Análisis de regresión entre variables del Censo y CASEN a nivel de Zona Censal 2017

Análisis diferenciados para zonas Urbanas y Rurales a nivel nacional.

DataIntelligence

Abstract

Nuestro objetivo es expandir variables de la CASEN sobre las del CENSO, ambos del año 2017, para poder realizar predicciones a nivel de Zona Censal, tanto a nivel urbano como a nivel zonal.

El primer paso será construir nuestra tabla de trabajo.

El segundo, será calcular las correlaciones entre el ingreso total promedio por comuna multiplicado por la población de la misma, y la frecuencia de categorías específicas de respuestas de variables de calidad de la vivienda. Ésto lo haremos para la pregunta P01: Tipo de vivienda y la P03B: Material en la cubierta del techo. Para ésta última también calcularemos la correlación entre la frecuencia de respuestas dividida por la cantidad de personas a nivel comunal y los ingresos expandidos.

En específico, expandiremos los ingresos promedios comunales obtenidos de la CASEN sobre la categoría de respuesta: “tejas o tejuelas de arcilla, metálicas, de cemento, de madera, asfálticas o plásticas” del campo P03B del CENSO de viviendas, que fue la categoría de respuesta que más alto correlaciona con los ingresos expandidos (obtenidos de la multiplicación del ingreso promedio y los habitantes), ambos a nivel comunal.

Por último calcularemos regresiones lineales.

Ensayaremos diferentes modelos dentro del análisis de regresión cuya variable independiente será: “frecuencia de población que posee la variable Censal respecto a la zona” y la dependiente: “ingreso expandido por zona”

Lo anterior para elegir el que posea el mayor coeficiente de determinación y así contruir una tabla de valores predichos.

Repetiremos los mismos pasos señalados pero a nivel rural.

---

1 Variable CENSO

Necesitamos calcular las frecuencias a nivel censal de las respuestas correspondientes a la categoría: “tejas o tejuelas de arcilla, metálicas, de cemento, de madera, asfálticas o plásticas” del campo P03B del Censo de viviendas. Recordemos que ésta fué la más alta correlación en relación a los ingresos expandidos (ver punto 1.2 aquí).

1.1 Lectura y filtrado de la tabla censal de viviendas

Leemos la tabla Casen 2017 de viviendas que ya tiene integrada la clave zonal:

Filtramos por área = 1 -URBANO-

R-01

Urbano

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 1)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15201	1	1	1	1767	1	1	1	2	1	3	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	2	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	3	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	4	1	1	2	3	1	99	1	2	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	5	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	6	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	7	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	8	1	1	3	3	1	99	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	9	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	10	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	11	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	12	1	1	4	3	3	3	4	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	13	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	14	1	1	2	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	15	1	1	2	1	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	16	1	1	5	1	5	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	17	1	1	5	3	1	3	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	18	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	19	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	20	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	21	4	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	22	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	23	1	1	5	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	24	1	1	3	1	1	6	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	25	1	1	5	6	1	1	4	2	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	26	4	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	27	1	1	5	99	3	3	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	28	1	1	6	3	2	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	29	1	1	2	3	1	99	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	30	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	31	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	32	1	1	3	1	1	2	1	1	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	33	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	34	3	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	35	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	36	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	37	1	1	5	3	3	1	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	38	1	1	1	1	4	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	39	1	1	5	3	4	3	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	40	1	1	5	3	2	1	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	41	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	42	1	1	2	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	43	1	1	1	1	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	44	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	45	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	46	1	1	5	6	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	47	1	1	5	3	4	0	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	48	1	1	3	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	49	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	50	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	51	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	52	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	53	1	1	1	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	54	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	55	1	1	2	2	1	4	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	56	8	1	98	98	98	98	98	98	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	57	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	58	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	59	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	60	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	61	1	1	2	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	62	1	1	5	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	63	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	64	1	1	5	3	2	4	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	65	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	66	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	67	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	68	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	69	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	70	1	1	1	3	3	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	71	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	72	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	73	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	74	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	75	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	76	1	1	5	3	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	77	1	1	3	6	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	78	5	1	3	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	79	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	80	1	1	2	1	1	3	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	81	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	82	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	83	1	1	5	3	2	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	84	4	1	5	3	2	0	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	85	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	86	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	87	1	1	3	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	88	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	89	5	1	4	3	2	1	2	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	90	1	1	5	3	3	2	1	3	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	91	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	92	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	93	5	1	4	3	5	1	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	94	1	1	5	3	3	99	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	95	1	1	5	3	2	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	96	1	1	2	3	4	6	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	97	1	1	5	1	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	98	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	99	1	1	2	7	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	100	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 1:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 1)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	1101011001	298	2017	01101
2	1101011002	95	2017	01101
3	1101021001	55	2017	01101
4	1101021002	2	2017	01101
5	1101021003	265	2017	01101
6	1101021004	178	2017	01101
7	1101021005	337	2017	01101
8	1101031001	194	2017	01101
9	1101031002	482	2017	01101
10	1101031003	328	2017	01101
11	1101031004	171	2017	01101
12	1101041001	135	2017	01101
13	1101041002	228	2017	01101
14	1101041003	235	2017	01101
15	1101041004	636	2017	01101
16	1101041005	403	2017	01101
17	1101041006	214	2017	01101
18	1101051001	364	2017	01101
19	1101051002	350	2017	01101
20	1101051003	303	2017	01101
21	1101051004	200	2017	01101
22	1101051005	269	2017	01101
23	1101051006	256	2017	01101
24	1101061001	105	2017	01101
25	1101061002	349	2017	01101
26	1101061003	259	2017	01101
27	1101061004	134	2017	01101
28	1101061005	144	2017	01101
29	1101071001	271	2017	01101
30	1101071002	453	2017	01101
31	1101071003	483	2017	01101
32	1101071004	211	2017	01101
33	1101081001	601	2017	01101
34	1101081002	469	2017	01101
35	1101081003	328	2017	01101
36	1101081004	284	2017	01101
37	1101101001	230	2017	01101
38	1101101002	420	2017	01101
39	1101101003	323	2017	01101
40	1101101004	249	2017	01101
41	1101101005	481	2017	01101
42	1101101006	356	2017	01101
43	1101111001	249	2017	01101
44	1101111002	184	2017	01101
45	1101111003	353	2017	01101
46	1101111004	279	2017	01101
47	1101111005	359	2017	01101
48	1101111006	60	2017	01101
49	1101111007	234	2017	01101
50	1101111008	322	2017	01101
51	1101111009	317	2017	01101
52	1101111010	21	2017	01101
53	1101111011	309	2017	01101
54	1101111012	109	2017	01101
55	1101111013	227	2017	01101
56	1101111014	138	2017	01101
57	1101991999	68	2017	01101
144	1107011001	245	2017	01107
145	1107011002	284	2017	01107
146	1107011003	280	2017	01107
147	1107021001	738	2017	01107
148	1107021002	407	2017	01107
149	1107021003	323	2017	01107
150	1107021004	466	2017	01107
151	1107021005	471	2017	01107
152	1107021006	201	2017	01107
153	1107021007	466	2017	01107
154	1107021008	416	2017	01107
155	1107031001	358	2017	01107
156	1107031002	594	2017	01107
157	1107031003	251	2017	01107
158	1107041001	233	2017	01107
159	1107041002	342	2017	01107
160	1107041003	252	2017	01107
161	1107041004	272	2017	01107
162	1107041005	207	2017	01107
163	1107041006	249	2017	01107
164	1107041007	359	2017	01107
165	1107991999	41	2017	01107
252	1401011001	197	2017	01401
253	1401011002	411	2017	01401
254	1401991999	4	2017	01401
341	1404011001	104	2017	01404
342	1404991999	5	2017	01404
429	1405011001	298	2017	01405
430	1405991999	5	2017	01405
NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA
NA.2	NA	NA	NA	NA
NA.3	NA	NA	NA	NA
NA.4	NA	NA	NA	NA
NA.5	NA	NA	NA	NA
NA.6	NA	NA	NA	NA
NA.7	NA	NA	NA	NA
NA.8	NA	NA	NA	NA
NA.9	NA	NA	NA	NA
NA.10	NA	NA	NA	NA
NA.11	NA	NA	NA	NA
NA.12	NA	NA	NA	NA
NA.13	NA	NA	NA	NA

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_urbano_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
01107	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
01401	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
01405	Pica	330061.1	2017	1405	9296	3068247619	Urbano
02101	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02102	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02104	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02201	Calama	416281.1	2017	2201	165731	68990679686	Urbano
02203	San Pedro de Atacama	437934.7	2017	2203	10996	4815529626	Urbano
02301	Tocopilla	271720.8	2017	2301	25186	6843559467	Urbano
02302	María Elena	466266.9	2017	2302	6457	3010685220	Urbano
03101	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
03102	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
03103	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
03201	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
03202	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
03301	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
03303	Freirina	289049.9	2017	3303	7041	2035200054	Urbano
03304	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
04101	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04102	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04103	Andacollo	251267.7	2017	4103	11044	2775000288	Urbano
04104	La Higuera	214257.0	2017	4104	4241	908664019	Urbano
04106	Vicuña	245957.4	2017	4106	27771	6830481918	Urbano
04201	Illapel	270316.5	2017	4201	30848	8338722128	Urbano
04202	Canela	233397.3	2017	4202	9093	2122281844	Urbano
04203	Los Vilos	282415.6	2017	4203	21382	6038609501	Urbano
04204	Salamanca	262056.9	2017	4204	29347	7690585032	Urbano
04301	Ovalle	274771.4	2017	4301	111272	30574361012	Urbano
04302	Combarbalá	228990.4	2017	4302	13322	3050610572	Urbano
04303	Monte Patria	225369.1	2017	4303	30751	6930326684	Urbano
04304	Punitaqui	212496.1	2017	4304	10956	2328107498	Urbano
05101	Valparaíso	297929.0	2017	5101	296655	88382118059	Urbano
05102	Casablanca	341641.8	2017	5102	26867	9178890241	Urbano
05103	Concón	318496.3	2017	5103	42152	13425257057	Urbano
05105	Puchuncaví	296035.5	2017	5105	18546	5490274928	Urbano
05107	Quintero	308224.7	2017	5107	31923	9839456903	Urbano
05109	Viña del Mar	337006.1	2017	5109	334248	112643604611	Urbano
05301	Los Andes	339720.2	2017	5301	66708	22662055502	Urbano
05302	Calle Larga	246387.3	2017	5302	14832	3654416747	Urbano
05303	Rinconada	273904.7	2017	5303	10207	2795744821	Urbano
05304	San Esteban	219571.6	2017	5304	18855	4140022481	Urbano
05401	La Ligua	250134.4	2017	5401	35390	8852256241	Urbano
05402	Cabildo	262745.9	2017	5402	19388	5094117762	Urbano
05403	Papudo	294355.2	2017	5403	6356	1870921373	Urbano
05404	Petorca	237510.8	2017	5404	9826	2333781007	Urbano
05405	Zapallar	294389.2	2017	5405	7339	2160521991	Urbano
05501	Quillota	286029.5	2017	5501	90517	25890529852	Urbano
05502	Calera	277181.9	2017	5502	50554	14012652087	Urbano
05503	Hijuelas	254094.0	2017	5503	17988	4570642363	Urbano

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
1	01101	1101021001	55	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
2	01101	1101021002	2	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
3	01101	1101011001	298	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
4	01101	1101011002	95	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
5	01101	1101021005	337	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
6	01101	1101031001	194	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
7	01101	1101031002	482	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
8	01101	1101031003	328	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
9	01101	1101031004	171	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
10	01101	1101041001	135	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
11	01101	1101041002	228	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
12	01101	1101041003	235	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
13	01101	1101041004	636	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
14	01101	1101041005	403	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
15	01101	1101041006	214	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
16	01101	1101021003	265	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
17	01101	1101021004	178	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
18	01101	1101051003	303	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
19	01101	1101051004	200	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
20	01101	1101051005	269	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
21	01101	1101051006	256	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
22	01101	1101061001	105	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
23	01101	1101061002	349	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
24	01101	1101061003	259	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
25	01101	1101061004	134	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
26	01101	1101061005	144	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
27	01101	1101071001	271	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
28	01101	1101071002	453	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
29	01101	1101051001	364	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
30	01101	1101051002	350	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
31	01101	1101081001	601	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
32	01101	1101081002	469	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
33	01101	1101081003	328	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
34	01101	1101081004	284	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
35	01101	1101101001	230	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
36	01101	1101101002	420	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
37	01101	1101101003	323	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
38	01101	1101101004	249	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
39	01101	1101101005	481	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
40	01101	1101101006	356	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
41	01101	1101111001	249	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
42	01101	1101071003	483	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
43	01101	1101071004	211	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
44	01101	1101111004	279	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
45	01101	1101111005	359	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
46	01101	1101111006	60	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
47	01101	1101111007	234	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
48	01101	1101111008	322	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
49	01101	1101111009	317	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
50	01101	1101111010	21	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
51	01101	1101111011	309	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
52	01101	1101111012	109	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
53	01101	1101111013	227	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
54	01101	1101111014	138	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
55	01101	1101111002	184	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
56	01101	1101111003	353	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
57	01101	1101991999	68	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
58	01107	1107011002	284	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
59	01107	1107011003	280	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
60	01107	1107021001	738	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
61	01107	1107021003	323	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
62	01107	1107021004	466	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
63	01107	1107021005	471	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
64	01107	1107021002	407	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
65	01107	1107021007	466	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
66	01107	1107021008	416	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
67	01107	1107031001	358	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
68	01107	1107021006	201	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
69	01107	1107011001	245	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
70	01107	1107041001	233	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
71	01107	1107041002	342	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
72	01107	1107041003	252	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
73	01107	1107041004	272	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
74	01107	1107041005	207	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
75	01107	1107041006	249	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
76	01107	1107041007	359	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
77	01107	1107991999	41	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
78	01107	1107031003	251	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
79	01107	1107031002	594	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
80	01401	1401011002	411	2017	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
81	01401	1401991999	4	2017	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
82	01401	1401011001	197	2017	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
83	01404	1404011001	104	2017	NA	NA	NA	NA	NA	NA	NA
84	01404	1404991999	5	2017	NA	NA	NA	NA	NA	NA	NA
85	01405	1405011001	298	2017	Pica	330061.1	2017	1405	9296	3068247619	Urbano
86	01405	1405991999	5	2017	Pica	330061.1	2017	1405	9296	3068247619	Urbano
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
1	01101	1101021001	55	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
2	01101	1101021002	2	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
3	01101	1101011001	298	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
4	01101	1101011002	95	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
5	01101	1101021005	337	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
6	01101	1101031001	194	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
7	01101	1101031002	482	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
8	01101	1101031003	328	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
9	01101	1101031004	171	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
10	01101	1101041001	135	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
11	01101	1101041002	228	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
12	01101	1101041003	235	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
13	01101	1101041004	636	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
14	01101	1101041005	403	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
15	01101	1101041006	214	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
16	01101	1101021003	265	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
17	01101	1101021004	178	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
18	01101	1101051003	303	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
19	01101	1101051004	200	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
20	01101	1101051005	269	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
21	01101	1101051006	256	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
22	01101	1101061001	105	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
23	01101	1101061002	349	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
24	01101	1101061003	259	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
25	01101	1101061004	134	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
26	01101	1101061005	144	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
27	01101	1101071001	271	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
28	01101	1101071002	453	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
29	01101	1101051001	364	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
30	01101	1101051002	350	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
31	01101	1101081001	601	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
32	01101	1101081002	469	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
33	01101	1101081003	328	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
34	01101	1101081004	284	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
35	01101	1101101001	230	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
36	01101	1101101002	420	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
37	01101	1101101003	323	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
38	01101	1101101004	249	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
39	01101	1101101005	481	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
40	01101	1101101006	356	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
41	01101	1101111001	249	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
42	01101	1101071003	483	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
43	01101	1101071004	211	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
44	01101	1101111004	279	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
45	01101	1101111005	359	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
46	01101	1101111006	60	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
47	01101	1101111007	234	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
48	01101	1101111008	322	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
49	01101	1101111009	317	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
50	01101	1101111010	21	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
51	01101	1101111011	309	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
52	01101	1101111012	109	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
53	01101	1101111013	227	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
54	01101	1101111014	138	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
55	01101	1101111002	184	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
56	01101	1101111003	353	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
57	01101	1101991999	68	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
58	01107	1107011002	284	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
59	01107	1107011003	280	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
60	01107	1107021001	738	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
61	01107	1107021003	323	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
62	01107	1107021004	466	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
63	01107	1107021005	471	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
64	01107	1107021002	407	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
65	01107	1107021007	466	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
66	01107	1107021008	416	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
67	01107	1107031001	358	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
68	01107	1107021006	201	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
69	01107	1107011001	245	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
70	01107	1107041001	233	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
71	01107	1107041002	342	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
72	01107	1107041003	252	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
73	01107	1107041004	272	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
74	01107	1107041005	207	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
75	01107	1107041006	249	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
76	01107	1107041007	359	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
77	01107	1107991999	41	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
78	01107	1107031003	251	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
79	01107	1107031002	594	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
80	01401	1401011002	411	2017	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
81	01401	1401991999	4	2017	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
82	01401	1401011001	197	2017	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
83	01404	1404011001	104	2017	NA	NA	NA	NA	NA	NA	NA
84	01404	1404991999	5	2017	NA	NA	NA	NA	NA	NA	NA
85	01405	1405011001	298	2017	Pica	330061.1	2017	1405	9296	3068247619	Urbano
86	01405	1405991999	5	2017	Pica	330061.1	2017	1405	9296	3068247619	Urbano
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
1101011001	01101	298	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2491	0.0130100	01101
1101011002	01101	95	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1475	0.0077036	01101
1101021001	01101	55	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1003	0.0052385	01101
1101021002	01101	2	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	54	0.0002820	01101
1101021003	01101	265	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2895	0.0151200	01101
1101021004	01101	178	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2398	0.0125243	01101
1101021005	01101	337	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4525	0.0236332	01101
1101031001	01101	194	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2725	0.0142321	01101
1101031002	01101	482	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3554	0.0185618	01101
1101031003	01101	328	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	5246	0.0273988	01101
1101031004	01101	171	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3389	0.0177001	01101
1101041001	01101	135	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1800	0.0094010	01101
1101041002	01101	228	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2538	0.0132555	01101
1101041003	01101	235	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3855	0.0201339	01101
1101041004	01101	636	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	5663	0.0295767	01101
1101041005	01101	403	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4162	0.0217373	01101
1101041006	01101	214	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2689	0.0140441	01101
1101051001	01101	364	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3296	0.0172144	01101
1101051002	01101	350	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4465	0.0233198	01101
1101051003	01101	303	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4656	0.0243174	01101
1101051004	01101	200	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2097	0.0109522	01101
1101051005	01101	269	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3569	0.0186402	01101
1101051006	01101	256	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2741	0.0143157	01101
1101061001	01101	105	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1625	0.0084871	01101
1101061002	01101	349	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4767	0.0248971	01101
1101061003	01101	259	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4826	0.0252053	01101
1101061004	01101	134	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4077	0.0212934	01101
1101061005	01101	144	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2166	0.0113126	01101
1101071001	01101	271	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2324	0.0121378	01101
1101071002	01101	453	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2801	0.0146291	01101
1101071003	01101	483	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3829	0.0199981	01101
1101071004	01101	211	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1987	0.0103777	01101
1101081001	01101	601	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	5133	0.0268087	01101
1101081002	01101	469	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3233	0.0168853	01101
1101081003	01101	328	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2122	0.0110828	01101
1101081004	01101	284	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2392	0.0124929	01101
1101101001	01101	230	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2672	0.0139553	01101
1101101002	01101	420	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4398	0.0229699	01101
1101101003	01101	323	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4524	0.0236280	01101
1101101004	01101	249	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3544	0.0185096	01101
1101101005	01101	481	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4911	0.0256492	01101
1101101006	01101	356	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3688	0.0192617	01101
1101111001	01101	249	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3886	0.0202958	01101
1101111002	01101	184	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2312	0.0120751	01101
1101111003	01101	353	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4874	0.0254560	01101
1101111004	01101	279	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4543	0.0237272	01101
1101111005	01101	359	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4331	0.0226200	01101
1101111006	01101	60	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3253	0.0169898	01101
1101111007	01101	234	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4639	0.0242286	01101
1101111008	01101	322	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4881	0.0254925	01101
1101111009	01101	317	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	5006	0.0261454	01101
1101111010	01101	21	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	366	0.0019115	01101
1101111011	01101	309	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4351	0.0227244	01101
1101111012	01101	109	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2926	0.0152819	01101
1101111013	01101	227	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3390	0.0177053	01101
1101111014	01101	138	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2940	0.0153550	01101
1101991999	01101	68	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1062	0.0055466	01101
1107011001	01107	245	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4104	0.0378685	01107
1107011002	01107	284	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4360	0.0402307	01107
1107011003	01107	280	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	8549	0.0788835	01107
1107021001	01107	738	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	6701	0.0618316	01107
1107021002	01107	407	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	3971	0.0366413	01107
1107021003	01107	323	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	6349	0.0585836	01107
1107021004	01107	466	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	5125	0.0472895	01107
1107021005	01107	471	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4451	0.0410704	01107
1107021006	01107	201	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	3864	0.0356540	01107
1107021007	01107	466	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	5235	0.0483045	01107
1107021008	01107	416	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4566	0.0421315	01107
1107031001	01107	358	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4195	0.0387082	01107
1107031002	01107	594	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	7099	0.0655040	01107
1107031003	01107	251	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4720	0.0435525	01107
1107041001	01107	233	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	3630	0.0334948	01107
1107041002	01107	342	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	5358	0.0494394	01107
1107041003	01107	252	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4520	0.0417070	01107
1107041004	01107	272	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	5317	0.0490611	01107
1107041005	01107	207	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	3725	0.0343714	01107
1107041006	01107	249	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4009	0.0369919	01107
1107041007	01107	359	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	5217	0.0481384	01107
1107991999	01107	41	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	819	0.0075571	01107
1401011001	01401	197	2017	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano	2771	0.1763732	01401
1401011002	01401	411	2017	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano	6506	0.4141048	01401
1401991999	01401	4	2017	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano	818	0.0520654	01401
1404011001	01404	104	2017	NA	NA	NA	NA	NA	NA	NA	1082	0.3963370	01404
1404991999	01404	5	2017	NA	NA	NA	NA	NA	NA	NA	27	0.0098901	01404
1405011001	01405	298	2017	Pica	330061.1	2017	1405	9296	3068247619	Urbano	3876	0.4169535	01405
1405991999	01405	5	2017	Pica	330061.1	2017	1405	9296	3068247619	Urbano	36	0.0038726	01405

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
1101011001	01101	298	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2491	0.0130100	01101	888010727
1101011002	01101	95	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1475	0.0077036	01101	525819278
1101021001	01101	55	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1003	0.0052385	01101	357557109
1101021002	01101	2	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	54	0.0002820	01101	19250333
1101021003	01101	265	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2895	0.0151200	01101	1032031736
1101021004	01101	178	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2398	0.0125243	01101	854857376
1101021005	01101	337	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4525	0.0236332	01101	1613106600
1101031001	01101	194	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2725	0.0142321	01101	971428836
1101031002	01101	482	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3554	0.0185618	01101	1266957095
1101031003	01101	328	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	5246	0.0273988	01101	1870134193
1101031004	01101	171	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3389	0.0177001	01101	1208136633
1101041001	01101	135	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1800	0.0094010	01101	641677763
1101041002	01101	228	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2538	0.0132555	01101	904765646
1101041003	01101	235	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3855	0.0201339	01101	1374259877
1101041004	01101	636	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	5663	0.0295767	01101	2018789541
1101041005	01101	403	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4162	0.0217373	01101	1483701584
1101041006	01101	214	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2689	0.0140441	01101	958595281
1101051001	01101	364	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3296	0.0172144	01101	1174983282
1101051002	01101	350	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4465	0.0233198	01101	1591717341
1101051003	01101	303	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4656	0.0243174	01101	1659806481
1101051004	01101	200	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2097	0.0109522	01101	747554594
1101051005	01101	269	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3569	0.0186402	01101	1272304410
1101051006	01101	256	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2741	0.0143157	01101	977132639
1101061001	01101	105	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1625	0.0084871	01101	579292425
1101061002	01101	349	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4767	0.0248971	01101	1699376610
1101061003	01101	259	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4826	0.0252053	01101	1720409381
1101061004	01101	134	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4077	0.0212934	01101	1453400134
1101061005	01101	144	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2166	0.0113126	01101	772152242
1101071001	01101	271	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2324	0.0121378	01101	828477290
1101071002	01101	453	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2801	0.0146291	01101	998521897
1101071003	01101	483	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3829	0.0199981	01101	1364991198
1101071004	01101	211	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1987	0.0103777	01101	708340953
1101081001	01101	601	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	5133	0.0268087	01101	1829851089
1101081002	01101	469	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3233	0.0168853	01101	1152524561
1101081003	01101	328	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2122	0.0110828	01101	756466785
1101081004	01101	284	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2392	0.0124929	01101	852718450
1101101001	01101	230	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2672	0.0139553	01101	952534991
1101101002	01101	420	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4398	0.0229699	01101	1567832668
1101101003	01101	323	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4524	0.0236280	01101	1612750112
1101101004	01101	249	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3544	0.0185096	01101	1263392219
1101101005	01101	481	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4911	0.0256492	01101	1750710831
1101101006	01101	356	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3688	0.0192617	01101	1314726440
1101111001	01101	249	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3886	0.0202958	01101	1385310994
1101111002	01101	184	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2312	0.0120751	01101	824199438
1101111003	01101	353	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4874	0.0254560	01101	1737520788
1101111004	01101	279	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4543	0.0237272	01101	1619523377
1101111005	01101	359	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4331	0.0226200	01101	1543947996
1101111006	01101	60	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3253	0.0169898	01101	1159654313
1101111007	01101	234	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4639	0.0242286	01101	1653746191
1101111008	01101	322	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4881	0.0254925	01101	1740016202
1101111009	01101	317	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	5006	0.0261454	01101	1784577157
1101111010	01101	21	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	366	0.0019115	01101	130474479
1101111011	01101	309	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4351	0.0227244	01101	1551077749
1101111012	01101	109	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2926	0.0152819	01101	1043082853
1101111013	01101	227	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3390	0.0177053	01101	1208493121
1101111014	01101	138	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2940	0.0153550	01101	1048073680
1101991999	01101	68	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1062	0.0055466	01101	378589880
1107011001	01107	245	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4104	0.0378685	01107	1239134756
1107011002	01107	284	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4360	0.0402307	01107	1316429711
1107011003	01107	280	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	8549	0.0788835	01107	2581228808
1107021001	01107	738	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	6701	0.0618316	01107	2023255848
1107021002	01107	407	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	3971	0.0366413	01107	1198977611
1107021003	01107	323	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	6349	0.0585836	01107	1916975284
1107021004	01107	466	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	5125	0.0472895	01107	1547408778
1107021005	01107	471	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4451	0.0410704	01107	1343905653
1107021006	01107	201	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	3864	0.0356540	01107	1166670735
1107021007	01107	466	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	5235	0.0483045	01107	1580621454
1107021008	01107	416	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4566	0.0421315	01107	1378627996
1107031001	01107	358	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4195	0.0387082	01107	1266610697
1107031002	01107	594	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	7099	0.0655040	01107	2143425349
1107031003	01107	251	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4720	0.0435525	01107	1425125743
1107041001	01107	233	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	3630	0.0334948	01107	1096018315
1107041002	01107	342	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	5358	0.0494394	01107	1617759265
1107041003	01107	252	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4520	0.0417070	01107	1364739059
1107041004	01107	272	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	5317	0.0490611	01107	1605379994
1107041005	01107	207	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	3725	0.0343714	01107	1124701990
1107041006	01107	249	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4009	0.0369919	01107	1210451081
1107041007	01107	359	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	5217	0.0481384	01107	1575186652
1107991999	01107	41	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	819	0.0075571	01107	247283471
1401011001	01401	197	2017	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano	2771	0.1763732	01401	831296153
1401011002	01401	411	2017	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano	6506	0.4141048	01401	1951790968
1401991999	01401	4	2017	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano	818	0.0520654	01401	245398864
1404011001	01404	104	2017	NA	NA	NA	NA	NA	NA	NA	1082	0.3963370	01404	NA
1404991999	01404	5	2017	NA	NA	NA	NA	NA	NA	NA	27	0.0098901	01404	NA
1405011001	01405	298	2017	Pica	330061.1	2017	1405	9296	3068247619	Urbano	3876	0.4169535	01405	1279316671
1405991999	01405	5	2017	Pica	330061.1	2017	1405	9296	3068247619	Urbano	36	0.0038726	01405	11882198

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -659459104 -246675287  -28538400  241475681 1353558418 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 531213909   83093042   6.393 9.37e-09 ***
## Freq.x        2487345     261754   9.503 7.21e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 346400000 on 82 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.5241, Adjusted R-squared:  0.5183 
## F-statistic:  90.3 on 1 and 82 DF,  p-value: 7.214e-15

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.518280693041906"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.518280693041906"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.535673326421852"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.593875917940282"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.663064744202599"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.593062101177527"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.703058649606228"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.789797595923559"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq ),]

##          modelo                rq
## 1    cuadrático 0.518280693041906
## 2        cúbico 0.518280693041906
## 3   logarítmico 0.535673326421852
## 6      log-raíz 0.593062101177527
## 4 raíz cuadrada 0.593875917940282
## 5     raíz-raíz 0.663064744202599
## 7      raíz-log 0.703058649606228
## 8       log-log 0.789797595923559
##                                                                     sintaxis
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 8
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.68750 -0.16988 -0.01449  0.16773  1.50466 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.79289    0.22799   73.66   <2e-16 ***
## log(Freq.x)  0.73639    0.04163   17.69   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3813 on 82 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.7923, Adjusted R-squared:  0.7898 
## F-statistic: 312.9 on 1 and 82 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    16.79289

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##   0.7363857

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.7898 ).

9.1 Diagrama de dispersión sobre log-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.68750 -0.16988 -0.01449  0.16773  1.50466 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.79289    0.22799   73.66   <2e-16 ***
## log(Freq.x)  0.73639    0.04163   17.69   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3813 on 82 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.7923, Adjusted R-squared:  0.7898 
## F-statistic: 312.9 on 1 and 82 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = e^{16.79289+0.7363857 \cdot ln{X}} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
1	1101011001	01101	298	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2491	0.0130100	01101	888010727	1303275876
2	1101011002	01101	95	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1475	0.0077036	01101	525819278	561598941
3	1101021001	01101	55	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1003	0.0052385	01101	357557109	375523486
4	1101021002	01101	2	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	54	0.0002820	01101	19250333	32713962
5	1101021003	01101	265	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2895	0.0151200	01101	1032031736	1195370384
6	1101021004	01101	178	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2398	0.0125243	01101	854857376	891735379
7	1101021005	01101	337	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4525	0.0236332	01101	1613106600	1426820672
8	1101031001	01101	194	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2725	0.0142321	01101	971428836	950086978
9	1101031002	01101	482	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3554	0.0185618	01101	1266957095	1857018990
10	1101031003	01101	328	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	5246	0.0273988	01101	1870134193	1398660793
11	1101031004	01101	171	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3389	0.0177001	01101	1208136633	865775503
12	1101041001	01101	135	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1800	0.0094010	01101	641677763	727455139
13	1101041002	01101	228	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2538	0.0132555	01101	904765646	1070060635
14	1101041003	01101	235	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3855	0.0201339	01101	1374259877	1094156267
15	1101041004	01101	636	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	5663	0.0295767	01101	2018789541	2277637475
16	1101041005	01101	403	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4162	0.0217373	01101	1483701584	1627677111
17	1101041006	01101	214	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2689	0.0140441	01101	958595281	1021273972
18	1101051001	01101	364	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3296	0.0172144	01101	1174983282	1510140354
19	1101051002	01101	350	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4465	0.0233198	01101	1591717341	1467148947
20	1101051003	01101	303	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4656	0.0243174	01101	1659806481	1319343093
21	1101051004	01101	200	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2097	0.0109522	01101	747554594	971637955
22	1101051005	01101	269	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3569	0.0186402	01101	1272304410	1208630963
23	1101051006	01101	256	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2741	0.0143157	01101	977132639	1165339201
24	1101061001	01101	105	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1625	0.0084871	01101	579292425	604552183
25	1101061002	01101	349	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4767	0.0248971	01101	1699376610	1464060961
26	1101061003	01101	259	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4826	0.0252053	01101	1720409381	1175380061
27	1101061004	01101	134	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4077	0.0212934	01101	1453400134	723483197
28	1101061005	01101	144	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2166	0.0113126	01101	772152242	762862324
29	1101071001	01101	271	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2324	0.0121378	01101	828477290	1215241737
30	1101071002	01101	453	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2801	0.0146291	01101	998521897	1774073498
31	1101071003	01101	483	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3829	0.0199981	01101	1364991198	1859855315
32	1101071004	01101	211	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1987	0.0103777	01101	708340953	1010711597
33	1101081001	01101	601	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	5133	0.0268087	01101	1829851089	2184652135
34	1101081002	01101	469	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3233	0.0168853	01101	1152524561	1820004063
35	1101081003	01101	328	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2122	0.0110828	01101	756466785	1398660793
36	1101081004	01101	284	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2392	0.0124929	01101	852718450	1257903788
37	1101101001	01101	230	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2672	0.0139553	01101	952534991	1076964755
38	1101101002	01101	420	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4398	0.0229699	01101	1567832668	1677962075
39	1101101003	01101	323	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4524	0.0236280	01101	1612750112	1382928528
40	1101101004	01101	249	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3544	0.0185096	01101	1263392219	1141788901
41	1101101005	01101	481	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4911	0.0256492	01101	1750710831	1854181114
42	1101101006	01101	356	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3688	0.0192617	01101	1314726440	1485628325
43	1101111001	01101	249	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3886	0.0202958	01101	1385310994	1141788901
44	1101111002	01101	184	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2312	0.0120751	01101	824199438	913773052
45	1101111003	01101	353	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4874	0.0254560	01101	1737520788	1476398986
46	1101111004	01101	279	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4543	0.0237272	01101	1619523377	1241557521
47	1101111005	01101	359	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4331	0.0226200	01101	1543947996	1494837183
48	1101111006	01101	60	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3253	0.0169898	01101	1159654313	400372333
49	1101111007	01101	234	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4639	0.0242286	01101	1653746191	1090725740
50	1101111008	01101	322	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4881	0.0254925	01101	1740016202	1379774395
51	1101111009	01101	317	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	5006	0.0261454	01101	1784577157	1363964778
52	1101111010	01101	21	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	366	0.0019115	01101	130474479	184808653
53	1101111011	01101	309	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4351	0.0227244	01101	1551077749	1338531815
54	1101111012	01101	109	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2926	0.0152819	01101	1043082853	621427738
55	1101111013	01101	227	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3390	0.0177053	01101	1208493121	1066602593
56	1101111014	01101	138	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2940	0.0153550	01101	1048073680	739324761
57	1101991999	01101	68	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1062	0.0055466	01101	378589880	439028043
58	1107011001	01107	245	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4104	0.0378685	01107	1239134756	1128253328
59	1107011002	01107	284	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4360	0.0402307	01107	1316429711	1257903788
60	1107011003	01107	280	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	8549	0.0788835	01107	2581228808	1244832911
61	1107021001	01107	738	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	6701	0.0618316	01107	2023255848	2541291938
62	1107021002	01107	407	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	3971	0.0366413	01107	1198977611	1639558367
63	1107021003	01107	323	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	6349	0.0585836	01107	1916975284	1382928528
64	1107021004	01107	466	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	5125	0.0472895	01107	1547408778	1811423949
65	1107021005	01107	471	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4451	0.0410704	01107	1343905653	1825716101
66	1107021006	01107	201	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	3864	0.0356540	01107	1166670735	975213103
67	1107021007	01107	466	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	5235	0.0483045	01107	1580621454	1811423949
68	1107021008	01107	416	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4566	0.0421315	01107	1378627996	1666179364
69	1107031001	01107	358	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4195	0.0387082	01107	1266610697	1491769826
70	1107031002	01107	594	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	7099	0.0655040	01107	2143425349	2165885747
71	1107031003	01107	251	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4720	0.0435525	01107	1425125743	1148535165
72	1107041001	01107	233	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	3630	0.0334948	01107	1096018315	1087291347
73	1107041002	01107	342	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	5358	0.0494394	01107	1617759265	1442379252
74	1107041003	01107	252	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4520	0.0417070	01107	1364739059	1151902980
75	1107041004	01107	272	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	5317	0.0490611	01107	1605379994	1218542298
76	1107041005	01107	207	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	3725	0.0343714	01107	1124701990	996566607
77	1107041006	01107	249	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4009	0.0369919	01107	1210451081	1141788901
78	1107041007	01107	359	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	5217	0.0481384	01107	1575186652	1494837183
79	1107991999	01107	41	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	819	0.0075571	01107	247283471	302475275
80	1401011001	01401	197	2017	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano	2771	0.1763732	01401	831296153	960884096
81	1401011002	01401	411	2017	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano	6506	0.4141048	01401	1951790968	1651408881
82	1401991999	01401	4	2017	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano	818	0.0520654	01401	245398864	54501357
83	1404011001	01404	104	2017	NA	NA	NA	NA	NA	NA	NA	1082	0.3963370	01404	NA	600306996
84	1404991999	01404	5	2017	NA	NA	NA	NA	NA	NA	NA	27	0.0098901	01404	NA	64234812
85	1405011001	01405	298	2017	Pica	330061.1	2017	1405	9296	3068247619	Urbano	3876	0.4169535	01405	1279316671	1303275876
86	1405991999	01405	5	2017	Pica	330061.1	2017	1405	9296	3068247619	Urbano	36	0.0038726	01405	11882198	64234812
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
1	1101011001	01101	298	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2491	0.0130100	01101	888010727	1303275876	523193.85
2	1101011002	01101	95	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1475	0.0077036	01101	525819278	561598941	380745.04
3	1101021001	01101	55	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1003	0.0052385	01101	357557109	375523486	374400.29
4	1101021002	01101	2	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	54	0.0002820	01101	19250333	32713962	605814.11
5	1101021003	01101	265	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2895	0.0151200	01101	1032031736	1195370384	412908.60
6	1101021004	01101	178	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2398	0.0125243	01101	854857376	891735379	371866.30
7	1101021005	01101	337	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4525	0.0236332	01101	1613106600	1426820672	315319.49
8	1101031001	01101	194	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2725	0.0142321	01101	971428836	950086978	348655.77
9	1101031002	01101	482	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3554	0.0185618	01101	1266957095	1857018990	522515.19
10	1101031003	01101	328	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	5246	0.0273988	01101	1870134193	1398660793	266614.71
11	1101031004	01101	171	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3389	0.0177001	01101	1208136633	865775503	255466.36
12	1101041001	01101	135	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1800	0.0094010	01101	641677763	727455139	404141.74
13	1101041002	01101	228	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2538	0.0132555	01101	904765646	1070060635	421615.70
14	1101041003	01101	235	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3855	0.0201339	01101	1374259877	1094156267	283827.83
15	1101041004	01101	636	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	5663	0.0295767	01101	2018789541	2277637475	402196.27
16	1101041005	01101	403	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4162	0.0217373	01101	1483701584	1627677111	391080.52
17	1101041006	01101	214	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2689	0.0140441	01101	958595281	1021273972	379796.94
18	1101051001	01101	364	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3296	0.0172144	01101	1174983282	1510140354	458173.65
19	1101051002	01101	350	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4465	0.0233198	01101	1591717341	1467148947	328588.79
20	1101051003	01101	303	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4656	0.0243174	01101	1659806481	1319343093	283364.07
21	1101051004	01101	200	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2097	0.0109522	01101	747554594	971637955	463346.66
22	1101051005	01101	269	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3569	0.0186402	01101	1272304410	1208630963	338646.95
23	1101051006	01101	256	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2741	0.0143157	01101	977132639	1165339201	425151.11
24	1101061001	01101	105	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1625	0.0084871	01101	579292425	604552183	372032.11
25	1101061002	01101	349	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4767	0.0248971	01101	1699376610	1464060961	307124.18
26	1101061003	01101	259	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4826	0.0252053	01101	1720409381	1175380061	243551.61
27	1101061004	01101	134	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4077	0.0212934	01101	1453400134	723483197	177454.79
28	1101061005	01101	144	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2166	0.0113126	01101	772152242	762862324	352198.67
29	1101071001	01101	271	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2324	0.0121378	01101	828477290	1215241737	522909.53
30	1101071002	01101	453	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2801	0.0146291	01101	998521897	1774073498	633371.47
31	1101071003	01101	483	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3829	0.0199981	01101	1364991198	1859855315	485728.73
32	1101071004	01101	211	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1987	0.0103777	01101	708340953	1010711597	508662.10
33	1101081001	01101	601	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	5133	0.0268087	01101	1829851089	2184652135	425609.22
34	1101081002	01101	469	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3233	0.0168853	01101	1152524561	1820004063	562945.89
35	1101081003	01101	328	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2122	0.0110828	01101	756466785	1398660793	659123.84
36	1101081004	01101	284	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2392	0.0124929	01101	852718450	1257903788	525879.51
37	1101101001	01101	230	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2672	0.0139553	01101	952534991	1076964755	403055.67
38	1101101002	01101	420	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4398	0.0229699	01101	1567832668	1677962075	381528.44
39	1101101003	01101	323	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4524	0.0236280	01101	1612750112	1382928528	305687.12
40	1101101004	01101	249	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3544	0.0185096	01101	1263392219	1141788901	322175.20
41	1101101005	01101	481	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4911	0.0256492	01101	1750710831	1854181114	377556.73
42	1101101006	01101	356	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3688	0.0192617	01101	1314726440	1485628325	402827.64
43	1101111001	01101	249	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3886	0.0202958	01101	1385310994	1141788901	293821.13
44	1101111002	01101	184	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2312	0.0120751	01101	824199438	913773052	395230.56
45	1101111003	01101	353	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4874	0.0254560	01101	1737520788	1476398986	302913.21
46	1101111004	01101	279	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4543	0.0237272	01101	1619523377	1241557521	273290.23
47	1101111005	01101	359	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4331	0.0226200	01101	1543947996	1494837183	345148.28
48	1101111006	01101	60	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3253	0.0169898	01101	1159654313	400372333	123077.88
49	1101111007	01101	234	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4639	0.0242286	01101	1653746191	1090725740	235120.88
50	1101111008	01101	322	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4881	0.0254925	01101	1740016202	1379774395	282682.73
51	1101111009	01101	317	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	5006	0.0261454	01101	1784577157	1363964778	272466.00
52	1101111010	01101	21	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	366	0.0019115	01101	130474479	184808653	504941.68
53	1101111011	01101	309	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	4351	0.0227244	01101	1551077749	1338531815	307637.74
54	1101111012	01101	109	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2926	0.0152819	01101	1043082853	621427738	212381.32
55	1101111013	01101	227	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	3390	0.0177053	01101	1208493121	1066602593	314632.03
56	1101111014	01101	138	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	2940	0.0153550	01101	1048073680	739324761	251471.01
57	1101991999	01101	68	2017	Iquique	356487.6	2017	1101	191468	68255976664	Urbano	1062	0.0055466	01101	378589880	439028043	413397.40
58	1107011001	01107	245	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4104	0.0378685	01107	1239134756	1128253328	274915.53
59	1107011002	01107	284	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4360	0.0402307	01107	1316429711	1257903788	288510.04
60	1107011003	01107	280	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	8549	0.0788835	01107	2581228808	1244832911	145611.52
61	1107021001	01107	738	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	6701	0.0618316	01107	2023255848	2541291938	379240.70
62	1107021002	01107	407	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	3971	0.0366413	01107	1198977611	1639558367	412882.99
63	1107021003	01107	323	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	6349	0.0585836	01107	1916975284	1382928528	217818.32
64	1107021004	01107	466	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	5125	0.0472895	01107	1547408778	1811423949	353448.58
65	1107021005	01107	471	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4451	0.0410704	01107	1343905653	1825716101	410181.11
66	1107021006	01107	201	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	3864	0.0356540	01107	1166670735	975213103	252384.34
67	1107021007	01107	466	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	5235	0.0483045	01107	1580621454	1811423949	346021.77
68	1107021008	01107	416	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4566	0.0421315	01107	1378627996	1666179364	364910.07
69	1107031001	01107	358	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4195	0.0387082	01107	1266610697	1491769826	355606.63
70	1107031002	01107	594	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	7099	0.0655040	01107	2143425349	2165885747	305097.30
71	1107031003	01107	251	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4720	0.0435525	01107	1425125743	1148535165	243333.72
72	1107041001	01107	233	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	3630	0.0334948	01107	1096018315	1087291347	299529.30
73	1107041002	01107	342	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	5358	0.0494394	01107	1617759265	1442379252	269201.05
74	1107041003	01107	252	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4520	0.0417070	01107	1364739059	1151902980	254845.79
75	1107041004	01107	272	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	5317	0.0490611	01107	1605379994	1218542298	229178.54
76	1107041005	01107	207	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	3725	0.0343714	01107	1124701990	996566607	267534.66
77	1107041006	01107	249	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	4009	0.0369919	01107	1210451081	1141788901	284806.41
78	1107041007	01107	359	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	5217	0.0481384	01107	1575186652	1494837183	286531.95
79	1107991999	01107	41	2017	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano	819	0.0075571	01107	247283471	302475275	369322.68
80	1401011001	01401	197	2017	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano	2771	0.1763732	01401	831296153	960884096	346764.38
81	1401011002	01401	411	2017	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano	6506	0.4141048	01401	1951790968	1651408881	253828.60
82	1401991999	01401	4	2017	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano	818	0.0520654	01401	245398864	54501357	66627.58
83	1404011001	01404	104	2017	NA	NA	NA	NA	NA	NA	NA	1082	0.3963370	01404	NA	600306996	NA
84	1404991999	01404	5	2017	NA	NA	NA	NA	NA	NA	NA	27	0.0098901	01404	NA	64234812	NA
85	1405011001	01405	298	2017	Pica	330061.1	2017	1405	9296	3068247619	Urbano	3876	0.4169535	01405	1279316671	1303275876	336242.49
86	1405991999	01405	5	2017	Pica	330061.1	2017	1405	9296	3068247619	Urbano	36	0.0038726	01405	11882198	64234812	1784300.34
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_urbano_nivel_nacional_17_r01.rds")

---

Rural

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 2)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15202	1	2	6	13225	1	3	1	5	3	5	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	3	1	1	5	3	5	2	3	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	4	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	5	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	6	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	8	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	9	3	1	5	3	5	1	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	10	1	1	5	3	4	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	11	1	2	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	12	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	13	1	1	5	3	4	1	4	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	14	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	16	1	1	5	3	5	1	3	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	17	1	1	5	3	5	2	4	1	8	15	152	15202	15202012006
15	152	15202	1	2	6	13225	18	3	1	5	3	5	1	1	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	19	3	1	5	3	5	1	3	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	20	1	1	5	3	5	2	1	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	21	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	22	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	24	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	25	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	26	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	27	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	28	3	1	5	3	5	1	4	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	29	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	30	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	31	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	32	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	33	1	1	5	3	5	3	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	34	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	35	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	36	1	1	5	3	5	3	2	1	9	15	152	15202	15202012006
15	152	15202	1	2	6	13225	37	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	38	1	1	5	3	5	99	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	39	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	40	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	41	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	42	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	43	3	1	5	3	5	2	1	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	44	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	45	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	8	13910	1	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	3	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	4	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	5	3	1	5	3	5	2	3	1	3	15	152	15202	15202012008
15	152	15202	1	2	8	13910	6	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	7	1	1	5	99	5	2	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	8	3	1	5	3	5	3	3	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	9	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	10	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	11	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	12	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	13	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	14	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	16	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	18	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	20	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	21	1	1	5	3	5	1	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	22	3	1	5	3	5	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	23	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	24	3	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	25	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	26	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	27	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	28	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	29	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	30	1	1	5	1	4	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	12	8394	1	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	2	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	3	3	1	5	3	5	2	3	1	4	15	152	15202	15202012012
15	152	15202	1	2	12	8394	4	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	5	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	6	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	8	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	9	3	1	5	3	5	1	4	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	10	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	11	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	12	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	13	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	14	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	15	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	16	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	18	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	20	3	1	5	3	5	3	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	21	3	1	5	99	5	1	4	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	22	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	24	3	1	5	3	5	1	2	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	25	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 1:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 1)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Veamos los primeros 100 registros:

r3_100 <- d[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	unlist.c.	unlist.d.	Freq	anio
1	1101092004	1	1101	6	2017
2	1101092005	1	1101	1	2017
3	1101092006	1	1101	35	2017
4	1101092007	1	1101	1	2017
5	1101092010	1	1101	36	2017
6	1101092012	1	1101	3	2017
7	1101092016	1	1101	1	2017
8	1101092017	1	1101	5	2017
9	1101092018	1	1101	6	2017
10	1101092019	1	1101	2	2017
11	1101092021	1	1101	11	2017
12	1101092023	1	1101	13	2017
13	1101092024	1	1101	1	2017
14	1101112003	1	1101	9	2017
15	1101112013	1	1101	9	2017
16	1101112025	1	1101	1	2017
17	1101112901	1	1101	1	2017
99	1107032005	1	1107	5	2017
100	1107042002	1	1107	3	2017
182	1401012001	1	1401	62	2017
183	1401012018	1	1401	1	2017
184	1401012021	1	1401	2	2017
185	1401012901	1	1401	3	2017
186	1401022014	1	1401	2	2017
187	1401022015	1	1401	16	2017
188	1401022019	1	1401	4	2017
189	1401022024	1	1401	1	2017
190	1401032002	1	1401	4	2017
191	1401032007	1	1401	1	2017
192	1401032011	1	1401	38	2017
193	1401032012	1	1401	162	2017
194	1401032026	1	1401	3	2017
195	1401032901	1	1401	1	2017
196	1401052009	1	1401	7	2017
197	1401052020	1	1401	10	2017
198	1401052901	1	1401	3	2017
199	1401072008	1	1401	1	2017
281	1402012001	1	1402	4	2017
282	1402012002	1	1402	38	2017
283	1402012003	1	1402	10	2017
284	1402012004	1	1402	1	2017
285	1402012005	1	1402	4	2017
286	1402012006	1	1402	8	2017
287	1402012007	1	1402	1	2017
288	1402012008	1	1402	6	2017
289	1402012009	1	1402	6	2017
290	1402012010	1	1402	2	2017
291	1402992999	1	1402	2	2017
373	1403012008	1	1403	18	2017
374	1403012009	1	1403	9	2017
375	1403012012	1	1403	1	2017
376	1403022002	1	1403	1	2017
377	1403022005	1	1403	4	2017
378	1403022901	1	1403	2	2017
379	1403992999	1	1403	6	2017
461	1404022013	1	1404	1	2017
462	1404022016	1	1404	2	2017
463	1404022022	1	1404	14	2017
464	1404022024	1	1404	1	2017
465	1404022034	1	1404	16	2017
466	1404032014	1	1404	1	2017
467	1404032017	1	1404	4	2017
468	1404032020	1	1404	2	2017
469	1404032028	1	1404	1	2017
470	1404042023	1	1404	25	2017
471	1404042037	1	1404	2	2017
472	1404042901	1	1404	5	2017
473	1404052025	1	1404	1	2017
474	1404062005	1	1404	1	2017
475	1404062018	1	1404	2	2017
476	1404062901	1	1404	5	2017
477	1404072004	1	1404	6	2017
478	1404072015	1	1404	1	2017
479	1404072031	1	1404	5	2017
480	1404082901	1	1404	1	2017
562	1405012008	1	1405	51	2017
563	1405012010	1	1405	5	2017
564	1405012014	1	1405	3	2017
565	1405012901	1	1405	2	2017
566	1405022901	1	1405	2	2017
567	1405032009	1	1405	2	2017
NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	1101092004	6	2017	01101
2	1101092005	1	2017	01101
3	1101092006	35	2017	01101
4	1101092007	1	2017	01101
5	1101092010	36	2017	01101
6	1101092012	3	2017	01101
7	1101092016	1	2017	01101
8	1101092017	5	2017	01101
9	1101092018	6	2017	01101
10	1101092019	2	2017	01101
11	1101092021	11	2017	01101
12	1101092023	13	2017	01101
13	1101092024	1	2017	01101
14	1101112003	9	2017	01101
15	1101112013	9	2017	01101
16	1101112025	1	2017	01101
17	1101112901	1	2017	01101
99	1107032005	5	2017	01107
100	1107042002	3	2017	01107
182	1401012001	62	2017	01401
183	1401012018	1	2017	01401
184	1401012021	2	2017	01401
185	1401012901	3	2017	01401
186	1401022014	2	2017	01401
187	1401022015	16	2017	01401
188	1401022019	4	2017	01401
189	1401022024	1	2017	01401
190	1401032002	4	2017	01401
191	1401032007	1	2017	01401
192	1401032011	38	2017	01401
193	1401032012	162	2017	01401
194	1401032026	3	2017	01401
195	1401032901	1	2017	01401
196	1401052009	7	2017	01401
197	1401052020	10	2017	01401
198	1401052901	3	2017	01401
199	1401072008	1	2017	01401
281	1402012001	4	2017	01402
282	1402012002	38	2017	01402
283	1402012003	10	2017	01402
284	1402012004	1	2017	01402
285	1402012005	4	2017	01402
286	1402012006	8	2017	01402
287	1402012007	1	2017	01402
288	1402012008	6	2017	01402
289	1402012009	6	2017	01402
290	1402012010	2	2017	01402
291	1402992999	2	2017	01402
373	1403012008	18	2017	01403
374	1403012009	9	2017	01403
375	1403012012	1	2017	01403
376	1403022002	1	2017	01403
377	1403022005	4	2017	01403
378	1403022901	2	2017	01403
379	1403992999	6	2017	01403
461	1404022013	1	2017	01404
462	1404022016	2	2017	01404
463	1404022022	14	2017	01404
464	1404022024	1	2017	01404
465	1404022034	16	2017	01404
466	1404032014	1	2017	01404
467	1404032017	4	2017	01404
468	1404032020	2	2017	01404
469	1404032028	1	2017	01404
470	1404042023	25	2017	01404
471	1404042037	2	2017	01404
472	1404042901	5	2017	01404
473	1404052025	1	2017	01404
474	1404062005	1	2017	01404
475	1404062018	2	2017	01404
476	1404062901	5	2017	01404
477	1404072004	6	2017	01404
478	1404072015	1	2017	01404
479	1404072031	5	2017	01404
480	1404082901	1	2017	01404
562	1405012008	51	2017	01405
563	1405012010	5	2017	01405
564	1405012014	3	2017	01405
565	1405012901	2	2017	01405
566	1405022901	2	2017	01405
567	1405032009	2	2017	01405
NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA
NA.2	NA	NA	NA	NA
NA.3	NA	NA	NA	NA
NA.4	NA	NA	NA	NA
NA.5	NA	NA	NA	NA
NA.6	NA	NA	NA	NA
NA.7	NA	NA	NA	NA
NA.8	NA	NA	NA	NA
NA.9	NA	NA	NA	NA
NA.10	NA	NA	NA	NA
NA.11	NA	NA	NA	NA
NA.12	NA	NA	NA	NA
NA.13	NA	NA	NA	NA
NA.14	NA	NA	NA	NA
NA.15	NA	NA	NA	NA
NA.16	NA	NA	NA	NA
NA.17	NA	NA	NA	NA
NA.18	NA	NA	NA	NA

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_rural_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	289375.3	2017	1101	191468	55406102543	Rural
01401	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
01402	Camiña	262850.3	2017	1402	1250	328562901	Rural
01404	Huara	253968.5	2017	1404	2730	693334131	Rural
01405	Pica	290496.7	2017	1405	9296	2700457509	Rural
02103	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
02104	Taltal	345494.0	2017	2104	13317	4600943086	Rural
02201	Calama	310025.0	2017	2201	165731	51380756402	Rural
02203	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
02301	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
03101	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03103	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03202	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural
03301	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03302	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03303	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03304	Huasco	227560.7	2017	3304	10149	2309513927	Rural
04101	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04102	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04103	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04104	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04105	Paiguano	205942.1	2017	4105	4497	926121774	Rural
04106	Vicuña	176130.6	2017	4106	27771	4891322768	Rural
04201	Illapel	191976.8	2017	4201	30848	5922099530	Rural
04202	Canela	171370.3	2017	4202	9093	1558270441	Rural
04203	Los Vilos	173238.5	2017	4203	21382	3704185607	Rural
04204	Salamanca	223234.2	2017	4204	29347	6551254640	Rural
04301	Ovalle	241393.7	2017	4301	111272	26860360045	Rural
04302	Combarbalá	179139.6	2017	4302	13322	2386498044	Rural
04303	Monte Patria	201205.8	2017	4303	30751	6187280931	Rural
04304	Punitaqui	171931.7	2017	4304	10956	1883683880	Rural
04305	Río Hurtado	182027.2	2017	4305	4278	778712384	Rural
05101	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05102	Casablanca	268917.1	2017	5102	26867	7224996933	Rural
05105	Puchuncaví	279614.4	2017	5105	18546	5185728335	Rural
05107	Quintero	334628.2	2017	5107	31923	10682335196	Rural
05301	Los Andes	324402.1	2017	5301	66708	21640215030	Rural
05302	Calle Larga	242743.8	2017	5302	14832	3600375502	Rural
05303	Rinconada	326532.5	2017	5303	10207	3332917471	Rural
05304	San Esteban	223168.6	2017	5304	18855	4207844130	Rural
05401	La Ligua	181468.0	2017	5401	35390	6422154059	Rural
05402	Cabildo	231277.8	2017	5402	19388	4484014285	Rural
05404	Petorca	298208.9	2017	5404	9826	2930200178	Rural
05405	Zapallar	292882.3	2017	5405	7339	2149463129	Rural
05501	Quillota	220926.8	2017	5501	90517	19997628209	Rural
05502	Calera	226906.2	2017	5502	50554	11471016698	Rural
05503	Hijuelas	253739.9	2017	5503	17988	4564273201	Rural
05504	La Cruz	291124.1	2017	5504	22098	6433259569	Rural
05506	Nogales	264475.3	2017	5506	22120	5850194593	Rural
05601	San Antonio	266331.2	2017	5601	91350	24329353815	Rural

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
1	01101	1101092007	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
2	01101	1101092016	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
3	01101	1101092017	5	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
4	01101	1101092018	6	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
5	01101	1101092004	6	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
6	01101	1101092005	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
7	01101	1101092006	35	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
8	01101	1101112901	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
9	01101	1101092010	36	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
10	01101	1101092012	3	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
11	01101	1101092021	11	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
12	01101	1101092023	13	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
13	01101	1101092024	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
14	01101	1101092019	2	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
15	01101	1101112013	9	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
16	01101	1101112025	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
17	01101	1101112003	9	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
18	01107	1107032005	5	2017	NA	NA	NA	NA	NA	NA	NA
19	01107	1107042002	3	2017	NA	NA	NA	NA	NA	NA	NA
20	01401	1401012021	2	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
21	01401	1401012001	62	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
22	01401	1401012018	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
23	01401	1401032011	38	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
24	01401	1401032012	162	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
25	01401	1401032026	3	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
26	01401	1401032901	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
27	01401	1401052009	7	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
28	01401	1401052020	10	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
29	01401	1401012901	3	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
30	01401	1401022014	2	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
31	01401	1401022015	16	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
32	01401	1401022019	4	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
33	01401	1401022024	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
34	01401	1401032002	4	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
35	01401	1401032007	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
36	01401	1401052901	3	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
37	01401	1401072008	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
38	01402	1402012006	8	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural
39	01402	1402012007	1	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural
40	01402	1402012010	2	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural
41	01402	1402992999	2	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural
42	01402	1402012008	6	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural
43	01402	1402012009	6	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural
44	01402	1402012001	4	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural
45	01402	1402012002	38	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural
46	01402	1402012003	10	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural
47	01402	1402012004	1	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural
48	01402	1402012005	4	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural
49	01403	1403022005	4	2017	NA	NA	NA	NA	NA	NA	NA
50	01403	1403022901	2	2017	NA	NA	NA	NA	NA	NA	NA
51	01403	1403992999	6	2017	NA	NA	NA	NA	NA	NA	NA
52	01403	1403012012	1	2017	NA	NA	NA	NA	NA	NA	NA
53	01403	1403012009	9	2017	NA	NA	NA	NA	NA	NA	NA
54	01403	1403022002	1	2017	NA	NA	NA	NA	NA	NA	NA
55	01403	1403012008	18	2017	NA	NA	NA	NA	NA	NA	NA
56	01404	1404022013	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
57	01404	1404022016	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
58	01404	1404022022	14	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
59	01404	1404022024	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
60	01404	1404022034	16	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
61	01404	1404032014	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
62	01404	1404032017	4	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
63	01404	1404032020	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
64	01404	1404032028	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
65	01404	1404042023	25	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
66	01404	1404042037	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
67	01404	1404042901	5	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
68	01404	1404052025	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
69	01404	1404062005	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
70	01404	1404062018	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
71	01404	1404062901	5	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
72	01404	1404072004	6	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
73	01404	1404072015	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
74	01404	1404072031	5	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
75	01404	1404082901	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
76	01405	1405012008	51	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural
77	01405	1405012010	5	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural
78	01405	1405012014	3	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural
79	01405	1405012901	2	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural
80	01405	1405022901	2	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural
81	01405	1405032009	2	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
1	01101	1101092007	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
2	01101	1101092016	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
3	01101	1101092017	5	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
4	01101	1101092018	6	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
5	01101	1101092004	6	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
6	01101	1101092005	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
7	01101	1101092006	35	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
8	01101	1101112901	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
9	01101	1101092010	36	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
10	01101	1101092012	3	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
11	01101	1101092021	11	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
12	01101	1101092023	13	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
13	01101	1101092024	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
14	01101	1101092019	2	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
15	01101	1101112013	9	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
16	01101	1101112025	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
17	01101	1101112003	9	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural
18	01107	1107032005	5	2017	NA	NA	NA	NA	NA	NA	NA
19	01107	1107042002	3	2017	NA	NA	NA	NA	NA	NA	NA
20	01401	1401012021	2	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
21	01401	1401012001	62	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
22	01401	1401012018	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
23	01401	1401032011	38	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
24	01401	1401032012	162	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
25	01401	1401032026	3	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
26	01401	1401032901	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
27	01401	1401052009	7	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
28	01401	1401052020	10	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
29	01401	1401012901	3	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
30	01401	1401022014	2	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
31	01401	1401022015	16	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
32	01401	1401022019	4	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
33	01401	1401022024	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
34	01401	1401032002	4	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
35	01401	1401032007	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
36	01401	1401052901	3	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
37	01401	1401072008	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
38	01402	1402012006	8	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural
39	01402	1402012007	1	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural
40	01402	1402012010	2	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural
41	01402	1402992999	2	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural
42	01402	1402012008	6	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural
43	01402	1402012009	6	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural
44	01402	1402012001	4	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural
45	01402	1402012002	38	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural
46	01402	1402012003	10	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural
47	01402	1402012004	1	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural
48	01402	1402012005	4	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural
49	01403	1403022005	4	2017	NA	NA	NA	NA	NA	NA	NA
50	01403	1403022901	2	2017	NA	NA	NA	NA	NA	NA	NA
51	01403	1403992999	6	2017	NA	NA	NA	NA	NA	NA	NA
52	01403	1403012012	1	2017	NA	NA	NA	NA	NA	NA	NA
53	01403	1403012009	9	2017	NA	NA	NA	NA	NA	NA	NA
54	01403	1403022002	1	2017	NA	NA	NA	NA	NA	NA	NA
55	01403	1403012008	18	2017	NA	NA	NA	NA	NA	NA	NA
56	01404	1404022013	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
57	01404	1404022016	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
58	01404	1404022022	14	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
59	01404	1404022024	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
60	01404	1404022034	16	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
61	01404	1404032014	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
62	01404	1404032017	4	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
63	01404	1404032020	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
64	01404	1404032028	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
65	01404	1404042023	25	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
66	01404	1404042037	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
67	01404	1404042901	5	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
68	01404	1404052025	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
69	01404	1404062005	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
70	01404	1404062018	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
71	01404	1404062901	5	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
72	01404	1404072004	6	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
73	01404	1404072015	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
74	01404	1404072031	5	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
75	01404	1404082901	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural
76	01405	1405012008	51	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural
77	01405	1405012010	5	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural
78	01405	1405012014	3	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural
79	01405	1405012901	2	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural
80	01405	1405022901	2	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural
81	01405	1405032009	2	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
1101092004	01101	6	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	247	0.0012900	01101
1101092005	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	76	0.0003969	01101
1101092006	01101	35	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	603	0.0031494	01101
1101092007	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	84	0.0004387	01101
1101092010	01101	36	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	398	0.0020787	01101
1101092012	01101	3	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	58	0.0003029	01101
1101092016	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	20	0.0001045	01101
1101092017	01101	5	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	8	0.0000418	01101
1101092018	01101	6	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	74	0.0003865	01101
1101092019	01101	2	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	25	0.0001306	01101
1101092021	01101	11	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	177	0.0009244	01101
1101092023	01101	13	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	288	0.0015042	01101
1101092024	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	14	0.0000731	01101
1101112003	01101	9	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	33	0.0001724	01101
1101112013	01101	9	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	104	0.0005432	01101
1101112025	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	21	0.0001097	01101
1101112901	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	6	0.0000313	01101
1107032005	01107	5	2017	NA	NA	NA	NA	NA	NA	NA	38	0.0003506	01107
1107042002	01107	3	2017	NA	NA	NA	NA	NA	NA	NA	30	0.0002768	01107
1401012001	01401	62	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	684	0.0435364	01401
1401012018	01401	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	6	0.0003819	01401
1401012021	01401	2	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	66	0.0042009	01401
1401012901	01401	3	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	20	0.0012730	01401
1401022014	01401	2	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	34	0.0021641	01401
1401022015	01401	16	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	371	0.0236140	01401
1401022019	01401	4	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	24	0.0015276	01401
1401022024	01401	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	16	0.0010184	01401
1401032002	01401	4	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	53	0.0033734	01401
1401032007	01401	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	21	0.0013366	01401
1401032011	01401	38	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	446	0.0283878	01401
1401032012	01401	162	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	2025	0.1288906	01401
1401032026	01401	3	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	13	0.0008274	01401
1401032901	01401	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	8	0.0005092	01401
1401052009	01401	7	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	55	0.0035007	01401
1401052020	01401	10	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	143	0.0091019	01401
1401052901	01401	3	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	118	0.0075107	01401
1401072008	01401	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	83	0.0052829	01401
1402012001	01402	4	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	64	0.0512000	01402
1402012002	01402	38	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	434	0.3472000	01402
1402012003	01402	10	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	140	0.1120000	01402
1402012004	01402	1	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	53	0.0424000	01402
1402012005	01402	4	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	134	0.1072000	01402
1402012006	01402	8	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	198	0.1584000	01402
1402012007	01402	1	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	44	0.0352000	01402
1402012008	01402	6	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	87	0.0696000	01402
1402012009	01402	6	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	56	0.0448000	01402
1402012010	01402	2	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	32	0.0256000	01402
1402992999	01402	2	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	8	0.0064000	01402
1403012008	01403	18	2017	NA	NA	NA	NA	NA	NA	NA	676	0.3912037	01403
1403012009	01403	9	2017	NA	NA	NA	NA	NA	NA	NA	61	0.0353009	01403
1403012012	01403	1	2017	NA	NA	NA	NA	NA	NA	NA	11	0.0063657	01403
1403022002	01403	1	2017	NA	NA	NA	NA	NA	NA	NA	49	0.0283565	01403
1403022005	01403	4	2017	NA	NA	NA	NA	NA	NA	NA	136	0.0787037	01403
1403022901	01403	2	2017	NA	NA	NA	NA	NA	NA	NA	32	0.0185185	01403
1403992999	01403	6	2017	NA	NA	NA	NA	NA	NA	NA	415	0.2401620	01403
1404022013	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	16	0.0058608	01404
1404022016	01404	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	35	0.0128205	01404
1404022022	01404	14	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	186	0.0681319	01404
1404022024	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	9	0.0032967	01404
1404022034	01404	16	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	202	0.0739927	01404
1404032014	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	37	0.0135531	01404
1404032017	01404	4	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	64	0.0234432	01404
1404032020	01404	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	12	0.0043956	01404
1404032028	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	106	0.0388278	01404
1404042023	01404	25	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	287	0.1051282	01404
1404042037	01404	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	13	0.0047619	01404
1404042901	01404	5	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	143	0.0523810	01404
1404052025	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	68	0.0249084	01404
1404062005	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	15	0.0054945	01404
1404062018	01404	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	25	0.0091575	01404
1404062901	01404	5	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	25	0.0091575	01404
1404072004	01404	6	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	54	0.0197802	01404
1404072015	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	29	0.0106227	01404
1404072031	01404	5	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	49	0.0179487	01404
1404082901	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	26	0.0095238	01404
1405012008	01405	51	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	645	0.0693847	01405
1405012010	01405	5	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	53	0.0057014	01405
1405012014	01405	3	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	67	0.0072074	01405
1405012901	01405	2	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	115	0.0123709	01405
1405022901	01405	2	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	20	0.0021515	01405
1405032009	01405	2	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	11	0.0011833	01405

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
1101092004	01101	6	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	247	0.0012900	01101	71475690
1101092005	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	76	0.0003969	01101	21992520
1101092006	01101	35	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	603	0.0031494	01101	174493283
1101092007	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	84	0.0004387	01101	24307522
1101092010	01101	36	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	398	0.0020787	01101	115171354
1101092012	01101	3	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	58	0.0003029	01101	16783765
1101092016	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	20	0.0001045	01101	5787505
1101092017	01101	5	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	8	0.0000418	01101	2315002
1101092018	01101	6	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	74	0.0003865	01101	21413769
1101092019	01101	2	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	25	0.0001306	01101	7234382
1101092021	01101	11	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	177	0.0009244	01101	51219421
1101092023	01101	13	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	288	0.0015042	01101	83340075
1101092024	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	14	0.0000731	01101	4051254
1101112003	01101	9	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	33	0.0001724	01101	9549384
1101112013	01101	9	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	104	0.0005432	01101	30095027
1101112025	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	21	0.0001097	01101	6076880
1101112901	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	6	0.0000313	01101	1736252
1107032005	01107	5	2017	NA	NA	NA	NA	NA	NA	NA	38	0.0003506	01107	NA
1107042002	01107	3	2017	NA	NA	NA	NA	NA	NA	NA	30	0.0002768	01107	NA
1401012001	01401	62	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	684	0.0435364	01401	179939617
1401012018	01401	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	6	0.0003819	01401	1578418
1401012021	01401	2	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	66	0.0042009	01401	17362595
1401012901	01401	3	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	20	0.0012730	01401	5261392
1401022014	01401	2	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	34	0.0021641	01401	8944367
1401022015	01401	16	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	371	0.0236140	01401	97598827
1401022019	01401	4	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	24	0.0015276	01401	6313671
1401022024	01401	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	16	0.0010184	01401	4209114
1401032002	01401	4	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	53	0.0033734	01401	13942690
1401032007	01401	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	21	0.0013366	01401	5524462
1401032011	01401	38	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	446	0.0283878	01401	117329048
1401032012	01401	162	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	2025	0.1288906	01401	532715971
1401032026	01401	3	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	13	0.0008274	01401	3419905
1401032901	01401	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	8	0.0005092	01401	2104557
1401052009	01401	7	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	55	0.0035007	01401	14468829
1401052020	01401	10	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	143	0.0091019	01401	37618955
1401052901	01401	3	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	118	0.0075107	01401	31042215
1401072008	01401	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	83	0.0052829	01401	21834778
1402012001	01402	4	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	64	0.0512000	01402	16822421
1402012002	01402	38	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	434	0.3472000	01402	114077039
1402012003	01402	10	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	140	0.1120000	01402	36799045
1402012004	01402	1	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	53	0.0424000	01402	13931067
1402012005	01402	4	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	134	0.1072000	01402	35221943
1402012006	01402	8	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	198	0.1584000	01402	52044364
1402012007	01402	1	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	44	0.0352000	01402	11565414
1402012008	01402	6	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	87	0.0696000	01402	22867978
1402012009	01402	6	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	56	0.0448000	01402	14719618
1402012010	01402	2	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	32	0.0256000	01402	8411210
1402992999	01402	2	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	8	0.0064000	01402	2102803
1403012008	01403	18	2017	NA	NA	NA	NA	NA	NA	NA	676	0.3912037	01403	NA
1403012009	01403	9	2017	NA	NA	NA	NA	NA	NA	NA	61	0.0353009	01403	NA
1403012012	01403	1	2017	NA	NA	NA	NA	NA	NA	NA	11	0.0063657	01403	NA
1403022002	01403	1	2017	NA	NA	NA	NA	NA	NA	NA	49	0.0283565	01403	NA
1403022005	01403	4	2017	NA	NA	NA	NA	NA	NA	NA	136	0.0787037	01403	NA
1403022901	01403	2	2017	NA	NA	NA	NA	NA	NA	NA	32	0.0185185	01403	NA
1403992999	01403	6	2017	NA	NA	NA	NA	NA	NA	NA	415	0.2401620	01403	NA
1404022013	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	16	0.0058608	01404	4063497
1404022016	01404	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	35	0.0128205	01404	8888899
1404022022	01404	14	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	186	0.0681319	01404	47238150
1404022024	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	9	0.0032967	01404	2285717
1404022034	01404	16	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	202	0.0739927	01404	51301646
1404032014	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	37	0.0135531	01404	9396836
1404032017	01404	4	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	64	0.0234432	01404	16253987
1404032020	01404	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	12	0.0043956	01404	3047623
1404032028	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	106	0.0388278	01404	26920666
1404042023	01404	25	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	287	0.1051282	01404	72888973
1404042037	01404	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	13	0.0047619	01404	3301591
1404042901	01404	5	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	143	0.0523810	01404	36317502
1404052025	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	68	0.0249084	01404	17269861
1404062005	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	15	0.0054945	01404	3809528
1404062018	01404	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	25	0.0091575	01404	6349214
1404062901	01404	5	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	25	0.0091575	01404	6349214
1404072004	01404	6	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	54	0.0197802	01404	13714302
1404072015	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	29	0.0106227	01404	7365088
1404072031	01404	5	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	49	0.0179487	01404	12444459
1404082901	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	26	0.0095238	01404	6603182
1405012008	01405	51	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	645	0.0693847	01405	187370384
1405012010	01405	5	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	53	0.0057014	01405	15396326
1405012014	01405	3	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	67	0.0072074	01405	19463280
1405012901	01405	2	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	115	0.0123709	01405	33407123
1405022901	01405	2	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	20	0.0021515	01405	5809934
1405032009	01405	2	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	11	0.0011833	01405	3195464

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -27486873  -7907690  -3694770   4356223  54965400 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  5585244    1927735   2.897  0.00502 ** 
## Freq.x       3255504      81086  40.149  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14910000 on 70 degrees of freedom
##   (9 observations deleted due to missingness)
## Multiple R-squared:  0.9584, Adjusted R-squared:  0.9578 
## F-statistic:  1612 on 1 and 70 DF,  p-value: < 2.2e-16

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.957786989243641"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.957786989243641"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.527549428690524"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.841784700866463"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.864014333020971"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.607636709149579"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.702439083384413"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.635607026159519"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 3   logarítmico 0.527549428690524
## 6      log-raíz 0.607636709149579
## 8       log-log 0.635607026159519
## 7      raíz-log 0.702439083384413
## 4 raíz cuadrada 0.841784700866463
## 5     raíz-raíz 0.864014333020971
## 1    cuadrático 0.957786989243641
## 2        cúbico 0.957786989243641
##                                                                     sintaxis
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)

metodo <- 1
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x^2), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -27486873  -7907690  -3694770   4356223  54965400 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  5585244    1927735   2.897  0.00502 ** 
## Freq.x       3255504      81086  40.149  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14910000 on 70 degrees of freedom
##   (9 observations deleted due to missingness)
## Multiple R-squared:  0.9584, Adjusted R-squared:  0.9578 
## F-statistic:  1612 on 1 and 70 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##     5585244

bb <- linearMod$coefficients[2]
bb

##  Freq.x 
## 3255504

9 Modelo cuadrático (cuadrático)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.9578 ).

9.1 Diagrama de dispersión sobre cuadrático

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=(h_y_m_comuna_corr_01$Freq.x^2), y=(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo cuadrático

Observemos nuevamente el resultado sobre cuadrático.

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x^2), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -27486873  -7907690  -3694770   4356223  54965400 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  5585244    1927735   2.897  0.00502 ** 
## Freq.x       3255504      81086  40.149  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14910000 on 70 degrees of freedom
##   (9 observations deleted due to missingness)
## Multiple R-squared:  0.9584, Adjusted R-squared:  0.9578 
## F-statistic:  1612 on 1 and 70 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = (Freq.x^2) , y = (multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = 5585244 + 3255504\cdot X^2 \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- aa+bb * (h_y_m_comuna_corr_01$Freq.x^2)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
1	1101092004	01101	6	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	247	0.0012900	01101	71475690	122783387
2	1101092005	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	76	0.0003969	01101	21992520	8840748
3	1101092006	01101	35	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	603	0.0031494	01101	174493283	3993577608
4	1101092007	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	84	0.0004387	01101	24307522	8840748
5	1101092010	01101	36	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	398	0.0020787	01101	115171354	4224718390
6	1101092012	01101	3	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	58	0.0003029	01101	16783765	34884779
7	1101092016	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	20	0.0001045	01101	5787505	8840748
8	1101092017	01101	5	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	8	0.0000418	01101	2315002	86972843
9	1101092018	01101	6	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	74	0.0003865	01101	21413769	122783387
10	1101092019	01101	2	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	25	0.0001306	01101	7234382	18607260
11	1101092021	01101	11	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	177	0.0009244	01101	51219421	399501224
12	1101092023	01101	13	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	288	0.0015042	01101	83340075	555765415
13	1101092024	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	14	0.0000731	01101	4051254	8840748
14	1101112003	01101	9	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	33	0.0001724	01101	9549384	269281065
15	1101112013	01101	9	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	104	0.0005432	01101	30095027	269281065
16	1101112025	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	21	0.0001097	01101	6076880	8840748
17	1101112901	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	6	0.0000313	01101	1736252	8840748
18	1107032005	01107	5	2017	NA	NA	NA	NA	NA	NA	NA	38	0.0003506	01107	NA	86972843
19	1107042002	01107	3	2017	NA	NA	NA	NA	NA	NA	NA	30	0.0002768	01107	NA	34884779
20	1401012001	01401	62	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	684	0.0435364	01401	179939617	12519742508
21	1401012018	01401	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	6	0.0003819	01401	1578418	8840748
22	1401012021	01401	2	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	66	0.0042009	01401	17362595	18607260
23	1401012901	01401	3	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	20	0.0012730	01401	5261392	34884779
24	1401022014	01401	2	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	34	0.0021641	01401	8944367	18607260
25	1401022015	01401	16	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	371	0.0236140	01401	97598827	838994260
26	1401022019	01401	4	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	24	0.0015276	01401	6313671	57673307
27	1401022024	01401	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	16	0.0010184	01401	4209114	8840748
28	1401032002	01401	4	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	53	0.0033734	01401	13942690	57673307
29	1401032007	01401	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	21	0.0013366	01401	5524462	8840748
30	1401032011	01401	38	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	446	0.0283878	01401	117329048	4706532978
31	1401032012	01401	162	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	2025	0.1288906	01401	532715971	85443031454
32	1401032026	01401	3	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	13	0.0008274	01401	3419905	34884779
33	1401032901	01401	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	8	0.0005092	01401	2104557	8840748
34	1401052009	01401	7	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	55	0.0035007	01401	14468829	165104938
35	1401052020	01401	10	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	143	0.0091019	01401	37618955	331135641
36	1401052901	01401	3	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	118	0.0075107	01401	31042215	34884779
37	1401072008	01401	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	83	0.0052829	01401	21834778	8840748
38	1402012001	01402	4	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	64	0.0512000	01402	16822421	57673307
39	1402012002	01402	38	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	434	0.3472000	01402	114077039	4706532978
40	1402012003	01402	10	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	140	0.1120000	01402	36799045	331135641
41	1402012004	01402	1	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	53	0.0424000	01402	13931067	8840748
42	1402012005	01402	4	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	134	0.1072000	01402	35221943	57673307
43	1402012006	01402	8	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	198	0.1584000	01402	52044364	213937498
44	1402012007	01402	1	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	44	0.0352000	01402	11565414	8840748
45	1402012008	01402	6	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	87	0.0696000	01402	22867978	122783387
46	1402012009	01402	6	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	56	0.0448000	01402	14719618	122783387
47	1402012010	01402	2	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	32	0.0256000	01402	8411210	18607260
48	1402992999	01402	2	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	8	0.0064000	01402	2102803	18607260
49	1403012008	01403	18	2017	NA	NA	NA	NA	NA	NA	NA	676	0.3912037	01403	NA	1060368530
50	1403012009	01403	9	2017	NA	NA	NA	NA	NA	NA	NA	61	0.0353009	01403	NA	269281065
51	1403012012	01403	1	2017	NA	NA	NA	NA	NA	NA	NA	11	0.0063657	01403	NA	8840748
52	1403022002	01403	1	2017	NA	NA	NA	NA	NA	NA	NA	49	0.0283565	01403	NA	8840748
53	1403022005	01403	4	2017	NA	NA	NA	NA	NA	NA	NA	136	0.0787037	01403	NA	57673307
54	1403022901	01403	2	2017	NA	NA	NA	NA	NA	NA	NA	32	0.0185185	01403	NA	18607260
55	1403992999	01403	6	2017	NA	NA	NA	NA	NA	NA	NA	415	0.2401620	01403	NA	122783387
56	1404022013	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	16	0.0058608	01404	4063497	8840748
57	1404022016	01404	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	35	0.0128205	01404	8888899	18607260
58	1404022022	01404	14	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	186	0.0681319	01404	47238150	643664022
59	1404022024	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	9	0.0032967	01404	2285717	8840748
60	1404022034	01404	16	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	202	0.0739927	01404	51301646	838994260
61	1404032014	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	37	0.0135531	01404	9396836	8840748
62	1404032017	01404	4	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	64	0.0234432	01404	16253987	57673307
63	1404032020	01404	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	12	0.0043956	01404	3047623	18607260
64	1404032028	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	106	0.0388278	01404	26920666	8840748
65	1404042023	01404	25	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	287	0.1051282	01404	72888973	2040275225
66	1404042037	01404	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	13	0.0047619	01404	3301591	18607260
67	1404042901	01404	5	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	143	0.0523810	01404	36317502	86972843
68	1404052025	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	68	0.0249084	01404	17269861	8840748
69	1404062005	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	15	0.0054945	01404	3809528	8840748
70	1404062018	01404	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	25	0.0091575	01404	6349214	18607260
71	1404062901	01404	5	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	25	0.0091575	01404	6349214	86972843
72	1404072004	01404	6	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	54	0.0197802	01404	13714302	122783387
73	1404072015	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	29	0.0106227	01404	7365088	8840748
74	1404072031	01404	5	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	49	0.0179487	01404	12444459	86972843
75	1404082901	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	26	0.0095238	01404	6603182	8840748
76	1405012008	01405	51	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	645	0.0693847	01405	187370384	8473151072
77	1405012010	01405	5	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	53	0.0057014	01405	15396326	86972843
78	1405012014	01405	3	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	67	0.0072074	01405	19463280	34884779
79	1405012901	01405	2	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	115	0.0123709	01405	33407123	18607260
80	1405022901	01405	2	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	20	0.0021515	01405	5809934	18607260
81	1405032009	01405	2	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	11	0.0011833	01405	3195464	18607260
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
1	1101092004	01101	6	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	247	0.0012900	01101	71475690	122783387	497098.73
2	1101092005	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	76	0.0003969	01101	21992520	8840748	116325.63
3	1101092006	01101	35	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	603	0.0031494	01101	174493283	3993577608	6622848.44
4	1101092007	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	84	0.0004387	01101	24307522	8840748	105247.00
5	1101092010	01101	36	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	398	0.0020787	01101	115171354	4224718390	10614870.33
6	1101092012	01101	3	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	58	0.0003029	01101	16783765	34884779	601461.71
7	1101092016	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	20	0.0001045	01101	5787505	8840748	442037.38
8	1101092017	01101	5	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	8	0.0000418	01101	2315002	86972843	10871605.36
9	1101092018	01101	6	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	74	0.0003865	01101	21413769	122783387	1659234.95
10	1101092019	01101	2	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	25	0.0001306	01101	7234382	18607260	744290.38
11	1101092021	01101	11	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	177	0.0009244	01101	51219421	399501224	2257069.06
12	1101092023	01101	13	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	288	0.0015042	01101	83340075	555765415	1929741.02
13	1101092024	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	14	0.0000731	01101	4051254	8840748	631481.97
14	1101112003	01101	9	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	33	0.0001724	01101	9549384	269281065	8160032.28
15	1101112013	01101	9	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	104	0.0005432	01101	30095027	269281065	2589241.01
16	1101112025	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	21	0.0001097	01101	6076880	8840748	420987.98
17	1101112901	01101	1	2017	Iquique	289375.3	2017	1101	191468	55406102543	Rural	6	0.0000313	01101	1736252	8840748	1473457.93
18	1107032005	01107	5	2017	NA	NA	NA	NA	NA	NA	NA	38	0.0003506	01107	NA	86972843	NA
19	1107042002	01107	3	2017	NA	NA	NA	NA	NA	NA	NA	30	0.0002768	01107	NA	34884779	NA
20	1401012001	01401	62	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	684	0.0435364	01401	179939617	12519742508	18303717.12
21	1401012018	01401	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	6	0.0003819	01401	1578418	8840748	1473457.93
22	1401012021	01401	2	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	66	0.0042009	01401	17362595	18607260	281928.17
23	1401012901	01401	3	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	20	0.0012730	01401	5261392	34884779	1744238.97
24	1401022014	01401	2	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	34	0.0021641	01401	8944367	18607260	547272.34
25	1401022015	01401	16	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	371	0.0236140	01401	97598827	838994260	2261440.05
26	1401022019	01401	4	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	24	0.0015276	01401	6313671	57673307	2403054.47
27	1401022024	01401	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	16	0.0010184	01401	4209114	8840748	552546.72
28	1401032002	01401	4	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	53	0.0033734	01401	13942690	57673307	1088175.61
29	1401032007	01401	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	21	0.0013366	01401	5524462	8840748	420987.98
30	1401032011	01401	38	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	446	0.0283878	01401	117329048	4706532978	10552764.52
31	1401032012	01401	162	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	2025	0.1288906	01401	532715971	85443031454	42194089.61
32	1401032026	01401	3	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	13	0.0008274	01401	3419905	34884779	2683444.57
33	1401032901	01401	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	8	0.0005092	01401	2104557	8840748	1105093.45
34	1401052009	01401	7	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	55	0.0035007	01401	14468829	165104938	3001907.97
35	1401052020	01401	10	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	143	0.0091019	01401	37618955	331135641	2315633.85
36	1401052901	01401	3	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	118	0.0075107	01401	31042215	34884779	295633.72
37	1401072008	01401	1	2017	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural	83	0.0052829	01401	21834778	8840748	106515.03
38	1402012001	01402	4	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	64	0.0512000	01402	16822421	57673307	901145.42
39	1402012002	01402	38	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	434	0.3472000	01402	114077039	4706532978	10844546.03
40	1402012003	01402	10	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	140	0.1120000	01402	36799045	331135641	2365254.58
41	1402012004	01402	1	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	53	0.0424000	01402	13931067	8840748	166806.56
42	1402012005	01402	4	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	134	0.1072000	01402	35221943	57673307	430397.81
43	1402012006	01402	8	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	198	0.1584000	01402	52044364	213937498	1080492.41
44	1402012007	01402	1	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	44	0.0352000	01402	11565414	8840748	200926.08
45	1402012008	01402	6	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	87	0.0696000	01402	22867978	122783387	1411303.29
46	1402012009	01402	6	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	56	0.0448000	01402	14719618	122783387	2192560.47
47	1402012010	01402	2	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	32	0.0256000	01402	8411210	18607260	581476.86
48	1402992999	01402	2	2017	Camiña	262850.3	2017	1402	1250	328562901	Rural	8	0.0064000	01402	2102803	18607260	2325907.44
49	1403012008	01403	18	2017	NA	NA	NA	NA	NA	NA	NA	676	0.3912037	01403	NA	1060368530	NA
50	1403012009	01403	9	2017	NA	NA	NA	NA	NA	NA	NA	61	0.0353009	01403	NA	269281065	NA
51	1403012012	01403	1	2017	NA	NA	NA	NA	NA	NA	NA	11	0.0063657	01403	NA	8840748	NA
52	1403022002	01403	1	2017	NA	NA	NA	NA	NA	NA	NA	49	0.0283565	01403	NA	8840748	NA
53	1403022005	01403	4	2017	NA	NA	NA	NA	NA	NA	NA	136	0.0787037	01403	NA	57673307	NA
54	1403022901	01403	2	2017	NA	NA	NA	NA	NA	NA	NA	32	0.0185185	01403	NA	18607260	NA
55	1403992999	01403	6	2017	NA	NA	NA	NA	NA	NA	NA	415	0.2401620	01403	NA	122783387	NA
56	1404022013	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	16	0.0058608	01404	4063497	8840748	552546.72
57	1404022016	01404	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	35	0.0128205	01404	8888899	18607260	531635.99
58	1404022022	01404	14	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	186	0.0681319	01404	47238150	643664022	3460559.26
59	1404022024	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	9	0.0032967	01404	2285717	8840748	982305.29
60	1404022034	01404	16	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	202	0.0739927	01404	51301646	838994260	4153436.93
61	1404032014	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	37	0.0135531	01404	9396836	8840748	238939.12
62	1404032017	01404	4	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	64	0.0234432	01404	16253987	57673307	901145.42
63	1404032020	01404	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	12	0.0043956	01404	3047623	18607260	1550604.96
64	1404032028	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	106	0.0388278	01404	26920666	8840748	83403.28
65	1404042023	01404	25	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	287	0.1051282	01404	72888973	2040275225	7108972.91
66	1404042037	01404	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	13	0.0047619	01404	3301591	18607260	1431327.65
67	1404042901	01404	5	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	143	0.0523810	01404	36317502	86972843	608201.70
68	1404052025	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	68	0.0249084	01404	17269861	8840748	130010.99
69	1404062005	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	15	0.0054945	01404	3809528	8840748	589383.17
70	1404062018	01404	2	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	25	0.0091575	01404	6349214	18607260	744290.38
71	1404062901	01404	5	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	25	0.0091575	01404	6349214	86972843	3478913.72
72	1404072004	01404	6	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	54	0.0197802	01404	13714302	122783387	2273766.42
73	1404072015	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	29	0.0106227	01404	7365088	8840748	304853.37
74	1404072031	01404	5	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	49	0.0179487	01404	12444459	86972843	1774955.98
75	1404082901	01404	1	2017	Huara	253968.5	2017	1404	2730	693334131	Rural	26	0.0095238	01404	6603182	8840748	340028.75
76	1405012008	01405	51	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	645	0.0693847	01405	187370384	8473151072	13136668.33
77	1405012010	01405	5	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	53	0.0057014	01405	15396326	86972843	1640997.04
78	1405012014	01405	3	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	67	0.0072074	01405	19463280	34884779	520668.35
79	1405012901	01405	2	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	115	0.0123709	01405	33407123	18607260	161802.26
80	1405022901	01405	2	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	20	0.0021515	01405	5809934	18607260	930362.98
81	1405032009	01405	2	2017	Pica	290496.7	2017	1405	9296	2700457509	Rural	11	0.0011833	01405	3195464	18607260	1691569.05
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_rural_nivel_nacional_17_r01.rds")

---

R-02

Urbano

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 1)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15201	1	1	1	1767	1	1	1	2	1	3	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	2	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	3	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	4	1	1	2	3	1	99	1	2	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	5	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	6	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	7	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	8	1	1	3	3	1	99	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	9	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	10	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	11	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	12	1	1	4	3	3	3	4	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	13	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	14	1	1	2	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	15	1	1	2	1	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	16	1	1	5	1	5	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	17	1	1	5	3	1	3	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	18	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	19	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	20	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	21	4	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	22	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	23	1	1	5	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	24	1	1	3	1	1	6	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	25	1	1	5	6	1	1	4	2	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	26	4	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	27	1	1	5	99	3	3	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	28	1	1	6	3	2	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	29	1	1	2	3	1	99	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	30	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	31	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	32	1	1	3	1	1	2	1	1	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	33	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	34	3	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	35	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	36	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	37	1	1	5	3	3	1	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	38	1	1	1	1	4	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	39	1	1	5	3	4	3	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	40	1	1	5	3	2	1	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	41	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	42	1	1	2	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	43	1	1	1	1	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	44	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	45	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	46	1	1	5	6	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	47	1	1	5	3	4	0	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	48	1	1	3	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	49	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	50	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	51	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	52	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	53	1	1	1	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	54	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	55	1	1	2	2	1	4	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	56	8	1	98	98	98	98	98	98	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	57	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	58	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	59	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	60	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	61	1	1	2	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	62	1	1	5	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	63	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	64	1	1	5	3	2	4	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	65	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	66	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	67	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	68	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	69	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	70	1	1	1	3	3	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	71	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	72	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	73	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	74	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	75	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	76	1	1	5	3	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	77	1	1	3	6	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	78	5	1	3	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	79	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	80	1	1	2	1	1	3	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	81	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	82	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	83	1	1	5	3	2	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	84	4	1	5	3	2	0	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	85	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	86	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	87	1	1	3	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	88	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	89	5	1	4	3	2	1	2	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	90	1	1	5	3	3	2	1	3	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	91	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	92	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	93	5	1	4	3	5	1	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	94	1	1	5	3	3	99	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	95	1	1	5	3	2	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	96	1	1	2	3	4	6	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	97	1	1	5	1	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	98	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	99	1	1	2	7	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	100	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 2:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 2)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	2101011001	240	2017	02101
2	2101011002	442	2017	02101
3	2101011003	410	2017	02101
4	2101011004	872	2017	02101
5	2101011005	542	2017	02101
6	2101011006	97	2017	02101
7	2101011008	318	2017	02101
8	2101011009	462	2017	02101
9	2101011010	264	2017	02101
10	2101011011	79	2017	02101
11	2101011012	282	2017	02101
12	2101011013	735	2017	02101
13	2101011014	516	2017	02101
14	2101011015	424	2017	02101
15	2101011016	121	2017	02101
16	2101011017	317	2017	02101
17	2101011018	745	2017	02101
18	2101011019	166	2017	02101
19	2101011020	604	2017	02101
20	2101011021	124	2017	02101
21	2101011022	136	2017	02101
22	2101021001	287	2017	02101
23	2101021002	267	2017	02101
24	2101021003	194	2017	02101
25	2101021004	351	2017	02101
26	2101021005	301	2017	02101
27	2101031001	295	2017	02101
28	2101031002	256	2017	02101
29	2101031003	123	2017	02101
30	2101031004	352	2017	02101
31	2101031005	168	2017	02101
32	2101031006	327	2017	02101
33	2101041001	324	2017	02101
34	2101041002	311	2017	02101
35	2101041003	232	2017	02101
36	2101041004	232	2017	02101
37	2101041005	252	2017	02101
38	2101051001	389	2017	02101
39	2101051002	332	2017	02101
40	2101051003	318	2017	02101
41	2101061001	262	2017	02101
42	2101061002	302	2017	02101
43	2101061003	356	2017	02101
44	2101071001	315	2017	02101
45	2101071002	223	2017	02101
46	2101071003	390	2017	02101
47	2101071004	273	2017	02101
48	2101071005	224	2017	02101
49	2101081001	242	2017	02101
50	2101081002	305	2017	02101
51	2101081003	198	2017	02101
52	2101081004	111	2017	02101
53	2101091001	293	2017	02101
54	2101091002	376	2017	02101
55	2101091003	93	2017	02101
56	2101091004	303	2017	02101
57	2101091005	429	2017	02101
58	2101091006	200	2017	02101
59	2101091007	153	2017	02101
60	2101091008	287	2017	02101
61	2101091009	210	2017	02101
62	2101091010	251	2017	02101
63	2101101001	375	2017	02101
64	2101101002	134	2017	02101
65	2101101003	175	2017	02101
66	2101141001	589	2017	02101
67	2101141002	454	2017	02101
68	2101141003	204	2017	02101
69	2101141004	436	2017	02101
70	2101141005	360	2017	02101
71	2101141006	436	2017	02101
72	2101141007	196	2017	02101
73	2101141008	180	2017	02101
74	2101141009	396	2017	02101
75	2101151001	229	2017	02101
76	2101151002	278	2017	02101
77	2101151003	240	2017	02101
78	2101151004	453	2017	02101
79	2101161001	291	2017	02101
80	2101161002	258	2017	02101
81	2101161003	184	2017	02101
82	2101161004	254	2017	02101
83	2101161005	257	2017	02101
84	2101171001	114	2017	02101
85	2101171002	240	2017	02101
86	2101171003	393	2017	02101
87	2101171004	385	2017	02101
88	2101181001	559	2017	02101
89	2101181002	484	2017	02101
90	2101181003	348	2017	02101
91	2101181004	115	2017	02101
92	2101991999	150	2017	02101
248	2102011001	267	2017	02102
249	2102011002	432	2017	02102
250	2102991999	8	2017	02102
406	2104011001	109	2017	02104
407	2104021001	176	2017	02104
408	2104031001	346	2017	02104
409	2104991999	7	2017	02104
565	2201011001	354	2017	02201

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_urbano_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
01107	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
01401	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
01405	Pica	330061.1	2017	1405	9296	3068247619	Urbano
02101	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02102	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02104	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02201	Calama	416281.1	2017	2201	165731	68990679686	Urbano
02203	San Pedro de Atacama	437934.7	2017	2203	10996	4815529626	Urbano
02301	Tocopilla	271720.8	2017	2301	25186	6843559467	Urbano
02302	María Elena	466266.9	2017	2302	6457	3010685220	Urbano
03101	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
03102	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
03103	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
03201	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
03202	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
03301	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
03303	Freirina	289049.9	2017	3303	7041	2035200054	Urbano
03304	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
04101	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04102	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04103	Andacollo	251267.7	2017	4103	11044	2775000288	Urbano
04104	La Higuera	214257.0	2017	4104	4241	908664019	Urbano
04106	Vicuña	245957.4	2017	4106	27771	6830481918	Urbano
04201	Illapel	270316.5	2017	4201	30848	8338722128	Urbano
04202	Canela	233397.3	2017	4202	9093	2122281844	Urbano
04203	Los Vilos	282415.6	2017	4203	21382	6038609501	Urbano
04204	Salamanca	262056.9	2017	4204	29347	7690585032	Urbano
04301	Ovalle	274771.4	2017	4301	111272	30574361012	Urbano
04302	Combarbalá	228990.4	2017	4302	13322	3050610572	Urbano
04303	Monte Patria	225369.1	2017	4303	30751	6930326684	Urbano
04304	Punitaqui	212496.1	2017	4304	10956	2328107498	Urbano
05101	Valparaíso	297929.0	2017	5101	296655	88382118059	Urbano
05102	Casablanca	341641.8	2017	5102	26867	9178890241	Urbano
05103	Concón	318496.3	2017	5103	42152	13425257057	Urbano
05105	Puchuncaví	296035.5	2017	5105	18546	5490274928	Urbano
05107	Quintero	308224.7	2017	5107	31923	9839456903	Urbano
05109	Viña del Mar	337006.1	2017	5109	334248	112643604611	Urbano
05301	Los Andes	339720.2	2017	5301	66708	22662055502	Urbano
05302	Calle Larga	246387.3	2017	5302	14832	3654416747	Urbano
05303	Rinconada	273904.7	2017	5303	10207	2795744821	Urbano
05304	San Esteban	219571.6	2017	5304	18855	4140022481	Urbano
05401	La Ligua	250134.4	2017	5401	35390	8852256241	Urbano
05402	Cabildo	262745.9	2017	5402	19388	5094117762	Urbano
05403	Papudo	294355.2	2017	5403	6356	1870921373	Urbano
05404	Petorca	237510.8	2017	5404	9826	2333781007	Urbano
05405	Zapallar	294389.2	2017	5405	7339	2160521991	Urbano
05501	Quillota	286029.5	2017	5501	90517	25890529852	Urbano
05502	Calera	277181.9	2017	5502	50554	14012652087	Urbano
05503	Hijuelas	254094.0	2017	5503	17988	4570642363	Urbano

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
02101	2101011001	240	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011002	442	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011003	410	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011004	872	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011005	542	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011006	97	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011008	318	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011009	462	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011010	264	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011011	79	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011012	282	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011013	735	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011014	516	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011015	424	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011016	121	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011017	317	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011018	745	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011019	166	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011020	604	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011021	124	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011022	136	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101021001	287	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101021002	267	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101021003	194	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101021004	351	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101021005	301	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101031001	295	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101031002	256	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101031003	123	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101031004	352	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101031005	168	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101031006	327	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101041001	324	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101041002	311	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101041003	232	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101041004	232	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101041005	252	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101051001	389	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101051002	332	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101051003	318	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101061001	262	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101061002	302	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101061003	356	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101071001	315	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101071002	223	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101071003	390	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101071004	273	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101071005	224	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101081001	242	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101081002	305	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101081003	198	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101081004	111	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101091001	293	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101091002	376	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101091003	93	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101091004	303	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101091005	429	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101091006	200	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101091007	153	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101091008	287	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101091009	210	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101091010	251	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101101001	375	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101101002	134	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101101003	175	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101141001	589	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101141002	454	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101141003	204	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101141004	436	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101141005	360	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101141006	436	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101141007	196	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101141008	180	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101141009	396	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101151001	229	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101151002	278	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101151003	240	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101151004	453	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101161001	291	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101161002	258	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101161003	184	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101161004	254	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101161005	257	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101171001	114	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101171002	240	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101171003	393	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101171004	385	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101181001	559	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101181002	484	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101181003	348	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101181004	115	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101991999	150	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02102	2102011001	267	2017	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02102	2102011002	432	2017	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02102	2102991999	8	2017	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02104	2104011001	109	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02104	2104021001	176	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02104	2104031001	346	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02104	2104991999	7	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02201	2201011001	354	2017	Calama	416281.1	2017	2201	165731	68990679686	Urbano

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
02101	2101011001	240	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011002	442	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011003	410	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011004	872	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011005	542	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011006	97	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011008	318	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011009	462	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011010	264	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011011	79	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011012	282	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011013	735	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011014	516	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011015	424	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011016	121	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011017	317	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011018	745	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011019	166	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011020	604	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011021	124	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101011022	136	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101021001	287	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101021002	267	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101021003	194	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101021004	351	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101021005	301	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101031001	295	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101031002	256	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101031003	123	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101031004	352	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101031005	168	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101031006	327	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101041001	324	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101041002	311	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101041003	232	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101041004	232	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101041005	252	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101051001	389	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101051002	332	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101051003	318	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101061001	262	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101061002	302	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101061003	356	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101071001	315	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101071002	223	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101071003	390	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101071004	273	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101071005	224	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101081001	242	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101081002	305	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101081003	198	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101081004	111	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101091001	293	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101091002	376	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101091003	93	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101091004	303	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101091005	429	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101091006	200	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101091007	153	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101091008	287	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101091009	210	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101091010	251	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101101001	375	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101101002	134	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101101003	175	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101141001	589	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101141002	454	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101141003	204	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101141004	436	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101141005	360	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101141006	436	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101141007	196	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101141008	180	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101141009	396	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101151001	229	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101151002	278	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101151003	240	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101151004	453	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101161001	291	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101161002	258	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101161003	184	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101161004	254	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101161005	257	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101171001	114	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101171002	240	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101171003	393	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101171004	385	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101181001	559	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101181002	484	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101181003	348	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101181004	115	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02101	2101991999	150	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02102	2102011001	267	2017	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02102	2102011002	432	2017	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02102	2102991999	8	2017	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02104	2104011001	109	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02104	2104021001	176	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02104	2104031001	346	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02104	2104991999	7	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02201	2201011001	354	2017	Calama	416281.1	2017	2201	165731	68990679686	Urbano

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
2101011001	02101	240	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4618	0.0127614	02101
2101011002	02101	442	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3644	0.0100698	02101
2101011003	02101	410	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5645	0.0155994	02101
2101011004	02101	872	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4385	0.0121175	02101
2101011005	02101	542	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2383	0.0065852	02101
2101011006	02101	97	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1466	0.0040511	02101
2101011008	02101	318	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	6487	0.0179262	02101
2101011009	02101	462	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	6152	0.0170004	02101
2101011010	02101	264	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4495	0.0124215	02101
2101011011	02101	79	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2445	0.0067565	02101
2101011012	02101	282	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4275	0.0118135	02101
2101011013	02101	735	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2947	0.0081437	02101
2101011014	02101	516	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	6247	0.0172630	02101
2101011015	02101	424	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2887	0.0079779	02101
2101011016	02101	121	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1643	0.0045403	02101
2101011017	02101	317	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4126	0.0114018	02101
2101011018	02101	745	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4567	0.0126204	02101
2101011019	02101	166	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1843	0.0050929	02101
2101011020	02101	604	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2874	0.0079420	02101
2101011021	02101	124	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2753	0.0076076	02101
2101011022	02101	136	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2805	0.0077513	02101
2101021001	02101	287	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3642	0.0100643	02101
2101021002	02101	267	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4658	0.0128719	02101
2101021003	02101	194	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2620	0.0072401	02101
2101021004	02101	351	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4532	0.0125237	02101
2101021005	02101	301	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5748	0.0158840	02101
2101031001	02101	295	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3779	0.0104429	02101
2101031002	02101	256	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2511	0.0069389	02101
2101031003	02101	123	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2087	0.0057672	02101
2101031004	02101	352	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3200	0.0088429	02101
2101031005	02101	168	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3138	0.0086716	02101
2101031006	02101	327	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3335	0.0092159	02101
2101041001	02101	324	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4379	0.0121009	02101
2101041002	02101	311	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4125	0.0113990	02101
2101041003	02101	232	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2518	0.0069582	02101
2101041004	02101	232	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2923	0.0080774	02101
2101041005	02101	252	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4618	0.0127614	02101
2101051001	02101	389	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4321	0.0119407	02101
2101051002	02101	332	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5027	0.0138916	02101
2101051003	02101	318	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5139	0.0142011	02101
2101061001	02101	262	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3741	0.0103379	02101
2101061002	02101	302	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2750	0.0075994	02101
2101061003	02101	356	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3370	0.0093127	02101
2101071001	02101	315	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4201	0.0116090	02101
2101071002	02101	223	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2844	0.0078591	02101
2101071003	02101	390	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	7624	0.0210682	02101
2101071004	02101	273	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3724	0.0102909	02101
2101071005	02101	224	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3220	0.0088981	02101
2101081001	02101	242	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2981	0.0082377	02101
2101081002	02101	305	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4598	0.0127061	02101
2101081003	02101	198	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3327	0.0091938	02101
2101081004	02101	111	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2616	0.0072291	02101
2101091001	02101	293	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2670	0.0073783	02101
2101091002	02101	376	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3682	0.0101748	02101
2101091003	02101	93	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3061	0.0084588	02101
2101091004	02101	303	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4034	0.0111476	02101
2101091005	02101	429	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3472	0.0095945	02101
2101091006	02101	200	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4565	0.0126149	02101
2101091007	02101	153	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1884	0.0052062	02101
2101091008	02101	287	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2451	0.0067731	02101
2101091009	02101	210	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2064	0.0057037	02101
2101091010	02101	251	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2643	0.0073037	02101
2101101001	02101	375	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3814	0.0105396	02101
2101101002	02101	134	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1578	0.0043606	02101
2101101003	02101	175	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2526	0.0069803	02101
2101141001	02101	589	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	7735	0.0213749	02101
2101141002	02101	454	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	6365	0.0175890	02101
2101141003	02101	204	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3005	0.0083040	02101
2101141004	02101	436	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4535	0.0125320	02101
2101141005	02101	360	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4116	0.0113742	02101
2101141006	02101	436	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	7236	0.0199960	02101
2101141007	02101	196	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2982	0.0082405	02101
2101141008	02101	180	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2989	0.0082598	02101
2101141009	02101	396	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	6367	0.0175946	02101
2101151001	02101	229	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2879	0.0079558	02101
2101151002	02101	278	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3675	0.0101555	02101
2101151003	02101	240	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3768	0.0104125	02101
2101151004	02101	453	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	8961	0.0247628	02101
2101161001	02101	291	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2974	0.0082184	02101
2101161002	02101	258	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3413	0.0094315	02101
2101161003	02101	184	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1805	0.0049879	02101
2101161004	02101	254	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2437	0.0067344	02101
2101161005	02101	257	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3265	0.0090225	02101
2101171001	02101	114	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2503	0.0069168	02101
2101171002	02101	240	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4463	0.0123331	02101
2101171003	02101	393	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4570	0.0126287	02101
2101171004	02101	385	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5586	0.0154364	02101
2101181001	02101	559	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5341	0.0147593	02101
2101181002	02101	484	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5876	0.0162377	02101
2101181003	02101	348	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4235	0.0117030	02101
2101181004	02101	115	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4475	0.0123662	02101
2101991999	02101	150	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4121	0.0113880	02101
2102011001	02102	267	2017	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano	5020	0.3727631	02102
2102011002	02102	432	2017	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano	7764	0.5765204	02102
2102991999	02102	8	2017	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano	170	0.0126234	02102
2104011001	02104	109	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano	2174	0.1632500	02104
2104021001	02104	176	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano	2812	0.2111587	02104
2104031001	02104	346	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano	5947	0.4465721	02104
2104991999	02104	7	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano	190	0.0142675	02104
2201011001	02201	354	2017	Calama	416281.1	2017	2201	165731	68990679686	Urbano	3387	0.0204367	02201

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
2101011001	02101	240	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4618	0.0127614	02101	1605125412
2101011002	02101	442	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3644	0.0100698	02101	1266582287
2101011003	02101	410	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5645	0.0155994	02101	1962090288
2101011004	02101	872	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4385	0.0121175	02101	1524139223
2101011005	02101	542	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2383	0.0065852	02101	828283642
2101011006	02101	97	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1466	0.0040511	02101	509552589
2101011008	02101	318	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	6487	0.0179262	02101	2254752826
2101011009	02101	462	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	6152	0.0170004	02101	2138313455
2101011010	02101	264	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4495	0.0124215	02101	1562373046
2101011011	02101	79	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2445	0.0067565	02101	849833615
2101011012	02101	282	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4275	0.0118135	02101	1485905400
2101011013	02101	735	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2947	0.0081437	02101	1024318880
2101011014	02101	516	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	6247	0.0172630	02101	2171333575
2101011015	02101	424	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2887	0.0079779	02101	1003464068
2101011016	02101	121	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1643	0.0045403	02101	571074286
2101011017	02101	317	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4126	0.0114018	02101	1434115949
2101011018	02101	745	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4567	0.0126204	02101	1587398821
2101011019	02101	166	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1843	0.0050929	02101	640590328
2101011020	02101	604	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2874	0.0079420	02101	998945525
2101011021	02101	124	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2753	0.0076076	02101	956888320
2101011022	02101	136	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2805	0.0077513	02101	974962490
2101021001	02101	287	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3642	0.0100643	02101	1265887127
2101021002	02101	267	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4658	0.0128719	02101	1619028621
2101021003	02101	194	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2620	0.0072401	02101	910660152
2101021004	02101	351	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4532	0.0125237	02101	1575233514
2101021005	02101	301	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5748	0.0158840	02101	1997891050
2101031001	02101	295	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3779	0.0104429	02101	1313505616
2101031002	02101	256	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2511	0.0069389	02101	872773909
2101031003	02101	123	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2087	0.0057672	02101	725399899
2101031004	02101	352	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3200	0.0088429	02101	1112256674
2101031005	02101	168	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3138	0.0086716	02101	1090706701
2101031006	02101	327	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3335	0.0092159	02101	1159180002
2101041001	02101	324	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4379	0.0121009	02101	1522053742
2101041002	02101	311	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4125	0.0113990	02101	1433768368
2101041003	02101	232	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2518	0.0069582	02101	875206970
2101041004	02101	232	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2923	0.0080774	02101	1015976955
2101041005	02101	252	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4618	0.0127614	02101	1605125412
2101051001	02101	389	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4321	0.0119407	02101	1501894090
2101051002	02101	332	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5027	0.0138916	02101	1747285718
2101051003	02101	318	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5139	0.0142011	02101	1786214702
2101061001	02101	262	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3741	0.0103379	02101	1300297568
2101061002	02101	302	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2750	0.0075994	02101	955845579
2101061003	02101	356	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3370	0.0093127	02101	1171345309
2101071001	02101	315	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4201	0.0116090	02101	1460184464
2101071002	02101	223	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2844	0.0078591	02101	988518119
2101071003	02101	390	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	7624	0.0210682	02101	2649951525
2101071004	02101	273	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3724	0.0102909	02101	1294388704
2101071005	02101	224	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3220	0.0088981	02101	1119208278
2101081001	02101	242	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2981	0.0082377	02101	1036136608
2101081002	02101	305	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4598	0.0127061	02101	1598173808
2101081003	02101	198	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3327	0.0091938	02101	1156399360
2101081004	02101	111	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2616	0.0072291	02101	909269831
2101091001	02101	293	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2670	0.0073783	02101	928039162
2101091002	02101	376	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3682	0.0101748	02101	1279790335
2101091003	02101	93	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3061	0.0084588	02101	1063943024
2101091004	02101	303	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4034	0.0111476	02101	1402138569
2101091005	02101	429	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3472	0.0095945	02101	1206798491
2101091006	02101	200	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4565	0.0126149	02101	1586703661
2101091007	02101	153	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1884	0.0052062	02101	654841117
2101091008	02101	287	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2451	0.0067731	02101	851919096
2101091009	02101	210	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2064	0.0057037	02101	717405554
2101091010	02101	251	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2643	0.0073037	02101	918654496
2101101001	02101	375	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3814	0.0105396	02101	1325670923
2101101002	02101	134	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1578	0.0043606	02101	548481572
2101101003	02101	175	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2526	0.0069803	02101	877987612
2101141001	02101	589	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	7735	0.0213749	02101	2688532928
2101141002	02101	454	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	6365	0.0175890	02101	2212348040
2101141003	02101	204	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3005	0.0083040	02101	1044478533
2101141004	02101	436	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4535	0.0125320	02101	1576276255
2101141005	02101	360	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4116	0.0113742	02101	1430640146
2101141006	02101	436	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	7236	0.0199960	02101	2515090403
2101141007	02101	196	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2982	0.0082405	02101	1036484188
2101141008	02101	180	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2989	0.0082598	02101	1038917249
2101141009	02101	396	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	6367	0.0175946	02101	2213043200
2101151001	02101	229	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2879	0.0079558	02101	1000683426
2101151002	02101	278	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3675	0.0101555	02101	1277357274
2101151003	02101	240	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3768	0.0104125	02101	1309682233
2101151004	02101	453	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	8961	0.0247628	02101	3114666266
2101161001	02101	291	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2974	0.0082184	02101	1033703546
2101161002	02101	258	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3413	0.0094315	02101	1186291258
2101161003	02101	184	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1805	0.0049879	02101	627382280
2101161004	02101	254	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2437	0.0067344	02101	847052973
2101161005	02101	257	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3265	0.0090225	02101	1134849387
2101171001	02101	114	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2503	0.0069168	02101	869993267
2101171002	02101	240	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4463	0.0123331	02101	1551250479
2101171003	02101	393	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4570	0.0126287	02101	1588441562
2101171004	02101	385	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5586	0.0154364	02101	1941583056
2101181001	02101	559	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5341	0.0147593	02101	1856425904
2101181002	02101	484	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5876	0.0162377	02101	2042381317
2101181003	02101	348	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4235	0.0117030	02101	1472002191
2101181004	02101	115	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4475	0.0123662	02101	1555421442
2101991999	02101	150	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4121	0.0113880	02101	1432378048
2102011001	02102	267	2017	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano	5020	0.3727631	02102	1856249020
2102011002	02102	432	2017	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano	7764	0.5765204	02102	2870899879
2102991999	02102	8	2017	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano	170	0.0126234	02102	62861023
2104011001	02104	109	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano	2174	0.1632500	02104	818139039
2104021001	02104	176	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano	2812	0.2111587	02104	1058236880
2104031001	02104	346	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano	5947	0.4465721	02104	2238027997
2104991999	02104	7	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano	190	0.0142675	02104	71502492
2201011001	02201	354	2017	Calama	416281.1	2017	2201	165731	68990679686	Urbano	3387	0.0204367	02201	1409944018

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -1.562e+09 -2.740e+08  2.337e+06  2.146e+08  1.340e+09 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 469548368   80042316   5.866 2.66e-08 ***
## Freq.x        2880607     231096  12.465  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 466500000 on 153 degrees of freedom
## Multiple R-squared:  0.5039, Adjusted R-squared:  0.5006 
## F-statistic: 155.4 on 1 and 153 DF,  p-value: < 2.2e-16

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.500608794420092"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.500608794420092"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.409374856166196"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.543531488486724"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.629998732218126"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.615607359693425"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.581881737589711"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq               
## [1,] "log-log" "0.7430801516998"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 3   logarítmico 0.409374856166196
## 1    cuadrático 0.500608794420092
## 2        cúbico 0.500608794420092
## 4 raíz cuadrada 0.543531488486724
## 7      raíz-log 0.581881737589711
## 6      log-raíz 0.615607359693425
## 5     raíz-raíz 0.629998732218126
## 8       log-log   0.7430801516998
##                                                                     sintaxis
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 8
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.47838 -0.21365  0.04744  0.22507  1.82867 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.20627    0.17460   98.55   <2e-16 ***
## log(Freq.x)  0.66382    0.03142   21.13   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4088 on 153 degrees of freedom
## Multiple R-squared:  0.7447, Adjusted R-squared:  0.7431 
## F-statistic: 446.4 on 1 and 153 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    17.20627

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##   0.6638183

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.7431 ).

9.1 Diagrama de dispersión sobre log-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.47838 -0.21365  0.04744  0.22507  1.82867 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.20627    0.17460   98.55   <2e-16 ***
## log(Freq.x)  0.66382    0.03142   21.13   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4088 on 153 degrees of freedom
## Multiple R-squared:  0.7447, Adjusted R-squared:  0.7431 
## F-statistic: 446.4 on 1 and 153 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = e^{17.361982+0.641075 \cdot ln{X}} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
2101011001	02101	240	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4618	0.0127614	02101	1605125412	1128793584
2101011002	02101	442	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3644	0.0100698	02101	1266582287	1693037107
2101011003	02101	410	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5645	0.0155994	02101	1962090288	1610647489
2101011004	02101	872	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4385	0.0121175	02101	1524139223	2658002773
2101011005	02101	542	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2383	0.0065852	02101	828283642	1938498751
2101011006	02101	97	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1466	0.0040511	02101	509552589	618645945
2101011008	02101	318	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	6487	0.0179262	02101	2254752826	1360641377
2101011009	02101	462	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	6152	0.0170004	02101	2138313455	1743511717
2101011010	02101	264	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4495	0.0124215	02101	1562373046	1202518470
2101011011	02101	79	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2445	0.0067565	02101	849833615	539841567
2101011012	02101	282	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4275	0.0118135	02101	1485905400	1256339321
2101011013	02101	735	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2947	0.0081437	02101	1024318880	2372907239
2101011014	02101	516	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	6247	0.0172630	02101	2171333575	1876261121
2101011015	02101	424	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2887	0.0079779	02101	1003464068	1646949561
2101011016	02101	121	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1643	0.0045403	02101	571074286	716436632
2101011017	02101	317	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4126	0.0114018	02101	1434115949	1357799564
2101011018	02101	745	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4567	0.0126204	02101	1587398821	2394289533
2101011019	02101	166	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1843	0.0050929	02101	640590328	883761589
2101011020	02101	604	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2874	0.0079420	02101	998945525	2083003414
2101011021	02101	124	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2753	0.0076076	02101	956888320	728179359
2101011022	02101	136	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2805	0.0077513	02101	974962490	774228070
2101021001	02101	287	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3642	0.0100643	02101	1265887127	1271082494
2101021002	02101	267	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4658	0.0128719	02101	1619028621	1211572297
2101021003	02101	194	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2620	0.0072401	02101	910660152	980102288
2101021004	02101	351	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4532	0.0125237	02101	1575233514	1452807931
2101021005	02101	301	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5748	0.0158840	02101	1997891050	1311911544
2101031001	02101	295	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3779	0.0104429	02101	1313505616	1294493302
2101031002	02101	256	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2511	0.0069389	02101	872773909	1178204055
2101031003	02101	123	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2087	0.0057672	02101	725399899	724275839
2101031004	02101	352	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3200	0.0088429	02101	1112256674	1455554197
2101031005	02101	168	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3138	0.0086716	02101	1090706701	890815510
2101031006	02101	327	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3335	0.0092159	02101	1159180002	1386084069
2101041001	02101	324	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4379	0.0121009	02101	1522053742	1377629640
2101041002	02101	311	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4125	0.0113990	02101	1433768368	1340684908
2101041003	02101	232	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2518	0.0069582	02101	875206970	1103674388
2101041004	02101	232	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2923	0.0080774	02101	1015976955	1103674388
2101041005	02101	252	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4618	0.0127614	02101	1605125412	1165951210
2101051001	02101	389	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4321	0.0119407	02101	1501894090	1555402176
2101051002	02101	332	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5027	0.0138916	02101	1747285718	1400117084
2101051003	02101	318	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5139	0.0142011	02101	1786214702	1360641377
2101061001	02101	262	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3741	0.0103379	02101	1300297568	1196463366
2101061002	02101	302	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2750	0.0075994	02101	955845579	1314803190
2101061003	02101	356	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3370	0.0093127	02101	1171345309	1466513143
2101071001	02101	315	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4201	0.0116090	02101	1460184464	1352106877
2101071002	02101	223	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2844	0.0078591	02101	988518119	1075064430
2101071003	02101	390	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	7624	0.0210682	02101	2649951525	1558055284
2101071004	02101	273	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3724	0.0102909	02101	1294388704	1229578049
2101071005	02101	224	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3220	0.0088981	02101	1119208278	1078262235
2101081001	02101	242	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2981	0.0082377	02101	1036136608	1135029152
2101081002	02101	305	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4598	0.0127061	02101	1598173808	1323458880
2101081003	02101	198	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3327	0.0091938	02101	1156399360	993470855
2101081004	02101	111	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2616	0.0072291	02101	909269831	676565052
2101091001	02101	293	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2670	0.0073783	02101	928039162	1288660823
2101091002	02101	376	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3682	0.0101748	02101	1279790335	1520700111
2101091003	02101	93	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3061	0.0084588	02101	1063943024	601591566
2101091004	02101	303	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4034	0.0111476	02101	1402138569	1317691618
2101091005	02101	429	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3472	0.0095945	02101	1206798491	1659816537
2101091006	02101	200	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4565	0.0126149	02101	1586703661	1000121051
2101091007	02101	153	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1884	0.0052062	02101	654841117	837191664
2101091008	02101	287	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2451	0.0067731	02101	851919096	1271082494
2101091009	02101	210	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2064	0.0057037	02101	717405554	1033043035
2101091010	02101	251	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2643	0.0073037	02101	918654496	1162877809
2101101001	02101	375	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3814	0.0105396	02101	1325670923	1518014152
2101101002	02101	134	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1578	0.0043606	02101	548481572	766651223
2101101003	02101	175	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2526	0.0069803	02101	877987612	915285222
2101141001	02101	589	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	7735	0.0213749	02101	2688532928	2048518990
2101141002	02101	454	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	6365	0.0175890	02101	2212348040	1723411803
2101141003	02101	204	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3005	0.0083040	02101	1044478533	1013354780
2101141004	02101	436	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4535	0.0125320	02101	1576276255	1677745942
2101141005	02101	360	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4116	0.0113742	02101	1430640146	1477430770
2101141006	02101	436	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	7236	0.0199960	02101	2515090403	1677745942
2101141007	02101	196	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2982	0.0082405	02101	1036484188	986798037
2101141008	02101	180	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2989	0.0082598	02101	1038917249	932562413
2101141009	02101	396	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	6367	0.0175946	02101	2213043200	1573926195
2101151001	02101	229	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2879	0.0079558	02101	1000683426	1094179892
2101151002	02101	278	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3675	0.0101555	02101	1277357274	1244481418
2101151003	02101	240	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3768	0.0104125	02101	1309682233	1128793584
2101151004	02101	453	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	8961	0.0247628	02101	3114666266	1720890974
2101161001	02101	291	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2974	0.0082184	02101	1033703546	1282814945
2101161002	02101	258	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3413	0.0094315	02101	1186291258	1184306320
2101161003	02101	184	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1805	0.0049879	02101	627382280	946268241
2101161004	02101	254	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2437	0.0067344	02101	847052973	1172085741
2101161005	02101	257	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3265	0.0090225	02101	1134849387	1181257183
2101171001	02101	114	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2503	0.0069168	02101	869993267	688648840
2101171002	02101	240	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4463	0.0123331	02101	1551250479	1128793584
2101171003	02101	393	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4570	0.0126287	02101	1588441562	1566000922
2101171004	02101	385	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5586	0.0154364	02101	1941583056	1544766728
2101181001	02101	559	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5341	0.0147593	02101	1856425904	1978650108
2101181002	02101	484	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5876	0.0162377	02101	2042381317	1798192776
2101181003	02101	348	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4235	0.0117030	02101	1472002191	1444553304
2101181004	02101	115	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4475	0.0123662	02101	1555421442	692652930
2101991999	02101	150	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4121	0.0113880	02101	1432378048	826258506
2102011001	02102	267	2017	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano	5020	0.3727631	02102	1856249020	1211572297
2102011002	02102	432	2017	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano	7764	0.5765204	02102	2870899879	1667512519
2102991999	02102	8	2017	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano	170	0.0126234	02102	62861023	118052272
2104011001	02104	109	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano	2174	0.1632500	02104	818139039	668448159
2104021001	02104	176	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano	2812	0.2111587	02104	1058236880	918753800
2104031001	02104	346	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano	5947	0.4465721	02104	2238027997	1439036926
2104991999	02104	7	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano	190	0.0142675	02104	71502492	108038422
2201011001	02201	354	2017	Calama	416281.1	2017	2201	165731	68990679686	Urbano	3387	0.0204367	02201	1409944018	1461038873

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
2101011001	02101	240	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4618	0.0127614	02101	1605125412	1128793584	244433.4
2101011002	02101	442	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3644	0.0100698	02101	1266582287	1693037107	464609.5
2101011003	02101	410	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5645	0.0155994	02101	1962090288	1610647489	285322.9
2101011004	02101	872	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4385	0.0121175	02101	1524139223	2658002773	606158.0
2101011005	02101	542	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2383	0.0065852	02101	828283642	1938498751	813469.9
2101011006	02101	97	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1466	0.0040511	02101	509552589	618645945	421995.9
2101011008	02101	318	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	6487	0.0179262	02101	2254752826	1360641377	209748.9
2101011009	02101	462	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	6152	0.0170004	02101	2138313455	1743511717	283405.7
2101011010	02101	264	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4495	0.0124215	02101	1562373046	1202518470	267523.6
2101011011	02101	79	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2445	0.0067565	02101	849833615	539841567	220794.1
2101011012	02101	282	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4275	0.0118135	02101	1485905400	1256339321	293880.5
2101011013	02101	735	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2947	0.0081437	02101	1024318880	2372907239	805194.2
2101011014	02101	516	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	6247	0.0172630	02101	2171333575	1876261121	300345.9
2101011015	02101	424	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2887	0.0079779	02101	1003464068	1646949561	570470.9
2101011016	02101	121	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1643	0.0045403	02101	571074286	716436632	436053.9
2101011017	02101	317	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4126	0.0114018	02101	1434115949	1357799564	329083.8
2101011018	02101	745	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4567	0.0126204	02101	1587398821	2394289533	524258.7
2101011019	02101	166	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1843	0.0050929	02101	640590328	883761589	479523.4
2101011020	02101	604	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2874	0.0079420	02101	998945525	2083003414	724775.0
2101011021	02101	124	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2753	0.0076076	02101	956888320	728179359	264503.9
2101011022	02101	136	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2805	0.0077513	02101	974962490	774228070	276017.1
2101021001	02101	287	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3642	0.0100643	02101	1265887127	1271082494	349006.7
2101021002	02101	267	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4658	0.0128719	02101	1619028621	1211572297	260105.7
2101021003	02101	194	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2620	0.0072401	02101	910660152	980102288	374084.8
2101021004	02101	351	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4532	0.0125237	02101	1575233514	1452807931	320566.6
2101021005	02101	301	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5748	0.0158840	02101	1997891050	1311911544	228237.9
2101031001	02101	295	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3779	0.0104429	02101	1313505616	1294493302	342549.2
2101031002	02101	256	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2511	0.0069389	02101	872773909	1178204055	469217.1
2101031003	02101	123	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2087	0.0057672	02101	725399899	724275839	347041.6
2101031004	02101	352	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3200	0.0088429	02101	1112256674	1455554197	454860.7
2101031005	02101	168	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3138	0.0086716	02101	1090706701	890815510	283880.0
2101031006	02101	327	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3335	0.0092159	02101	1159180002	1386084069	415617.4
2101041001	02101	324	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4379	0.0121009	02101	1522053742	1377629640	314599.1
2101041002	02101	311	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4125	0.0113990	02101	1433768368	1340684908	325014.5
2101041003	02101	232	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2518	0.0069582	02101	875206970	1103674388	438313.9
2101041004	02101	232	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2923	0.0080774	02101	1015976955	1103674388	377582.8
2101041005	02101	252	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4618	0.0127614	02101	1605125412	1165951210	252479.7
2101051001	02101	389	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4321	0.0119407	02101	1501894090	1555402176	359963.5
2101051002	02101	332	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5027	0.0138916	02101	1747285718	1400117084	278519.4
2101051003	02101	318	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5139	0.0142011	02101	1786214702	1360641377	264767.7
2101061001	02101	262	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3741	0.0103379	02101	1300297568	1196463366	319824.5
2101061002	02101	302	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2750	0.0075994	02101	955845579	1314803190	478110.3
2101061003	02101	356	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3370	0.0093127	02101	1171345309	1466513143	435167.1
2101071001	02101	315	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4201	0.0116090	02101	1460184464	1352106877	321853.6
2101071002	02101	223	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2844	0.0078591	02101	988518119	1075064430	378011.4
2101071003	02101	390	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	7624	0.0210682	02101	2649951525	1558055284	204361.9
2101071004	02101	273	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3724	0.0102909	02101	1294388704	1229578049	330176.7
2101071005	02101	224	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3220	0.0088981	02101	1119208278	1078262235	334864.0
2101081001	02101	242	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2981	0.0082377	02101	1036136608	1135029152	380754.5
2101081002	02101	305	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4598	0.0127061	02101	1598173808	1323458880	287833.6
2101081003	02101	198	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3327	0.0091938	02101	1156399360	993470855	298608.6
2101081004	02101	111	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2616	0.0072291	02101	909269831	676565052	258625.8
2101091001	02101	293	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2670	0.0073783	02101	928039162	1288660823	482644.5
2101091002	02101	376	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3682	0.0101748	02101	1279790335	1520700111	413009.3
2101091003	02101	93	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3061	0.0084588	02101	1063943024	601591566	196534.3
2101091004	02101	303	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4034	0.0111476	02101	1402138569	1317691618	326646.4
2101091005	02101	429	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3472	0.0095945	02101	1206798491	1659816537	478057.8
2101091006	02101	200	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4565	0.0126149	02101	1586703661	1000121051	219084.6
2101091007	02101	153	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1884	0.0052062	02101	654841117	837191664	444369.2
2101091008	02101	287	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2451	0.0067731	02101	851919096	1271082494	518597.5
2101091009	02101	210	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2064	0.0057037	02101	717405554	1033043035	500505.3
2101091010	02101	251	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2643	0.0073037	02101	918654496	1162877809	439984.0
2101101001	02101	375	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3814	0.0105396	02101	1325670923	1518014152	398011.1
2101101002	02101	134	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1578	0.0043606	02101	548481572	766651223	485837.3
2101101003	02101	175	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2526	0.0069803	02101	877987612	915285222	362345.7
2101141001	02101	589	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	7735	0.0213749	02101	2688532928	2048518990	264837.6
2101141002	02101	454	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	6365	0.0175890	02101	2212348040	1723411803	270763.8
2101141003	02101	204	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3005	0.0083040	02101	1044478533	1013354780	337222.9
2101141004	02101	436	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4535	0.0125320	02101	1576276255	1677745942	369955.0
2101141005	02101	360	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4116	0.0113742	02101	1430640146	1477430770	358948.2
2101141006	02101	436	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	7236	0.0199960	02101	2515090403	1677745942	231861.0
2101141007	02101	196	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2982	0.0082405	02101	1036484188	986798037	330918.2
2101141008	02101	180	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2989	0.0082598	02101	1038917249	932562413	311998.1
2101141009	02101	396	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	6367	0.0175946	02101	2213043200	1573926195	247200.6
2101151001	02101	229	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2879	0.0079558	02101	1000683426	1094179892	380055.5
2101151002	02101	278	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3675	0.0101555	02101	1277357274	1244481418	338634.4
2101151003	02101	240	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3768	0.0104125	02101	1309682233	1128793584	299573.7
2101151004	02101	453	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	8961	0.0247628	02101	3114666266	1720890974	192042.3
2101161001	02101	291	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2974	0.0082184	02101	1033703546	1282814945	431343.3
2101161002	02101	258	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3413	0.0094315	02101	1186291258	1184306320	346998.6
2101161003	02101	184	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	1805	0.0049879	02101	627382280	946268241	524248.3
2101161004	02101	254	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2437	0.0067344	02101	847052973	1172085741	480954.3
2101161005	02101	257	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	3265	0.0090225	02101	1134849387	1181257183	361793.9
2101171001	02101	114	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	2503	0.0069168	02101	869993267	688648840	275129.4
2101171002	02101	240	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4463	0.0123331	02101	1551250479	1128793584	252922.6
2101171003	02101	393	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4570	0.0126287	02101	1588441562	1566000922	342669.8
2101171004	02101	385	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5586	0.0154364	02101	1941583056	1544766728	276542.6
2101181001	02101	559	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5341	0.0147593	02101	1856425904	1978650108	370464.4
2101181002	02101	484	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	5876	0.0162377	02101	2042381317	1798192776	306023.3
2101181003	02101	348	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4235	0.0117030	02101	1472002191	1444553304	341098.8
2101181004	02101	115	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4475	0.0123662	02101	1555421442	692652930	154782.8
2101991999	02101	150	2017	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano	4121	0.0113880	02101	1432378048	826258506	200499.5
2102011001	02102	267	2017	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano	5020	0.3727631	02102	1856249020	1211572297	241349.1
2102011002	02102	432	2017	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano	7764	0.5765204	02102	2870899879	1667512519	214774.9
2102991999	02102	8	2017	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano	170	0.0126234	02102	62861023	118052272	694425.1
2104011001	02104	109	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano	2174	0.1632500	02104	818139039	668448159	307473.9
2104021001	02104	176	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano	2812	0.2111587	02104	1058236880	918753800	326726.1
2104031001	02104	346	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano	5947	0.4465721	02104	2238027997	1439036926	241977.0
2104991999	02104	7	2017	Taltal	376328.9	2017	2104	13317	5011572025	Urbano	190	0.0142675	02104	71502492	108038422	568623.3
2201011001	02201	354	2017	Calama	416281.1	2017	2201	165731	68990679686	Urbano	3387	0.0204367	02201	1409944018	1461038873	431366.7

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_urbano_nivel_nacional_17_r02.rds")

---

Rural

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 2)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15202	1	2	6	13225	1	3	1	5	3	5	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	3	1	1	5	3	5	2	3	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	4	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	5	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	6	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	8	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	9	3	1	5	3	5	1	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	10	1	1	5	3	4	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	11	1	2	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	12	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	13	1	1	5	3	4	1	4	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	14	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	16	1	1	5	3	5	1	3	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	17	1	1	5	3	5	2	4	1	8	15	152	15202	15202012006
15	152	15202	1	2	6	13225	18	3	1	5	3	5	1	1	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	19	3	1	5	3	5	1	3	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	20	1	1	5	3	5	2	1	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	21	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	22	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	24	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	25	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	26	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	27	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	28	3	1	5	3	5	1	4	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	29	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	30	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	31	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	32	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	33	1	1	5	3	5	3	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	34	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	35	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	36	1	1	5	3	5	3	2	1	9	15	152	15202	15202012006
15	152	15202	1	2	6	13225	37	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	38	1	1	5	3	5	99	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	39	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	40	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	41	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	42	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	43	3	1	5	3	5	2	1	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	44	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	45	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	8	13910	1	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	3	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	4	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	5	3	1	5	3	5	2	3	1	3	15	152	15202	15202012008
15	152	15202	1	2	8	13910	6	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	7	1	1	5	99	5	2	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	8	3	1	5	3	5	3	3	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	9	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	10	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	11	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	12	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	13	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	14	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	16	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	18	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	20	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	21	1	1	5	3	5	1	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	22	3	1	5	3	5	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	23	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	24	3	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	25	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	26	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	27	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	28	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	29	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	30	1	1	5	1	4	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	12	8394	1	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	2	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	3	3	1	5	3	5	2	3	1	4	15	152	15202	15202012012
15	152	15202	1	2	12	8394	4	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	5	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	6	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	8	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	9	3	1	5	3	5	1	4	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	10	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	11	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	12	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	13	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	14	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	15	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	16	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	18	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	20	3	1	5	3	5	3	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	21	3	1	5	99	5	1	4	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	22	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	24	3	1	5	3	5	1	2	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	25	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 2:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 2)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Veamos los primeros 100 registros:

r3_100 <- d[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	unlist.c.	unlist.d.	Freq	anio
1	2101012001	1	2101	1	2017
2	2101012005	1	2101	1	2017
3	2101012012	1	2101	1	2017
4	2101012013	1	2101	10	2017
5	2101012901	1	2101	1	2017
6	2101102007	1	2101	18	2017
7	2101102014	1	2101	5	2017
8	2101102016	1	2101	1	2017
9	2101122014	1	2101	12	2017
10	2101122019	1	2101	2	2017
70	2102012001	1	2102	5	2017
71	2102022003	1	2102	20	2017
72	2102022006	1	2102	16	2017
73	2102022008	1	2102	4	2017
74	2102022901	1	2102	1	2017
134	2103012008	1	2103	18	2017
135	2103012901	1	2103	1	2017
136	2103032002	1	2103	29	2017
196	2104012008	1	2104	8	2017
197	2104012014	1	2104	10	2017
198	2104012901	1	2104	6	2017
199	2104022015	1	2104	8	2017
200	2104022022	1	2104	2	2017
201	2104032901	1	2104	1	2017
202	2104042020	1	2104	27	2017
203	2104072901	1	2104	1	2017
263	2201032006	1	2201	2	2017
264	2201032013	1	2201	2	2017
265	2201082002	1	2201	45	2017
266	2201082012	1	2201	6	2017
267	2201122002	1	2201	1	2017
268	2201132004	1	2201	30	2017
269	2201132010	1	2201	9	2017
270	2201132901	1	2201	1	2017
271	2201142002	1	2201	2	2017
272	2201152001	1	2201	3	2017
273	2201152003	1	2201	8	2017
274	2201152014	1	2201	8	2017
275	2201152015	1	2201	1	2017
335	2202012005	1	2202	20	2017
395	2203012013	1	2203	2	2017
396	2203012014	1	2203	147	2017
397	2203012901	1	2203	4	2017
398	2203022008	1	2203	3	2017
399	2203022017	1	2203	50	2017
400	2203032012	1	2203	11	2017
401	2203032015	1	2203	19	2017
461	2301032003	1	2301	1	2017
462	2301032005	1	2301	1	2017
463	2301032006	1	2301	2	2017
464	2301032018	1	2301	2	2017
465	2301032020	1	2301	1	2017
466	2301032023	1	2301	2	2017
467	2301052004	1	2301	9	2017
468	2301052007	1	2301	1	2017
469	2301052008	1	2301	4	2017
470	2301052901	1	2301	3	2017
530	2302012011	1	2302	9	2017
531	2302012901	1	2302	3	2017
NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	2101012001	1	2017	02101
2	2101012005	1	2017	02101
3	2101012012	1	2017	02101
4	2101012013	10	2017	02101
5	2101012901	1	2017	02101
6	2101102007	18	2017	02101
7	2101102014	5	2017	02101
8	2101102016	1	2017	02101
9	2101122014	12	2017	02101
10	2101122019	2	2017	02101
70	2102012001	5	2017	02102
71	2102022003	20	2017	02102
72	2102022006	16	2017	02102
73	2102022008	4	2017	02102
74	2102022901	1	2017	02102
134	2103012008	18	2017	02103
135	2103012901	1	2017	02103
136	2103032002	29	2017	02103
196	2104012008	8	2017	02104
197	2104012014	10	2017	02104
198	2104012901	6	2017	02104
199	2104022015	8	2017	02104
200	2104022022	2	2017	02104
201	2104032901	1	2017	02104
202	2104042020	27	2017	02104
203	2104072901	1	2017	02104
263	2201032006	2	2017	02201
264	2201032013	2	2017	02201
265	2201082002	45	2017	02201
266	2201082012	6	2017	02201
267	2201122002	1	2017	02201
268	2201132004	30	2017	02201
269	2201132010	9	2017	02201
270	2201132901	1	2017	02201
271	2201142002	2	2017	02201
272	2201152001	3	2017	02201
273	2201152003	8	2017	02201
274	2201152014	8	2017	02201
275	2201152015	1	2017	02201
335	2202012005	20	2017	02202
395	2203012013	2	2017	02203
396	2203012014	147	2017	02203
397	2203012901	4	2017	02203
398	2203022008	3	2017	02203
399	2203022017	50	2017	02203
400	2203032012	11	2017	02203
401	2203032015	19	2017	02203
461	2301032003	1	2017	02301
462	2301032005	1	2017	02301
463	2301032006	2	2017	02301
464	2301032018	2	2017	02301
465	2301032020	1	2017	02301
466	2301032023	2	2017	02301
467	2301052004	9	2017	02301
468	2301052007	1	2017	02301
469	2301052008	4	2017	02301
470	2301052901	3	2017	02301
530	2302012011	9	2017	02302
531	2302012901	3	2017	02302
NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA
NA.2	NA	NA	NA	NA
NA.3	NA	NA	NA	NA
NA.4	NA	NA	NA	NA
NA.5	NA	NA	NA	NA
NA.6	NA	NA	NA	NA
NA.7	NA	NA	NA	NA
NA.8	NA	NA	NA	NA
NA.9	NA	NA	NA	NA
NA.10	NA	NA	NA	NA
NA.11	NA	NA	NA	NA
NA.12	NA	NA	NA	NA
NA.13	NA	NA	NA	NA
NA.14	NA	NA	NA	NA
NA.15	NA	NA	NA	NA
NA.16	NA	NA	NA	NA
NA.17	NA	NA	NA	NA
NA.18	NA	NA	NA	NA
NA.19	NA	NA	NA	NA
NA.20	NA	NA	NA	NA
NA.21	NA	NA	NA	NA
NA.22	NA	NA	NA	NA
NA.23	NA	NA	NA	NA
NA.24	NA	NA	NA	NA
NA.25	NA	NA	NA	NA
NA.26	NA	NA	NA	NA
NA.27	NA	NA	NA	NA
NA.28	NA	NA	NA	NA
NA.29	NA	NA	NA	NA
NA.30	NA	NA	NA	NA
NA.31	NA	NA	NA	NA
NA.32	NA	NA	NA	NA
NA.33	NA	NA	NA	NA
NA.34	NA	NA	NA	NA
NA.35	NA	NA	NA	NA
NA.36	NA	NA	NA	NA
NA.37	NA	NA	NA	NA
NA.38	NA	NA	NA	NA
NA.39	NA	NA	NA	NA
NA.40	NA	NA	NA	NA

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_rural_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	289375.3	2017	1101	191468	55406102543	Rural
01401	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
01402	Camiña	262850.3	2017	1402	1250	328562901	Rural
01404	Huara	253968.5	2017	1404	2730	693334131	Rural
01405	Pica	290496.7	2017	1405	9296	2700457509	Rural
02103	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
02104	Taltal	345494.0	2017	2104	13317	4600943086	Rural
02201	Calama	310025.0	2017	2201	165731	51380756402	Rural
02203	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
02301	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
03101	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03103	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03202	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural
03301	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03302	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03303	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03304	Huasco	227560.7	2017	3304	10149	2309513927	Rural
04101	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04102	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04103	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04104	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04105	Paiguano	205942.1	2017	4105	4497	926121774	Rural
04106	Vicuña	176130.6	2017	4106	27771	4891322768	Rural
04201	Illapel	191976.8	2017	4201	30848	5922099530	Rural
04202	Canela	171370.3	2017	4202	9093	1558270441	Rural
04203	Los Vilos	173238.5	2017	4203	21382	3704185607	Rural
04204	Salamanca	223234.2	2017	4204	29347	6551254640	Rural
04301	Ovalle	241393.7	2017	4301	111272	26860360045	Rural
04302	Combarbalá	179139.6	2017	4302	13322	2386498044	Rural
04303	Monte Patria	201205.8	2017	4303	30751	6187280931	Rural
04304	Punitaqui	171931.7	2017	4304	10956	1883683880	Rural
04305	Río Hurtado	182027.2	2017	4305	4278	778712384	Rural
05101	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05102	Casablanca	268917.1	2017	5102	26867	7224996933	Rural
05105	Puchuncaví	279614.4	2017	5105	18546	5185728335	Rural
05107	Quintero	334628.2	2017	5107	31923	10682335196	Rural
05301	Los Andes	324402.1	2017	5301	66708	21640215030	Rural
05302	Calle Larga	242743.8	2017	5302	14832	3600375502	Rural
05303	Rinconada	326532.5	2017	5303	10207	3332917471	Rural
05304	San Esteban	223168.6	2017	5304	18855	4207844130	Rural
05401	La Ligua	181468.0	2017	5401	35390	6422154059	Rural
05402	Cabildo	231277.8	2017	5402	19388	4484014285	Rural
05404	Petorca	298208.9	2017	5404	9826	2930200178	Rural
05405	Zapallar	292882.3	2017	5405	7339	2149463129	Rural
05501	Quillota	220926.8	2017	5501	90517	19997628209	Rural
05502	Calera	226906.2	2017	5502	50554	11471016698	Rural
05503	Hijuelas	253739.9	2017	5503	17988	4564273201	Rural
05504	La Cruz	291124.1	2017	5504	22098	6433259569	Rural
05506	Nogales	264475.3	2017	5506	22120	5850194593	Rural
05601	San Antonio	266331.2	2017	5601	91350	24329353815	Rural

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
1	02101	2101012001	1	2017	NA	NA	NA	NA	NA	NA	NA
2	02101	2101012005	1	2017	NA	NA	NA	NA	NA	NA	NA
3	02101	2101012012	1	2017	NA	NA	NA	NA	NA	NA	NA
4	02101	2101012013	10	2017	NA	NA	NA	NA	NA	NA	NA
5	02101	2101012901	1	2017	NA	NA	NA	NA	NA	NA	NA
6	02101	2101102007	18	2017	NA	NA	NA	NA	NA	NA	NA
7	02101	2101102014	5	2017	NA	NA	NA	NA	NA	NA	NA
8	02101	2101102016	1	2017	NA	NA	NA	NA	NA	NA	NA
9	02101	2101122014	12	2017	NA	NA	NA	NA	NA	NA	NA
10	02101	2101122019	2	2017	NA	NA	NA	NA	NA	NA	NA
11	02102	2102012001	5	2017	NA	NA	NA	NA	NA	NA	NA
12	02102	2102022003	20	2017	NA	NA	NA	NA	NA	NA	NA
13	02102	2102022006	16	2017	NA	NA	NA	NA	NA	NA	NA
14	02102	2102022008	4	2017	NA	NA	NA	NA	NA	NA	NA
15	02102	2102022901	1	2017	NA	NA	NA	NA	NA	NA	NA
16	02103	2103012901	1	2017	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
17	02103	2103032002	29	2017	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
18	02103	2103012008	18	2017	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
19	02104	2104012901	6	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural
20	02104	2104022015	8	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural
21	02104	2104032901	1	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural
22	02104	2104042020	27	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural
23	02104	2104072901	1	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural
24	02104	2104022022	2	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural
25	02104	2104012008	8	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural
26	02104	2104012014	10	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural
27	02201	2201082012	6	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
28	02201	2201122002	1	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
29	02201	2201032013	2	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
30	02201	2201082002	45	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
31	02201	2201132901	1	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
32	02201	2201142002	2	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
33	02201	2201152001	3	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
34	02201	2201152003	8	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
35	02201	2201152014	8	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
36	02201	2201152015	1	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
37	02201	2201032006	2	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
38	02201	2201132004	30	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
39	02201	2201132010	9	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
40	02202	2202012005	20	2017	NA	NA	NA	NA	NA	NA	NA
41	02203	2203012901	4	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
42	02203	2203022008	3	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
43	02203	2203012013	2	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
44	02203	2203012014	147	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
45	02203	2203032015	19	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
46	02203	2203022017	50	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
47	02203	2203032012	11	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
48	02301	2301032003	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
49	02301	2301032005	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
50	02301	2301032020	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
51	02301	2301032023	2	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
52	02301	2301032006	2	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
53	02301	2301032018	2	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
54	02301	2301052008	4	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
55	02301	2301052901	3	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
56	02301	2301052004	9	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
57	02301	2301052007	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
58	02302	2302012901	3	2017	NA	NA	NA	NA	NA	NA	NA
59	02302	2302012011	9	2017	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
1	02101	2101012001	1	2017	NA	NA	NA	NA	NA	NA	NA
2	02101	2101012005	1	2017	NA	NA	NA	NA	NA	NA	NA
3	02101	2101012012	1	2017	NA	NA	NA	NA	NA	NA	NA
4	02101	2101012013	10	2017	NA	NA	NA	NA	NA	NA	NA
5	02101	2101012901	1	2017	NA	NA	NA	NA	NA	NA	NA
6	02101	2101102007	18	2017	NA	NA	NA	NA	NA	NA	NA
7	02101	2101102014	5	2017	NA	NA	NA	NA	NA	NA	NA
8	02101	2101102016	1	2017	NA	NA	NA	NA	NA	NA	NA
9	02101	2101122014	12	2017	NA	NA	NA	NA	NA	NA	NA
10	02101	2101122019	2	2017	NA	NA	NA	NA	NA	NA	NA
11	02102	2102012001	5	2017	NA	NA	NA	NA	NA	NA	NA
12	02102	2102022003	20	2017	NA	NA	NA	NA	NA	NA	NA
13	02102	2102022006	16	2017	NA	NA	NA	NA	NA	NA	NA
14	02102	2102022008	4	2017	NA	NA	NA	NA	NA	NA	NA
15	02102	2102022901	1	2017	NA	NA	NA	NA	NA	NA	NA
16	02103	2103012901	1	2017	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
17	02103	2103032002	29	2017	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
18	02103	2103012008	18	2017	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
19	02104	2104012901	6	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural
20	02104	2104022015	8	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural
21	02104	2104032901	1	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural
22	02104	2104042020	27	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural
23	02104	2104072901	1	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural
24	02104	2104022022	2	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural
25	02104	2104012008	8	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural
26	02104	2104012014	10	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural
27	02201	2201082012	6	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
28	02201	2201122002	1	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
29	02201	2201032013	2	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
30	02201	2201082002	45	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
31	02201	2201132901	1	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
32	02201	2201142002	2	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
33	02201	2201152001	3	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
34	02201	2201152003	8	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
35	02201	2201152014	8	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
36	02201	2201152015	1	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
37	02201	2201032006	2	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
38	02201	2201132004	30	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
39	02201	2201132010	9	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural
40	02202	2202012005	20	2017	NA	NA	NA	NA	NA	NA	NA
41	02203	2203012901	4	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
42	02203	2203022008	3	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
43	02203	2203012013	2	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
44	02203	2203012014	147	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
45	02203	2203032015	19	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
46	02203	2203022017	50	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
47	02203	2203032012	11	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
48	02301	2301032003	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
49	02301	2301032005	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
50	02301	2301032020	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
51	02301	2301032023	2	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
52	02301	2301032006	2	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
53	02301	2301032018	2	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
54	02301	2301052008	4	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
55	02301	2301052901	3	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
56	02301	2301052004	9	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
57	02301	2301052007	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
58	02302	2302012901	3	2017	NA	NA	NA	NA	NA	NA	NA
59	02302	2302012011	9	2017	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
2101012001	02101	1	2017	NA	NA	NA	NA	NA	NA	NA	6	0.0000166	02101
2101012005	02101	1	2017	NA	NA	NA	NA	NA	NA	NA	59	0.0001630	02101
2101012012	02101	1	2017	NA	NA	NA	NA	NA	NA	NA	53	0.0001465	02101
2101012013	02101	10	2017	NA	NA	NA	NA	NA	NA	NA	121	0.0003344	02101
2101012901	02101	1	2017	NA	NA	NA	NA	NA	NA	NA	16	0.0000442	02101
2101102007	02101	18	2017	NA	NA	NA	NA	NA	NA	NA	401	0.0011081	02101
2101102014	02101	5	2017	NA	NA	NA	NA	NA	NA	NA	78	0.0002155	02101
2101102016	02101	1	2017	NA	NA	NA	NA	NA	NA	NA	9	0.0000249	02101
2101122014	02101	12	2017	NA	NA	NA	NA	NA	NA	NA	268	0.0007406	02101
2101122019	02101	2	2017	NA	NA	NA	NA	NA	NA	NA	1438	0.0039738	02101
2102012001	02102	5	2017	NA	NA	NA	NA	NA	NA	NA	31	0.0023019	02102
2102022003	02102	20	2017	NA	NA	NA	NA	NA	NA	NA	293	0.0217569	02102
2102022006	02102	16	2017	NA	NA	NA	NA	NA	NA	NA	127	0.0094305	02102
2102022008	02102	4	2017	NA	NA	NA	NA	NA	NA	NA	31	0.0023019	02102
2102022901	02102	1	2017	NA	NA	NA	NA	NA	NA	NA	12	0.0008911	02102
2103012008	02103	18	2017	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural	2684	0.2634989	02103
2103012901	02103	1	2017	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural	8	0.0007854	02103
2103032002	02103	29	2017	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural	942	0.0924799	02103
2104012008	02104	8	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	59	0.0044304	02104
2104012014	02104	10	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	19	0.0014267	02104
2104012901	02104	6	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	26	0.0019524	02104
2104022015	02104	8	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	59	0.0044304	02104
2104022022	02104	2	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	57	0.0042802	02104
2104032901	02104	1	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	11	0.0008260	02104
2104042020	02104	27	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	729	0.0547421	02104
2104072901	02104	1	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	56	0.0042052	02104
2201032006	02201	2	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	36	0.0002172	02201
2201032013	02201	2	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	13	0.0000784	02201
2201082002	02201	45	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	1364	0.0082302	02201
2201082012	02201	6	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	227	0.0013697	02201
2201122002	02201	1	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	151	0.0009111	02201
2201132004	02201	30	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	542	0.0032704	02201
2201132010	02201	9	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	108	0.0006517	02201
2201132901	02201	1	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	42	0.0002534	02201
2201142002	02201	2	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	122	0.0007361	02201
2201152001	02201	3	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	29	0.0001750	02201
2201152003	02201	8	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	119	0.0007180	02201
2201152014	02201	8	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	60	0.0003620	02201
2201152015	02201	1	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	39	0.0002353	02201
2202012005	02202	20	2017	NA	NA	NA	NA	NA	NA	NA	240	0.7476636	02202
2203012013	02203	2	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	27	0.0024554	02203
2203012014	02203	147	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	2621	0.2383594	02203
2203012901	02203	4	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	40	0.0036377	02203
2203022008	02203	3	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	83	0.0075482	02203
2203022017	02203	50	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	742	0.0674791	02203
2203032012	02203	11	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	1475	0.1341397	02203
2203032015	02203	19	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	408	0.0371044	02203
2301032003	02301	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	23	0.0009132	02301
2301032005	02301	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	37	0.0014691	02301
2301032006	02301	2	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	118	0.0046851	02301
2301032018	02301	2	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	30	0.0011911	02301
2301032020	02301	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	13	0.0005162	02301
2301032023	02301	2	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	50	0.0019852	02301
2301052004	02301	9	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	71	0.0028190	02301
2301052007	02301	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	15	0.0005956	02301
2301052008	02301	4	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	23	0.0009132	02301
2301052901	02301	3	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	93	0.0036925	02301
2302012011	02302	9	2017	NA	NA	NA	NA	NA	NA	NA	141	0.0218368	02302
2302012901	02302	3	2017	NA	NA	NA	NA	NA	NA	NA	57	0.0088276	02302

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
2101012001	02101	1	2017	NA	NA	NA	NA	NA	NA	NA	6	0.0000166	02101	NA
2101012005	02101	1	2017	NA	NA	NA	NA	NA	NA	NA	59	0.0001630	02101	NA
2101012012	02101	1	2017	NA	NA	NA	NA	NA	NA	NA	53	0.0001465	02101	NA
2101012013	02101	10	2017	NA	NA	NA	NA	NA	NA	NA	121	0.0003344	02101	NA
2101012901	02101	1	2017	NA	NA	NA	NA	NA	NA	NA	16	0.0000442	02101	NA
2101102007	02101	18	2017	NA	NA	NA	NA	NA	NA	NA	401	0.0011081	02101	NA
2101102014	02101	5	2017	NA	NA	NA	NA	NA	NA	NA	78	0.0002155	02101	NA
2101102016	02101	1	2017	NA	NA	NA	NA	NA	NA	NA	9	0.0000249	02101	NA
2101122014	02101	12	2017	NA	NA	NA	NA	NA	NA	NA	268	0.0007406	02101	NA
2101122019	02101	2	2017	NA	NA	NA	NA	NA	NA	NA	1438	0.0039738	02101	NA
2102012001	02102	5	2017	NA	NA	NA	NA	NA	NA	NA	31	0.0023019	02102	NA
2102022003	02102	20	2017	NA	NA	NA	NA	NA	NA	NA	293	0.0217569	02102	NA
2102022006	02102	16	2017	NA	NA	NA	NA	NA	NA	NA	127	0.0094305	02102	NA
2102022008	02102	4	2017	NA	NA	NA	NA	NA	NA	NA	31	0.0023019	02102	NA
2102022901	02102	1	2017	NA	NA	NA	NA	NA	NA	NA	12	0.0008911	02102	NA
2103012008	02103	18	2017	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural	2684	0.2634989	02103	1082882590
2103012901	02103	1	2017	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural	8	0.0007854	02103	3227668
2103032002	02103	29	2017	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural	942	0.0924799	02103	380057898
2104012008	02104	8	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	59	0.0044304	02104	20384144
2104012014	02104	10	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	19	0.0014267	02104	6564385
2104012901	02104	6	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	26	0.0019524	02104	8982843
2104022015	02104	8	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	59	0.0044304	02104	20384144
2104022022	02104	2	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	57	0.0042802	02104	19693156
2104032901	02104	1	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	11	0.0008260	02104	3800434
2104042020	02104	27	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	729	0.0547421	02104	251865098
2104072901	02104	1	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	56	0.0042052	02104	19347662
2201032006	02201	2	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	36	0.0002172	02201	11160901
2201032013	02201	2	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	13	0.0000784	02201	4030325
2201082002	02201	45	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	1364	0.0082302	02201	422874126
2201082012	02201	6	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	227	0.0013697	02201	70375679
2201122002	02201	1	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	151	0.0009111	02201	46813778
2201132004	02201	30	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	542	0.0032704	02201	168033560
2201132010	02201	9	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	108	0.0006517	02201	33482702
2201132901	02201	1	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	42	0.0002534	02201	13021051
2201142002	02201	2	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	122	0.0007361	02201	37823052
2201152001	02201	3	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	29	0.0001750	02201	8990726
2201152003	02201	8	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	119	0.0007180	02201	36892977
2201152014	02201	8	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	60	0.0003620	02201	18601501
2201152015	02201	1	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	39	0.0002353	02201	12090976
2202012005	02202	20	2017	NA	NA	NA	NA	NA	NA	NA	240	0.7476636	02202	NA
2203012013	02203	2	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	27	0.0024554	02203	9615995
2203012014	02203	147	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	2621	0.2383594	02203	933463770
2203012901	02203	4	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	40	0.0036377	02203	14245918
2203022008	02203	3	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	83	0.0075482	02203	29560280
2203022017	02203	50	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	742	0.0674791	02203	264261777
2203032012	02203	11	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	1475	0.1341397	02203	525318222
2203032015	02203	19	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	408	0.0371044	02203	145308363
2301032003	02301	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	23	0.0009132	02301	4145016
2301032005	02301	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	37	0.0014691	02301	6668068
2301032006	02301	2	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	118	0.0046851	02301	21265732
2301032018	02301	2	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	30	0.0011911	02301	5406542
2301032020	02301	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	13	0.0005162	02301	2342835
2301032023	02301	2	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	50	0.0019852	02301	9010903
2301052004	02301	9	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	71	0.0028190	02301	12795483
2301052007	02301	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	15	0.0005956	02301	2703271
2301052008	02301	4	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	23	0.0009132	02301	4145016
2301052901	02301	3	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	93	0.0036925	02301	16760280
2302012011	02302	9	2017	NA	NA	NA	NA	NA	NA	NA	141	0.0218368	02302	NA
2302012901	02302	3	2017	NA	NA	NA	NA	NA	NA	NA	57	0.0088276	02302	NA

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -111631952  -55937792  -36457073  -23657073  926683414 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 32620989   30018819   1.087    0.284    
## Freq.x       6865455    1107886   6.197 2.76e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 172400000 on 39 degrees of freedom
##   (18 observations deleted due to missingness)
## Multiple R-squared:  0.4961, Adjusted R-squared:  0.4832 
## F-statistic:  38.4 on 1 and 39 DF,  p-value: 2.759e-07

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.483214321192485"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.483214321192485"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                
## [1,] "logarítmico" "0.42457206343907"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.531898928627414"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.638527969918706"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.599584061810376"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.577599181706715"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.640590862494669"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 3   logarítmico  0.42457206343907
## 1    cuadrático 0.483214321192485
## 2        cúbico 0.483214321192485
## 4 raíz cuadrada 0.531898928627414
## 7      raíz-log 0.577599181706715
## 6      log-raíz 0.599584061810376
## 5     raíz-raíz 0.638527969918706
## 8       log-log 0.640590862494669
##                                                                     sintaxis
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 8
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1627 -0.6956 -0.1130  0.5815  2.3421 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  15.5055     0.2385  65.023  < 2e-16 ***
## log(Freq.x)   1.0225     0.1203   8.503 2.05e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.005 on 39 degrees of freedom
##   (18 observations deleted due to missingness)
## Multiple R-squared:  0.6496, Adjusted R-squared:  0.6406 
## F-statistic: 72.29 on 1 and 39 DF,  p-value: 2.048e-10

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    15.50554

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##    1.022457

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.6406 ).

9.1 Diagrama de dispersión sobre log-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1627 -0.6956 -0.1130  0.5815  2.3421 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  15.5055     0.2385  65.023  < 2e-16 ***
## log(Freq.x)   1.0225     0.1203   8.503 2.05e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.005 on 39 degrees of freedom
##   (18 observations deleted due to missingness)
## Multiple R-squared:  0.6496, Adjusted R-squared:  0.6406 
## F-statistic: 72.29 on 1 and 39 DF,  p-value: 2.048e-10

ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = e^{15.50554 +1.022457 \cdot ln{X}} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
1	2101012001	02101	1	2017	NA	NA	NA	NA	NA	NA	NA	6	0.0000166	02101	NA	5419631
2	2101012005	02101	1	2017	NA	NA	NA	NA	NA	NA	NA	59	0.0001630	02101	NA	5419631
3	2101012012	02101	1	2017	NA	NA	NA	NA	NA	NA	NA	53	0.0001465	02101	NA	5419631
4	2101012013	02101	10	2017	NA	NA	NA	NA	NA	NA	NA	121	0.0003344	02101	NA	57072532
5	2101012901	02101	1	2017	NA	NA	NA	NA	NA	NA	NA	16	0.0000442	02101	NA	5419631
6	2101102007	02101	18	2017	NA	NA	NA	NA	NA	NA	NA	401	0.0011081	02101	NA	104095608
7	2101102014	02101	5	2017	NA	NA	NA	NA	NA	NA	NA	78	0.0002155	02101	NA	28095501
8	2101102016	02101	1	2017	NA	NA	NA	NA	NA	NA	NA	9	0.0000249	02101	NA	5419631
9	2101122014	02101	12	2017	NA	NA	NA	NA	NA	NA	NA	268	0.0007406	02101	NA	68768031
10	2101122019	02101	2	2017	NA	NA	NA	NA	NA	NA	NA	1438	0.0039738	02101	NA	11009309
11	2102012001	02102	5	2017	NA	NA	NA	NA	NA	NA	NA	31	0.0023019	02102	NA	28095501
12	2102022003	02102	20	2017	NA	NA	NA	NA	NA	NA	NA	293	0.0217569	02102	NA	115935782
13	2102022006	02102	16	2017	NA	NA	NA	NA	NA	NA	NA	127	0.0094305	02102	NA	92285003
14	2102022008	02102	4	2017	NA	NA	NA	NA	NA	NA	NA	31	0.0023019	02102	NA	22364048
15	2102022901	02102	1	2017	NA	NA	NA	NA	NA	NA	NA	12	0.0008911	02102	NA	5419631
16	2103012008	02103	18	2017	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural	2684	0.2634989	02103	1082882590	104095608
17	2103012901	02103	1	2017	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural	8	0.0007854	02103	3227668	5419631
18	2103032002	02103	29	2017	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural	942	0.0924799	02103	380057898	169515497
19	2104012008	02104	8	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	59	0.0044304	02104	20384144	45429795
20	2104012014	02104	10	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	19	0.0014267	02104	6564385	57072532
21	2104012901	02104	6	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	26	0.0019524	02104	8982843	33852928
22	2104022015	02104	8	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	59	0.0044304	02104	20384144	45429795
23	2104022022	02104	2	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	57	0.0042802	02104	19693156	11009309
24	2104032901	02104	1	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	11	0.0008260	02104	3800434	5419631
25	2104042020	02104	27	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	729	0.0547421	02104	251865098	157571701
26	2104072901	02104	1	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	56	0.0042052	02104	19347662	5419631
27	2201032006	02201	2	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	36	0.0002172	02201	11160901	11009309
28	2201032013	02201	2	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	13	0.0000784	02201	4030325	11009309
29	2201082002	02201	45	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	1364	0.0082302	02201	422874126	265649575
30	2201082012	02201	6	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	227	0.0013697	02201	70375679	33852928
31	2201122002	02201	1	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	151	0.0009111	02201	46813778	5419631
32	2201132004	02201	30	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	542	0.0032704	02201	168033560	175494419
33	2201132010	02201	9	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	108	0.0006517	02201	33482702	51243886
34	2201132901	02201	1	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	42	0.0002534	02201	13021051	5419631
35	2201142002	02201	2	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	122	0.0007361	02201	37823052	11009309
36	2201152001	02201	3	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	29	0.0001750	02201	8990726	16665022
37	2201152003	02201	8	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	119	0.0007180	02201	36892977	45429795
38	2201152014	02201	8	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	60	0.0003620	02201	18601501	45429795
39	2201152015	02201	1	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	39	0.0002353	02201	12090976	5419631
40	2202012005	02202	20	2017	NA	NA	NA	NA	NA	NA	NA	240	0.7476636	02202	NA	115935782
41	2203012013	02203	2	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	27	0.0024554	02203	9615995	11009309
42	2203012014	02203	147	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	2621	0.2383594	02203	933463770	891167661
43	2203012901	02203	4	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	40	0.0036377	02203	14245918	22364048
44	2203022008	02203	3	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	83	0.0075482	02203	29560280	16665022
45	2203022017	02203	50	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	742	0.0674791	02203	264261777	295865422
46	2203032012	02203	11	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	1475	0.1341397	02203	525318222	62914304
47	2203032015	02203	19	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	408	0.0371044	02203	145308363	110012195
48	2301032003	02301	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	23	0.0009132	02301	4145016	5419631
49	2301032005	02301	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	37	0.0014691	02301	6668068	5419631
50	2301032006	02301	2	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	118	0.0046851	02301	21265732	11009309
51	2301032018	02301	2	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	30	0.0011911	02301	5406542	11009309
52	2301032020	02301	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	13	0.0005162	02301	2342835	5419631
53	2301032023	02301	2	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	50	0.0019852	02301	9010903	11009309
54	2301052004	02301	9	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	71	0.0028190	02301	12795483	51243886
55	2301052007	02301	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	15	0.0005956	02301	2703271	5419631
56	2301052008	02301	4	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	23	0.0009132	02301	4145016	22364048
57	2301052901	02301	3	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	93	0.0036925	02301	16760280	16665022
58	2302012011	02302	9	2017	NA	NA	NA	NA	NA	NA	NA	141	0.0218368	02302	NA	51243886
59	2302012901	02302	3	2017	NA	NA	NA	NA	NA	NA	NA	57	0.0088276	02302	NA	16665022
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
1	2101012001	02101	1	2017	NA	NA	NA	NA	NA	NA	NA	6	0.0000166	02101	NA	5419631	NA
2	2101012005	02101	1	2017	NA	NA	NA	NA	NA	NA	NA	59	0.0001630	02101	NA	5419631	NA
3	2101012012	02101	1	2017	NA	NA	NA	NA	NA	NA	NA	53	0.0001465	02101	NA	5419631	NA
4	2101012013	02101	10	2017	NA	NA	NA	NA	NA	NA	NA	121	0.0003344	02101	NA	57072532	NA
5	2101012901	02101	1	2017	NA	NA	NA	NA	NA	NA	NA	16	0.0000442	02101	NA	5419631	NA
6	2101102007	02101	18	2017	NA	NA	NA	NA	NA	NA	NA	401	0.0011081	02101	NA	104095608	NA
7	2101102014	02101	5	2017	NA	NA	NA	NA	NA	NA	NA	78	0.0002155	02101	NA	28095501	NA
8	2101102016	02101	1	2017	NA	NA	NA	NA	NA	NA	NA	9	0.0000249	02101	NA	5419631	NA
9	2101122014	02101	12	2017	NA	NA	NA	NA	NA	NA	NA	268	0.0007406	02101	NA	68768031	NA
10	2101122019	02101	2	2017	NA	NA	NA	NA	NA	NA	NA	1438	0.0039738	02101	NA	11009309	NA
11	2102012001	02102	5	2017	NA	NA	NA	NA	NA	NA	NA	31	0.0023019	02102	NA	28095501	NA
12	2102022003	02102	20	2017	NA	NA	NA	NA	NA	NA	NA	293	0.0217569	02102	NA	115935782	NA
13	2102022006	02102	16	2017	NA	NA	NA	NA	NA	NA	NA	127	0.0094305	02102	NA	92285003	NA
14	2102022008	02102	4	2017	NA	NA	NA	NA	NA	NA	NA	31	0.0023019	02102	NA	22364048	NA
15	2102022901	02102	1	2017	NA	NA	NA	NA	NA	NA	NA	12	0.0008911	02102	NA	5419631	NA
16	2103012008	02103	18	2017	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural	2684	0.2634989	02103	1082882590	104095608	38783.76
17	2103012901	02103	1	2017	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural	8	0.0007854	02103	3227668	5419631	677453.86
18	2103032002	02103	29	2017	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural	942	0.0924799	02103	380057898	169515497	179952.76
19	2104012008	02104	8	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	59	0.0044304	02104	20384144	45429795	769996.52
20	2104012014	02104	10	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	19	0.0014267	02104	6564385	57072532	3003817.47
21	2104012901	02104	6	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	26	0.0019524	02104	8982843	33852928	1302035.69
22	2104022015	02104	8	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	59	0.0044304	02104	20384144	45429795	769996.52
23	2104022022	02104	2	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	57	0.0042802	02104	19693156	11009309	193145.77
24	2104032901	02104	1	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	11	0.0008260	02104	3800434	5419631	492693.72
25	2104042020	02104	27	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	729	0.0547421	02104	251865098	157571701	216147.74
26	2104072901	02104	1	2017	Taltal	345494.0	2017	2104	13317	4600943086	Rural	56	0.0042052	02104	19347662	5419631	96779.12
27	2201032006	02201	2	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	36	0.0002172	02201	11160901	11009309	305814.14
28	2201032013	02201	2	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	13	0.0000784	02201	4030325	11009309	846869.93
29	2201082002	02201	45	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	1364	0.0082302	02201	422874126	265649575	194757.75
30	2201082012	02201	6	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	227	0.0013697	02201	70375679	33852928	149131.84
31	2201122002	02201	1	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	151	0.0009111	02201	46813778	5419631	35891.60
32	2201132004	02201	30	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	542	0.0032704	02201	168033560	175494419	323790.44
33	2201132010	02201	9	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	108	0.0006517	02201	33482702	51243886	474480.42
34	2201132901	02201	1	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	42	0.0002534	02201	13021051	5419631	129038.83
35	2201142002	02201	2	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	122	0.0007361	02201	37823052	11009309	90240.24
36	2201152001	02201	3	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	29	0.0001750	02201	8990726	16665022	574655.92
37	2201152003	02201	8	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	119	0.0007180	02201	36892977	45429795	381762.98
38	2201152014	02201	8	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	60	0.0003620	02201	18601501	45429795	757163.25
39	2201152015	02201	1	2017	Calama	310025.0	2017	2201	165731	51380756402	Rural	39	0.0002353	02201	12090976	5419631	138964.90
40	2202012005	02202	20	2017	NA	NA	NA	NA	NA	NA	NA	240	0.7476636	02202	NA	115935782	NA
41	2203012013	02203	2	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	27	0.0024554	02203	9615995	11009309	407752.19
42	2203012014	02203	147	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	2621	0.2383594	02203	933463770	891167661	340010.55
43	2203012901	02203	4	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	40	0.0036377	02203	14245918	22364048	559101.21
44	2203022008	02203	3	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	83	0.0075482	02203	29560280	16665022	200783.39
45	2203022017	02203	50	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	742	0.0674791	02203	264261777	295865422	398740.46
46	2203032012	02203	11	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	1475	0.1341397	02203	525318222	62914304	42653.77
47	2203032015	02203	19	2017	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural	408	0.0371044	02203	145308363	110012195	269637.73
48	2301032003	02301	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	23	0.0009132	02301	4145016	5419631	235636.13
49	2301032005	02301	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	37	0.0014691	02301	6668068	5419631	146476.51
50	2301032006	02301	2	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	118	0.0046851	02301	21265732	11009309	93299.23
51	2301032018	02301	2	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	30	0.0011911	02301	5406542	11009309	366976.97
52	2301032020	02301	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	13	0.0005162	02301	2342835	5419631	416894.69
53	2301032023	02301	2	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	50	0.0019852	02301	9010903	11009309	220186.18
54	2301052004	02301	9	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	71	0.0028190	02301	12795483	51243886	721744.87
55	2301052007	02301	1	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	15	0.0005956	02301	2703271	5419631	361308.73
56	2301052008	02301	4	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	23	0.0009132	02301	4145016	22364048	972349.93
57	2301052901	02301	3	2017	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural	93	0.0036925	02301	16760280	16665022	179193.78
58	2302012011	02302	9	2017	NA	NA	NA	NA	NA	NA	NA	141	0.0218368	02302	NA	51243886	NA
59	2302012901	02302	3	2017	NA	NA	NA	NA	NA	NA	NA	57	0.0088276	02302	NA	16665022	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_rural_nivel_nacional_17_r02.rds")

R-03

Urbano

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 1)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15201	1	1	1	1767	1	1	1	2	1	3	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	2	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	3	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	4	1	1	2	3	1	99	1	2	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	5	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	6	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	7	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	8	1	1	3	3	1	99	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	9	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	10	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	11	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	12	1	1	4	3	3	3	4	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	13	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	14	1	1	2	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	15	1	1	2	1	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	16	1	1	5	1	5	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	17	1	1	5	3	1	3	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	18	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	19	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	20	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	21	4	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	22	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	23	1	1	5	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	24	1	1	3	1	1	6	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	25	1	1	5	6	1	1	4	2	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	26	4	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	27	1	1	5	99	3	3	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	28	1	1	6	3	2	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	29	1	1	2	3	1	99	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	30	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	31	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	32	1	1	3	1	1	2	1	1	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	33	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	34	3	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	35	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	36	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	37	1	1	5	3	3	1	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	38	1	1	1	1	4	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	39	1	1	5	3	4	3	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	40	1	1	5	3	2	1	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	41	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	42	1	1	2	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	43	1	1	1	1	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	44	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	45	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	46	1	1	5	6	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	47	1	1	5	3	4	0	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	48	1	1	3	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	49	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	50	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	51	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	52	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	53	1	1	1	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	54	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	55	1	1	2	2	1	4	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	56	8	1	98	98	98	98	98	98	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	57	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	58	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	59	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	60	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	61	1	1	2	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	62	1	1	5	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	63	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	64	1	1	5	3	2	4	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	65	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	66	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	67	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	68	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	69	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	70	1	1	1	3	3	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	71	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	72	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	73	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	74	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	75	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	76	1	1	5	3	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	77	1	1	3	6	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	78	5	1	3	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	79	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	80	1	1	2	1	1	3	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	81	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	82	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	83	1	1	5	3	2	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	84	4	1	5	3	2	0	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	85	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	86	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	87	1	1	3	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	88	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	89	5	1	4	3	2	1	2	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	90	1	1	5	3	3	2	1	3	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	91	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	92	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	93	5	1	4	3	5	1	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	94	1	1	5	3	3	99	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	95	1	1	5	3	2	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	96	1	1	2	3	4	6	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	97	1	1	5	1	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	98	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	99	1	1	2	7	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	100	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 3:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 3)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	3101011001	59	2017	03101
2	3101021001	77	2017	03101
3	3101031001	45	2017	03101
4	3101041001	91	2017	03101
5	3101051001	71	2017	03101
6	3101061001	55	2017	03101
7	3101061002	12	2017	03101
8	3101061003	25	2017	03101
9	3101061004	87	2017	03101
10	3101061005	75	2017	03101
11	3101061006	89	2017	03101
12	3101061007	35	2017	03101
13	3101061008	30	2017	03101
14	3101061009	109	2017	03101
15	3101071001	162	2017	03101
16	3101071002	162	2017	03101
17	3101081001	68	2017	03101
18	3101091001	102	2017	03101
19	3101101001	2	2017	03101
20	3101111001	665	2017	03101
21	3101111002	187	2017	03101
22	3101111003	640	2017	03101
23	3101161001	186	2017	03101
24	3101161002	654	2017	03101
25	3101161003	264	2017	03101
26	3101161004	334	2017	03101
27	3101211001	367	2017	03101
28	3101211002	80	2017	03101
29	3101211003	108	2017	03101
30	3101211004	169	2017	03101
31	3101211005	301	2017	03101
32	3101211006	219	2017	03101
33	3101211007	248	2017	03101
34	3101231001	16	2017	03101
35	3101231002	81	2017	03101
36	3101231003	122	2017	03101
37	3101231004	81	2017	03101
38	3101231005	36	2017	03101
39	3101241001	515	2017	03101
40	3101241002	719	2017	03101
41	3101241003	149	2017	03101
42	3101241004	98	2017	03101
43	3101241005	287	2017	03101
129	3102011001	179	2017	03102
130	3102011002	148	2017	03102
131	3102011003	228	2017	03102
132	3102011007	343	2017	03102
133	3102991999	9	2017	03102
219	3103011001	73	2017	03103
220	3103011002	19	2017	03103
221	3103011003	127	2017	03103
222	3103991999	2	2017	03103
308	3201011001	49	2017	03201
309	3201011002	65	2017	03201
310	3201011003	44	2017	03201
311	3201011004	15	2017	03201
312	3201011005	31	2017	03201
313	3201011006	11	2017	03201
399	3202011001	81	2017	03202
400	3202011002	38	2017	03202
401	3202011003	89	2017	03202
402	3202021001	56	2017	03202
403	3202021002	231	2017	03202
404	3202021003	119	2017	03202
490	3301011001	280	2017	03301
491	3301021001	397	2017	03301
492	3301021002	317	2017	03301
493	3301031001	164	2017	03301
494	3301031002	229	2017	03301
495	3301031003	149	2017	03301
496	3301031004	132	2017	03301
497	3301041001	314	2017	03301
498	3301041002	223	2017	03301
499	3301051001	1020	2017	03301
500	3301051002	254	2017	03301
501	3301051003	586	2017	03301
502	3301051004	270	2017	03301
503	3301991999	40	2017	03301
589	3303021001	217	2017	03303
590	3303021002	27	2017	03303
676	3304011001	64	2017	03304
677	3304011002	82	2017	03304
678	3304011003	225	2017	03304
679	3304011004	97	2017	03304
680	3304991999	8	2017	03304
NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA
NA.2	NA	NA	NA	NA
NA.3	NA	NA	NA	NA
NA.4	NA	NA	NA	NA
NA.5	NA	NA	NA	NA
NA.6	NA	NA	NA	NA
NA.7	NA	NA	NA	NA
NA.8	NA	NA	NA	NA
NA.9	NA	NA	NA	NA
NA.10	NA	NA	NA	NA
NA.11	NA	NA	NA	NA
NA.12	NA	NA	NA	NA
NA.13	NA	NA	NA	NA
NA.14	NA	NA	NA	NA

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_urbano_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
01107	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
01401	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
01405	Pica	330061.1	2017	1405	9296	3068247619	Urbano
02101	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02102	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02104	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02201	Calama	416281.1	2017	2201	165731	68990679686	Urbano
02203	San Pedro de Atacama	437934.7	2017	2203	10996	4815529626	Urbano
02301	Tocopilla	271720.8	2017	2301	25186	6843559467	Urbano
02302	María Elena	466266.9	2017	2302	6457	3010685220	Urbano
03101	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
03102	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
03103	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
03201	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
03202	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
03301	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
03303	Freirina	289049.9	2017	3303	7041	2035200054	Urbano
03304	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
04101	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04102	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04103	Andacollo	251267.7	2017	4103	11044	2775000288	Urbano
04104	La Higuera	214257.0	2017	4104	4241	908664019	Urbano
04106	Vicuña	245957.4	2017	4106	27771	6830481918	Urbano
04201	Illapel	270316.5	2017	4201	30848	8338722128	Urbano
04202	Canela	233397.3	2017	4202	9093	2122281844	Urbano
04203	Los Vilos	282415.6	2017	4203	21382	6038609501	Urbano
04204	Salamanca	262056.9	2017	4204	29347	7690585032	Urbano
04301	Ovalle	274771.4	2017	4301	111272	30574361012	Urbano
04302	Combarbalá	228990.4	2017	4302	13322	3050610572	Urbano
04303	Monte Patria	225369.1	2017	4303	30751	6930326684	Urbano
04304	Punitaqui	212496.1	2017	4304	10956	2328107498	Urbano
05101	Valparaíso	297929.0	2017	5101	296655	88382118059	Urbano
05102	Casablanca	341641.8	2017	5102	26867	9178890241	Urbano
05103	Concón	318496.3	2017	5103	42152	13425257057	Urbano
05105	Puchuncaví	296035.5	2017	5105	18546	5490274928	Urbano
05107	Quintero	308224.7	2017	5107	31923	9839456903	Urbano
05109	Viña del Mar	337006.1	2017	5109	334248	112643604611	Urbano
05301	Los Andes	339720.2	2017	5301	66708	22662055502	Urbano
05302	Calle Larga	246387.3	2017	5302	14832	3654416747	Urbano
05303	Rinconada	273904.7	2017	5303	10207	2795744821	Urbano
05304	San Esteban	219571.6	2017	5304	18855	4140022481	Urbano
05401	La Ligua	250134.4	2017	5401	35390	8852256241	Urbano
05402	Cabildo	262745.9	2017	5402	19388	5094117762	Urbano
05403	Papudo	294355.2	2017	5403	6356	1870921373	Urbano
05404	Petorca	237510.8	2017	5404	9826	2333781007	Urbano
05405	Zapallar	294389.2	2017	5405	7339	2160521991	Urbano
05501	Quillota	286029.5	2017	5501	90517	25890529852	Urbano
05502	Calera	277181.9	2017	5502	50554	14012652087	Urbano
05503	Hijuelas	254094.0	2017	5503	17988	4570642363	Urbano

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
1	03101	3101011001	59	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
2	03101	3101021001	77	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
3	03101	3101031001	45	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
4	03101	3101041001	91	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
5	03101	3101051001	71	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
6	03101	3101061001	55	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
7	03101	3101061002	12	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
8	03101	3101061003	25	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
9	03101	3101061004	87	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
10	03101	3101061005	75	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
11	03101	3101061006	89	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
12	03101	3101061007	35	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
13	03101	3101061008	30	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
14	03101	3101061009	109	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
15	03101	3101071001	162	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
16	03101	3101071002	162	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
17	03101	3101081001	68	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
18	03101	3101091001	102	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
19	03101	3101101001	2	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
20	03101	3101111001	665	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
21	03101	3101111002	187	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
22	03101	3101111003	640	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
23	03101	3101161001	186	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
24	03101	3101161002	654	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
25	03101	3101161003	264	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
26	03101	3101161004	334	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
27	03101	3101211001	367	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
28	03101	3101211002	80	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
29	03101	3101211003	108	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
30	03101	3101211004	169	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
31	03101	3101211005	301	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
32	03101	3101211006	219	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
33	03101	3101211007	248	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
34	03101	3101231001	16	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
35	03101	3101231002	81	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
36	03101	3101231003	122	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
37	03101	3101231004	81	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
38	03101	3101231005	36	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
39	03101	3101241001	515	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
40	03101	3101241002	719	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
41	03101	3101241003	149	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
42	03101	3101241004	98	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
43	03101	3101241005	287	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
44	03102	3102011001	179	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
45	03102	3102011002	148	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
46	03102	3102011003	228	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
47	03102	3102011007	343	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
48	03102	3102991999	9	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
49	03103	3103011001	73	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
50	03103	3103011002	19	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
51	03103	3103011003	127	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
52	03103	3103991999	2	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
53	03201	3201011001	49	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
54	03201	3201011002	65	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
55	03201	3201011003	44	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
56	03201	3201011004	15	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
57	03201	3201011005	31	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
58	03201	3201011006	11	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
59	03202	3202011001	81	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
60	03202	3202011002	38	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
61	03202	3202011003	89	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
62	03202	3202021001	56	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
63	03202	3202021002	231	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
64	03202	3202021003	119	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
65	03301	3301011001	280	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
66	03301	3301021001	397	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
67	03301	3301021002	317	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
68	03301	3301031001	164	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
69	03301	3301031002	229	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
70	03301	3301031003	149	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
71	03301	3301031004	132	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
72	03301	3301041001	314	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
73	03301	3301041002	223	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
74	03301	3301051001	1020	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
75	03301	3301051002	254	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
76	03301	3301051003	586	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
77	03301	3301051004	270	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
78	03301	3301991999	40	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
79	03303	3303021001	217	2017	Freirina	289049.9	2017	3303	7041	2035200054	Urbano
80	03303	3303021002	27	2017	Freirina	289049.9	2017	3303	7041	2035200054	Urbano
81	03304	3304011001	64	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
82	03304	3304011002	82	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
83	03304	3304011003	225	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
84	03304	3304011004	97	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
85	03304	3304991999	8	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
1	03101	3101011001	59	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
2	03101	3101021001	77	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
3	03101	3101031001	45	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
4	03101	3101041001	91	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
5	03101	3101051001	71	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
6	03101	3101061001	55	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
7	03101	3101061002	12	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
8	03101	3101061003	25	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
9	03101	3101061004	87	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
10	03101	3101061005	75	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
11	03101	3101061006	89	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
12	03101	3101061007	35	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
13	03101	3101061008	30	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
14	03101	3101061009	109	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
15	03101	3101071001	162	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
16	03101	3101071002	162	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
17	03101	3101081001	68	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
18	03101	3101091001	102	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
19	03101	3101101001	2	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
20	03101	3101111001	665	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
21	03101	3101111002	187	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
22	03101	3101111003	640	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
23	03101	3101161001	186	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
24	03101	3101161002	654	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
25	03101	3101161003	264	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
26	03101	3101161004	334	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
27	03101	3101211001	367	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
28	03101	3101211002	80	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
29	03101	3101211003	108	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
30	03101	3101211004	169	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
31	03101	3101211005	301	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
32	03101	3101211006	219	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
33	03101	3101211007	248	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
34	03101	3101231001	16	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
35	03101	3101231002	81	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
36	03101	3101231003	122	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
37	03101	3101231004	81	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
38	03101	3101231005	36	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
39	03101	3101241001	515	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
40	03101	3101241002	719	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
41	03101	3101241003	149	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
42	03101	3101241004	98	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
43	03101	3101241005	287	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
44	03102	3102011001	179	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
45	03102	3102011002	148	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
46	03102	3102011003	228	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
47	03102	3102011007	343	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
48	03102	3102991999	9	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
49	03103	3103011001	73	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
50	03103	3103011002	19	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
51	03103	3103011003	127	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
52	03103	3103991999	2	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
53	03201	3201011001	49	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
54	03201	3201011002	65	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
55	03201	3201011003	44	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
56	03201	3201011004	15	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
57	03201	3201011005	31	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
58	03201	3201011006	11	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
59	03202	3202011001	81	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
60	03202	3202011002	38	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
61	03202	3202011003	89	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
62	03202	3202021001	56	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
63	03202	3202021002	231	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
64	03202	3202021003	119	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
65	03301	3301011001	280	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
66	03301	3301021001	397	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
67	03301	3301021002	317	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
68	03301	3301031001	164	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
69	03301	3301031002	229	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
70	03301	3301031003	149	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
71	03301	3301031004	132	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
72	03301	3301041001	314	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
73	03301	3301041002	223	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
74	03301	3301051001	1020	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
75	03301	3301051002	254	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
76	03301	3301051003	586	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
77	03301	3301051004	270	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
78	03301	3301991999	40	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
79	03303	3303021001	217	2017	Freirina	289049.9	2017	3303	7041	2035200054	Urbano
80	03303	3303021002	27	2017	Freirina	289049.9	2017	3303	7041	2035200054	Urbano
81	03304	3304011001	64	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
82	03304	3304011002	82	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
83	03304	3304011003	225	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
84	03304	3304011004	97	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
85	03304	3304991999	8	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
3101011001	03101	59	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	869	0.0056452	03101
3101021001	03101	77	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	1437	0.0093350	03101
3101031001	03101	45	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	1502	0.0097572	03101
3101041001	03101	91	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	1734	0.0112643	03101
3101051001	03101	71	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	1576	0.0102380	03101
3101061001	03101	55	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4376	0.0284272	03101
3101061002	03101	12	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2049	0.0133106	03101
3101061003	03101	25	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4199	0.0272774	03101
3101061004	03101	87	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	5838	0.0379246	03101
3101061005	03101	75	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3217	0.0208982	03101
3101061006	03101	89	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	1930	0.0125376	03101
3101061007	03101	35	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3446	0.0223858	03101
3101061008	03101	30	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2624	0.0170459	03101
3101061009	03101	109	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	5319	0.0345531	03101
3101071001	03101	162	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3367	0.0218726	03101
3101071002	03101	162	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2651	0.0172213	03101
3101081001	03101	68	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2352	0.0152790	03101
3101091001	03101	102	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4467	0.0290184	03101
3101101001	03101	2	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	94	0.0006106	03101
3101111001	03101	665	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3046	0.0197873	03101
3101111002	03101	187	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2128	0.0138238	03101
3101111003	03101	640	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4579	0.0297459	03101
3101161001	03101	186	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3897	0.0253156	03101
3101161002	03101	654	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	5267	0.0342153	03101
3101161003	03101	264	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4789	0.0311101	03101
3101161004	03101	334	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4382	0.0284662	03101
3101211001	03101	367	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4698	0.0305190	03101
3101211002	03101	80	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2574	0.0167211	03101
3101211003	03101	108	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4857	0.0315519	03101
3101211004	03101	169	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4381	0.0284597	03101
3101211005	03101	301	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3957	0.0257053	03101
3101211006	03101	219	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	5331	0.0346311	03101
3101211007	03101	248	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2203	0.0143110	03101
3101231001	03101	16	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2431	0.0157922	03101
3101231002	03101	81	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4099	0.0266278	03101
3101231003	03101	122	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	6102	0.0396396	03101
3101231004	03101	81	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3368	0.0218791	03101
3101231005	03101	36	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3855	0.0250427	03101
3101241001	03101	515	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	5023	0.0326302	03101
3101241002	03101	719	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	6270	0.0407309	03101
3101241003	03101	149	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3082	0.0200212	03101
3101241004	03101	98	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3115	0.0202356	03101
3101241005	03101	287	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4323	0.0280829	03101
3102011001	03102	179	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano	2174	0.1230891	03102
3102011002	03102	148	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano	2696	0.1526441	03102
3102011003	03102	228	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano	3928	0.2223984	03102
3102011007	03102	343	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano	6749	0.3821198	03102
3102991999	03102	9	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano	228	0.0129091	03102
3103011001	03103	73	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano	6039	0.4307725	03103
3103011002	03103	19	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano	1412	0.1007205	03103
3103011003	03103	127	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano	2406	0.1716242	03103
3103991999	03103	2	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano	78	0.0055639	03103
3201011001	03201	49	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	4870	0.3985596	03201
3201011002	03201	65	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	1606	0.1314347	03201
3201011003	03201	44	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	2325	0.1902774	03201
3201011004	03201	15	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	1169	0.0956707	03201
3201011005	03201	31	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	735	0.0601522	03201
3201011006	03201	11	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	368	0.0301170	03201
3202011001	03202	81	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	2416	0.1735009	03202
3202011002	03202	38	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	1650	0.1184919	03202
3202011003	03202	89	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	3157	0.2267145	03202
3202021001	03202	56	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	1494	0.1072890	03202
3202021002	03202	231	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	2848	0.2045242	03202
3202021003	03202	119	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	1690	0.1213645	03202
3301011001	03301	280	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	3793	0.0730589	03301
3301021001	03301	397	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	3947	0.0760252	03301
3301021002	03301	317	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	2498	0.0481153	03301
3301031001	03301	164	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	1903	0.0366547	03301
3301031002	03301	229	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	2039	0.0392742	03301
3301031003	03301	149	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	3371	0.0649306	03301
3301031004	03301	132	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	2241	0.0431651	03301
3301041001	03301	314	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	4893	0.0942466	03301
3301041002	03301	223	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	2552	0.0491554	03301
3301051001	03301	1020	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	5354	0.1031261	03301
3301051002	03301	254	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	3313	0.0638134	03301
3301051003	03301	586	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	5518	0.1062850	03301
3301051004	03301	270	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	3876	0.0746576	03301
3301991999	03301	40	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	721	0.0138876	03301
3303021001	03303	217	2017	Freirina	289049.9	2017	3303	7041	2035200054	Urbano	3504	0.4976566	03303
3303021002	03303	27	2017	Freirina	289049.9	2017	3303	7041	2035200054	Urbano	1037	0.1472802	03303
3304011001	03304	64	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano	1426	0.1405065	03304
3304011002	03304	82	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano	1476	0.1454330	03304
3304011003	03304	225	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano	4169	0.4107794	03304
3304011004	03304	97	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano	1589	0.1565671	03304
3304991999	03304	8	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano	242	0.0238447	03304

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
3101011001	03101	59	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	869	0.0056452	03101	287269336
3101021001	03101	77	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	1437	0.0093350	03101	475035714
3101031001	03101	45	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	1502	0.0097572	03101	496523064
3101041001	03101	91	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	1734	0.0112643	03101	573216373
3101051001	03101	71	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	1576	0.0102380	03101	520985585
3101061001	03101	55	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4376	0.0284272	03101	1446594493
3101061002	03101	12	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2049	0.0133106	03101	677347376
3101061003	03101	25	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4199	0.0272774	03101	1388082787
3101061004	03101	87	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	5838	0.0379246	03101	1929894572
3101061005	03101	75	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3217	0.0208982	03101	1063458520
3101061006	03101	89	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	1930	0.0125376	03101	638008997
3101061007	03101	35	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3446	0.0223858	03101	1139160106
3101061008	03101	30	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2624	0.0170459	03101	867427776
3101061009	03101	109	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	5319	0.0345531	03101	1758326350
3101071001	03101	162	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3367	0.0218726	03101	1113044711
3101071002	03101	162	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2651	0.0172213	03101	876353291
3101081001	03101	68	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2352	0.0152790	03101	777511482
3101091001	03101	102	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4467	0.0290184	03101	1476676782
3101101001	03101	2	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	94	0.0006106	03101	31074013
3101111001	03101	665	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3046	0.0197873	03101	1006930262
3101111002	03101	187	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2128	0.0138238	03101	703462770
3101111003	03101	640	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4579	0.0297459	03101	1513701139
3101161001	03101	186	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3897	0.0253156	03101	1288249255
3101161002	03101	654	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	5267	0.0342153	03101	1741136470
3101161003	03101	264	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4789	0.0311101	03101	1583121807
3101161004	03101	334	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4382	0.0284662	03101	1448577940
3101211001	03101	367	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4698	0.0305190	03101	1553039517
3101211002	03101	80	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2574	0.0167211	03101	850899046
3101211003	03101	108	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4857	0.0315519	03101	1605600880
3101211004	03101	169	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4381	0.0284597	03101	1448247366
3101211005	03101	301	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3957	0.0257053	03101	1308083731
3101211006	03101	219	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	5331	0.0346311	03101	1762293245
3101211007	03101	248	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2203	0.0143110	03101	728255866
3101231001	03101	16	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2431	0.0157922	03101	803626877
3101231002	03101	81	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4099	0.0266278	03101	1355025326
3101231003	03101	122	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	6102	0.0396396	03101	2017166269
3101231004	03101	81	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3368	0.0218791	03101	1113375286
3101231005	03101	36	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3855	0.0250427	03101	1274365121
3101241001	03101	515	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	5023	0.0326302	03101	1660476265
3101241002	03101	719	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	6270	0.0407309	03101	2072702804
3101241003	03101	149	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3082	0.0200212	03101	1018830948
3101241004	03101	98	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3115	0.0202356	03101	1029739910
3101241005	03101	287	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4323	0.0280829	03101	1429074038
3102011001	03102	179	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano	2174	0.1230891	03102	650710405
3102011002	03102	148	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano	2696	0.1526441	03102	806952738
3102011003	03102	228	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano	3928	0.2223984	03102	1175708588
3102011007	03102	343	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano	6749	0.3821198	03102	2020075678
3102991999	03102	9	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano	228	0.0129091	03102	68243778
3103011001	03103	73	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano	6039	0.4307725	03103	1907482241
3103011002	03103	19	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano	1412	0.1007205	03103	445995185
3103011003	03103	127	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano	2406	0.1716242	03103	759960635
3103991999	03103	2	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano	78	0.0055639	03103	24637128
3201011001	03201	49	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	4870	0.3985596	03201	1394716026
3201011002	03201	65	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	1606	0.1314347	03201	459941260
3201011003	03201	44	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	2325	0.1902774	03201	665855187
3201011004	03201	15	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	1169	0.0956707	03201	334789124
3201011005	03201	31	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	735	0.0601522	03201	210496156
3201011006	03201	11	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	368	0.0301170	03201	105391273
3202011001	03202	81	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	2416	0.1735009	03202	787281371
3202011002	03202	38	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	1650	0.1184919	03202	537671466
3202011003	03202	89	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	3157	0.2267145	03202	1028744738
3202021001	03202	56	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	1494	0.1072890	03202	486837073
3202021002	03202	231	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	2848	0.2045242	03202	928053537
3202021003	03202	119	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	1690	0.1213645	03202	550705926
3301011001	03301	280	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	3793	0.0730589	03301	1181811700
3301021001	03301	397	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	3947	0.0760252	03301	1229794563
3301021002	03301	317	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	2498	0.0481153	03301	778319437
3301031001	03301	164	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	1903	0.0366547	03301	592931101
3301031002	03301	229	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	2039	0.0392742	03301	635305578
3301031003	03301	149	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	3371	0.0649306	03301	1050326190
3301031004	03301	132	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	2241	0.0431651	03301	698244139
3301041001	03301	314	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	4893	0.0942466	03301	1524546440
3301041002	03301	223	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	2552	0.0491554	03301	795144597
3301051001	03301	1020	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	5354	0.1031261	03301	1668183454
3301051002	03301	254	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	3313	0.0638134	03301	1032254722
3301051003	03301	586	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	5518	0.1062850	03301	1719282088
3301051004	03301	270	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	3876	0.0746576	03301	1207672594
3301991999	03301	40	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	721	0.0138876	03301	224647043
3303021001	03303	217	2017	Freirina	289049.9	2017	3303	7041	2035200054	Urbano	3504	0.4976566	03303	1012830704
3303021002	03303	27	2017	Freirina	289049.9	2017	3303	7041	2035200054	Urbano	1037	0.1472802	03303	299744703
3304011001	03304	64	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano	1426	0.1405065	03304	481153497
3304011002	03304	82	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano	1476	0.1454330	03304	498024237
3304011003	03304	225	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano	4169	0.4107794	03304	1406682278
3304011004	03304	97	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano	1589	0.1565671	03304	536152108
3304991999	03304	8	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano	242	0.0238447	03304	81654380

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -726321170 -334949144  -54763915  271330000 1112553429 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 718436214   66018000   10.88  < 2e-16 ***
## Freq.x        1526038     258218    5.91 7.31e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 442200000 on 83 degrees of freedom
## Multiple R-squared:  0.2962, Adjusted R-squared:  0.2877 
## F-statistic: 34.93 on 1 and 83 DF,  p-value: 7.307e-08

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.287693479921362"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.287693479921362"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.403831640886372"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.367943647397227"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                
## [1,] "raíz-raíz" "0.38755764915871"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                
## [1,] "log-raíz" "0.35108178154676"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.501984912163272"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.576282910777499"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 1    cuadrático 0.287693479921362
## 2        cúbico 0.287693479921362
## 6      log-raíz  0.35108178154676
## 4 raíz cuadrada 0.367943647397227
## 5     raíz-raíz  0.38755764915871
## 3   logarítmico 0.403831640886372
## 7      raíz-log 0.501984912163272
## 8       log-log 0.576282910777499
##                                                                     sintaxis
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 8
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.35469 -0.33356 -0.08175  0.28990  1.32071 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 18.00232    0.23813   75.60   <2e-16 ***
## log(Freq.x)  0.53688    0.05001   10.73   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5602 on 83 degrees of freedom
## Multiple R-squared:  0.5813, Adjusted R-squared:  0.5763 
## F-statistic: 115.2 on 1 and 83 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    18.00232

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##   0.5368848

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.5763 ).

9.1 Diagrama de dispersión sobre log-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.35469 -0.33356 -0.08175  0.28990  1.32071 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 18.00232    0.23813   75.60   <2e-16 ***
## log(Freq.x)  0.53688    0.05001   10.73   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5602 on 83 degrees of freedom
## Multiple R-squared:  0.5813, Adjusted R-squared:  0.5763 
## F-statistic: 115.2 on 1 and 83 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

(Intercept)

log(Freq.x)

\[ \hat Y = e^{17.361982+0.5368848 \cdot ln{X}} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
1	3101011001	03101	59	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	869	0.0056452	03101	287269336	587557021
2	3101021001	03101	77	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	1437	0.0093350	03101	475035714	677851695
3	3101031001	03101	45	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	1502	0.0097572	03101	496523064	508032112
4	3101041001	03101	91	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	1734	0.0112643	03101	573216373	741457112
5	3101051001	03101	71	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	1576	0.0102380	03101	520985585	648961540
6	3101061001	03101	55	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4376	0.0284272	03101	1446594493	565823222
7	3101061002	03101	12	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2049	0.0133106	03101	677347376	249863323
8	3101061003	03101	25	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4199	0.0272774	03101	1388082787	370543536
9	3101061004	03101	87	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	5838	0.0379246	03101	1929894572	723777196
10	3101061005	03101	75	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3217	0.0208982	03101	1063458520	668341428
11	3101061006	03101	89	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	1930	0.0125376	03101	638008997	732663157
12	3101061007	03101	35	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3446	0.0223858	03101	1139160106	443908192
13	3101061008	03101	30	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2624	0.0170459	03101	867427776	408649006
14	3101061009	03101	109	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	5319	0.0345531	03101	1758326350	816901711
15	3101071001	03101	162	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3367	0.0218726	03101	1113044711	1010558621
16	3101071002	03101	162	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2651	0.0172213	03101	876353291	1010558621
17	3101081001	03101	68	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2352	0.0152790	03101	777511482	634092582
18	3101091001	03101	102	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4467	0.0290184	03101	1476676782	788303382
19	3101101001	03101	2	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	94	0.0006106	03101	31074013	95482756
20	3101111001	03101	665	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3046	0.0197873	03101	1006930262	2156932270
21	3101111002	03101	187	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2128	0.0138238	03101	703462770	1091499925
22	3101111003	03101	640	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4579	0.0297459	03101	1513701139	2113011438
23	3101161001	03101	186	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3897	0.0253156	03101	1288249255	1088362292
24	3101161002	03101	654	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	5267	0.0342153	03101	1741136470	2137703016
25	3101161003	03101	264	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4789	0.0311101	03101	1583121807	1313496200
26	3101161004	03101	334	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4382	0.0284662	03101	1448577940	1490279049
27	3101211001	03101	367	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4698	0.0305190	03101	1553039517	1567605235
28	3101211002	03101	80	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2574	0.0167211	03101	850899046	691905172
29	3101211003	03101	108	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4857	0.0315519	03101	1605600880	812869435
30	3101211004	03101	169	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4381	0.0284597	03101	1448247366	1033772541
31	3101211005	03101	301	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3957	0.0257053	03101	1308083731	1409325004
32	3101211006	03101	219	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	5331	0.0346311	03101	1762293245	1188106448
33	3101211007	03101	248	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2203	0.0143110	03101	728255866	1270138829
34	3101231001	03101	16	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2431	0.0157922	03101	803626877	291595098
35	3101231002	03101	81	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4099	0.0266278	03101	1355025326	696535230
36	3101231003	03101	122	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	6102	0.0396396	03101	2017166269	867843405
37	3101231004	03101	81	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3368	0.0218791	03101	1113375286	696535230
38	3101231005	03101	36	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3855	0.0250427	03101	1274365121	450673117
39	3101241001	03101	515	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	5023	0.0326302	03101	1660476265	1880332088
40	3101241002	03101	719	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	6270	0.0407309	03101	2072702804	2249265863
41	3101241003	03101	149	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3082	0.0200212	03101	1018830948	966178036
42	3101241004	03101	98	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3115	0.0202356	03101	1029739910	771552534
43	3101241005	03101	287	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4323	0.0280829	03101	1429074038	1373744323
44	3102011001	03102	179	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano	2174	0.1230891	03102	650710405	1066176321
45	3102011002	03102	148	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano	2696	0.1526441	03102	806952738	962691223
46	3102011003	03102	228	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano	3928	0.2223984	03102	1175708588	1214075963
47	3102011007	03102	343	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano	6749	0.3821198	03102	2020075678	1511706065
48	3102991999	03102	9	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano	228	0.0129091	03102	68243778	214104012
49	3103011001	03103	73	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano	6039	0.4307725	03103	1907482241	658712966
50	3103011002	03103	19	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano	1412	0.1007205	03103	445995185	319778950
51	3103011003	03103	127	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano	2406	0.1716242	03103	759960635	886761290
52	3103991999	03103	2	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano	78	0.0055639	03103	24637128	95482756
53	3201011001	03201	49	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	4870	0.3985596	03201	1394716026	531798464
54	3201011002	03201	65	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	1606	0.1314347	03201	459941260	618916579
55	3201011003	03201	44	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	2325	0.1902774	03201	665855187	501939364
56	3201011004	03201	15	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	1169	0.0956707	03201	334789124	281664442
57	3201011005	03201	31	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	735	0.0601522	03201	210496156	415906705
58	3201011006	03201	11	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	368	0.0301170	03201	105391273	238459379
59	3202011001	03202	81	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	2416	0.1735009	03202	787281371	696535230
60	3202011002	03202	38	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	1650	0.1184919	03202	537671466	463946920
61	3202011003	03202	89	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	3157	0.2267145	03202	1028744738	732663157
62	3202021001	03202	56	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	1494	0.1072890	03202	486837073	571323479
63	3202021002	03202	231	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	2848	0.2045242	03202	928053537	1222626563
64	3202021003	03202	119	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	1690	0.1213645	03202	550705926	856320029
65	3301011001	03301	280	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	3793	0.0730589	03301	1181811700	1355652660
66	3301021001	03301	397	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	3947	0.0760252	03301	1229794563	1635150001
67	3301021002	03301	317	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	2498	0.0481153	03301	778319437	1449062712
68	3301031001	03301	164	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	1903	0.0366547	03301	592931101	1017237779
69	3301031002	03301	229	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	2039	0.0392742	03301	635305578	1216931920
70	3301031003	03301	149	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	3371	0.0649306	03301	1050326190	966178036
71	3301031004	03301	132	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	2241	0.0431651	03301	698244139	905337265
72	3301041001	03301	314	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	4893	0.0942466	03301	1524546440	1441683919
73	3301041002	03301	223	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	2552	0.0491554	03301	795144597	1199708315
74	3301051001	03301	1020	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	5354	0.1031261	03301	1668183454	2713802085
75	3301051002	03301	254	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	3313	0.0638134	03301	1032254722	1286545500
76	3301051003	03301	586	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	5518	0.1062850	03301	1719282088	2015341429
77	3301051004	03301	270	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	3876	0.0746576	03301	1207672594	1329439961
78	3301991999	03301	40	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	721	0.0138876	03301	224647043	476900913
79	3303021001	03303	217	2017	Freirina	289049.9	2017	3303	7041	2035200054	Urbano	3504	0.4976566	03303	1012830704	1182268720
80	3303021002	03303	27	2017	Freirina	289049.9	2017	3303	7041	2035200054	Urbano	1037	0.1472802	03303	299744703	386174815
81	3304011001	03304	64	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano	1426	0.1405065	03304	481153497	613786124
82	3304011002	03304	82	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano	1476	0.1454330	03304	498024237	701138891
83	3304011003	03304	225	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano	4169	0.4107794	03304	1406682278	1205473098
84	3304011004	03304	97	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano	1589	0.1565671	03304	536152108	767315611
85	3304991999	03304	8	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano	242	0.0238447	03304	81654380	200984143
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
1	3101011001	03101	59	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	869	0.0056452	03101	287269336	587557021	676130.06
2	3101021001	03101	77	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	1437	0.0093350	03101	475035714	677851695	471713.08
3	3101031001	03101	45	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	1502	0.0097572	03101	496523064	508032112	338237.09
4	3101041001	03101	91	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	1734	0.0112643	03101	573216373	741457112	427599.26
5	3101051001	03101	71	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	1576	0.0102380	03101	520985585	648961540	411777.63
6	3101061001	03101	55	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4376	0.0284272	03101	1446594493	565823222	129301.47
7	3101061002	03101	12	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2049	0.0133106	03101	677347376	249863323	121944.03
8	3101061003	03101	25	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4199	0.0272774	03101	1388082787	370543536	88245.66
9	3101061004	03101	87	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	5838	0.0379246	03101	1929894572	723777196	123976.91
10	3101061005	03101	75	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3217	0.0208982	03101	1063458520	668341428	207753.01
11	3101061006	03101	89	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	1930	0.0125376	03101	638008997	732663157	379618.22
12	3101061007	03101	35	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3446	0.0223858	03101	1139160106	443908192	128818.40
13	3101061008	03101	30	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2624	0.0170459	03101	867427776	408649006	155735.14
14	3101061009	03101	109	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	5319	0.0345531	03101	1758326350	816901711	153581.82
15	3101071001	03101	162	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3367	0.0218726	03101	1113044711	1010558621	300136.21
16	3101071002	03101	162	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2651	0.0172213	03101	876353291	1010558621	381199.03
17	3101081001	03101	68	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2352	0.0152790	03101	777511482	634092582	269597.19
18	3101091001	03101	102	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4467	0.0290184	03101	1476676782	788303382	176472.66
19	3101101001	03101	2	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	94	0.0006106	03101	31074013	95482756	1015774.00
20	3101111001	03101	665	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3046	0.0197873	03101	1006930262	2156932270	708119.59
21	3101111002	03101	187	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2128	0.0138238	03101	703462770	1091499925	512922.90
22	3101111003	03101	640	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4579	0.0297459	03101	1513701139	2113011438	461456.96
23	3101161001	03101	186	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3897	0.0253156	03101	1288249255	1088362292	279282.09
24	3101161002	03101	654	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	5267	0.0342153	03101	1741136470	2137703016	405867.29
25	3101161003	03101	264	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4789	0.0311101	03101	1583121807	1313496200	274273.59
26	3101161004	03101	334	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4382	0.0284662	03101	1448577940	1490279049	340091.07
27	3101211001	03101	367	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4698	0.0305190	03101	1553039517	1567605235	333675.02
28	3101211002	03101	80	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2574	0.0167211	03101	850899046	691905172	268805.43
29	3101211003	03101	108	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4857	0.0315519	03101	1605600880	812869435	167360.39
30	3101211004	03101	169	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4381	0.0284597	03101	1448247366	1033772541	235967.25
31	3101211005	03101	301	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3957	0.0257053	03101	1308083731	1409325004	356159.97
32	3101211006	03101	219	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	5331	0.0346311	03101	1762293245	1188106448	222867.46
33	3101211007	03101	248	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2203	0.0143110	03101	728255866	1270138829	576549.63
34	3101231001	03101	16	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	2431	0.0157922	03101	803626877	291595098	119948.62
35	3101231002	03101	81	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4099	0.0266278	03101	1355025326	696535230	169928.09
36	3101231003	03101	122	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	6102	0.0396396	03101	2017166269	867843405	142222.78
37	3101231004	03101	81	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3368	0.0218791	03101	1113375286	696535230	206809.75
38	3101231005	03101	36	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3855	0.0250427	03101	1274365121	450673117	116906.13
39	3101241001	03101	515	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	5023	0.0326302	03101	1660476265	1880332088	374344.43
40	3101241002	03101	719	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	6270	0.0407309	03101	2072702804	2249265863	358734.59
41	3101241003	03101	149	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3082	0.0200212	03101	1018830948	966178036	313490.60
42	3101241004	03101	98	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	3115	0.0202356	03101	1029739910	771552534	247689.42
43	3101241005	03101	287	2017	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano	4323	0.0280829	03101	1429074038	1373744323	317775.69
44	3102011001	03102	179	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano	2174	0.1230891	03102	650710405	1066176321	490421.49
45	3102011002	03102	148	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano	2696	0.1526441	03102	806952738	962691223	357081.31
46	3102011003	03102	228	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano	3928	0.2223984	03102	1175708588	1214075963	309082.48
47	3102011007	03102	343	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano	6749	0.3821198	03102	2020075678	1511706065	223989.64
48	3102991999	03102	9	2017	Caldera	299314.8	2017	3102	17662	5286498241	Urbano	228	0.0129091	03102	68243778	214104012	939052.69
49	3103011001	03103	73	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano	6039	0.4307725	03103	1907482241	658712966	109076.50
50	3103011002	03103	19	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano	1412	0.1007205	03103	445995185	319778950	226472.34
51	3103011003	03103	127	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano	2406	0.1716242	03103	759960635	886761290	368562.46
52	3103991999	03103	2	2017	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano	78	0.0055639	03103	24637128	95482756	1224137.90
53	3201011001	03201	49	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	4870	0.3985596	03201	1394716026	531798464	109198.86
54	3201011002	03201	65	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	1606	0.1314347	03201	459941260	618916579	385377.70
55	3201011003	03201	44	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	2325	0.1902774	03201	665855187	501939364	215887.90
56	3201011004	03201	15	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	1169	0.0956707	03201	334789124	281664442	240944.77
57	3201011005	03201	31	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	735	0.0601522	03201	210496156	415906705	565859.46
58	3201011006	03201	11	2017	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano	368	0.0301170	03201	105391273	238459379	647987.44
59	3202011001	03202	81	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	2416	0.1735009	03202	787281371	696535230	288301.01
60	3202011002	03202	38	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	1650	0.1184919	03202	537671466	463946920	281179.95
61	3202011003	03202	89	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	3157	0.2267145	03202	1028744738	732663157	232075.75
62	3202021001	03202	56	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	1494	0.1072890	03202	486837073	571323479	382411.97
63	3202021002	03202	231	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	2848	0.2045242	03202	928053537	1222626563	429293.03
64	3202021003	03202	119	2017	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano	1690	0.1213645	03202	550705926	856320029	506698.24
65	3301011001	03301	280	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	3793	0.0730589	03301	1181811700	1355652660	357409.09
66	3301021001	03301	397	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	3947	0.0760252	03301	1229794563	1635150001	414276.67
67	3301021002	03301	317	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	2498	0.0481153	03301	778319437	1449062712	580089.16
68	3301031001	03301	164	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	1903	0.0366547	03301	592931101	1017237779	534544.29
69	3301031002	03301	229	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	2039	0.0392742	03301	635305578	1216931920	596827.82
70	3301031003	03301	149	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	3371	0.0649306	03301	1050326190	966178036	286614.66
71	3301031004	03301	132	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	2241	0.0431651	03301	698244139	905337265	403988.07
72	3301041001	03301	314	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	4893	0.0942466	03301	1524546440	1441683919	294642.13
73	3301041002	03301	223	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	2552	0.0491554	03301	795144597	1199708315	470105.14
74	3301051001	03301	1020	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	5354	0.1031261	03301	1668183454	2713802085	506873.76
75	3301051002	03301	254	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	3313	0.0638134	03301	1032254722	1286545500	388332.48
76	3301051003	03301	586	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	5518	0.1062850	03301	1719282088	2015341429	365230.41
77	3301051004	03301	270	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	3876	0.0746576	03301	1207672594	1329439961	342992.77
78	3301991999	03301	40	2017	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano	721	0.0138876	03301	224647043	476900913	661443.71
79	3303021001	03303	217	2017	Freirina	289049.9	2017	3303	7041	2035200054	Urbano	3504	0.4976566	03303	1012830704	1182268720	337405.46
80	3303021002	03303	27	2017	Freirina	289049.9	2017	3303	7041	2035200054	Urbano	1037	0.1472802	03303	299744703	386174815	372396.16
81	3304011001	03304	64	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano	1426	0.1405065	03304	481153497	613786124	430425.05
82	3304011002	03304	82	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano	1476	0.1454330	03304	498024237	701138891	475026.35
83	3304011003	03304	225	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano	4169	0.4107794	03304	1406682278	1205473098	289151.62
84	3304011004	03304	97	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano	1589	0.1565671	03304	536152108	767315611	482892.14
85	3304991999	03304	8	2017	Huasco	337414.8	2017	3304	10149	3424422750	Urbano	242	0.0238447	03304	81654380	200984143	830512.99
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_urbano_nivel_nacional_17_r03.rds")

---

Rural

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 2)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15202	1	2	6	13225	1	3	1	5	3	5	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	3	1	1	5	3	5	2	3	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	4	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	5	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	6	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	8	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	9	3	1	5	3	5	1	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	10	1	1	5	3	4	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	11	1	2	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	12	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	13	1	1	5	3	4	1	4	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	14	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	16	1	1	5	3	5	1	3	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	17	1	1	5	3	5	2	4	1	8	15	152	15202	15202012006
15	152	15202	1	2	6	13225	18	3	1	5	3	5	1	1	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	19	3	1	5	3	5	1	3	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	20	1	1	5	3	5	2	1	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	21	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	22	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	24	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	25	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	26	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	27	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	28	3	1	5	3	5	1	4	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	29	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	30	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	31	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	32	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	33	1	1	5	3	5	3	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	34	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	35	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	36	1	1	5	3	5	3	2	1	9	15	152	15202	15202012006
15	152	15202	1	2	6	13225	37	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	38	1	1	5	3	5	99	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	39	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	40	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	41	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	42	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	43	3	1	5	3	5	2	1	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	44	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	45	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	8	13910	1	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	3	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	4	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	5	3	1	5	3	5	2	3	1	3	15	152	15202	15202012008
15	152	15202	1	2	8	13910	6	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	7	1	1	5	99	5	2	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	8	3	1	5	3	5	3	3	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	9	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	10	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	11	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	12	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	13	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	14	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	16	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	18	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	20	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	21	1	1	5	3	5	1	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	22	3	1	5	3	5	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	23	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	24	3	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	25	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	26	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	27	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	28	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	29	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	30	1	1	5	1	4	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	12	8394	1	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	2	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	3	3	1	5	3	5	2	3	1	4	15	152	15202	15202012012
15	152	15202	1	2	12	8394	4	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	5	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	6	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	8	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	9	3	1	5	3	5	1	4	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	10	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	11	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	12	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	13	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	14	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	15	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	16	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	18	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	20	3	1	5	3	5	3	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	21	3	1	5	99	5	1	4	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	22	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	24	3	1	5	3	5	1	2	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	25	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 3:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 3)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Veamos los primeros 100 registros:

r3_100 <- d[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	unlist.c.	unlist.d.	Freq	anio
1	3101102901	1	3101	2	2017
2	3101122013	1	3101	54	2017
3	3101122047	1	3101	1	2017
4	3101122901	1	3101	1	2017
5	3101132901	1	3101	1	2017
6	3101162050	1	3101	2	2017
7	3101172013	1	3101	11	2017
8	3101172017	1	3101	18	2017
9	3101172021	1	3101	1	2017
10	3101172026	1	3101	16	2017
11	3101172035	1	3101	15	2017
12	3101172037	1	3101	12	2017
13	3101182901	1	3101	1	2017
14	3101222015	1	3101	1	2017
15	3101222048	1	3101	1	2017
123	3102012001	1	3102	66	2017
124	3102012004	1	3102	2	2017
125	3102022010	1	3102	22	2017
126	3102022901	1	3102	2	2017
127	3102032003	1	3102	10	2017
128	3102032007	1	3102	4	2017
129	3102042002	1	3102	1	2017
237	3103012003	1	3103	2	2017
238	3103012022	1	3103	5	2017
239	3103012029	1	3103	1	2017
240	3103032006	1	3103	1	2017
241	3103032009	1	3103	1	2017
242	3103032014	1	3103	1	2017
243	3103042028	1	3103	1	2017
244	3103052020	1	3103	4	2017
245	3103062901	1	3103	1	2017
246	3103072012	1	3103	1	2017
354	3201012003	1	3201	4	2017
355	3201012005	1	3201	1	2017
356	3201022006	1	3201	6	2017
357	3201032007	1	3201	3	2017
465	3202022901	1	3202	18	2017
466	3202042008	1	3202	14	2017
574	3301032017	1	3301	14	2017
575	3301032901	1	3301	1	2017
576	3301042005	1	3301	10	2017
577	3301042060	1	3301	6	2017
578	3301052002	1	3301	19	2017
579	3301052006	1	3301	1	2017
580	3301052008	1	3301	1	2017
581	3301052010	1	3301	30	2017
582	3301052014	1	3301	2	2017
583	3301052028	1	3301	30	2017
584	3301052036	1	3301	67	2017
585	3301052038	1	3301	3	2017
586	3301052063	1	3301	11	2017
587	3301062901	1	3301	1	2017
588	3301072901	1	3301	3	2017
589	3301082012	1	3301	42	2017
590	3301082901	1	3301	3	2017
591	3301092043	1	3301	1	2017
592	3301092901	1	3301	8	2017
593	3301102901	1	3301	1	2017
594	3301112901	1	3301	1	2017
595	3301122009	1	3301	5	2017
596	3301122025	1	3301	9	2017
597	3301122030	1	3301	12	2017
598	3301122032	1	3301	1	2017
599	3301122033	1	3301	6	2017
600	3301122062	1	3301	1	2017
601	3301132026	1	3301	6	2017
602	3301132901	1	3301	1	2017
603	3301152010	1	3301	18	2017
604	3301152024	1	3301	4	2017
605	3301152901	1	3301	6	2017
713	3302012002	1	3302	19	2017
714	3302012005	1	3302	4	2017
715	3302012029	1	3302	12	2017
716	3302022005	1	3302	28	2017
717	3302032005	1	3302	15	2017
718	3302032018	1	3302	31	2017
719	3302042005	1	3302	31	2017
720	3302052005	1	3302	3	2017
721	3302072003	1	3302	6	2017
722	3302072025	1	3302	2	2017
723	3302072034	1	3302	31	2017
724	3302072901	1	3302	3	2017
725	3302082025	1	3302	1	2017
726	3302092013	1	3302	5	2017
727	3302092033	1	3302	8	2017
728	3302102010	1	3302	4	2017
729	3302102030	1	3302	2	2017
730	3302112015	1	3302	8	2017
731	3302112901	1	3302	1	2017
839	3303022004	1	3303	2	2017
840	3303022005	1	3303	5	2017
841	3303022007	1	3303	3	2017
842	3303022008	1	3303	16	2017
843	3303022009	1	3303	14	2017
844	3303022012	1	3303	5	2017
845	3303022013	1	3303	2	2017
846	3303032010	1	3303	3	2017
847	3303042002	1	3303	9	2017
848	3303042003	1	3303	12	2017
956	3304012009	1	3304	2	2017

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	3101102901	2	2017	03101
2	3101122013	54	2017	03101
3	3101122047	1	2017	03101
4	3101122901	1	2017	03101
5	3101132901	1	2017	03101
6	3101162050	2	2017	03101
7	3101172013	11	2017	03101
8	3101172017	18	2017	03101
9	3101172021	1	2017	03101
10	3101172026	16	2017	03101
11	3101172035	15	2017	03101
12	3101172037	12	2017	03101
13	3101182901	1	2017	03101
14	3101222015	1	2017	03101
15	3101222048	1	2017	03101
123	3102012001	66	2017	03102
124	3102012004	2	2017	03102
125	3102022010	22	2017	03102
126	3102022901	2	2017	03102
127	3102032003	10	2017	03102
128	3102032007	4	2017	03102
129	3102042002	1	2017	03102
237	3103012003	2	2017	03103
238	3103012022	5	2017	03103
239	3103012029	1	2017	03103
240	3103032006	1	2017	03103
241	3103032009	1	2017	03103
242	3103032014	1	2017	03103
243	3103042028	1	2017	03103
244	3103052020	4	2017	03103
245	3103062901	1	2017	03103
246	3103072012	1	2017	03103
354	3201012003	4	2017	03201
355	3201012005	1	2017	03201
356	3201022006	6	2017	03201
357	3201032007	3	2017	03201
465	3202022901	18	2017	03202
466	3202042008	14	2017	03202
574	3301032017	14	2017	03301
575	3301032901	1	2017	03301
576	3301042005	10	2017	03301
577	3301042060	6	2017	03301
578	3301052002	19	2017	03301
579	3301052006	1	2017	03301
580	3301052008	1	2017	03301
581	3301052010	30	2017	03301
582	3301052014	2	2017	03301
583	3301052028	30	2017	03301
584	3301052036	67	2017	03301
585	3301052038	3	2017	03301
586	3301052063	11	2017	03301
587	3301062901	1	2017	03301
588	3301072901	3	2017	03301
589	3301082012	42	2017	03301
590	3301082901	3	2017	03301
591	3301092043	1	2017	03301
592	3301092901	8	2017	03301
593	3301102901	1	2017	03301
594	3301112901	1	2017	03301
595	3301122009	5	2017	03301
596	3301122025	9	2017	03301
597	3301122030	12	2017	03301
598	3301122032	1	2017	03301
599	3301122033	6	2017	03301
600	3301122062	1	2017	03301
601	3301132026	6	2017	03301
602	3301132901	1	2017	03301
603	3301152010	18	2017	03301
604	3301152024	4	2017	03301
605	3301152901	6	2017	03301
713	3302012002	19	2017	03302
714	3302012005	4	2017	03302
715	3302012029	12	2017	03302
716	3302022005	28	2017	03302
717	3302032005	15	2017	03302
718	3302032018	31	2017	03302
719	3302042005	31	2017	03302
720	3302052005	3	2017	03302
721	3302072003	6	2017	03302
722	3302072025	2	2017	03302
723	3302072034	31	2017	03302
724	3302072901	3	2017	03302
725	3302082025	1	2017	03302
726	3302092013	5	2017	03302
727	3302092033	8	2017	03302
728	3302102010	4	2017	03302
729	3302102030	2	2017	03302
730	3302112015	8	2017	03302
731	3302112901	1	2017	03302
839	3303022004	2	2017	03303
840	3303022005	5	2017	03303
841	3303022007	3	2017	03303
842	3303022008	16	2017	03303
843	3303022009	14	2017	03303
844	3303022012	5	2017	03303
845	3303022013	2	2017	03303
846	3303032010	3	2017	03303
847	3303042002	9	2017	03303
848	3303042003	12	2017	03303
956	3304012009	2	2017	03304

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_rural_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	289375.3	2017	1101	191468	55406102543	Rural
01401	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
01402	Camiña	262850.3	2017	1402	1250	328562901	Rural
01404	Huara	253968.5	2017	1404	2730	693334131	Rural
01405	Pica	290496.7	2017	1405	9296	2700457509	Rural
02103	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
02104	Taltal	345494.0	2017	2104	13317	4600943086	Rural
02201	Calama	310025.0	2017	2201	165731	51380756402	Rural
02203	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
02301	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
03101	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03103	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03202	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural
03301	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03302	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03303	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03304	Huasco	227560.7	2017	3304	10149	2309513927	Rural
04101	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04102	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04103	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04104	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04105	Paiguano	205942.1	2017	4105	4497	926121774	Rural
04106	Vicuña	176130.6	2017	4106	27771	4891322768	Rural
04201	Illapel	191976.8	2017	4201	30848	5922099530	Rural
04202	Canela	171370.3	2017	4202	9093	1558270441	Rural
04203	Los Vilos	173238.5	2017	4203	21382	3704185607	Rural
04204	Salamanca	223234.2	2017	4204	29347	6551254640	Rural
04301	Ovalle	241393.7	2017	4301	111272	26860360045	Rural
04302	Combarbalá	179139.6	2017	4302	13322	2386498044	Rural
04303	Monte Patria	201205.8	2017	4303	30751	6187280931	Rural
04304	Punitaqui	171931.7	2017	4304	10956	1883683880	Rural
04305	Río Hurtado	182027.2	2017	4305	4278	778712384	Rural
05101	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05102	Casablanca	268917.1	2017	5102	26867	7224996933	Rural
05105	Puchuncaví	279614.4	2017	5105	18546	5185728335	Rural
05107	Quintero	334628.2	2017	5107	31923	10682335196	Rural
05301	Los Andes	324402.1	2017	5301	66708	21640215030	Rural
05302	Calle Larga	242743.8	2017	5302	14832	3600375502	Rural
05303	Rinconada	326532.5	2017	5303	10207	3332917471	Rural
05304	San Esteban	223168.6	2017	5304	18855	4207844130	Rural
05401	La Ligua	181468.0	2017	5401	35390	6422154059	Rural
05402	Cabildo	231277.8	2017	5402	19388	4484014285	Rural
05404	Petorca	298208.9	2017	5404	9826	2930200178	Rural
05405	Zapallar	292882.3	2017	5405	7339	2149463129	Rural
05501	Quillota	220926.8	2017	5501	90517	19997628209	Rural
05502	Calera	226906.2	2017	5502	50554	11471016698	Rural
05503	Hijuelas	253739.9	2017	5503	17988	4564273201	Rural
05504	La Cruz	291124.1	2017	5504	22098	6433259569	Rural
05506	Nogales	264475.3	2017	5506	22120	5850194593	Rural
05601	San Antonio	266331.2	2017	5601	91350	24329353815	Rural

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
03101	3101172035	15	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101102901	2	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101122013	54	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101222048	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101172037	12	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101182901	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101222015	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101162050	2	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101122047	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101122901	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101132901	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101172026	16	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101172013	11	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101172017	18	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101172021	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03102	3102022010	22	2017	NA	NA	NA	NA	NA	NA	NA
03102	3102022901	2	2017	NA	NA	NA	NA	NA	NA	NA
03102	3102012001	66	2017	NA	NA	NA	NA	NA	NA	NA
03102	3102012004	2	2017	NA	NA	NA	NA	NA	NA	NA
03102	3102042002	1	2017	NA	NA	NA	NA	NA	NA	NA
03102	3102032003	10	2017	NA	NA	NA	NA	NA	NA	NA
03102	3102032007	4	2017	NA	NA	NA	NA	NA	NA	NA
03103	3103012022	5	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03103	3103032014	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03103	3103012029	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03103	3103032006	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03103	3103032009	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03103	3103072012	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03103	3103042028	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03103	3103052020	4	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03103	3103062901	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03103	3103012003	2	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03201	3201032007	3	2017	NA	NA	NA	NA	NA	NA	NA
03201	3201012005	1	2017	NA	NA	NA	NA	NA	NA	NA
03201	3201022006	6	2017	NA	NA	NA	NA	NA	NA	NA
03201	3201012003	4	2017	NA	NA	NA	NA	NA	NA	NA
03202	3202022901	18	2017	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural
03202	3202042008	14	2017	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural
03301	3301032017	14	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301032901	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301042005	10	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301042060	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301052002	19	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301052006	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301052008	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301052010	30	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301052014	2	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301052028	30	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301052036	67	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301052038	3	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301052063	11	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301062901	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301072901	3	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301082012	42	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301082901	3	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301092043	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301092901	8	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301102901	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301112901	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301122009	5	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301122025	9	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301122030	12	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301122032	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301122033	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301122062	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301132026	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301132901	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301152010	18	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301152024	4	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301152901	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03302	3302012002	19	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302012005	4	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302012029	12	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302022005	28	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302032005	15	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302032018	31	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302042005	31	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302052005	3	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302072003	6	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302072025	2	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302072034	31	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302072901	3	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302082025	1	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302092013	5	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302092033	8	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302102010	4	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302102030	2	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302112015	8	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302112901	1	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03303	3303022004	2	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03303	3303022005	5	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03303	3303022007	3	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03303	3303022008	16	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03303	3303022009	14	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03303	3303022012	5	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03303	3303022013	2	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03303	3303032010	3	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03303	3303042002	9	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03303	3303042003	12	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03304	3304012009	2	2017	Huasco	227560.7	2017	3304	10149	2309513927	Rural

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
03101	3101172035	15	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101102901	2	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101122013	54	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101222048	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101172037	12	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101182901	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101222015	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101162050	2	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101122047	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101122901	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101132901	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101172026	16	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101172013	11	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101172017	18	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03101	3101172021	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03102	3102022010	22	2017	NA	NA	NA	NA	NA	NA	NA
03102	3102022901	2	2017	NA	NA	NA	NA	NA	NA	NA
03102	3102012001	66	2017	NA	NA	NA	NA	NA	NA	NA
03102	3102012004	2	2017	NA	NA	NA	NA	NA	NA	NA
03102	3102042002	1	2017	NA	NA	NA	NA	NA	NA	NA
03102	3102032003	10	2017	NA	NA	NA	NA	NA	NA	NA
03102	3102032007	4	2017	NA	NA	NA	NA	NA	NA	NA
03103	3103012022	5	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03103	3103032014	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03103	3103012029	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03103	3103032006	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03103	3103032009	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03103	3103072012	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03103	3103042028	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03103	3103052020	4	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03103	3103062901	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03103	3103012003	2	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03201	3201032007	3	2017	NA	NA	NA	NA	NA	NA	NA
03201	3201012005	1	2017	NA	NA	NA	NA	NA	NA	NA
03201	3201022006	6	2017	NA	NA	NA	NA	NA	NA	NA
03201	3201012003	4	2017	NA	NA	NA	NA	NA	NA	NA
03202	3202022901	18	2017	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural
03202	3202042008	14	2017	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural
03301	3301032017	14	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301032901	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301042005	10	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301042060	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301052002	19	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301052006	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301052008	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301052010	30	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301052014	2	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301052028	30	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301052036	67	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301052038	3	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301052063	11	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301062901	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301072901	3	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301082012	42	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301082901	3	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301092043	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301092901	8	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301102901	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301112901	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301122009	5	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301122025	9	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301122030	12	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301122032	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301122033	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301122062	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301132026	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301132901	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301152010	18	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301152024	4	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03301	3301152901	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03302	3302012002	19	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302012005	4	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302012029	12	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302022005	28	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302032005	15	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302032018	31	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302042005	31	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302052005	3	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302072003	6	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302072025	2	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302072034	31	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302072901	3	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302082025	1	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302092013	5	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302092033	8	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302102010	4	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302102030	2	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302112015	8	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03302	3302112901	1	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03303	3303022004	2	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03303	3303022005	5	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03303	3303022007	3	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03303	3303022008	16	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03303	3303022009	14	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03303	3303022012	5	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03303	3303022013	2	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03303	3303032010	3	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03303	3303042002	9	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03303	3303042003	12	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03304	3304012009	2	2017	Huasco	227560.7	2017	3304	10149	2309513927	Rural

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
3101102901	03101	2	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	26	0.0001689	03101
3101122013	03101	54	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	332	0.0021567	03101
3101122047	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	183	0.0011888	03101
3101122901	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	54	0.0003508	03101
3101132901	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	14	0.0000909	03101
3101162050	03101	2	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	33	0.0002144	03101
3101172013	03101	11	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	194	0.0012603	03101
3101172017	03101	18	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	121	0.0007860	03101
3101172021	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	74	0.0004807	03101
3101172026	03101	16	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	340	0.0022087	03101
3101172035	03101	15	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	293	0.0019034	03101
3101172037	03101	12	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	859	0.0055802	03101
3101182901	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	17	0.0001104	03101
3101222015	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	97	0.0006301	03101
3101222048	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	62	0.0004028	03101
3102012001	03102	66	2017	NA	NA	NA	NA	NA	NA	NA	590	0.0334051	03102
3102012004	03102	2	2017	NA	NA	NA	NA	NA	NA	NA	117	0.0066244	03102
3102022010	03102	22	2017	NA	NA	NA	NA	NA	NA	NA	542	0.0306874	03102
3102022901	03102	2	2017	NA	NA	NA	NA	NA	NA	NA	53	0.0030008	03102
3102032003	03102	10	2017	NA	NA	NA	NA	NA	NA	NA	297	0.0168158	03102
3102032007	03102	4	2017	NA	NA	NA	NA	NA	NA	NA	181	0.0102480	03102
3102042002	03102	1	2017	NA	NA	NA	NA	NA	NA	NA	22	0.0012456	03102
3103012003	03103	2	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	74	0.0052786	03103
3103012022	03103	5	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	553	0.0394465	03103
3103012029	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	86	0.0061345	03103
3103032006	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	476	0.0339539	03103
3103032009	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	22	0.0015693	03103
3103032014	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	41	0.0029246	03103
3103042028	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	195	0.0139097	03103
3103052020	03103	4	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	952	0.0679078	03103
3103062901	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	122	0.0087025	03103
3103072012	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	27	0.0019260	03103
3201012003	03201	4	2017	NA	NA	NA	NA	NA	NA	NA	27	0.0022097	03201
3201012005	03201	1	2017	NA	NA	NA	NA	NA	NA	NA	29	0.0023734	03201
3201022006	03201	6	2017	NA	NA	NA	NA	NA	NA	NA	699	0.0572060	03201
3201032007	03201	3	2017	NA	NA	NA	NA	NA	NA	NA	185	0.0151404	03201
3202022901	03202	18	2017	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural	294	0.0211131	03202
3202042008	03202	14	2017	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural	319	0.0229084	03202
3301032017	03301	14	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	386	0.0074349	03301
3301032901	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	9	0.0001734	03301
3301042005	03301	10	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	184	0.0035441	03301
3301042060	03301	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	172	0.0033130	03301
3301052002	03301	19	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	574	0.0110561	03301
3301052006	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	55	0.0010594	03301
3301052008	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	17	0.0003274	03301
3301052010	03301	30	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	343	0.0066067	03301
3301052014	03301	2	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	37	0.0007127	03301
3301052028	03301	30	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	523	0.0100738	03301
3301052036	03301	67	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	537	0.0103434	03301
3301052038	03301	3	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	87	0.0016758	03301
3301052063	03301	11	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	90	0.0017335	03301
3301062901	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	11	0.0002119	03301
3301072901	03301	3	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	19	0.0003660	03301
3301082012	03301	42	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	796	0.0153322	03301
3301082901	03301	3	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	177	0.0034093	03301
3301092043	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	60	0.0011557	03301
3301092901	03301	8	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	141	0.0027159	03301
3301102901	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	42	0.0008090	03301
3301112901	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	13	0.0002504	03301
3301122009	03301	5	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	198	0.0038138	03301
3301122025	03301	9	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	264	0.0050850	03301
3301122030	03301	12	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	159	0.0030626	03301
3301122032	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	43	0.0008282	03301
3301122033	03301	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	108	0.0020802	03301
3301122062	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	27	0.0005201	03301
3301132026	03301	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	146	0.0028122	03301
3301132901	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	33	0.0006356	03301
3301152010	03301	18	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	218	0.0041990	03301
3301152024	03301	4	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	43	0.0008282	03301
3301152901	03301	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	124	0.0023884	03301
3302012002	03302	19	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	645	0.1217211	03302
3302012005	03302	4	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	48	0.0090583	03302
3302012029	03302	12	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	117	0.0220796	03302
3302022005	03302	28	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	540	0.1019060	03302
3302032005	03302	15	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	821	0.1549349	03302
3302032018	03302	31	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	512	0.0966220	03302
3302042005	03302	31	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	560	0.1056803	03302
3302052005	03302	3	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	419	0.0790715	03302
3302072003	03302	6	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	73	0.0137762	03302
3302072025	03302	2	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	28	0.0052840	03302
3302072034	03302	31	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	229	0.0432157	03302
3302072901	03302	3	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	52	0.0098132	03302
3302082025	03302	1	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	24	0.0045292	03302
3302092013	03302	5	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	58	0.0109455	03302
3302092033	03302	8	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	149	0.0281185	03302
3302102010	03302	4	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	113	0.0213248	03302
3302102030	03302	2	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	115	0.0217022	03302
3302112015	03302	8	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	53	0.0100019	03302
3302112901	03302	1	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	27	0.0050953	03302
3303022004	03303	2	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	35	0.0049709	03303
3303022005	03303	5	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	208	0.0295413	03303
3303022007	03303	3	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	79	0.0112200	03303
3303022008	03303	16	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	693	0.0984235	03303
3303022009	03303	14	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	418	0.0593666	03303
3303022012	03303	5	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	206	0.0292572	03303
3303022013	03303	2	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	38	0.0053970	03303
3303032010	03303	3	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	73	0.0103678	03303
3303042002	03303	9	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	223	0.0316716	03303
3303042003	03303	12	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	319	0.0453061	03303
3304012009	03304	2	2017	Huasco	227560.7	2017	3304	10149	2309513927	Rural	201	0.0198049	03304

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
3101102901	03101	2	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	26	0.0001689	03101	8021072
3101122013	03101	54	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	332	0.0021567	03101	102422918
3101122047	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	183	0.0011888	03101	56456006
3101122901	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	54	0.0003508	03101	16659149
3101132901	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	14	0.0000909	03101	4319039
3101162050	03101	2	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	33	0.0002144	03101	10180591
3101172013	03101	11	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	194	0.0012603	03101	59849537
3101172017	03101	18	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	121	0.0007860	03101	37328835
3101172021	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	74	0.0004807	03101	22829205
3101172026	03101	16	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	340	0.0022087	03101	104890940
3101172035	03101	15	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	293	0.0019034	03101	90391310
3101172037	03101	12	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	859	0.0055802	03101	265003876
3101182901	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	17	0.0001104	03101	5244547
3101222015	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	97	0.0006301	03101	29924768
3101222048	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	62	0.0004028	03101	19127171
3102012001	03102	66	2017	NA	NA	NA	NA	NA	NA	NA	590	0.0334051	03102	NA
3102012004	03102	2	2017	NA	NA	NA	NA	NA	NA	NA	117	0.0066244	03102	NA
3102022010	03102	22	2017	NA	NA	NA	NA	NA	NA	NA	542	0.0306874	03102	NA
3102022901	03102	2	2017	NA	NA	NA	NA	NA	NA	NA	53	0.0030008	03102	NA
3102032003	03102	10	2017	NA	NA	NA	NA	NA	NA	NA	297	0.0168158	03102	NA
3102032007	03102	4	2017	NA	NA	NA	NA	NA	NA	NA	181	0.0102480	03102	NA
3102042002	03102	1	2017	NA	NA	NA	NA	NA	NA	NA	22	0.0012456	03102	NA
3103012003	03103	2	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	74	0.0052786	03103	23121842
3103012022	03103	5	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	553	0.0394465	03103	172788897
3103012029	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	86	0.0061345	03103	26871329
3103032006	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	476	0.0339539	03103	148729684
3103032009	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	22	0.0015693	03103	6874061
3103032014	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	41	0.0029246	03103	12810750
3103042028	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	195	0.0139097	03103	60929177
3103052020	03103	4	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	952	0.0679078	03103	297459368
3103062901	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	122	0.0087025	03103	38119793
3103072012	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	27	0.0019260	03103	8436348
3201012003	03201	4	2017	NA	NA	NA	NA	NA	NA	NA	27	0.0022097	03201	NA
3201012005	03201	1	2017	NA	NA	NA	NA	NA	NA	NA	29	0.0023734	03201	NA
3201022006	03201	6	2017	NA	NA	NA	NA	NA	NA	NA	699	0.0572060	03201	NA
3201032007	03201	3	2017	NA	NA	NA	NA	NA	NA	NA	185	0.0151404	03201	NA
3202022901	03202	18	2017	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural	294	0.0211131	03202	110106399
3202042008	03202	14	2017	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural	319	0.0229084	03202	119469188
3301032017	03301	14	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	386	0.0074349	03301	98156173
3301032901	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	9	0.0001734	03301	2288615
3301042005	03301	10	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	184	0.0035441	03301	46789471
3301042060	03301	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	172	0.0033130	03301	43737984
3301052002	03301	19	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	574	0.0110561	03301	145962807
3301052006	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	55	0.0010594	03301	13985983
3301052008	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	17	0.0003274	03301	4322940
3301052010	03301	30	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	343	0.0066067	03301	87221677
3301052014	03301	2	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	37	0.0007127	03301	9408752
3301052028	03301	30	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	523	0.0100738	03301	132993986
3301052036	03301	67	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	537	0.0103434	03301	136554055
3301052038	03301	3	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	87	0.0016758	03301	22123283
3301052063	03301	11	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	90	0.0017335	03301	22886154
3301062901	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	11	0.0002119	03301	2797197
3301072901	03301	3	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	19	0.0003660	03301	4831521
3301082012	03301	42	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	796	0.0153322	03301	202415321
3301082901	03301	3	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	177	0.0034093	03301	45009437
3301092043	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	60	0.0011557	03301	15257436
3301092901	03301	8	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	141	0.0027159	03301	35854975
3301102901	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	42	0.0008090	03301	10680205
3301112901	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	13	0.0002504	03301	3305778
3301122009	03301	5	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	198	0.0038138	03301	50349540
3301122025	03301	9	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	264	0.0050850	03301	67132720
3301122030	03301	12	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	159	0.0030626	03301	40432206
3301122032	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	43	0.0008282	03301	10934496
3301122033	03301	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	108	0.0020802	03301	27463385
3301122062	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	27	0.0005201	03301	6865846
3301132026	03301	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	146	0.0028122	03301	37126428
3301132901	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	33	0.0006356	03301	8391590
3301152010	03301	18	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	218	0.0041990	03301	55435352
3301152024	03301	4	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	43	0.0008282	03301	10934496
3301152901	03301	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	124	0.0023884	03301	31532035
3302012002	03302	19	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	645	0.1217211	03302	146499089
3302012005	03302	4	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	48	0.0090583	03302	10902258
3302012029	03302	12	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	117	0.0220796	03302	26574253
3302022005	03302	28	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	540	0.1019060	03302	122650400
3302032005	03302	15	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	821	0.1549349	03302	186474034
3302032018	03302	31	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	512	0.0966220	03302	116290750
3302042005	03302	31	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	560	0.1056803	03302	127193007
3302052005	03302	3	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	419	0.0790715	03302	95167625
3302072003	03302	6	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	73	0.0137762	03302	16580517
3302072025	03302	2	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	28	0.0052840	03302	6359650
3302072034	03302	31	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	229	0.0432157	03302	52012855
3302072901	03302	3	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	52	0.0098132	03302	11810779
3302082025	03302	1	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	24	0.0045292	03302	5451129
3302092013	03302	5	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	58	0.0109455	03302	13173561
3302092033	03302	8	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	149	0.0281185	03302	33842425
3302102010	03302	4	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	113	0.0213248	03302	25665732
3302102030	03302	2	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	115	0.0217022	03302	26119993
3302112015	03302	8	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	53	0.0100019	03302	12037910
3302112901	03302	1	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	27	0.0050953	03302	6132520
3303022004	03303	2	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	35	0.0049709	03303	7518115
3303022005	03303	5	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	208	0.0295413	03303	44679082
3303022007	03303	3	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	79	0.0112200	03303	16969459
3303022008	03303	16	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	693	0.0984235	03303	148858673
3303022009	03303	14	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	418	0.0593666	03303	89787771
3303022012	03303	5	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	206	0.0292572	03303	44249476
3303022013	03303	2	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	38	0.0053970	03303	8162525
3303032010	03303	3	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	73	0.0103678	03303	15680639
3303042002	03303	9	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	223	0.0316716	03303	47901131
3303042003	03303	12	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	319	0.0453061	03303	68522246
3304012009	03304	2	2017	Huasco	227560.7	2017	3304	10149	2309513927	Rural	201	0.0198049	03304	45739708

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -71946398 -26183927 -15541200   8128607 257914377 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 28817660    6411137   4.495 1.98e-05 ***
## Freq.x       2681833     439854   6.097 2.37e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 49860000 on 94 degrees of freedom
##   (11 observations deleted due to missingness)
## Multiple R-squared:  0.2834, Adjusted R-squared:  0.2758 
## F-statistic: 37.17 on 1 and 94 DF,  p-value: 2.373e-08

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.275774143510848"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.275774143510848"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.326750520471716"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.332836242802862"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.438720629789788"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.457998577298983"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                
## [1,] "raíz-log" "0.45031335351767"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.505108023787481"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 1    cuadrático 0.275774143510848
## 2        cúbico 0.275774143510848
## 3   logarítmico 0.326750520471716
## 4 raíz cuadrada 0.332836242802862
## 5     raíz-raíz 0.438720629789788
## 7      raíz-log  0.45031335351767
## 6      log-raíz 0.457998577298983
## 8       log-log 0.505108023787481
##                                                                     sintaxis
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 8
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.51650 -0.64384  0.00934  0.40410  2.67424 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.14340    0.13374 120.711  < 2e-16 ***
## log(Freq.x)  0.69522    0.07024   9.898 3.03e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8355 on 94 degrees of freedom
##   (11 observations deleted due to missingness)
## Multiple R-squared:  0.5103, Adjusted R-squared:  0.5051 
## F-statistic: 97.96 on 1 and 94 DF,  p-value: 3.034e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##     16.1434

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##   0.6952152

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.5051 ).

9.1 Diagrama de dispersión sobre log-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.51650 -0.64384  0.00934  0.40410  2.67424 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.14340    0.13374 120.711  < 2e-16 ***
## log(Freq.x)  0.69522    0.07024   9.898 3.03e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8355 on 94 degrees of freedom
##   (11 observations deleted due to missingness)
## Multiple R-squared:  0.5103, Adjusted R-squared:  0.5051 
## F-statistic: 97.96 on 1 and 94 DF,  p-value: 3.034e-16

ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = e^{16.1434 +0.6952152 \cdot ln{X}} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
3101102901	03101	2	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	26	0.0001689	03101	8021072	16606207
3101122013	03101	54	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	332	0.0021567	03101	102422918	164201191
3101122047	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	183	0.0011888	03101	56456006	10256278
3101122901	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	54	0.0003508	03101	16659149	10256278
3101132901	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	14	0.0000909	03101	4319039	10256278
3101162050	03101	2	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	33	0.0002144	03101	10180591	16606207
3101172013	03101	11	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	194	0.0012603	03101	59849537	54322762
3101172017	03101	18	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	121	0.0007860	03101	37328835	76502275
3101172021	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	74	0.0004807	03101	22829205	10256278
3101172026	03101	16	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	340	0.0022087	03101	104890940	70487537
3101172035	03101	15	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	293	0.0019034	03101	90391310	67394793
3101172037	03101	12	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	859	0.0055802	03101	265003876	57710259
3101182901	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	17	0.0001104	03101	5244547	10256278
3101222015	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	97	0.0006301	03101	29924768	10256278
3101222048	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	62	0.0004028	03101	19127171	10256278
3102012001	03102	66	2017	NA	NA	NA	NA	NA	NA	NA	590	0.0334051	03102	NA	188783673
3102012004	03102	2	2017	NA	NA	NA	NA	NA	NA	NA	117	0.0066244	03102	NA	16606207
3102022010	03102	22	2017	NA	NA	NA	NA	NA	NA	NA	542	0.0306874	03102	NA	87955393
3102022901	03102	2	2017	NA	NA	NA	NA	NA	NA	NA	53	0.0030008	03102	NA	16606207
3102032003	03102	10	2017	NA	NA	NA	NA	NA	NA	NA	297	0.0168158	03102	NA	50839939
3102032007	03102	4	2017	NA	NA	NA	NA	NA	NA	NA	181	0.0102480	03102	NA	26887540
3102042002	03102	1	2017	NA	NA	NA	NA	NA	NA	NA	22	0.0012456	03102	NA	10256278
3103012003	03103	2	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	74	0.0052786	03103	23121842	16606207
3103012022	03103	5	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	553	0.0394465	03103	172788897	31399620
3103012029	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	86	0.0061345	03103	26871329	10256278
3103032006	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	476	0.0339539	03103	148729684	10256278
3103032009	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	22	0.0015693	03103	6874061	10256278
3103032014	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	41	0.0029246	03103	12810750	10256278
3103042028	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	195	0.0139097	03103	60929177	10256278
3103052020	03103	4	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	952	0.0679078	03103	297459368	26887540
3103062901	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	122	0.0087025	03103	38119793	10256278
3103072012	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	27	0.0019260	03103	8436348	10256278
3201012003	03201	4	2017	NA	NA	NA	NA	NA	NA	NA	27	0.0022097	03201	NA	26887540
3201012005	03201	1	2017	NA	NA	NA	NA	NA	NA	NA	29	0.0023734	03201	NA	10256278
3201022006	03201	6	2017	NA	NA	NA	NA	NA	NA	NA	699	0.0572060	03201	NA	35642847
3201032007	03201	3	2017	NA	NA	NA	NA	NA	NA	NA	185	0.0151404	03201	NA	22013635
3202022901	03202	18	2017	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural	294	0.0211131	03202	110106399	76502275
3202042008	03202	14	2017	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural	319	0.0229084	03202	119469188	64238509
3301032017	03301	14	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	386	0.0074349	03301	98156173	64238509
3301032901	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	9	0.0001734	03301	2288615	10256278
3301042005	03301	10	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	184	0.0035441	03301	46789471	50839939
3301042060	03301	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	172	0.0033130	03301	43737984	35642847
3301052002	03301	19	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	574	0.0110561	03301	145962807	79432597
3301052006	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	55	0.0010594	03301	13985983	10256278
3301052008	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	17	0.0003274	03301	4322940	10256278
3301052010	03301	30	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	343	0.0066067	03301	87221677	109120657
3301052014	03301	2	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	37	0.0007127	03301	9408752	16606207
3301052028	03301	30	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	523	0.0100738	03301	132993986	109120657
3301052036	03301	67	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	537	0.0103434	03301	136554055	190767676
3301052038	03301	3	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	87	0.0016758	03301	22123283	22013635
3301052063	03301	11	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	90	0.0017335	03301	22886154	54322762
3301062901	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	11	0.0002119	03301	2797197	10256278
3301072901	03301	3	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	19	0.0003660	03301	4831521	22013635
3301082012	03301	42	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	796	0.0153322	03301	202415321	137878772
3301082901	03301	3	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	177	0.0034093	03301	45009437	22013635
3301092043	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	60	0.0011557	03301	15257436	10256278
3301092901	03301	8	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	141	0.0027159	03301	35854975	43534314
3301102901	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	42	0.0008090	03301	10680205	10256278
3301112901	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	13	0.0002504	03301	3305778	10256278
3301122009	03301	5	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	198	0.0038138	03301	50349540	31399620
3301122025	03301	9	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	264	0.0050850	03301	67132720	47249119
3301122030	03301	12	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	159	0.0030626	03301	40432206	57710259
3301122032	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	43	0.0008282	03301	10934496	10256278
3301122033	03301	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	108	0.0020802	03301	27463385	35642847
3301122062	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	27	0.0005201	03301	6865846	10256278
3301132026	03301	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	146	0.0028122	03301	37126428	35642847
3301132901	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	33	0.0006356	03301	8391590	10256278
3301152010	03301	18	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	218	0.0041990	03301	55435352	76502275
3301152024	03301	4	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	43	0.0008282	03301	10934496	26887540
3301152901	03301	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	124	0.0023884	03301	31532035	35642847
3302012002	03302	19	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	645	0.1217211	03302	146499089	79432597
3302012005	03302	4	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	48	0.0090583	03302	10902258	26887540
3302012029	03302	12	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	117	0.0220796	03302	26574253	57710259
3302022005	03302	28	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	540	0.1019060	03302	122650400	104010237
3302032005	03302	15	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	821	0.1549349	03302	186474034	67394793
3302032018	03302	31	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	512	0.0966220	03302	116290750	111636739
3302042005	03302	31	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	560	0.1056803	03302	127193007	111636739
3302052005	03302	3	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	419	0.0790715	03302	95167625	22013635
3302072003	03302	6	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	73	0.0137762	03302	16580517	35642847
3302072025	03302	2	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	28	0.0052840	03302	6359650	16606207
3302072034	03302	31	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	229	0.0432157	03302	52012855	111636739
3302072901	03302	3	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	52	0.0098132	03302	11810779	22013635
3302082025	03302	1	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	24	0.0045292	03302	5451129	10256278
3302092013	03302	5	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	58	0.0109455	03302	13173561	31399620
3302092033	03302	8	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	149	0.0281185	03302	33842425	43534314
3302102010	03302	4	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	113	0.0213248	03302	25665732	26887540
3302102030	03302	2	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	115	0.0217022	03302	26119993	16606207
3302112015	03302	8	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	53	0.0100019	03302	12037910	43534314
3302112901	03302	1	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	27	0.0050953	03302	6132520	10256278
3303022004	03303	2	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	35	0.0049709	03303	7518115	16606207
3303022005	03303	5	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	208	0.0295413	03303	44679082	31399620
3303022007	03303	3	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	79	0.0112200	03303	16969459	22013635
3303022008	03303	16	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	693	0.0984235	03303	148858673	70487537
3303022009	03303	14	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	418	0.0593666	03303	89787771	64238509
3303022012	03303	5	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	206	0.0292572	03303	44249476	31399620
3303022013	03303	2	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	38	0.0053970	03303	8162525	16606207
3303032010	03303	3	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	73	0.0103678	03303	15680639	22013635
3303042002	03303	9	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	223	0.0316716	03303	47901131	47249119
3303042003	03303	12	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	319	0.0453061	03303	68522246	57710259
3304012009	03304	2	2017	Huasco	227560.7	2017	3304	10149	2309513927	Rural	201	0.0198049	03304	45739708	16606207

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
3101102901	03101	2	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	26	0.0001689	03101	8021072	16606207	638700.25
3101122013	03101	54	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	332	0.0021567	03101	102422918	164201191	494581.90
3101122047	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	183	0.0011888	03101	56456006	10256278	56045.24
3101122901	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	54	0.0003508	03101	16659149	10256278	189931.08
3101132901	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	14	0.0000909	03101	4319039	10256278	732591.32
3101162050	03101	2	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	33	0.0002144	03101	10180591	16606207	503218.38
3101172013	03101	11	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	194	0.0012603	03101	59849537	54322762	280014.24
3101172017	03101	18	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	121	0.0007860	03101	37328835	76502275	632250.20
3101172021	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	74	0.0004807	03101	22829205	10256278	138598.36
3101172026	03101	16	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	340	0.0022087	03101	104890940	70487537	207316.29
3101172035	03101	15	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	293	0.0019034	03101	90391310	67394793	230016.36
3101172037	03101	12	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	859	0.0055802	03101	265003876	57710259	67183.07
3101182901	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	17	0.0001104	03101	5244547	10256278	603310.50
3101222015	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	97	0.0006301	03101	29924768	10256278	105734.83
3101222048	03101	1	2017	Copiapó	308502.8	2017	3101	153937	47489990283	Rural	62	0.0004028	03101	19127171	10256278	165423.85
3102012001	03102	66	2017	NA	NA	NA	NA	NA	NA	NA	590	0.0334051	03102	NA	188783673	NA
3102012004	03102	2	2017	NA	NA	NA	NA	NA	NA	NA	117	0.0066244	03102	NA	16606207	NA
3102022010	03102	22	2017	NA	NA	NA	NA	NA	NA	NA	542	0.0306874	03102	NA	87955393	NA
3102022901	03102	2	2017	NA	NA	NA	NA	NA	NA	NA	53	0.0030008	03102	NA	16606207	NA
3102032003	03102	10	2017	NA	NA	NA	NA	NA	NA	NA	297	0.0168158	03102	NA	50839939	NA
3102032007	03102	4	2017	NA	NA	NA	NA	NA	NA	NA	181	0.0102480	03102	NA	26887540	NA
3102042002	03102	1	2017	NA	NA	NA	NA	NA	NA	NA	22	0.0012456	03102	NA	10256278	NA
3103012003	03103	2	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	74	0.0052786	03103	23121842	16606207	224408.20
3103012022	03103	5	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	553	0.0394465	03103	172788897	31399620	56780.51
3103012029	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	86	0.0061345	03103	26871329	10256278	119259.05
3103032006	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	476	0.0339539	03103	148729684	10256278	21546.80
3103032009	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	22	0.0015693	03103	6874061	10256278	466194.48
3103032014	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	41	0.0029246	03103	12810750	10256278	250153.13
3103042028	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	195	0.0139097	03103	60929177	10256278	52596.30
3103052020	03103	4	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	952	0.0679078	03103	297459368	26887540	28243.21
3103062901	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	122	0.0087025	03103	38119793	10256278	84067.86
3103072012	03103	1	2017	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural	27	0.0019260	03103	8436348	10256278	379862.17
3201012003	03201	4	2017	NA	NA	NA	NA	NA	NA	NA	27	0.0022097	03201	NA	26887540	NA
3201012005	03201	1	2017	NA	NA	NA	NA	NA	NA	NA	29	0.0023734	03201	NA	10256278	NA
3201022006	03201	6	2017	NA	NA	NA	NA	NA	NA	NA	699	0.0572060	03201	NA	35642847	NA
3201032007	03201	3	2017	NA	NA	NA	NA	NA	NA	NA	185	0.0151404	03201	NA	22013635	NA
3202022901	03202	18	2017	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural	294	0.0211131	03202	110106399	76502275	260211.82
3202042008	03202	14	2017	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural	319	0.0229084	03202	119469188	64238509	201374.64
3301032017	03301	14	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	386	0.0074349	03301	98156173	64238509	166421.01
3301032901	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	9	0.0001734	03301	2288615	10256278	1139586.50
3301042005	03301	10	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	184	0.0035441	03301	46789471	50839939	276304.01
3301042060	03301	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	172	0.0033130	03301	43737984	35642847	207225.86
3301052002	03301	19	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	574	0.0110561	03301	145962807	79432597	138384.32
3301052006	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	55	0.0010594	03301	13985983	10256278	186477.79
3301052008	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	17	0.0003274	03301	4322940	10256278	603310.50
3301052010	03301	30	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	343	0.0066067	03301	87221677	109120657	318136.03
3301052014	03301	2	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	37	0.0007127	03301	9408752	16606207	448816.40
3301052028	03301	30	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	523	0.0100738	03301	132993986	109120657	208643.70
3301052036	03301	67	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	537	0.0103434	03301	136554055	190767676	355247.07
3301052038	03301	3	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	87	0.0016758	03301	22123283	22013635	253030.29
3301052063	03301	11	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	90	0.0017335	03301	22886154	54322762	603586.24
3301062901	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	11	0.0002119	03301	2797197	10256278	932388.95
3301072901	03301	3	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	19	0.0003660	03301	4831521	22013635	1158612.36
3301082012	03301	42	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	796	0.0153322	03301	202415321	137878772	173214.54
3301082901	03301	3	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	177	0.0034093	03301	45009437	22013635	124370.82
3301092043	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	60	0.0011557	03301	15257436	10256278	170937.97
3301092901	03301	8	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	141	0.0027159	03301	35854975	43534314	308754.00
3301102901	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	42	0.0008090	03301	10680205	10256278	244197.11
3301112901	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	13	0.0002504	03301	3305778	10256278	788944.50
3301122009	03301	5	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	198	0.0038138	03301	50349540	31399620	158583.94
3301122025	03301	9	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	264	0.0050850	03301	67132720	47249119	178973.94
3301122030	03301	12	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	159	0.0030626	03301	40432206	57710259	362957.61
3301122032	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	43	0.0008282	03301	10934496	10256278	238518.10
3301122033	03301	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	108	0.0020802	03301	27463385	35642847	330026.37
3301122062	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	27	0.0005201	03301	6865846	10256278	379862.17
3301132026	03301	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	146	0.0028122	03301	37126428	35642847	244129.09
3301132901	03301	1	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	33	0.0006356	03301	8391590	10256278	310796.32
3301152010	03301	18	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	218	0.0041990	03301	55435352	76502275	350927.86
3301152024	03301	4	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	43	0.0008282	03301	10934496	26887540	625291.63
3301152901	03301	6	2017	Vallenar	254290.6	2017	3301	51917	13202005308	Rural	124	0.0023884	03301	31532035	35642847	287442.32
3302012002	03302	19	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	645	0.1217211	03302	146499089	79432597	123151.31
3302012005	03302	4	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	48	0.0090583	03302	10902258	26887540	560157.08
3302012029	03302	12	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	117	0.0220796	03302	26574253	57710259	493250.08
3302022005	03302	28	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	540	0.1019060	03302	122650400	104010237	192611.55
3302032005	03302	15	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	821	0.1549349	03302	186474034	67394793	82088.66
3302032018	03302	31	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	512	0.0966220	03302	116290750	111636739	218040.51
3302042005	03302	31	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	560	0.1056803	03302	127193007	111636739	199351.32
3302052005	03302	3	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	419	0.0790715	03302	95167625	22013635	52538.51
3302072003	03302	6	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	73	0.0137762	03302	16580517	35642847	488258.18
3302072025	03302	2	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	28	0.0052840	03302	6359650	16606207	593078.81
3302072034	03302	31	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	229	0.0432157	03302	52012855	111636739	487496.68
3302072901	03302	3	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	52	0.0098132	03302	11810779	22013635	423339.13
3302082025	03302	1	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	24	0.0045292	03302	5451129	10256278	427344.94
3302092013	03302	5	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	58	0.0109455	03302	13173561	31399620	541372.75
3302092033	03302	8	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	149	0.0281185	03302	33842425	43534314	292176.60
3302102010	03302	4	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	113	0.0213248	03302	25665732	26887540	237942.83
3302102030	03302	2	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	115	0.0217022	03302	26119993	16606207	144401.80
3302112015	03302	8	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	53	0.0100019	03302	12037910	43534314	821402.15
3302112901	03302	1	2017	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural	27	0.0050953	03302	6132520	10256278	379862.17
3303022004	03303	2	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	35	0.0049709	03303	7518115	16606207	474463.05
3303022005	03303	5	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	208	0.0295413	03303	44679082	31399620	150959.71
3303022007	03303	3	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	79	0.0112200	03303	16969459	22013635	278653.61
3303022008	03303	16	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	693	0.0984235	03303	148858673	70487537	101713.62
3303022009	03303	14	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	418	0.0593666	03303	89787771	64238509	153680.64
3303022012	03303	5	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	206	0.0292572	03303	44249476	31399620	152425.34
3303022013	03303	2	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	38	0.0053970	03303	8162525	16606207	437005.44
3303032010	03303	3	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	73	0.0103678	03303	15680639	22013635	301556.64
3303042002	03303	9	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	223	0.0316716	03303	47901131	47249119	211879.46
3303042003	03303	12	2017	Freirina	214803.3	2017	3303	7041	1512429891	Rural	319	0.0453061	03303	68522246	57710259	180909.90
3304012009	03304	2	2017	Huasco	227560.7	2017	3304	10149	2309513927	Rural	201	0.0198049	03304	45739708	16606207	82617.94

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_rural_nivel_nacional_17_r03.rds")

---

R-04

Urbano

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 1)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15201	1	1	1	1767	1	1	1	2	1	3	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	2	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	3	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	4	1	1	2	3	1	99	1	2	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	5	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	6	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	7	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	8	1	1	3	3	1	99	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	9	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	10	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	11	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	12	1	1	4	3	3	3	4	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	13	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	14	1	1	2	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	15	1	1	2	1	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	16	1	1	5	1	5	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	17	1	1	5	3	1	3	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	18	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	19	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	20	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	21	4	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	22	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	23	1	1	5	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	24	1	1	3	1	1	6	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	25	1	1	5	6	1	1	4	2	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	26	4	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	27	1	1	5	99	3	3	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	28	1	1	6	3	2	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	29	1	1	2	3	1	99	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	30	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	31	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	32	1	1	3	1	1	2	1	1	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	33	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	34	3	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	35	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	36	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	37	1	1	5	3	3	1	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	38	1	1	1	1	4	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	39	1	1	5	3	4	3	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	40	1	1	5	3	2	1	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	41	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	42	1	1	2	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	43	1	1	1	1	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	44	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	45	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	46	1	1	5	6	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	47	1	1	5	3	4	0	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	48	1	1	3	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	49	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	50	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	51	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	52	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	53	1	1	1	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	54	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	55	1	1	2	2	1	4	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	56	8	1	98	98	98	98	98	98	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	57	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	58	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	59	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	60	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	61	1	1	2	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	62	1	1	5	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	63	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	64	1	1	5	3	2	4	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	65	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	66	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	67	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	68	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	69	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	70	1	1	1	3	3	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	71	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	72	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	73	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	74	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	75	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	76	1	1	5	3	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	77	1	1	3	6	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	78	5	1	3	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	79	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	80	1	1	2	1	1	3	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	81	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	82	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	83	1	1	5	3	2	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	84	4	1	5	3	2	0	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	85	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	86	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	87	1	1	3	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	88	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	89	5	1	4	3	2	1	2	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	90	1	1	5	3	3	2	1	3	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	91	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	92	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	93	5	1	4	3	5	1	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	94	1	1	5	3	3	99	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	95	1	1	5	3	2	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	96	1	1	2	3	4	6	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	97	1	1	5	1	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	98	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	99	1	1	2	7	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	100	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 4:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 4)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	4101011001	119	2017	04101
2	4101021001	89	2017	04101
3	4101021002	108	2017	04101
4	4101021003	158	2017	04101
5	4101021004	222	2017	04101
6	4101021005	217	2017	04101
7	4101031001	1023	2017	04101
8	4101031002	172	2017	04101
9	4101031003	670	2017	04101
10	4101041001	153	2017	04101
11	4101041002	89	2017	04101
12	4101041003	952	2017	04101
13	4101041004	452	2017	04101
14	4101041005	148	2017	04101
15	4101041006	130	2017	04101
16	4101051001	118	2017	04101
17	4101051002	216	2017	04101
18	4101051003	372	2017	04101
19	4101051004	286	2017	04101
20	4101051005	661	2017	04101
21	4101051006	1199	2017	04101
22	4101051007	560	2017	04101
23	4101051008	1047	2017	04101
24	4101051009	1119	2017	04101
25	4101051010	630	2017	04101
26	4101051011	712	2017	04101
27	4101061001	253	2017	04101
28	4101061002	349	2017	04101
29	4101061003	679	2017	04101
30	4101061004	755	2017	04101
31	4101061005	35	2017	04101
32	4101071001	35	2017	04101
33	4101091001	41	2017	04101
34	4101141001	154	2017	04101
35	4101141002	175	2017	04101
36	4101141003	378	2017	04101
37	4101141004	229	2017	04101
38	4101141005	510	2017	04101
39	4101141006	194	2017	04101
40	4101151001	1228	2017	04101
41	4101151002	372	2017	04101
42	4101151003	391	2017	04101
43	4101151004	372	2017	04101
44	4101151005	506	2017	04101
45	4101151006	569	2017	04101
46	4101161001	241	2017	04101
47	4101161002	183	2017	04101
48	4101161003	130	2017	04101
49	4101161004	166	2017	04101
50	4101161005	201	2017	04101
51	4101161006	189	2017	04101
52	4101161007	278	2017	04101
53	4101161008	191	2017	04101
54	4101161009	213	2017	04101
55	4101161010	109	2017	04101
56	4101171001	221	2017	04101
57	4101171002	148	2017	04101
58	4101171003	235	2017	04101
59	4101171004	276	2017	04101
60	4101171005	197	2017	04101
61	4101991999	45	2017	04101
252	4102011001	222	2017	04102
253	4102011002	92	2017	04102
254	4102021001	176	2017	04102
255	4102021002	101	2017	04102
256	4102021003	113	2017	04102
257	4102021004	196	2017	04102
258	4102021005	112	2017	04102
259	4102021006	112	2017	04102
260	4102031001	257	2017	04102
261	4102031002	291	2017	04102
262	4102031003	209	2017	04102
263	4102031004	183	2017	04102
264	4102041001	116	2017	04102
265	4102041002	106	2017	04102
266	4102051001	808	2017	04102
267	4102051002	488	2017	04102
268	4102051003	1164	2017	04102
269	4102051004	5	2017	04102
270	4102051005	536	2017	04102
271	4102051006	419	2017	04102
272	4102051007	143	2017	04102
273	4102051008	647	2017	04102
274	4102061001	253	2017	04102
275	4102061002	577	2017	04102
276	4102061003	58	2017	04102
277	4102061004	379	2017	04102
278	4102061005	1445	2017	04102
279	4102061006	364	2017	04102
280	4102081001	329	2017	04102
281	4102081002	525	2017	04102
282	4102091001	93	2017	04102
283	4102091002	212	2017	04102
284	4102091003	226	2017	04102
285	4102091004	178	2017	04102
286	4102091005	189	2017	04102
287	4102091006	263	2017	04102
288	4102091007	348	2017	04102
289	4102091008	423	2017	04102
290	4102101001	62	2017	04102

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_urbano_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
01107	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
01401	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
01405	Pica	330061.1	2017	1405	9296	3068247619	Urbano
02101	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02102	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02104	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02201	Calama	416281.1	2017	2201	165731	68990679686	Urbano
02203	San Pedro de Atacama	437934.7	2017	2203	10996	4815529626	Urbano
02301	Tocopilla	271720.8	2017	2301	25186	6843559467	Urbano
02302	María Elena	466266.9	2017	2302	6457	3010685220	Urbano
03101	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
03102	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
03103	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
03201	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
03202	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
03301	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
03303	Freirina	289049.9	2017	3303	7041	2035200054	Urbano
03304	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
04101	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04102	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04103	Andacollo	251267.7	2017	4103	11044	2775000288	Urbano
04104	La Higuera	214257.0	2017	4104	4241	908664019	Urbano
04106	Vicuña	245957.4	2017	4106	27771	6830481918	Urbano
04201	Illapel	270316.5	2017	4201	30848	8338722128	Urbano
04202	Canela	233397.3	2017	4202	9093	2122281844	Urbano
04203	Los Vilos	282415.6	2017	4203	21382	6038609501	Urbano
04204	Salamanca	262056.9	2017	4204	29347	7690585032	Urbano
04301	Ovalle	274771.4	2017	4301	111272	30574361012	Urbano
04302	Combarbalá	228990.4	2017	4302	13322	3050610572	Urbano
04303	Monte Patria	225369.1	2017	4303	30751	6930326684	Urbano
04304	Punitaqui	212496.1	2017	4304	10956	2328107498	Urbano
05101	Valparaíso	297929.0	2017	5101	296655	88382118059	Urbano
05102	Casablanca	341641.8	2017	5102	26867	9178890241	Urbano
05103	Concón	318496.3	2017	5103	42152	13425257057	Urbano
05105	Puchuncaví	296035.5	2017	5105	18546	5490274928	Urbano
05107	Quintero	308224.7	2017	5107	31923	9839456903	Urbano
05109	Viña del Mar	337006.1	2017	5109	334248	112643604611	Urbano
05301	Los Andes	339720.2	2017	5301	66708	22662055502	Urbano
05302	Calle Larga	246387.3	2017	5302	14832	3654416747	Urbano
05303	Rinconada	273904.7	2017	5303	10207	2795744821	Urbano
05304	San Esteban	219571.6	2017	5304	18855	4140022481	Urbano
05401	La Ligua	250134.4	2017	5401	35390	8852256241	Urbano
05402	Cabildo	262745.9	2017	5402	19388	5094117762	Urbano
05403	Papudo	294355.2	2017	5403	6356	1870921373	Urbano
05404	Petorca	237510.8	2017	5404	9826	2333781007	Urbano
05405	Zapallar	294389.2	2017	5405	7339	2160521991	Urbano
05501	Quillota	286029.5	2017	5501	90517	25890529852	Urbano
05502	Calera	277181.9	2017	5502	50554	14012652087	Urbano
05503	Hijuelas	254094.0	2017	5503	17988	4570642363	Urbano

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
04101	4101011001	119	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101021001	89	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101021002	108	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101021003	158	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101021004	222	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101021005	217	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101031001	1023	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101031002	172	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101031003	670	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101041001	153	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101041002	89	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101041003	952	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101041004	452	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101041005	148	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101041006	130	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101051001	118	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101051002	216	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101051003	372	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101051004	286	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101051005	661	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101051006	1199	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101051007	560	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101051008	1047	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101051009	1119	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101051010	630	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101051011	712	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101061001	253	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101061002	349	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101061003	679	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101061004	755	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101061005	35	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101071001	35	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101091001	41	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101141001	154	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101141002	175	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101141003	378	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101141004	229	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101141005	510	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101141006	194	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101151001	1228	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101151002	372	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101151003	391	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101151004	372	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101151005	506	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101151006	569	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101161001	241	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101161002	183	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101161003	130	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101161004	166	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101161005	201	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101161006	189	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101161007	278	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101161008	191	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101161009	213	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101161010	109	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101171001	221	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101171002	148	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101171003	235	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101171004	276	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101171005	197	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101991999	45	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04102	4102011001	222	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102011002	92	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102021001	176	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102021002	101	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102021003	113	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102021004	196	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102021005	112	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102021006	112	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102031001	257	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102031002	291	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102031003	209	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102031004	183	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102041001	116	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102041002	106	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102051001	808	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102051002	488	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102051003	1164	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102051004	5	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102051005	536	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102051006	419	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102051007	143	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102051008	647	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102061001	253	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102061002	577	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102061003	58	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102061004	379	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102061005	1445	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102061006	364	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102081001	329	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102081002	525	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102091001	93	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102091002	212	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102091003	226	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102091004	178	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102091005	189	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102091006	263	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102091007	348	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102091008	423	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102101001	62	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
04101	4101011001	119	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101021001	89	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101021002	108	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101021003	158	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101021004	222	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101021005	217	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101031001	1023	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101031002	172	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101031003	670	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101041001	153	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101041002	89	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101041003	952	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101041004	452	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101041005	148	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101041006	130	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101051001	118	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101051002	216	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101051003	372	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101051004	286	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101051005	661	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101051006	1199	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101051007	560	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101051008	1047	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101051009	1119	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101051010	630	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101051011	712	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101061001	253	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101061002	349	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101061003	679	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101061004	755	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101061005	35	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101071001	35	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101091001	41	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101141001	154	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101141002	175	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101141003	378	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101141004	229	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101141005	510	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101141006	194	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101151001	1228	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101151002	372	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101151003	391	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101151004	372	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101151005	506	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101151006	569	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101161001	241	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101161002	183	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101161003	130	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101161004	166	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101161005	201	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101161006	189	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101161007	278	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101161008	191	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101161009	213	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101161010	109	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101171001	221	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101171002	148	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101171003	235	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101171004	276	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101171005	197	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04101	4101991999	45	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04102	4102011001	222	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102011002	92	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102021001	176	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102021002	101	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102021003	113	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102021004	196	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102021005	112	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102021006	112	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102031001	257	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102031002	291	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102031003	209	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102031004	183	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102041001	116	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102041002	106	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102051001	808	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102051002	488	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102051003	1164	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102051004	5	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102051005	536	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102051006	419	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102051007	143	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102051008	647	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102061001	253	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102061002	577	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102061003	58	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102061004	379	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102061005	1445	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102061006	364	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102081001	329	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102081002	525	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102091001	93	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102091002	212	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102091003	226	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102091004	178	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102091005	189	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102091006	263	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102091007	348	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102091008	423	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04102	4102101001	62	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
4101011001	04101	119	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1455	0.0065821	04101
4101021001	04101	89	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2431	0.0109973	04101
4101021002	04101	108	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2926	0.0132366	04101
4101021003	04101	158	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2699	0.0122097	04101
4101021004	04101	222	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5323	0.0240801	04101
4101021005	04101	217	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2125	0.0096130	04101
4101031001	04101	1023	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4341	0.0196377	04101
4101031002	04101	172	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2583	0.0116849	04101
4101031003	04101	670	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4026	0.0182127	04101
4101041001	04101	153	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1108	0.0050123	04101
4101041002	04101	89	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1015	0.0045916	04101
4101041003	04101	952	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4721	0.0213568	04101
4101041004	04101	452	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3560	0.0161047	04101
4101041005	04101	148	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	812	0.0036733	04101
4101041006	04101	130	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	821	0.0037140	04101
4101051001	04101	118	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1419	0.0064192	04101
4101051002	04101	216	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2920	0.0132094	04101
4101051003	04101	372	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3348	0.0151456	04101
4101051004	04101	286	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2851	0.0128973	04101
4101051005	04101	661	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5493	0.0248491	04101
4101051006	04101	1199	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4295	0.0194296	04101
4101051007	04101	560	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2336	0.0105676	04101
4101051008	04101	1047	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4235	0.0191582	04101
4101051009	04101	1119	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3882	0.0175613	04101
4101051010	04101	630	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3226	0.0145937	04101
4101051011	04101	712	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2966	0.0134175	04101
4101061001	04101	253	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4424	0.0200132	04101
4101061002	04101	349	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3047	0.0137840	04101
4101061003	04101	679	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3472	0.0157066	04101
4101061004	04101	755	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5333	0.0241253	04101
4101061005	04101	35	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	288	0.0013028	04101
4101071001	04101	35	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1056	0.0047771	04101
4101091001	04101	41	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1292	0.0058447	04101
4101141001	04101	154	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2872	0.0129923	04101
4101141002	04101	175	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2750	0.0124404	04101
4101141003	04101	378	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4706	0.0212889	04101
4101141004	04101	229	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3750	0.0169642	04101
4101141005	04101	510	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5866	0.0265365	04101
4101141006	04101	194	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2114	0.0095633	04101
4101151001	04101	1228	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4957	0.0224244	04101
4101151002	04101	372	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1602	0.0072471	04101
4101151003	04101	391	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1900	0.0085952	04101
4101151004	04101	372	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2649	0.0119835	04101
4101151005	04101	506	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2047	0.0092602	04101
4101151006	04101	569	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3173	0.0143540	04101
4101161001	04101	241	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5756	0.0260389	04101
4101161002	04101	183	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3690	0.0166928	04101
4101161003	04101	130	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2952	0.0133542	04101
4101161004	04101	166	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5185	0.0234558	04101
4101161005	04101	201	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4746	0.0214699	04101
4101161006	04101	189	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4464	0.0201942	04101
4101161007	04101	278	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5497	0.0248672	04101
4101161008	04101	191	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4352	0.0196875	04101
4101161009	04101	213	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4309	0.0194930	04101
4101161010	04101	109	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4752	0.0214970	04101
4101171001	04101	221	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2644	0.0119609	04101
4101171002	04101	148	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2927	0.0132411	04101
4101171003	04101	235	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5093	0.0230396	04101
4101171004	04101	276	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4747	0.0214744	04101
4101171005	04101	197	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4515	0.0204249	04101
4101991999	04101	45	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	796	0.0036009	04101
4102011001	04102	222	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	6389	0.0280552	04102
4102011002	04102	92	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2328	0.0102226	04102
4102021001	04102	176	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4724	0.0207439	04102
4102021002	04102	101	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2101	0.0092258	04102
4102021003	04102	113	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2094	0.0091951	04102
4102021004	04102	196	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4472	0.0196373	04102
4102021005	04102	112	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2188	0.0096079	04102
4102021006	04102	112	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3498	0.0153603	04102
4102031001	04102	257	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2056	0.0090282	04102
4102031002	04102	291	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3483	0.0152944	04102
4102031003	04102	209	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2188	0.0096079	04102
4102031004	04102	183	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2324	0.0102051	04102
4102041001	04102	116	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	1800	0.0079041	04102
4102041002	04102	106	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	1873	0.0082247	04102
4102051001	04102	808	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4645	0.0203970	04102
4102051002	04102	488	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	5393	0.0236816	04102
4102051003	04102	1164	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4578	0.0201028	04102
4102051004	04102	5	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	15	0.0000659	04102
4102051005	04102	536	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4613	0.0202564	04102
4102051006	04102	419	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4305	0.0189040	04102
4102051007	04102	143	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2872	0.0126114	04102
4102051008	04102	647	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2455	0.0107803	04102
4102061001	04102	253	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4034	0.0177140	04102
4102061002	04102	577	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	6820	0.0299477	04102
4102061003	04102	58	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	309	0.0013569	04102
4102061004	04102	379	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4293	0.0188513	04102
4102061005	04102	1445	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	6111	0.0268344	04102
4102061006	04102	364	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4633	0.0203443	04102
4102081001	04102	329	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2323	0.0102007	04102
4102081002	04102	525	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3139	0.0137839	04102
4102091001	04102	93	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	1423	0.0062486	04102
4102091002	04102	212	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2821	0.0123875	04102
4102091003	04102	226	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3087	0.0135555	04102
4102091004	04102	178	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2580	0.0113292	04102
4102091005	04102	189	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3863	0.0169631	04102
4102091006	04102	263	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3170	0.0139200	04102
4102091007	04102	348	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	6290	0.0276204	04102
4102091008	04102	423	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	5782	0.0253897	04102
4102101001	04102	62	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	1476	0.0064814	04102

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
4101011001	04101	119	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1455	0.0065821	04101	395959023
4101021001	04101	89	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2431	0.0109973	04101	661564525
4101021002	04101	108	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2926	0.0132366	04101	796272234
4101021003	04101	158	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2699	0.0122097	04101	734497184
4101021004	04101	222	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5323	0.0240801	04101	1448584108
4101021005	04101	217	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2125	0.0096130	04101	578290669
4101031001	04101	1023	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4341	0.0196377	04101	1181345785
4101031002	04101	172	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2583	0.0116849	04101	702929317
4101031003	04101	670	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4026	0.0182127	04101	1095622698
4101041001	04101	153	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1108	0.0050123	04101	301527558
4101041002	04101	89	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1015	0.0045916	04101	276218837
4101041003	04101	952	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4721	0.0213568	04101	1284757764
4101041004	04101	452	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3560	0.0161047	04101	968806956
4101041005	04101	148	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	812	0.0036733	04101	220975070
4101041006	04101	130	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	821	0.0037140	04101	223424301
4101051001	04101	118	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1419	0.0064192	04101	386162098
4101051002	04101	216	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2920	0.0132094	04101	794639413
4101051003	04101	372	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3348	0.0151456	04101	911113957
4101051004	04101	286	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2851	0.0128973	04101	775861975
4101051005	04101	661	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5493	0.0248491	04101	1494847362
4101051006	04101	1199	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4295	0.0194296	04101	1168827493
4101051007	04101	560	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2336	0.0105676	04101	635711531
4101051008	04101	1047	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4235	0.0191582	04101	1152499286
4101051009	04101	1119	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3882	0.0175613	04101	1056435001
4101051010	04101	630	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3226	0.0145937	04101	877913270
4101051011	04101	712	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2966	0.0134175	04101	807157705
4101061001	04101	253	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4424	0.0200132	04101	1203933138
4101061002	04101	349	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3047	0.0137840	04101	829200785
4101061003	04101	679	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3472	0.0157066	04101	944858919
4101061004	04101	755	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5333	0.0241253	04101	1451305476
4101061005	04101	35	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	288	0.0013028	04101	78375394
4101071001	04101	35	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1056	0.0047771	04101	287376445
4101091001	04101	41	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1292	0.0058447	04101	351600727
4101141001	04101	154	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2872	0.0129923	04101	781576847
4101141002	04101	175	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2750	0.0124404	04101	748376160
4101141003	04101	378	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4706	0.0212889	04101	1280675712
4101141004	04101	229	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3750	0.0169642	04101	1020512945
4101141005	04101	510	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5866	0.0265365	04101	1596354383
4101141006	04101	194	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2114	0.0095633	04101	575297164
4101151001	04101	1228	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4957	0.0224244	04101	1348982045
4101151002	04101	372	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1602	0.0072471	04101	435963130
4101151003	04101	391	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1900	0.0085952	04101	517059892
4101151004	04101	372	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2649	0.0119835	04101	720890344
4101151005	04101	506	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2047	0.0092602	04101	557064000
4101151006	04101	569	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3173	0.0143540	04101	863490020
4101161001	04101	241	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5756	0.0260389	04101	1566419336
4101161002	04101	183	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3690	0.0166928	04101	1004184738
4101161003	04101	130	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2952	0.0133542	04101	803347790
4101161004	04101	166	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5185	0.0234558	04101	1411029232
4101161005	04101	201	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4746	0.0214699	04101	1291561183
4101161006	04101	189	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4464	0.0201942	04101	1214818610
4101161007	04101	278	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5497	0.0248672	04101	1495935909
4101161008	04101	191	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4352	0.0196875	04101	1184339290
4101161009	04101	213	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4309	0.0194930	04101	1172637408
4101161010	04101	109	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4752	0.0214970	04101	1293194004
4101171001	04101	221	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2644	0.0119609	04101	719529660
4101171002	04101	148	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2927	0.0132411	04101	796544371
4101171003	04101	235	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5093	0.0230396	04101	1385992648
4101171004	04101	276	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4747	0.0214744	04101	1291833320
4101171005	04101	197	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4515	0.0204249	04101	1228697586
4101991999	04101	45	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	796	0.0036009	04101	216620881
4102011001	04102	222	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	6389	0.0280552	04102	1688868566
4102011002	04102	92	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2328	0.0102226	04102	615383631
4102021001	04102	176	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4724	0.0207439	04102	1248742386
4102021002	04102	101	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2101	0.0092258	04102	555378440
4102021003	04102	113	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2094	0.0091951	04102	553528060
4102021004	04102	196	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4472	0.0196373	04102	1182128694
4102021005	04102	112	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2188	0.0096079	04102	578376025
4102021006	04102	112	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3498	0.0153603	04102	924661487
4102031001	04102	257	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2056	0.0090282	04102	543483138
4102031002	04102	291	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3483	0.0152944	04102	920696387
4102031003	04102	209	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2188	0.0096079	04102	578376025
4102031004	04102	183	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2324	0.0102051	04102	614326271
4102041001	04102	116	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	1800	0.0079041	04102	475812086
4102041002	04102	106	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	1873	0.0082247	04102	495108910
4102051001	04102	808	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4645	0.0203970	04102	1227859522
4102051002	04102	488	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	5393	0.0236816	04102	1425585878
4102051003	04102	1164	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4578	0.0201028	04102	1210148739
4102051004	04102	5	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	15	0.0000659	04102	3965101
4102051005	04102	536	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4613	0.0202564	04102	1219400641
4102051006	04102	419	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4305	0.0189040	04102	1137983906
4102051007	04102	143	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2872	0.0126114	04102	759184617
4102051008	04102	647	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2455	0.0107803	04102	648954817
4102061001	04102	253	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4034	0.0177140	04102	1066347753
4102061002	04102	577	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	6820	0.0299477	04102	1802799126
4102061003	04102	58	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	309	0.0013569	04102	81681075
4102061004	04102	379	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4293	0.0188513	04102	1134811825
4102061005	04102	1445	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	6111	0.0268344	04102	1615382032
4102061006	04102	364	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4633	0.0203443	04102	1224687442
4102081001	04102	329	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2323	0.0102007	04102	614061931
4102081002	04102	525	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3139	0.0137839	04102	829763410
4102091001	04102	93	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	1423	0.0062486	04102	376155888
4102091002	04102	212	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2821	0.0123875	04102	745703275
4102091003	04102	226	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3087	0.0135555	04102	816017728
4102091004	04102	178	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2580	0.0113292	04102	681997323
4102091005	04102	189	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3863	0.0169631	04102	1021145605
4102091006	04102	263	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3170	0.0139200	04102	837957952
4102091007	04102	348	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	6290	0.0276204	04102	1662698901
4102091008	04102	423	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	5782	0.0253897	04102	1528414157
4102101001	04102	62	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	1476	0.0064814	04102	390165911

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -636631539 -300147551  -49726731  291102513 1000968258 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 636192545   38993958  16.315  < 2e-16 ***
## Freq.x         880819     107380   8.203 3.61e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 380900000 on 188 degrees of freedom
## Multiple R-squared:  0.2636, Adjusted R-squared:  0.2597 
## F-statistic: 67.29 on 1 and 188 DF,  p-value: 3.613e-14

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.259655620096316"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.259655620096316"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.471107248619754"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.394581764399648"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.417403462835761"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.356195403460153"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.579546161379137"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.626426897420318"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 1    cuadrático 0.259655620096316
## 2        cúbico 0.259655620096316
## 6      log-raíz 0.356195403460153
## 4 raíz cuadrada 0.394581764399648
## 5     raíz-raíz 0.417403462835761
## 3   logarítmico 0.471107248619754
## 7      raíz-log 0.579546161379137
## 8       log-log 0.626426897420318
##                                                                     sintaxis
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 8
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3.14057 -0.31056  0.07506  0.40224  0.98218 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.38851    0.17019  102.17   <2e-16 ***
## log(Freq.x)  0.58722    0.03293   17.83   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5522 on 188 degrees of freedom
## Multiple R-squared:  0.6284, Adjusted R-squared:  0.6264 
## F-statistic: 317.9 on 1 and 188 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    17.38851

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##   0.5872229

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.6264 ).

9.1 Diagrama de dispersión sobre log-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3.14057 -0.31056  0.07506  0.40224  0.98218 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.38851    0.17019  102.17   <2e-16 ***
## log(Freq.x)  0.58722    0.03293   17.83   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5522 on 188 degrees of freedom
## Multiple R-squared:  0.6284, Adjusted R-squared:  0.6264 
## F-statistic: 317.9 on 1 and 188 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = e^{17.38851+0.5872229 \cdot ln{X}} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
4101011001	04101	119	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1455	0.0065821	04101	395959023	589579728
4101021001	04101	89	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2431	0.0109973	04101	661564525	497118919
4101021002	04101	108	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2926	0.0132366	04101	796272234	556937976
4101021003	04101	158	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2699	0.0122097	04101	734497184	696362902
4101021004	04101	222	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5323	0.0240801	04101	1448584108	850287966
4101021005	04101	217	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2125	0.0096130	04101	578290669	838989441
4101031001	04101	1023	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4341	0.0196377	04101	1181345785	2085459336
4101031002	04101	172	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2583	0.0116849	04101	702929317	731959974
4101031003	04101	670	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4026	0.0182127	04101	1095622698	1626558437
4101041001	04101	153	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1108	0.0050123	04101	301527558	683336586
4101041002	04101	89	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1015	0.0045916	04101	276218837	497118919
4101041003	04101	952	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4721	0.0213568	04101	1284757764	1999206499
4101041004	04101	452	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3560	0.0161047	04101	968806956	1290897985
4101041005	04101	148	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	812	0.0036733	04101	220975070	670133323
4101041006	04101	130	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	821	0.0037140	04101	223424301	620997401
4101051001	04101	118	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1419	0.0064192	04101	386162098	586665295
4101051002	04101	216	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2920	0.0132094	04101	794639413	836716891
4101051003	04101	372	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3348	0.0151456	04101	911113957	1151371597
4101051004	04101	286	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2851	0.0128973	04101	775861975	986661583
4101051005	04101	661	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5493	0.0248491	04101	1494847362	1613692235
4101051006	04101	1199	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4295	0.0194296	04101	1168827493	2289216968
4101051007	04101	560	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2336	0.0105676	04101	635711531	1463972009
4101051008	04101	1047	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4235	0.0191582	04101	1152499286	2114052050
4101051009	04101	1119	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3882	0.0175613	04101	1056435001	2198247927
4101051010	04101	630	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3226	0.0145937	04101	877913270	1568811287
4101051011	04101	712	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2966	0.0134175	04101	807157705	1685681045
4101061001	04101	253	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4424	0.0200132	04101	1203933138	918123778
4101061002	04101	349	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3047	0.0137840	04101	829200785	1109019427
4101061003	04101	679	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3472	0.0157066	04101	944858919	1639353494
4101061004	04101	755	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5333	0.0241253	04101	1451305476	1744737855
4101061005	04101	35	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	288	0.0013028	04101	78375394	287372997
4101071001	04101	35	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1056	0.0047771	04101	287376445	287372997
4101091001	04101	41	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1292	0.0058447	04101	351600727	315353354
4101141001	04101	154	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2872	0.0129923	04101	781576847	685955744
4101141002	04101	175	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2750	0.0124404	04101	748376160	739430130
4101141003	04101	378	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4706	0.0212889	04101	1280675712	1162240597
4101141004	04101	229	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3750	0.0169642	04101	1020512945	865930964
4101141005	04101	510	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5866	0.0265365	04101	1596354383	1385737729
4101141006	04101	194	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2114	0.0095633	04101	575297164	785567307
4101151001	04101	1228	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4957	0.0224244	04101	1348982045	2321570361
4101151002	04101	372	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1602	0.0072471	04101	435963130	1151371597
4101151003	04101	391	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1900	0.0085952	04101	517059892	1185548662
4101151004	04101	372	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2649	0.0119835	04101	720890344	1151371597
4101151005	04101	506	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2047	0.0092602	04101	557064000	1379345109
4101151006	04101	569	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3173	0.0143540	04101	863490020	1477742777
4101161001	04101	241	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5756	0.0260389	04101	1566419336	892295678
4101161002	04101	183	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3690	0.0166928	04101	1004184738	759096388
4101161003	04101	130	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2952	0.0133542	04101	803347790	620997401
4101161004	04101	166	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5185	0.0234558	04101	1411029232	716856364
4101161005	04101	201	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4746	0.0214699	04101	1291561183	802090371
4101161006	04101	189	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4464	0.0201942	04101	1214818610	773614030
4101161007	04101	278	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5497	0.0248672	04101	1495935909	970360045
4101161008	04101	191	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4352	0.0196875	04101	1184339290	778410820
4101161009	04101	213	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4309	0.0194930	04101	1172637408	829873043
4101161010	04101	109	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4752	0.0214970	04101	1293194004	559960424
4101171001	04101	221	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2644	0.0119609	04101	719529660	848036732
4101171002	04101	148	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2927	0.0132411	04101	796544371	670133323
4101171003	04101	235	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5093	0.0230396	04101	1385992648	879182794
4101171004	04101	276	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4747	0.0214744	04101	1291833320	966254530
4101171005	04101	197	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4515	0.0204249	04101	1228697586	792678258
4101991999	04101	45	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	796	0.0036009	04101	216620881	333071972
4102011001	04102	222	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	6389	0.0280552	04102	1688868566	850287966
4102011002	04102	92	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2328	0.0102226	04102	615383631	506891514
4102021001	04102	176	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4724	0.0207439	04102	1248742386	741908413
4102021002	04102	101	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2101	0.0092258	04102	555378440	535447934
4102021003	04102	113	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2094	0.0091951	04102	553528060	571937424
4102021004	04102	196	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4472	0.0196373	04102	1182128694	790312940
4102021005	04102	112	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2188	0.0096079	04102	578376025	568959807
4102021006	04102	112	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3498	0.0153603	04102	924661487	568959807
4102031001	04102	257	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2056	0.0090282	04102	543483138	926620174
4102031002	04102	291	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3483	0.0152944	04102	920696387	996754533
4102031003	04102	209	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2188	0.0096079	04102	578376025	820685698
4102031004	04102	183	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2324	0.0102051	04102	614326271	759096388
4102041001	04102	116	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	1800	0.0079041	04102	475812086	580805666
4102041002	04102	106	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	1873	0.0082247	04102	495108910	550858203
4102051001	04102	808	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4645	0.0203970	04102	1227859522	1815650943
4102051002	04102	488	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	5393	0.0236816	04102	1425585878	1350316316
4102051003	04102	1164	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4578	0.0201028	04102	1210148739	2249736360
4102051004	04102	5	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	15	0.0000659	04102	3965101	91661096
4102051005	04102	536	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4613	0.0202564	04102	1219400641	1426796032
4102051006	04102	419	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4305	0.0189040	04102	1137983906	1234690068
4102051007	04102	143	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2872	0.0126114	04102	759184617	656744606
4102051008	04102	647	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2455	0.0107803	04102	648954817	1593533482
4102061001	04102	253	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4034	0.0177140	04102	1066347753	918123778
4102061002	04102	577	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	6820	0.0299477	04102	1802799126	1489908159
4102061003	04102	58	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	309	0.0013569	04102	81681075	386597588
4102061004	04102	379	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4293	0.0188513	04102	1134811825	1164045153
4102061005	04102	1445	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	6111	0.0268344	04102	1615382032	2554351073
4102061006	04102	364	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4633	0.0203443	04102	1224687442	1136766362
4102081001	04102	329	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2323	0.0102007	04102	614061931	1071245246
4102081002	04102	525	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3139	0.0137839	04102	829763410	1409527863
4102091001	04102	93	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	1423	0.0062486	04102	376155888	510119710
4102091002	04102	212	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2821	0.0123875	04102	745703275	827582932
4102091003	04102	226	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3087	0.0135555	04102	816017728	859251341
4102091004	04102	178	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2580	0.0113292	04102	681997323	746847609
4102091005	04102	189	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3863	0.0169631	04102	1021145605	773614030
4102091006	04102	263	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3170	0.0139200	04102	837957952	939263111
4102091007	04102	348	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	6290	0.0276204	04102	1662698901	1107152300
4102091008	04102	423	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	5782	0.0253897	04102	1528414157	1241598097
4102101001	04102	62	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	1476	0.0064814	04102	390165911	402038168

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
4101011001	04101	119	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1455	0.0065821	04101	395959023	589579728	405209.4
4101021001	04101	89	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2431	0.0109973	04101	661564525	497118919	204491.5
4101021002	04101	108	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2926	0.0132366	04101	796272234	556937976	190341.1
4101021003	04101	158	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2699	0.0122097	04101	734497184	696362902	258007.7
4101021004	04101	222	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5323	0.0240801	04101	1448584108	850287966	159738.5
4101021005	04101	217	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2125	0.0096130	04101	578290669	838989441	394818.6
4101031001	04101	1023	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4341	0.0196377	04101	1181345785	2085459336	480409.9
4101031002	04101	172	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2583	0.0116849	04101	702929317	731959974	283375.9
4101031003	04101	670	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4026	0.0182127	04101	1095622698	1626558437	404013.5
4101041001	04101	153	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1108	0.0050123	04101	301527558	683336586	616729.8
4101041002	04101	89	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1015	0.0045916	04101	276218837	497118919	489772.3
4101041003	04101	952	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4721	0.0213568	04101	1284757764	1999206499	423471.0
4101041004	04101	452	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3560	0.0161047	04101	968806956	1290897985	362611.8
4101041005	04101	148	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	812	0.0036733	04101	220975070	670133323	825287.3
4101041006	04101	130	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	821	0.0037140	04101	223424301	620997401	756391.5
4101051001	04101	118	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1419	0.0064192	04101	386162098	586665295	413435.7
4101051002	04101	216	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2920	0.0132094	04101	794639413	836716891	286546.9
4101051003	04101	372	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3348	0.0151456	04101	911113957	1151371597	343898.3
4101051004	04101	286	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2851	0.0128973	04101	775861975	986661583	346075.6
4101051005	04101	661	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5493	0.0248491	04101	1494847362	1613692235	293772.5
4101051006	04101	1199	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4295	0.0194296	04101	1168827493	2289216968	532995.8
4101051007	04101	560	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2336	0.0105676	04101	635711531	1463972009	626700.3
4101051008	04101	1047	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4235	0.0191582	04101	1152499286	2114052050	499185.8
4101051009	04101	1119	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3882	0.0175613	04101	1056435001	2198247927	566266.9
4101051010	04101	630	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3226	0.0145937	04101	877913270	1568811287	486302.3
4101051011	04101	712	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2966	0.0134175	04101	807157705	1685681045	568334.8
4101061001	04101	253	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4424	0.0200132	04101	1203933138	918123778	207532.5
4101061002	04101	349	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3047	0.0137840	04101	829200785	1109019427	363970.9
4101061003	04101	679	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3472	0.0157066	04101	944858919	1639353494	472164.0
4101061004	04101	755	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5333	0.0241253	04101	1451305476	1744737855	327158.8
4101061005	04101	35	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	288	0.0013028	04101	78375394	287372997	997822.9
4101071001	04101	35	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1056	0.0047771	04101	287376445	287372997	272133.5
4101091001	04101	41	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1292	0.0058447	04101	351600727	315353354	244081.5
4101141001	04101	154	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2872	0.0129923	04101	781576847	685955744	238842.5
4101141002	04101	175	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2750	0.0124404	04101	748376160	739430130	268883.7
4101141003	04101	378	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4706	0.0212889	04101	1280675712	1162240597	246970.0
4101141004	04101	229	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3750	0.0169642	04101	1020512945	865930964	230914.9
4101141005	04101	510	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5866	0.0265365	04101	1596354383	1385737729	236232.1
4101141006	04101	194	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2114	0.0095633	04101	575297164	785567307	371602.3
4101151001	04101	1228	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4957	0.0224244	04101	1348982045	2321570361	468341.8
4101151002	04101	372	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1602	0.0072471	04101	435963130	1151371597	718708.9
4101151003	04101	391	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	1900	0.0085952	04101	517059892	1185548662	623973.0
4101151004	04101	372	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2649	0.0119835	04101	720890344	1151371597	434643.9
4101151005	04101	506	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2047	0.0092602	04101	557064000	1379345109	673837.4
4101151006	04101	569	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3173	0.0143540	04101	863490020	1477742777	465724.2
4101161001	04101	241	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5756	0.0260389	04101	1566419336	892295678	155020.1
4101161002	04101	183	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	3690	0.0166928	04101	1004184738	759096388	205717.2
4101161003	04101	130	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2952	0.0133542	04101	803347790	620997401	210365.0
4101161004	04101	166	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5185	0.0234558	04101	1411029232	716856364	138255.8
4101161005	04101	201	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4746	0.0214699	04101	1291561183	802090371	169003.4
4101161006	04101	189	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4464	0.0201942	04101	1214818610	773614030	173300.6
4101161007	04101	278	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5497	0.0248672	04101	1495935909	970360045	176525.4
4101161008	04101	191	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4352	0.0196875	04101	1184339290	778410820	178862.8
4101161009	04101	213	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4309	0.0194930	04101	1172637408	829873043	192590.6
4101161010	04101	109	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4752	0.0214970	04101	1293194004	559960424	117836.8
4101171001	04101	221	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2644	0.0119609	04101	719529660	848036732	320740.1
4101171002	04101	148	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	2927	0.0132411	04101	796544371	670133323	228948.9
4101171003	04101	235	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	5093	0.0230396	04101	1385992648	879182794	172625.7
4101171004	04101	276	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4747	0.0214744	04101	1291833320	966254530	203550.6
4101171005	04101	197	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	4515	0.0204249	04101	1228697586	792678258	175565.5
4101991999	04101	45	2017	La Serena	272136.8	2017	4101	221054	60156924947	Urbano	796	0.0036009	04101	216620881	333071972	418432.1
4102011001	04102	222	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	6389	0.0280552	04102	1688868566	850287966	133086.2
4102011002	04102	92	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2328	0.0102226	04102	615383631	506891514	217736.9
4102021001	04102	176	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4724	0.0207439	04102	1248742386	741908413	157050.9
4102021002	04102	101	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2101	0.0092258	04102	555378440	535447934	254853.8
4102021003	04102	113	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2094	0.0091951	04102	553528060	571937424	273131.5
4102021004	04102	196	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4472	0.0196373	04102	1182128694	790312940	176724.7
4102021005	04102	112	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2188	0.0096079	04102	578376025	568959807	260036.5
4102021006	04102	112	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3498	0.0153603	04102	924661487	568959807	162652.9
4102031001	04102	257	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2056	0.0090282	04102	543483138	926620174	450690.7
4102031002	04102	291	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3483	0.0152944	04102	920696387	996754533	286177.0
4102031003	04102	209	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2188	0.0096079	04102	578376025	820685698	375084.9
4102031004	04102	183	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2324	0.0102051	04102	614326271	759096388	326633.6
4102041001	04102	116	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	1800	0.0079041	04102	475812086	580805666	322669.8
4102041002	04102	106	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	1873	0.0082247	04102	495108910	550858203	294104.8
4102051001	04102	808	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4645	0.0203970	04102	1227859522	1815650943	390882.9
4102051002	04102	488	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	5393	0.0236816	04102	1425585878	1350316316	250383.1
4102051003	04102	1164	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4578	0.0201028	04102	1210148739	2249736360	491423.4
4102051004	04102	5	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	15	0.0000659	04102	3965101	91661096	6110739.7
4102051005	04102	536	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4613	0.0202564	04102	1219400641	1426796032	309298.9
4102051006	04102	419	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4305	0.0189040	04102	1137983906	1234690068	286803.7
4102051007	04102	143	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2872	0.0126114	04102	759184617	656744606	228671.5
4102051008	04102	647	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2455	0.0107803	04102	648954817	1593533482	649097.1
4102061001	04102	253	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4034	0.0177140	04102	1066347753	918123778	227596.4
4102061002	04102	577	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	6820	0.0299477	04102	1802799126	1489908159	218461.6
4102061003	04102	58	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	309	0.0013569	04102	81681075	386597588	1251124.9
4102061004	04102	379	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4293	0.0188513	04102	1134811825	1164045153	271149.6
4102061005	04102	1445	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	6111	0.0268344	04102	1615382032	2554351073	417992.3
4102061006	04102	364	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	4633	0.0203443	04102	1224687442	1136766362	245362.9
4102081001	04102	329	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2323	0.0102007	04102	614061931	1071245246	461147.3
4102081002	04102	525	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3139	0.0137839	04102	829763410	1409527863	449037.2
4102091001	04102	93	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	1423	0.0062486	04102	376155888	510119710	358481.9
4102091002	04102	212	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2821	0.0123875	04102	745703275	827582932	293365.1
4102091003	04102	226	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3087	0.0135555	04102	816017728	859251341	278345.1
4102091004	04102	178	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	2580	0.0113292	04102	681997323	746847609	289475.8
4102091005	04102	189	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3863	0.0169631	04102	1021145605	773614030	200262.5
4102091006	04102	263	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	3170	0.0139200	04102	837957952	939263111	296297.5
4102091007	04102	348	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	6290	0.0276204	04102	1662698901	1107152300	176017.9
4102091008	04102	423	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	5782	0.0253897	04102	1528414157	1241598097	214735.1
4102101001	04102	62	2017	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano	1476	0.0064814	04102	390165911	402038168	272383.6

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_urbano_nivel_nacional_17_r04.rds")

---

Rural

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 2)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15202	1	2	6	13225	1	3	1	5	3	5	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	3	1	1	5	3	5	2	3	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	4	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	5	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	6	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	8	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	9	3	1	5	3	5	1	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	10	1	1	5	3	4	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	11	1	2	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	12	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	13	1	1	5	3	4	1	4	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	14	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	16	1	1	5	3	5	1	3	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	17	1	1	5	3	5	2	4	1	8	15	152	15202	15202012006
15	152	15202	1	2	6	13225	18	3	1	5	3	5	1	1	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	19	3	1	5	3	5	1	3	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	20	1	1	5	3	5	2	1	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	21	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	22	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	24	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	25	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	26	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	27	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	28	3	1	5	3	5	1	4	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	29	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	30	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	31	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	32	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	33	1	1	5	3	5	3	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	34	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	35	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	36	1	1	5	3	5	3	2	1	9	15	152	15202	15202012006
15	152	15202	1	2	6	13225	37	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	38	1	1	5	3	5	99	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	39	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	40	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	41	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	42	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	43	3	1	5	3	5	2	1	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	44	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	45	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	8	13910	1	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	3	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	4	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	5	3	1	5	3	5	2	3	1	3	15	152	15202	15202012008
15	152	15202	1	2	8	13910	6	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	7	1	1	5	99	5	2	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	8	3	1	5	3	5	3	3	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	9	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	10	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	11	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	12	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	13	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	14	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	16	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	18	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	20	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	21	1	1	5	3	5	1	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	22	3	1	5	3	5	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	23	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	24	3	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	25	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	26	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	27	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	28	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	29	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	30	1	1	5	1	4	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	12	8394	1	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	2	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	3	3	1	5	3	5	2	3	1	4	15	152	15202	15202012012
15	152	15202	1	2	12	8394	4	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	5	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	6	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	8	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	9	3	1	5	3	5	1	4	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	10	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	11	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	12	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	13	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	14	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	15	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	16	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	18	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	20	3	1	5	3	5	3	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	21	3	1	5	99	5	1	4	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	22	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	24	3	1	5	3	5	1	2	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	25	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 4:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 4)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Veamos los primeros 100 registros:

r3_100 <- d[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	unlist.c.	unlist.d.	Freq	anio
1	4101022001	1	4101	85	2017
2	4101052007	1	4101	20	2017
3	4101062001	1	4101	48	2017
4	4101062004	1	4101	114	2017
5	4101062006	1	4101	25	2017
6	4101062024	1	4101	15	2017
7	4101062030	1	4101	1	2017
8	4101062049	1	4101	25	2017
9	4101062901	1	4101	11	2017
10	4101072002	1	4101	55	2017
11	4101072027	1	4101	17	2017
12	4101072029	1	4101	3	2017
13	4101072051	1	4101	20	2017
14	4101072053	1	4101	2	2017
15	4101072054	1	4101	4	2017
16	4101082011	1	4101	8	2017
17	4101082018	1	4101	25	2017
18	4101082021	1	4101	61	2017
19	4101082022	1	4101	1	2017
20	4101082023	1	4101	46	2017
21	4101082029	1	4101	7	2017
22	4101082043	1	4101	38	2017
23	4101082051	1	4101	5	2017
24	4101082052	1	4101	5	2017
25	4101082901	1	4101	6	2017
26	4101092003	1	4101	94	2017
27	4101092034	1	4101	46	2017
28	4101092038	1	4101	6	2017
29	4101092039	1	4101	7	2017
30	4101092901	1	4101	2	2017
31	4101102003	1	4101	9	2017
32	4101102013	1	4101	49	2017
33	4101102015	1	4101	21	2017
34	4101102020	1	4101	169	2017
35	4101102032	1	4101	1	2017
36	4101122010	1	4101	1	2017
37	4101132025	1	4101	30	2017
38	4101132032	1	4101	19	2017
39	4101132046	1	4101	1	2017
40	4101132055	1	4101	1	2017
41	4101132901	1	4101	1	2017
42	4101142008	1	4101	6	2017
43	4101142012	1	4101	25	2017
44	4101142014	1	4101	23	2017
45	4101142019	1	4101	3	2017
46	4101142030	1	4101	2	2017
47	4101142033	1	4101	10	2017
48	4101142036	1	4101	4	2017
49	4101142040	1	4101	1	2017
50	4101142056	1	4101	98	2017
51	4101142057	1	4101	5	2017
52	4101142901	1	4101	36	2017
53	4101162030	1	4101	1	2017
54	4101172030	1	4101	3	2017
492	4102062009	1	4102	4	2017
493	4102062014	1	4102	4	2017
494	4102062024	1	4102	238	2017
495	4102062901	1	4102	16	2017
496	4102072002	1	4102	64	2017
497	4102072011	1	4102	20	2017
498	4102072015	1	4102	2	2017
499	4102072021	1	4102	2	2017
500	4102072023	1	4102	30	2017
501	4102072024	1	4102	80	2017
502	4102082004	1	4102	20	2017
503	4102082007	1	4102	5	2017
504	4102082020	1	4102	1	2017
505	4102082032	1	4102	19	2017
506	4102082901	1	4102	4	2017
507	4102092026	1	4102	5	2017
508	4102102003	1	4102	1	2017
509	4102112006	1	4102	1	2017
510	4102112017	1	4102	36	2017
511	4102112025	1	4102	19	2017
512	4102112029	1	4102	53	2017
513	4102112901	1	4102	2	2017
514	4102122018	1	4102	36	2017
515	4102142012	1	4102	4	2017
516	4102152012	1	4102	2	2017
517	4102152030	1	4102	3	2017
518	4102162013	1	4102	23	2017
519	4102162901	1	4102	1	2017
957	4103012014	1	4103	2	2017
958	4103012901	1	4103	3	2017
959	4103032005	1	4103	17	2017
960	4103032013	1	4103	1	2017
961	4103032901	1	4103	4	2017
1399	4104012013	1	4104	1	2017
1400	4104022002	1	4104	22	2017
1401	4104022006	1	4104	1	2017
1402	4104032014	1	4104	3	2017
1403	4104042003	1	4104	8	2017
1404	4104042028	1	4104	1	2017
1405	4104052004	1	4104	13	2017
1406	4104052017	1	4104	13	2017
1407	4104052023	1	4104	22	2017
1408	4104062901	1	4104	2	2017
1409	4104082022	1	4104	10	2017
1410	4104092012	1	4104	9	2017
1848	4105012010	1	4105	20	2017

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	4101022001	85	2017	04101
2	4101052007	20	2017	04101
3	4101062001	48	2017	04101
4	4101062004	114	2017	04101
5	4101062006	25	2017	04101
6	4101062024	15	2017	04101
7	4101062030	1	2017	04101
8	4101062049	25	2017	04101
9	4101062901	11	2017	04101
10	4101072002	55	2017	04101
11	4101072027	17	2017	04101
12	4101072029	3	2017	04101
13	4101072051	20	2017	04101
14	4101072053	2	2017	04101
15	4101072054	4	2017	04101
16	4101082011	8	2017	04101
17	4101082018	25	2017	04101
18	4101082021	61	2017	04101
19	4101082022	1	2017	04101
20	4101082023	46	2017	04101
21	4101082029	7	2017	04101
22	4101082043	38	2017	04101
23	4101082051	5	2017	04101
24	4101082052	5	2017	04101
25	4101082901	6	2017	04101
26	4101092003	94	2017	04101
27	4101092034	46	2017	04101
28	4101092038	6	2017	04101
29	4101092039	7	2017	04101
30	4101092901	2	2017	04101
31	4101102003	9	2017	04101
32	4101102013	49	2017	04101
33	4101102015	21	2017	04101
34	4101102020	169	2017	04101
35	4101102032	1	2017	04101
36	4101122010	1	2017	04101
37	4101132025	30	2017	04101
38	4101132032	19	2017	04101
39	4101132046	1	2017	04101
40	4101132055	1	2017	04101
41	4101132901	1	2017	04101
42	4101142008	6	2017	04101
43	4101142012	25	2017	04101
44	4101142014	23	2017	04101
45	4101142019	3	2017	04101
46	4101142030	2	2017	04101
47	4101142033	10	2017	04101
48	4101142036	4	2017	04101
49	4101142040	1	2017	04101
50	4101142056	98	2017	04101
51	4101142057	5	2017	04101
52	4101142901	36	2017	04101
53	4101162030	1	2017	04101
54	4101172030	3	2017	04101
492	4102062009	4	2017	04102
493	4102062014	4	2017	04102
494	4102062024	238	2017	04102
495	4102062901	16	2017	04102
496	4102072002	64	2017	04102
497	4102072011	20	2017	04102
498	4102072015	2	2017	04102
499	4102072021	2	2017	04102
500	4102072023	30	2017	04102
501	4102072024	80	2017	04102
502	4102082004	20	2017	04102
503	4102082007	5	2017	04102
504	4102082020	1	2017	04102
505	4102082032	19	2017	04102
506	4102082901	4	2017	04102
507	4102092026	5	2017	04102
508	4102102003	1	2017	04102
509	4102112006	1	2017	04102
510	4102112017	36	2017	04102
511	4102112025	19	2017	04102
512	4102112029	53	2017	04102
513	4102112901	2	2017	04102
514	4102122018	36	2017	04102
515	4102142012	4	2017	04102
516	4102152012	2	2017	04102
517	4102152030	3	2017	04102
518	4102162013	23	2017	04102
519	4102162901	1	2017	04102
957	4103012014	2	2017	04103
958	4103012901	3	2017	04103
959	4103032005	17	2017	04103
960	4103032013	1	2017	04103
961	4103032901	4	2017	04103
1399	4104012013	1	2017	04104
1400	4104022002	22	2017	04104
1401	4104022006	1	2017	04104
1402	4104032014	3	2017	04104
1403	4104042003	8	2017	04104
1404	4104042028	1	2017	04104
1405	4104052004	13	2017	04104
1406	4104052017	13	2017	04104
1407	4104052023	22	2017	04104
1408	4104062901	2	2017	04104
1409	4104082022	10	2017	04104
1410	4104092012	9	2017	04104
1848	4105012010	20	2017	04105

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_rural_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	289375.3	2017	1101	191468	55406102543	Rural
01401	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
01402	Camiña	262850.3	2017	1402	1250	328562901	Rural
01404	Huara	253968.5	2017	1404	2730	693334131	Rural
01405	Pica	290496.7	2017	1405	9296	2700457509	Rural
02103	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
02104	Taltal	345494.0	2017	2104	13317	4600943086	Rural
02201	Calama	310025.0	2017	2201	165731	51380756402	Rural
02203	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
02301	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
03101	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03103	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03202	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural
03301	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03302	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03303	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03304	Huasco	227560.7	2017	3304	10149	2309513927	Rural
04101	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04102	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04103	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04104	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04105	Paiguano	205942.1	2017	4105	4497	926121774	Rural
04106	Vicuña	176130.6	2017	4106	27771	4891322768	Rural
04201	Illapel	191976.8	2017	4201	30848	5922099530	Rural
04202	Canela	171370.3	2017	4202	9093	1558270441	Rural
04203	Los Vilos	173238.5	2017	4203	21382	3704185607	Rural
04204	Salamanca	223234.2	2017	4204	29347	6551254640	Rural
04301	Ovalle	241393.7	2017	4301	111272	26860360045	Rural
04302	Combarbalá	179139.6	2017	4302	13322	2386498044	Rural
04303	Monte Patria	201205.8	2017	4303	30751	6187280931	Rural
04304	Punitaqui	171931.7	2017	4304	10956	1883683880	Rural
04305	Río Hurtado	182027.2	2017	4305	4278	778712384	Rural
05101	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05102	Casablanca	268917.1	2017	5102	26867	7224996933	Rural
05105	Puchuncaví	279614.4	2017	5105	18546	5185728335	Rural
05107	Quintero	334628.2	2017	5107	31923	10682335196	Rural
05301	Los Andes	324402.1	2017	5301	66708	21640215030	Rural
05302	Calle Larga	242743.8	2017	5302	14832	3600375502	Rural
05303	Rinconada	326532.5	2017	5303	10207	3332917471	Rural
05304	San Esteban	223168.6	2017	5304	18855	4207844130	Rural
05401	La Ligua	181468.0	2017	5401	35390	6422154059	Rural
05402	Cabildo	231277.8	2017	5402	19388	4484014285	Rural
05404	Petorca	298208.9	2017	5404	9826	2930200178	Rural
05405	Zapallar	292882.3	2017	5405	7339	2149463129	Rural
05501	Quillota	220926.8	2017	5501	90517	19997628209	Rural
05502	Calera	226906.2	2017	5502	50554	11471016698	Rural
05503	Hijuelas	253739.9	2017	5503	17988	4564273201	Rural
05504	La Cruz	291124.1	2017	5504	22098	6433259569	Rural
05506	Nogales	264475.3	2017	5506	22120	5850194593	Rural
05601	San Antonio	266331.2	2017	5601	91350	24329353815	Rural

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
04101	4101022001	85	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101052007	20	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101062001	48	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101062004	114	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101062006	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101062024	15	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101062030	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101062049	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101062901	11	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101072002	55	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101072027	17	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101072029	3	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101072051	20	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101072053	2	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101072054	4	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101082011	8	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101082018	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101082021	61	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101082022	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101082023	46	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101082029	7	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101082043	38	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101082051	5	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101082052	5	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101082901	6	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101092003	94	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101092034	46	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101092038	6	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101092039	7	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101092901	2	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101102003	9	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101102013	49	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101102015	21	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101102020	169	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101102032	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101122010	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101132025	30	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101132032	19	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101132046	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101132055	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101132901	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101142008	6	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101142012	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101142014	23	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101142019	3	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101142030	2	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101142033	10	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101142036	4	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101142040	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101142056	98	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101142057	5	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101142901	36	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101162030	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101172030	3	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04102	4102062009	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102062014	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102062024	238	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102062901	16	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102072002	64	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102072011	20	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102072015	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102072021	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102072023	30	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102072024	80	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102082004	20	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102082007	5	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102082020	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102082032	19	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102082901	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102092026	5	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102102003	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102112006	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102112017	36	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102112025	19	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102112029	53	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102112901	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102122018	36	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102142012	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102152012	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102152030	3	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102162013	23	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102162901	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04103	4103012014	2	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04103	4103012901	3	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04103	4103032005	17	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04103	4103032013	1	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04103	4103032901	4	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04104	4104012013	1	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04104	4104022002	22	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04104	4104022006	1	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04104	4104032014	3	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04104	4104042003	8	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04104	4104042028	1	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04104	4104052004	13	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04104	4104052017	13	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04104	4104052023	22	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04104	4104062901	2	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04104	4104082022	10	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04104	4104092012	9	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04105	4105012010	20	2017	Paiguano	205942.1	2017	4105	4497	926121774	Rural

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
04101	4101022001	85	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101052007	20	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101062001	48	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101062004	114	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101062006	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101062024	15	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101062030	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101062049	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101062901	11	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101072002	55	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101072027	17	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101072029	3	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101072051	20	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101072053	2	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101072054	4	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101082011	8	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101082018	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101082021	61	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101082022	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101082023	46	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101082029	7	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101082043	38	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101082051	5	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101082052	5	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101082901	6	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101092003	94	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101092034	46	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101092038	6	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101092039	7	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101092901	2	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101102003	9	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101102013	49	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101102015	21	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101102020	169	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101102032	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101122010	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101132025	30	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101132032	19	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101132046	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101132055	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101132901	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101142008	6	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101142012	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101142014	23	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101142019	3	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101142030	2	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101142033	10	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101142036	4	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101142040	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101142056	98	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101142057	5	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101142901	36	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101162030	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04101	4101172030	3	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04102	4102062009	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102062014	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102062024	238	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102062901	16	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102072002	64	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102072011	20	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102072015	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102072021	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102072023	30	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102072024	80	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102082004	20	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102082007	5	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102082020	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102082032	19	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102082901	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102092026	5	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102102003	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102112006	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102112017	36	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102112025	19	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102112029	53	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102112901	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102122018	36	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102142012	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102152012	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102152030	3	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102162013	23	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04102	4102162901	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04103	4103012014	2	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04103	4103012901	3	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04103	4103032005	17	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04103	4103032013	1	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04103	4103032901	4	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04104	4104012013	1	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04104	4104022002	22	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04104	4104022006	1	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04104	4104032014	3	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04104	4104042003	8	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04104	4104042028	1	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04104	4104052004	13	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04104	4104052017	13	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04104	4104052023	22	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04104	4104062901	2	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04104	4104082022	10	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04104	4104092012	9	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04105	4105012010	20	2017	Paiguano	205942.1	2017	4105	4497	926121774	Rural

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
4101022001	04101	85	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	1157	0.0052340	04101
4101052007	04101	20	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	125	0.0005655	04101
4101062001	04101	48	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	345	0.0015607	04101
4101062004	04101	114	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	740	0.0033476	04101
4101062006	04101	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	224	0.0010133	04101
4101062024	04101	15	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	2392	0.0108209	04101
4101062030	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	41	0.0001855	04101
4101062049	04101	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	115	0.0005202	04101
4101062901	04101	11	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	284	0.0012848	04101
4101072002	04101	55	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	394	0.0017824	04101
4101072027	04101	17	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	461	0.0020855	04101
4101072029	04101	3	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	119	0.0005383	04101
4101072051	04101	20	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	179	0.0008098	04101
4101072053	04101	2	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	35	0.0001583	04101
4101072054	04101	4	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	114	0.0005157	04101
4101082011	04101	8	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	304	0.0013752	04101
4101082018	04101	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	348	0.0015743	04101
4101082021	04101	61	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	748	0.0033838	04101
4101082022	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	13	0.0000588	04101
4101082023	04101	46	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	733	0.0033159	04101
4101082029	04101	7	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	140	0.0006333	04101
4101082043	04101	38	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	692	0.0031305	04101
4101082051	04101	5	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	36	0.0001629	04101
4101082052	04101	5	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	52	0.0002352	04101
4101082901	04101	6	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	37	0.0001674	04101
4101092003	04101	94	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	740	0.0033476	04101
4101092034	04101	46	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	742	0.0033566	04101
4101092038	04101	6	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	329	0.0014883	04101
4101092039	04101	7	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	63	0.0002850	04101
4101092901	04101	2	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	60	0.0002714	04101
4101102003	04101	9	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	70	0.0003167	04101
4101102013	04101	49	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	1201	0.0054331	04101
4101102015	04101	21	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	364	0.0016467	04101
4101102020	04101	169	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	2212	0.0100066	04101
4101102032	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	48	0.0002171	04101
4101122010	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	44	0.0001990	04101
4101132025	04101	30	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	987	0.0044650	04101
4101132032	04101	19	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	824	0.0037276	04101
4101132046	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	23	0.0001040	04101
4101132055	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	23	0.0001040	04101
4101132901	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	36	0.0001629	04101
4101142008	04101	6	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	95	0.0004298	04101
4101142012	04101	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	175	0.0007917	04101
4101142014	04101	23	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	519	0.0023478	04101
4101142019	04101	3	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	194	0.0008776	04101
4101142030	04101	2	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	147	0.0006650	04101
4101142033	04101	10	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	51	0.0002307	04101
4101142036	04101	4	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	225	0.0010179	04101
4101142040	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	92	0.0004162	04101
4101142056	04101	98	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	383	0.0017326	04101
4101142057	04101	5	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	45	0.0002036	04101
4101142901	04101	36	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	324	0.0014657	04101
4101162030	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	78	0.0003529	04101
4101172030	04101	3	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	113	0.0005112	04101
4102062009	04102	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	395	0.0017345	04102
4102062014	04102	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	128	0.0005621	04102
4102062024	04102	238	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	4341	0.0190620	04102
4102062901	04102	16	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	111	0.0004874	04102
4102072002	04102	64	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	816	0.0035832	04102
4102072011	04102	20	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	156	0.0006850	04102
4102072015	04102	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	50	0.0002196	04102
4102072021	04102	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	106	0.0004655	04102
4102072023	04102	30	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	565	0.0024810	04102
4102072024	04102	80	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	1893	0.0083125	04102
4102082004	04102	20	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	296	0.0012998	04102
4102082007	04102	5	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	52	0.0002283	04102
4102082020	04102	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	8	0.0000351	04102
4102082032	04102	19	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	307	0.0013481	04102
4102082901	04102	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	77	0.0003381	04102
4102092026	04102	5	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	40	0.0001756	04102
4102102003	04102	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	46	0.0002020	04102
4102112006	04102	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	29	0.0001273	04102
4102112017	04102	36	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	760	0.0033373	04102
4102112025	04102	19	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	233	0.0010231	04102
4102112029	04102	53	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	1312	0.0057612	04102
4102112901	04102	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	45	0.0001976	04102
4102122018	04102	36	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	460	0.0020199	04102
4102142012	04102	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	288	0.0012647	04102
4102152012	04102	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	50	0.0002196	04102
4102152030	04102	3	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	122	0.0005357	04102
4102162013	04102	23	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	252	0.0011066	04102
4102162901	04102	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	19	0.0000834	04102
4103012014	04103	2	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural	55	0.0049801	04103
4103012901	04103	3	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural	83	0.0075154	04103
4103032005	04103	17	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural	537	0.0486237	04103
4103032013	04103	1	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural	72	0.0065194	04103
4103032901	04103	4	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural	31	0.0028070	04103
4104012013	04104	1	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	53	0.0124971	04104
4104022002	04104	22	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	839	0.1978307	04104
4104022006	04104	1	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	43	0.0101391	04104
4104032014	04104	3	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	66	0.0155624	04104
4104042003	04104	8	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	267	0.0629568	04104
4104042028	04104	1	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	52	0.0122613	04104
4104052004	04104	13	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	144	0.0339543	04104
4104052017	04104	13	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	231	0.0544683	04104
4104052023	04104	22	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	311	0.0733318	04104
4104062901	04104	2	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	52	0.0122613	04104
4104082022	04104	10	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	324	0.0763971	04104
4104092012	04104	9	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	296	0.0697949	04104
4105012010	04105	20	2017	Paiguano	205942.1	2017	4105	4497	926121774	Rural	1024	0.2277074	04105

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
4101022001	04101	85	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	1157	0.0052340	04101	269794152
4101052007	04101	20	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	125	0.0005655	04101	29148028
4101062001	04101	48	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	345	0.0015607	04101	80448559
4101062004	04101	114	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	740	0.0033476	04101	172556329
4101062006	04101	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	224	0.0010133	04101	52233267
4101062024	04101	15	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	2392	0.0108209	04101	557776673
4101062030	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	41	0.0001855	04101	9560553
4101062049	04101	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	115	0.0005202	04101	26816186
4101062901	04101	11	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	284	0.0012848	04101	66224321
4101072002	04101	55	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	394	0.0017824	04101	91874586
4101072027	04101	17	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	461	0.0020855	04101	107497929
4101072029	04101	3	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	119	0.0005383	04101	27748923
4101072051	04101	20	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	179	0.0008098	04101	41739977
4101072053	04101	2	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	35	0.0001583	04101	8161448
4101072054	04101	4	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	114	0.0005157	04101	26583002
4101082011	04101	8	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	304	0.0013752	04101	70888005
4101082018	04101	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	348	0.0015743	04101	81148111
4101082021	04101	61	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	748	0.0033838	04101	174421802
4101082022	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	13	0.0000588	04101	3031395
4101082023	04101	46	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	733	0.0033159	04101	170924039
4101082029	04101	7	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	140	0.0006333	04101	32645792
4101082043	04101	38	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	692	0.0031305	04101	161363486
4101082051	04101	5	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	36	0.0001629	04101	8394632
4101082052	04101	5	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	52	0.0002352	04101	12125580
4101082901	04101	6	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	37	0.0001674	04101	8627816
4101092003	04101	94	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	740	0.0033476	04101	172556329
4101092034	04101	46	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	742	0.0033566	04101	173022697
4101092038	04101	6	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	329	0.0014883	04101	76717611
4101092039	04101	7	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	63	0.0002850	04101	14690606
4101092901	04101	2	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	60	0.0002714	04101	13991054
4101102003	04101	9	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	70	0.0003167	04101	16322896
4101102013	04101	49	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	1201	0.0054331	04101	280054258
4101102015	04101	21	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	364	0.0016467	04101	84879059
4101102020	04101	169	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	2212	0.0100066	04101	515803512
4101102032	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	48	0.0002171	04101	11192843
4101122010	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	44	0.0001990	04101	10260106
4101132025	04101	30	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	987	0.0044650	04101	230152833
4101132032	04101	19	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	824	0.0037276	04101	192143804
4101132046	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	23	0.0001040	04101	5363237
4101132055	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	23	0.0001040	04101	5363237
4101132901	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	36	0.0001629	04101	8394632
4101142008	04101	6	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	95	0.0004298	04101	22152502
4101142012	04101	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	175	0.0007917	04101	40807240
4101142014	04101	23	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	519	0.0023478	04101	121022614
4101142019	04101	3	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	194	0.0008776	04101	45237740
4101142030	04101	2	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	147	0.0006650	04101	34278081
4101142033	04101	10	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	51	0.0002307	04101	11892396
4101142036	04101	4	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	225	0.0010179	04101	52466451
4101142040	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	92	0.0004162	04101	21452949
4101142056	04101	98	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	383	0.0017326	04101	89309559
4101142057	04101	5	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	45	0.0002036	04101	10493290
4101142901	04101	36	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	324	0.0014657	04101	75551690
4101162030	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	78	0.0003529	04101	18188370
4101172030	04101	3	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	113	0.0005112	04101	26349818
4102062009	04102	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	395	0.0017345	04102	91565212
4102062014	04102	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	128	0.0005621	04102	29671765
4102062024	04102	238	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	4341	0.0190620	04102	1006290092
4102062901	04102	16	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	111	0.0004874	04102	25730984
4102072002	04102	64	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	816	0.0035832	04102	189157502
4102072011	04102	20	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	156	0.0006850	04102	36162464
4102072015	04102	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	50	0.0002196	04102	11590533
4102072021	04102	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	106	0.0004655	04102	24571930
4102072023	04102	30	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	565	0.0024810	04102	130973025
4102072024	04102	80	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	1893	0.0083125	04102	438817587
4102082004	04102	20	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	296	0.0012998	04102	68615956
4102082007	04102	5	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	52	0.0002283	04102	12054155
4102082020	04102	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	8	0.0000351	04102	1854485
4102082032	04102	19	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	307	0.0013481	04102	71165874
4102082901	04102	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	77	0.0003381	04102	17849421
4102092026	04102	5	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	40	0.0001756	04102	9272427
4102102003	04102	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	46	0.0002020	04102	10663291
4102112006	04102	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	29	0.0001273	04102	6722509
4102112017	04102	36	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	760	0.0033373	04102	176176104
4102112025	04102	19	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	233	0.0010231	04102	54011885
4102112029	04102	53	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	1312	0.0057612	04102	304135591
4102112901	04102	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	45	0.0001976	04102	10431480
4102122018	04102	36	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	460	0.0020199	04102	106632905
4102142012	04102	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	288	0.0012647	04102	66761471
4102152012	04102	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	50	0.0002196	04102	11590533
4102152030	04102	3	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	122	0.0005357	04102	28280901
4102162013	04102	23	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	252	0.0011066	04102	58416287
4102162901	04102	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	19	0.0000834	04102	4404403
4103012014	04103	2	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural	55	0.0049801	04103	13359952
4103012901	04103	3	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural	83	0.0075154	04103	20161382
4103032005	04103	17	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural	537	0.0486237	04103	130441712
4103032013	04103	1	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural	72	0.0065194	04103	17489392
4103032901	04103	4	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural	31	0.0028070	04103	7530155
4104012013	04104	1	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	53	0.0124971	04104	13287080
4104022002	04104	22	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	839	0.1978307	04104	210336977
4104022006	04104	1	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	43	0.0101391	04104	10780083
4104032014	04104	3	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	66	0.0155624	04104	16546175
4104042003	04104	8	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	267	0.0629568	04104	66936797
4104042028	04104	1	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	52	0.0122613	04104	13036380
4104052004	04104	13	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	144	0.0339543	04104	36100745
4104052017	04104	13	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	231	0.0544683	04104	57911611
4104052023	04104	22	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	311	0.0733318	04104	77967580
4104062901	04104	2	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	52	0.0122613	04104	13036380
4104082022	04104	10	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	324	0.0763971	04104	81226675
4104092012	04104	9	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	296	0.0697949	04104	74207086
4105012010	04105	20	2017	Paiguano	205942.1	2017	4105	4497	926121774	Rural	1024	0.2277074	04105	210884745

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -262845236  -25906067  -15203425   13455484  481065200 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 26932559    3151566   8.546   <2e-16 ***
## Freq.x       3318594     139443  23.799   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 57970000 on 435 degrees of freedom
## Multiple R-squared:  0.5656, Adjusted R-squared:  0.5646 
## F-statistic: 566.4 on 1 and 435 DF,  p-value: < 2.2e-16

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.564604886308557"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.564604886308557"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.389606150557925"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.528252660249392"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.555882751532979"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.440983949394383"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.524773484451479"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq               
## [1,] "log-log" "0.5103133666971"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 3   logarítmico 0.389606150557925
## 6      log-raíz 0.440983949394383
## 8       log-log   0.5103133666971
## 7      raíz-log 0.524773484451479
## 4 raíz cuadrada 0.528252660249392
## 5     raíz-raíz 0.555882751532979
## 1    cuadrático 0.564604886308557
## 2        cúbico 0.564604886308557
##                                                                     sintaxis
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)

metodo <- 1
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x^2), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -262845236  -25906067  -15203425   13455484  481065200 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 26932559    3151566   8.546   <2e-16 ***
## Freq.x       3318594     139443  23.799   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 57970000 on 435 degrees of freedom
## Multiple R-squared:  0.5656, Adjusted R-squared:  0.5646 
## F-statistic: 566.4 on 1 and 435 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    26932559

bb <- linearMod$coefficients[2]
bb

##  Freq.x 
## 3318594

9 Modelo cuadrático (cuadrático)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.5646 ).

9.1 Diagrama de dispersión sobre cuadrático

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=(h_y_m_comuna_corr_01$Freq.x^2), y=(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo cuadrático

Observemos nuevamente el resultado sobre cuadrático.

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x^2), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -262845236  -25906067  -15203425   13455484  481065200 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 26932559    3151566   8.546   <2e-16 ***
## Freq.x       3318594     139443  23.799   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 57970000 on 435 degrees of freedom
## Multiple R-squared:  0.5656, Adjusted R-squared:  0.5646 
## F-statistic: 566.4 on 1 and 435 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = (Freq.x^2) , y = (multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = 26932559 + 3318594\cdot X^2 \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- aa+bb * (h_y_m_comuna_corr_01$Freq.x^2)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
4101022001	04101	85	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	1157	0.0052340	04101	269794152	24003775950
4101052007	04101	20	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	125	0.0005655	04101	29148028	1354370256
4101062001	04101	48	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	345	0.0015607	04101	80448559	7672973690
4101062004	04101	114	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	740	0.0033476	04101	172556329	43155383314
4101062006	04101	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	224	0.0010133	04101	52233267	2101053960
4101062024	04101	15	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	2392	0.0108209	04101	557776673	773616263
4101062030	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	41	0.0001855	04101	9560553	30251154
4101062049	04101	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	115	0.0005202	04101	26816186	2101053960
4101062901	04101	11	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	284	0.0012848	04101	66224321	428482462
4101072002	04101	55	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	394	0.0017824	04101	91874586	10065680138
4101072027	04101	17	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	461	0.0020855	04101	107497929	986006295
4101072029	04101	3	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	119	0.0005383	04101	27748923	56799907
4101072051	04101	20	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	179	0.0008098	04101	41739977	1354370256
4101072053	04101	2	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	35	0.0001583	04101	8161448	40206936
4101072054	04101	4	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	114	0.0005157	04101	26583002	80030067
4101082011	04101	8	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	304	0.0013752	04101	70888005	239322591
4101082018	04101	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	348	0.0015743	04101	81148111	2101053960
4101082021	04101	61	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	748	0.0033838	04101	174421802	12375421730
4101082022	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	13	0.0000588	04101	3031395	30251154
4101082023	04101	46	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	733	0.0033159	04101	170924039	7049077973
4101082029	04101	7	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	140	0.0006333	04101	32645792	189543677
4101082043	04101	38	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	692	0.0031305	04101	161363486	4818982643
4101082051	04101	5	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	36	0.0001629	04101	8394632	109897415
4101082052	04101	5	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	52	0.0002352	04101	12125580	109897415
4101082901	04101	6	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	37	0.0001674	04101	8627816	146401952
4101092003	04101	94	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	740	0.0033476	04101	172556329	29350031272
4101092034	04101	46	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	742	0.0033566	04101	173022697	7049077973
4101092038	04101	6	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	329	0.0014883	04101	76717611	146401952
4101092039	04101	7	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	63	0.0002850	04101	14690606	189543677
4101092901	04101	2	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	60	0.0002714	04101	13991054	40206936
4101102003	04101	9	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	70	0.0003167	04101	16322896	295738693
4101102013	04101	49	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	1201	0.0054331	04101	280054258	7994877332
4101102015	04101	21	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	364	0.0016467	04101	84879059	1490432620
4101102020	04101	169	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	2212	0.0100066	04101	515803512	94809302673
4101102032	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	48	0.0002171	04101	11192843	30251154
4101122010	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	44	0.0001990	04101	10260106	30251154
4101132025	04101	30	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	987	0.0044650	04101	230152833	3013667376
4101132032	04101	19	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	824	0.0037276	04101	192143804	1224945080
4101132046	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	23	0.0001040	04101	5363237	30251154
4101132055	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	23	0.0001040	04101	5363237	30251154
4101132901	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	36	0.0001629	04101	8394632	30251154
4101142008	04101	6	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	95	0.0004298	04101	22152502	146401952
4101142012	04101	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	175	0.0007917	04101	40807240	2101053960
4101142014	04101	23	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	519	0.0023478	04101	121022614	1782468913
4101142019	04101	3	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	194	0.0008776	04101	45237740	56799907
4101142030	04101	2	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	147	0.0006650	04101	34278081	40206936
4101142033	04101	10	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	51	0.0002307	04101	11892396	358791983
4101142036	04101	4	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	225	0.0010179	04101	52466451	80030067
4101142040	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	92	0.0004162	04101	21452949	30251154
4101142056	04101	98	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	383	0.0017326	04101	89309559	31898711649
4101142057	04101	5	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	45	0.0002036	04101	10493290	109897415
4101142901	04101	36	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	324	0.0014657	04101	75551690	4327830695
4101162030	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	78	0.0003529	04101	18188370	30251154
4101172030	04101	3	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	113	0.0005112	04101	26349818	56799907
4102062009	04102	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	395	0.0017345	04102	91565212	80030067
4102062014	04102	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	128	0.0005621	04102	29671765	80030067
4102062024	04102	238	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	4341	0.0190620	04102	1006290092	188005384740
4102062901	04102	16	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	111	0.0004874	04102	25730984	876492685
4102072002	04102	64	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	816	0.0035832	04102	189157502	13619894570
4102072011	04102	20	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	156	0.0006850	04102	36162464	1354370256
4102072015	04102	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	50	0.0002196	04102	11590533	40206936
4102072021	04102	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	106	0.0004655	04102	24571930	40206936
4102072023	04102	30	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	565	0.0024810	04102	130973025	3013667376
4102072024	04102	80	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	1893	0.0083125	04102	438817587	21265935701
4102082004	04102	20	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	296	0.0012998	04102	68615956	1354370256
4102082007	04102	5	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	52	0.0002283	04102	12054155	109897415
4102082020	04102	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	8	0.0000351	04102	1854485	30251154
4102082032	04102	19	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	307	0.0013481	04102	71165874	1224945080
4102082901	04102	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	77	0.0003381	04102	17849421	80030067
4102092026	04102	5	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	40	0.0001756	04102	9272427	109897415
4102102003	04102	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	46	0.0002020	04102	10663291	30251154
4102112006	04102	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	29	0.0001273	04102	6722509	30251154
4102112017	04102	36	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	760	0.0033373	04102	176176104	4327830695
4102112025	04102	19	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	233	0.0010231	04102	54011885	1224945080
4102112029	04102	53	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	1312	0.0057612	04102	304135591	9348863782
4102112901	04102	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	45	0.0001976	04102	10431480	40206936
4102122018	04102	36	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	460	0.0020199	04102	106632905	4327830695
4102142012	04102	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	288	0.0012647	04102	66761471	80030067
4102152012	04102	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	50	0.0002196	04102	11590533	40206936
4102152030	04102	3	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	122	0.0005357	04102	28280901	56799907
4102162013	04102	23	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	252	0.0011066	04102	58416287	1782468913
4102162901	04102	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	19	0.0000834	04102	4404403	30251154
4103012014	04103	2	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural	55	0.0049801	04103	13359952	40206936
4103012901	04103	3	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural	83	0.0075154	04103	20161382	56799907
4103032005	04103	17	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural	537	0.0486237	04103	130441712	986006295
4103032013	04103	1	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural	72	0.0065194	04103	17489392	30251154
4103032901	04103	4	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural	31	0.0028070	04103	7530155	80030067
4104012013	04104	1	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	53	0.0124971	04104	13287080	30251154
4104022002	04104	22	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	839	0.1978307	04104	210336977	1633132172
4104022006	04104	1	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	43	0.0101391	04104	10780083	30251154
4104032014	04104	3	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	66	0.0155624	04104	16546175	56799907
4104042003	04104	8	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	267	0.0629568	04104	66936797	239322591
4104042028	04104	1	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	52	0.0122613	04104	13036380	30251154
4104052004	04104	13	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	144	0.0339543	04104	36100745	587774986
4104052017	04104	13	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	231	0.0544683	04104	57911611	587774986
4104052023	04104	22	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	311	0.0733318	04104	77967580	1633132172
4104062901	04104	2	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	52	0.0122613	04104	13036380	40206936
4104082022	04104	10	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	324	0.0763971	04104	81226675	358791983
4104092012	04104	9	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	296	0.0697949	04104	74207086	295738693
4105012010	04105	20	2017	Paiguano	205942.1	2017	4105	4497	926121774	Rural	1024	0.2277074	04105	210884745	1354370256

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
4101022001	04101	85	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	1157	0.0052340	04101	269794152	24003775950	20746565.2
4101052007	04101	20	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	125	0.0005655	04101	29148028	1354370256	10834962.0
4101062001	04101	48	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	345	0.0015607	04101	80448559	7672973690	22240503.5
4101062004	04101	114	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	740	0.0033476	04101	172556329	43155383314	58318085.6
4101062006	04101	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	224	0.0010133	04101	52233267	2101053960	9379705.2
4101062024	04101	15	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	2392	0.0108209	04101	557776673	773616263	323418.2
4101062030	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	41	0.0001855	04101	9560553	30251154	737833.0
4101062049	04101	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	115	0.0005202	04101	26816186	2101053960	18270034.4
4101062901	04101	11	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	284	0.0012848	04101	66224321	428482462	1508741.1
4101072002	04101	55	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	394	0.0017824	04101	91874586	10065680138	25547411.5
4101072027	04101	17	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	461	0.0020855	04101	107497929	986006295	2138842.3
4101072029	04101	3	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	119	0.0005383	04101	27748923	56799907	477310.1
4101072051	04101	20	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	179	0.0008098	04101	41739977	1354370256	7566314.3
4101072053	04101	2	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	35	0.0001583	04101	8161448	40206936	1148769.6
4101072054	04101	4	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	114	0.0005157	04101	26583002	80030067	702018.1
4101082011	04101	8	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	304	0.0013752	04101	70888005	239322591	787245.4
4101082018	04101	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	348	0.0015743	04101	81148111	2101053960	6037511.4
4101082021	04101	61	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	748	0.0033838	04101	174421802	12375421730	16544681.5
4101082022	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	13	0.0000588	04101	3031395	30251154	2327011.8
4101082023	04101	46	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	733	0.0033159	04101	170924039	7049077973	9616750.3
4101082029	04101	7	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	140	0.0006333	04101	32645792	189543677	1353883.4
4101082043	04101	38	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	692	0.0031305	04101	161363486	4818982643	6963847.8
4101082051	04101	5	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	36	0.0001629	04101	8394632	109897415	3052706.0
4101082052	04101	5	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	52	0.0002352	04101	12125580	109897415	2113411.8
4101082901	04101	6	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	37	0.0001674	04101	8627816	146401952	3956809.5
4101092003	04101	94	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	740	0.0033476	04101	172556329	29350031272	39662204.4
4101092034	04101	46	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	742	0.0033566	04101	173022697	7049077973	9500105.1
4101092038	04101	6	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	329	0.0014883	04101	76717611	146401952	444990.7
4101092039	04101	7	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	63	0.0002850	04101	14690606	189543677	3008629.8
4101092901	04101	2	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	60	0.0002714	04101	13991054	40206936	670115.6
4101102003	04101	9	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	70	0.0003167	04101	16322896	295738693	4224838.5
4101102013	04101	49	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	1201	0.0054331	04101	280054258	7994877332	6656850.4
4101102015	04101	21	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	364	0.0016467	04101	84879059	1490432620	4094595.1
4101102020	04101	169	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	2212	0.0100066	04101	515803512	94809302673	42861348.4
4101102032	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	48	0.0002171	04101	11192843	30251154	630232.4
4101122010	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	44	0.0001990	04101	10260106	30251154	687526.2
4101132025	04101	30	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	987	0.0044650	04101	230152833	3013667376	3053361.1
4101132032	04101	19	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	824	0.0037276	04101	192143804	1224945080	1486583.8
4101132046	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	23	0.0001040	04101	5363237	30251154	1315267.5
4101132055	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	23	0.0001040	04101	5363237	30251154	1315267.5
4101132901	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	36	0.0001629	04101	8394632	30251154	840309.8
4101142008	04101	6	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	95	0.0004298	04101	22152502	146401952	1541073.2
4101142012	04101	25	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	175	0.0007917	04101	40807240	2101053960	12006022.6
4101142014	04101	23	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	519	0.0023478	04101	121022614	1782468913	3434429.5
4101142019	04101	3	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	194	0.0008776	04101	45237740	56799907	292783.0
4101142030	04101	2	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	147	0.0006650	04101	34278081	40206936	273516.6
4101142033	04101	10	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	51	0.0002307	04101	11892396	358791983	7035136.9
4101142036	04101	4	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	225	0.0010179	04101	52466451	80030067	355689.2
4101142040	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	92	0.0004162	04101	21452949	30251154	328816.9
4101142056	04101	98	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	383	0.0017326	04101	89309559	31898711649	83286453.4
4101142057	04101	5	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	45	0.0002036	04101	10493290	109897415	2442164.8
4101142901	04101	36	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	324	0.0014657	04101	75551690	4327830695	13357502.1
4101162030	04101	1	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	78	0.0003529	04101	18188370	30251154	387835.3
4101172030	04101	3	2017	La Serena	233184.2	2017	4101	221054	51546306303	Rural	113	0.0005112	04101	26349818	56799907	502654.0
4102062009	04102	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	395	0.0017345	04102	91565212	80030067	202607.8
4102062014	04102	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	128	0.0005621	04102	29671765	80030067	625234.9
4102062024	04102	238	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	4341	0.0190620	04102	1006290092	188005384740	43309234.0
4102062901	04102	16	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	111	0.0004874	04102	25730984	876492685	7896330.5
4102072002	04102	64	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	816	0.0035832	04102	189157502	13619894570	16691047.3
4102072011	04102	20	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	156	0.0006850	04102	36162464	1354370256	8681860.6
4102072015	04102	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	50	0.0002196	04102	11590533	40206936	804138.7
4102072021	04102	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	106	0.0004655	04102	24571930	40206936	379310.7
4102072023	04102	30	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	565	0.0024810	04102	130973025	3013667376	5333924.6
4102072024	04102	80	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	1893	0.0083125	04102	438817587	21265935701	11233986.1
4102082004	04102	20	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	296	0.0012998	04102	68615956	1354370256	4575575.2
4102082007	04102	5	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	52	0.0002283	04102	12054155	109897415	2113411.8
4102082020	04102	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	8	0.0000351	04102	1854485	30251154	3781394.2
4102082032	04102	19	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	307	0.0013481	04102	71165874	1224945080	3990049.1
4102082901	04102	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	77	0.0003381	04102	17849421	80030067	1039351.5
4102092026	04102	5	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	40	0.0001756	04102	9272427	109897415	2747435.4
4102102003	04102	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	46	0.0002020	04102	10663291	30251154	657633.8
4102112006	04102	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	29	0.0001273	04102	6722509	30251154	1043143.2
4102112017	04102	36	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	760	0.0033373	04102	176176104	4327830695	5694514.1
4102112025	04102	19	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	233	0.0010231	04102	54011885	1224945080	5257275.0
4102112029	04102	53	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	1312	0.0057612	04102	304135591	9348863782	7125658.4
4102112901	04102	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	45	0.0001976	04102	10431480	40206936	893487.5
4102122018	04102	36	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	460	0.0020199	04102	106632905	4327830695	9408327.6
4102142012	04102	4	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	288	0.0012647	04102	66761471	80030067	277882.2
4102152012	04102	2	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	50	0.0002196	04102	11590533	40206936	804138.7
4102152030	04102	3	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	122	0.0005357	04102	28280901	56799907	465573.0
4102162013	04102	23	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	252	0.0011066	04102	58416287	1782468913	7073289.3
4102162901	04102	1	2017	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural	19	0.0000834	04102	4404403	30251154	1592166.0
4103012014	04103	2	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural	55	0.0049801	04103	13359952	40206936	731035.2
4103012901	04103	3	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural	83	0.0075154	04103	20161382	56799907	684336.2
4103032005	04103	17	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural	537	0.0486237	04103	130441712	986006295	1836138.4
4103032013	04103	1	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural	72	0.0065194	04103	17489392	30251154	420154.9
4103032901	04103	4	2017	Andacollo	242908.2	2017	4103	11044	2682678345	Rural	31	0.0028070	04103	7530155	80030067	2581615.1
4104012013	04104	1	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	53	0.0124971	04104	13287080	30251154	570776.5
4104022002	04104	22	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	839	0.1978307	04104	210336977	1633132172	1946522.3
4104022006	04104	1	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	43	0.0101391	04104	10780083	30251154	703515.2
4104032014	04104	3	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	66	0.0155624	04104	16546175	56799907	860604.7
4104042003	04104	8	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	267	0.0629568	04104	66936797	239322591	896339.3
4104042028	04104	1	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	52	0.0122613	04104	13036380	30251154	581753.0
4104052004	04104	13	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	144	0.0339543	04104	36100745	587774986	4081770.7
4104052017	04104	13	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	231	0.0544683	04104	57911611	587774986	2544480.5
4104052023	04104	22	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	311	0.0733318	04104	77967580	1633132172	5251228.8
4104062901	04104	2	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	52	0.0122613	04104	13036380	40206936	773210.3
4104082022	04104	10	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	324	0.0763971	04104	81226675	358791983	1107382.7
4104092012	04104	9	2017	La Higuera	250699.6	2017	4104	4241	1063217069	Rural	296	0.0697949	04104	74207086	295738693	999117.2
4105012010	04105	20	2017	Paiguano	205942.1	2017	4105	4497	926121774	Rural	1024	0.2277074	04105	210884745	1354370256	1322627.2

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_rural_nivel_nacional_17_r04.rds")

---

r-05

Urbano

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 1)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15201	1	1	1	1767	1	1	1	2	1	3	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	2	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	3	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	4	1	1	2	3	1	99	1	2	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	5	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	6	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	7	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	8	1	1	3	3	1	99	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	9	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	10	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	11	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	12	1	1	4	3	3	3	4	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	13	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	14	1	1	2	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	15	1	1	2	1	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	16	1	1	5	1	5	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	17	1	1	5	3	1	3	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	18	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	19	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	20	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	21	4	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	22	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	23	1	1	5	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	24	1	1	3	1	1	6	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	25	1	1	5	6	1	1	4	2	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	26	4	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	27	1	1	5	99	3	3	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	28	1	1	6	3	2	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	29	1	1	2	3	1	99	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	30	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	31	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	32	1	1	3	1	1	2	1	1	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	33	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	34	3	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	35	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	36	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	37	1	1	5	3	3	1	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	38	1	1	1	1	4	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	39	1	1	5	3	4	3	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	40	1	1	5	3	2	1	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	41	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	42	1	1	2	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	43	1	1	1	1	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	44	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	45	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	46	1	1	5	6	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	47	1	1	5	3	4	0	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	48	1	1	3	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	49	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	50	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	51	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	52	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	53	1	1	1	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	54	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	55	1	1	2	2	1	4	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	56	8	1	98	98	98	98	98	98	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	57	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	58	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	59	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	60	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	61	1	1	2	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	62	1	1	5	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	63	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	64	1	1	5	3	2	4	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	65	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	66	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	67	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	68	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	69	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	70	1	1	1	3	3	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	71	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	72	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	73	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	74	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	75	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	76	1	1	5	3	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	77	1	1	3	6	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	78	5	1	3	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	79	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	80	1	1	2	1	1	3	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	81	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	82	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	83	1	1	5	3	2	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	84	4	1	5	3	2	0	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	85	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	86	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	87	1	1	3	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	88	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	89	5	1	4	3	2	1	2	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	90	1	1	5	3	3	2	1	3	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	91	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	92	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	93	5	1	4	3	5	1	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	94	1	1	5	3	3	99	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	95	1	1	5	3	2	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	96	1	1	2	3	4	6	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	97	1	1	5	1	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	98	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	99	1	1	2	7	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	100	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 5:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 5)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	anio	código
5101011001	200	2017	05101
5101011002	183	2017	05101
5101011003	184	2017	05101
5101011004	49	2017	05101
5101011005	97	2017	05101
5101011006	193	2017	05101
5101011007	210	2017	05101
5101021001	5	2017	05101
5101021002	218	2017	05101
5101021003	140	2017	05101
5101021004	74	2017	05101
5101031001	86	2017	05101
5101031002	78	2017	05101
5101031003	81	2017	05101
5101031004	43	2017	05101
5101031005	29	2017	05101
5101031006	29	2017	05101
5101031007	84	2017	05101
5101031008	93	2017	05101
5101031009	29	2017	05101
5101031010	38	2017	05101
5101031011	104	2017	05101
5101031012	35	2017	05101
5101041001	10	2017	05101
5101051001	67	2017	05101
5101051002	49	2017	05101
5101051003	75	2017	05101
5101051004	65	2017	05101
5101051005	34	2017	05101
5101051006	53	2017	05101
5101051007	31	2017	05101
5101061001	52	2017	05101
5101061002	36	2017	05101
5101061003	83	2017	05101
5101061004	71	2017	05101
5101061005	102	2017	05101
5101071001	152	2017	05101
5101081001	68	2017	05101
5101081002	50	2017	05101
5101081003	23	2017	05101
5101081004	65	2017	05101
5101081005	70	2017	05101
5101081006	39	2017	05101
5101081007	63	2017	05101
5101081008	41	2017	05101
5101081009	57	2017	05101
5101081010	79	2017	05101
5101081011	25	2017	05101
5101091001	87	2017	05101
5101091002	97	2017	05101
5101091003	79	2017	05101
5101091004	37	2017	05101
5101101001	77	2017	05101
5101101002	115	2017	05101
5101101003	75	2017	05101
5101101004	47	2017	05101
5101101005	46	2017	05101
5101101006	87	2017	05101
5101101007	10	2017	05101
5101101008	38	2017	05101
5101101009	113	2017	05101
5101111001	142	2017	05101
5101121001	107	2017	05101
5101121002	79	2017	05101
5101121003	75	2017	05101
5101131001	49	2017	05101
5101131002	17	2017	05101
5101131003	20	2017	05101
5101131004	23	2017	05101
5101131005	24	2017	05101
5101141001	52	2017	05101
5101141002	27	2017	05101
5101141003	39	2017	05101
5101141004	9	2017	05101
5101141005	52	2017	05101
5101141006	53	2017	05101
5101151001	25	2017	05101
5101151002	35	2017	05101
5101151003	23	2017	05101
5101151004	20	2017	05101
5101151005	27	2017	05101
5101151006	28	2017	05101
5101151007	66	2017	05101
5101161001	17	2017	05101
5101161002	23	2017	05101
5101161003	8	2017	05101
5101161004	64	2017	05101
5101161005	13	2017	05101
5101161006	24	2017	05101
5101161007	24	2017	05101
5101161008	64	2017	05101
5101161009	24	2017	05101
5101161010	23	2017	05101
5101161011	40	2017	05101
5101161012	50	2017	05101
5101171001	79	2017	05101
5101171002	147	2017	05101
5101171003	114	2017	05101
5101171004	54	2017	05101
5101171005	60	2017	05101

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_urbano_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
01107	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
01401	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
01405	Pica	330061.1	2017	1405	9296	3068247619	Urbano
02101	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02102	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02104	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02201	Calama	416281.1	2017	2201	165731	68990679686	Urbano
02203	San Pedro de Atacama	437934.7	2017	2203	10996	4815529626	Urbano
02301	Tocopilla	271720.8	2017	2301	25186	6843559467	Urbano
02302	María Elena	466266.9	2017	2302	6457	3010685220	Urbano
03101	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
03102	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
03103	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
03201	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
03202	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
03301	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
03303	Freirina	289049.9	2017	3303	7041	2035200054	Urbano
03304	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
04101	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04102	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04103	Andacollo	251267.7	2017	4103	11044	2775000288	Urbano
04104	La Higuera	214257.0	2017	4104	4241	908664019	Urbano
04106	Vicuña	245957.4	2017	4106	27771	6830481918	Urbano
04201	Illapel	270316.5	2017	4201	30848	8338722128	Urbano
04202	Canela	233397.3	2017	4202	9093	2122281844	Urbano
04203	Los Vilos	282415.6	2017	4203	21382	6038609501	Urbano
04204	Salamanca	262056.9	2017	4204	29347	7690585032	Urbano
04301	Ovalle	274771.4	2017	4301	111272	30574361012	Urbano
04302	Combarbalá	228990.4	2017	4302	13322	3050610572	Urbano
04303	Monte Patria	225369.1	2017	4303	30751	6930326684	Urbano
04304	Punitaqui	212496.1	2017	4304	10956	2328107498	Urbano
05101	Valparaíso	297929.0	2017	5101	296655	88382118059	Urbano
05102	Casablanca	341641.8	2017	5102	26867	9178890241	Urbano
05103	Concón	318496.3	2017	5103	42152	13425257057	Urbano
05105	Puchuncaví	296035.5	2017	5105	18546	5490274928	Urbano
05107	Quintero	308224.7	2017	5107	31923	9839456903	Urbano
05109	Viña del Mar	337006.1	2017	5109	334248	112643604611	Urbano
05301	Los Andes	339720.2	2017	5301	66708	22662055502	Urbano
05302	Calle Larga	246387.3	2017	5302	14832	3654416747	Urbano
05303	Rinconada	273904.7	2017	5303	10207	2795744821	Urbano
05304	San Esteban	219571.6	2017	5304	18855	4140022481	Urbano
05401	La Ligua	250134.4	2017	5401	35390	8852256241	Urbano
05402	Cabildo	262745.9	2017	5402	19388	5094117762	Urbano
05403	Papudo	294355.2	2017	5403	6356	1870921373	Urbano
05404	Petorca	237510.8	2017	5404	9826	2333781007	Urbano
05405	Zapallar	294389.2	2017	5405	7339	2160521991	Urbano
05501	Quillota	286029.5	2017	5501	90517	25890529852	Urbano
05502	Calera	277181.9	2017	5502	50554	14012652087	Urbano
05503	Hijuelas	254094.0	2017	5503	17988	4570642363	Urbano

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
05101	5101011004	49	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101011005	97	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101011006	193	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101011007	210	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101021001	5	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101021002	218	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101021003	140	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101021004	74	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031001	86	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031002	78	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101011001	200	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101011002	183	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101011003	184	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031006	29	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031007	84	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031008	93	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031009	29	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031010	38	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031011	104	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031012	35	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101041001	10	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101051001	67	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101051002	49	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101051003	75	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101051004	65	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101051005	34	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101051006	53	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101051007	31	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101061001	52	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101061002	36	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101061003	83	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101061004	71	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101061005	102	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101071001	152	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101081001	68	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101081002	50	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101081003	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101081004	65	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101081005	70	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101081006	39	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101081007	63	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101081008	41	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101081009	57	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101081010	79	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101081011	25	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101091001	87	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101091002	97	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101091003	79	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101091004	37	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101101001	77	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031003	81	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031004	43	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031005	29	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101101005	46	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101101006	87	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101101007	10	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101101008	38	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101101009	113	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101111001	142	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101121001	107	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101121002	79	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101121003	75	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101131001	49	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101131002	17	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101131003	20	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101131004	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101131005	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101141001	52	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101141002	27	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101141003	39	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101141004	9	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101141005	52	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101141006	53	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101151001	25	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101151002	35	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101151003	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101151004	20	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101151005	27	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101151006	28	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101151007	66	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101161001	17	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101161002	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101161003	8	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101161004	64	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101161005	13	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101161006	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101161007	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101161008	64	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101161009	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101161010	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101101002	115	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101101003	75	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101101004	47	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101171002	147	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101171003	114	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101171004	54	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101171005	60	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101171006	113	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101171007	133	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101171008	7	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
05101	5101011004	49	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101011005	97	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101011006	193	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101011007	210	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101021001	5	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101021002	218	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101021003	140	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101021004	74	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031001	86	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031002	78	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101011001	200	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101011002	183	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101011003	184	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031006	29	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031007	84	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031008	93	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031009	29	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031010	38	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031011	104	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031012	35	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101041001	10	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101051001	67	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101051002	49	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101051003	75	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101051004	65	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101051005	34	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101051006	53	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101051007	31	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101061001	52	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101061002	36	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101061003	83	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101061004	71	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101061005	102	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101071001	152	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101081001	68	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101081002	50	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101081003	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101081004	65	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101081005	70	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101081006	39	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101081007	63	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101081008	41	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101081009	57	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101081010	79	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101081011	25	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101091001	87	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101091002	97	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101091003	79	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101091004	37	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101101001	77	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031003	81	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031004	43	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101031005	29	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101101005	46	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101101006	87	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101101007	10	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101101008	38	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101101009	113	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101111001	142	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101121001	107	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101121002	79	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101121003	75	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101131001	49	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101131002	17	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101131003	20	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101131004	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101131005	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101141001	52	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101141002	27	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101141003	39	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101141004	9	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101141005	52	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101141006	53	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101151001	25	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101151002	35	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101151003	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101151004	20	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101151005	27	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101151006	28	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101151007	66	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101161001	17	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101161002	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101161003	8	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101161004	64	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101161005	13	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101161006	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101161007	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101161008	64	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101161009	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101161010	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101101002	115	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101101003	75	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101101004	47	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101171002	147	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101171003	114	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101171004	54	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101171005	60	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101171006	113	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101171007	133	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano
05101	5101171008	7	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
5101011001	05101	200	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3280	0.0110566	05101
5101011002	05101	183	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3761	0.0126780	05101
5101011003	05101	184	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2365	0.0079722	05101
5101011004	05101	49	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2690	0.0090678	05101
5101011005	05101	97	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2518	0.0084880	05101
5101011006	05101	193	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2848	0.0096004	05101
5101011007	05101	210	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3131	0.0105543	05101
5101021001	05101	5	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	273	0.0009203	05101
5101021002	05101	218	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1881	0.0063407	05101
5101021003	05101	140	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1696	0.0057171	05101
5101021004	05101	74	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1415	0.0047699	05101
5101031001	05101	86	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1402	0.0047260	05101
5101031002	05101	78	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1780	0.0060002	05101
5101031003	05101	81	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1071	0.0036103	05101
5101031004	05101	43	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	739	0.0024911	05101
5101031005	05101	29	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	457	0.0015405	05101
5101031006	05101	29	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1051	0.0035428	05101
5101031007	05101	84	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2154	0.0072610	05101
5101031008	05101	93	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2337	0.0078778	05101
5101031009	05101	29	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1059	0.0035698	05101
5101031010	05101	38	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1678	0.0056564	05101
5101031011	05101	104	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2610	0.0087981	05101
5101031012	05101	35	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	659	0.0022214	05101
5101041001	05101	10	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	177	0.0005967	05101
5101051001	05101	67	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1872	0.0063104	05101
5101051002	05101	49	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1714	0.0057778	05101
5101051003	05101	75	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2345	0.0079048	05101
5101051004	05101	65	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3014	0.0101600	05101
5101051005	05101	34	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1688	0.0056901	05101
5101051006	05101	53	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3387	0.0114173	05101
5101051007	05101	31	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2233	0.0075273	05101
5101061001	05101	52	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1196	0.0040316	05101
5101061002	05101	36	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	878	0.0029597	05101
5101061003	05101	83	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1642	0.0055350	05101
5101061004	05101	71	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	862	0.0029057	05101
5101061005	05101	102	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1546	0.0052114	05101
5101071001	05101	152	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1265	0.0042642	05101
5101081001	05101	68	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	687	0.0023158	05101
5101081002	05101	50	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	634	0.0021372	05101
5101081003	05101	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	619	0.0020866	05101
5101081004	05101	65	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	620	0.0020900	05101
5101081005	05101	70	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	979	0.0033001	05101
5101081006	05101	39	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1435	0.0048373	05101
5101081007	05101	63	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1791	0.0060373	05101
5101081008	05101	41	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1274	0.0042946	05101
5101081009	05101	57	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1519	0.0051204	05101
5101081010	05101	79	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1320	0.0044496	05101
5101081011	05101	25	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1143	0.0038530	05101
5101091001	05101	87	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1689	0.0056935	05101
5101091002	05101	97	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1322	0.0044564	05101
5101091003	05101	79	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1275	0.0042979	05101
5101091004	05101	37	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	4201	0.0141612	05101
5101101001	05101	77	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1044	0.0035192	05101
5101101002	05101	115	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	988	0.0033305	05101
5101101003	05101	75	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	849	0.0028619	05101
5101101004	05101	47	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	717	0.0024169	05101
5101101005	05101	46	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	936	0.0031552	05101
5101101006	05101	87	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1453	0.0048979	05101
5101101007	05101	10	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	604	0.0020360	05101
5101101008	05101	38	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2358	0.0079486	05101
5101101009	05101	113	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3502	0.0118050	05101
5101111001	05101	142	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2120	0.0071463	05101
5101121001	05101	107	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1435	0.0048373	05101
5101121002	05101	79	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1941	0.0065430	05101
5101121003	05101	75	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1528	0.0051508	05101
5101131001	05101	49	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1170	0.0039440	05101
5101131002	05101	17	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	572	0.0019282	05101
5101131003	05101	20	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	638	0.0021506	05101
5101131004	05101	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	880	0.0029664	05101
5101131005	05101	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	664	0.0022383	05101
5101141001	05101	52	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1502	0.0050631	05101
5101141002	05101	27	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1122	0.0037822	05101
5101141003	05101	39	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1031	0.0034754	05101
5101141004	05101	9	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	638	0.0021506	05101
5101141005	05101	52	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	704	0.0023731	05101
5101141006	05101	53	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1265	0.0042642	05101
5101151001	05101	25	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	532	0.0017933	05101
5101151002	05101	35	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	455	0.0015338	05101
5101151003	05101	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	640	0.0021574	05101
5101151004	05101	20	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	543	0.0018304	05101
5101151005	05101	27	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	503	0.0016956	05101
5101151006	05101	28	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	774	0.0026091	05101
5101151007	05101	66	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1448	0.0048811	05101
5101161001	05101	17	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	625	0.0021068	05101
5101161002	05101	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	697	0.0023495	05101
5101161003	05101	8	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	496	0.0016720	05101
5101161004	05101	64	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	892	0.0030069	05101
5101161005	05101	13	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	518	0.0017461	05101
5101161006	05101	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1016	0.0034249	05101
5101161007	05101	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	950	0.0032024	05101
5101161008	05101	64	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1194	0.0040249	05101
5101161009	05101	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	856	0.0028855	05101
5101161010	05101	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1235	0.0041631	05101
5101161011	05101	40	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1840	0.0062025	05101
5101161012	05101	50	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2467	0.0083161	05101
5101171001	05101	79	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	932	0.0031417	05101
5101171002	05101	147	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1142	0.0038496	05101
5101171003	05101	114	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1447	0.0048777	05101
5101171004	05101	54	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1122	0.0037822	05101
5101171005	05101	60	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1452	0.0048946	05101

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
5101011001	05101	200	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3280	0.0110566	05101	977207016
5101011002	05101	183	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3761	0.0126780	05101	1120510849
5101011003	05101	184	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2365	0.0079722	05101	704602010
5101011004	05101	49	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2690	0.0090678	05101	801428924
5101011005	05101	97	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2518	0.0084880	05101	750185142
5101011006	05101	193	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2848	0.0096004	05101	848501701
5101011007	05101	210	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3131	0.0105543	05101	932815599
5101021001	05101	5	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	273	0.0009203	05101	81334608
5101021002	05101	218	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1881	0.0063407	05101	560404389
5101021003	05101	140	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1696	0.0057171	05101	505287530
5101021004	05101	74	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1415	0.0047699	05101	421569490
5101031001	05101	86	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1402	0.0047260	05101	417696413
5101031002	05101	78	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1780	0.0060002	05101	530313563
5101031003	05101	81	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1071	0.0036103	05101	319081925
5101031004	05101	43	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	739	0.0024911	05101	220169507
5101031005	05101	29	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	457	0.0015405	05101	136153538
5101031006	05101	29	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1051	0.0035428	05101	313123346
5101031007	05101	84	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2154	0.0072610	05101	641738997
5101031008	05101	93	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2337	0.0078778	05101	696259999
5101031009	05101	29	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1059	0.0035698	05101	315506777
5101031010	05101	38	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1678	0.0056564	05101	499924809
5101031011	05101	104	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2610	0.0087981	05101	777594607
5101031012	05101	35	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	659	0.0022214	05101	196335190
5101041001	05101	10	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	177	0.0005967	05101	52733427
5101051001	05101	67	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1872	0.0063104	05101	557723028
5101051002	05101	49	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1714	0.0057778	05101	510650251
5101051003	05101	75	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2345	0.0079048	05101	698643430
5101051004	05101	65	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3014	0.0101600	05101	897957910
5101051005	05101	34	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1688	0.0056901	05101	502904098
5101051006	05101	53	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3387	0.0114173	05101	1009085415
5101051007	05101	31	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2233	0.0075273	05101	665275386
5101061001	05101	52	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1196	0.0040316	05101	356323046
5101061002	05101	36	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	878	0.0029597	05101	261581634
5101061003	05101	83	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1642	0.0055350	05101	489199366
5101061004	05101	71	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	862	0.0029057	05101	256814771
5101061005	05101	102	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1546	0.0052114	05101	460598185
5101071001	05101	152	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1265	0.0042642	05101	376880145
5101081001	05101	68	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	687	0.0023158	05101	204677201
5101081002	05101	50	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	634	0.0021372	05101	188886966
5101081003	05101	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	619	0.0020866	05101	184418031
5101081004	05101	65	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	620	0.0020900	05101	184715960
5101081005	05101	70	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	979	0.0033001	05101	291672460
5101081006	05101	39	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1435	0.0048373	05101	427528069
5101081007	05101	63	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1791	0.0060373	05101	533590782
5101081008	05101	41	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1274	0.0042946	05101	379561505
5101081009	05101	57	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1519	0.0051204	05101	452554103
5101081010	05101	79	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1320	0.0044496	05101	393266238
5101081011	05101	25	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1143	0.0038530	05101	340532811
5101091001	05101	87	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1689	0.0056935	05101	503202027
5101091002	05101	97	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1322	0.0044564	05101	393862096
5101091003	05101	79	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1275	0.0042979	05101	379859434
5101091004	05101	37	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	4201	0.0141612	05101	1251599595
5101101001	05101	77	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1044	0.0035192	05101	311037843
5101101002	05101	115	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	988	0.0033305	05101	294353821
5101101003	05101	75	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	849	0.0028619	05101	252941694
5101101004	05101	47	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	717	0.0024169	05101	213615070
5101101005	05101	46	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	936	0.0031552	05101	278861514
5101101006	05101	87	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1453	0.0048979	05101	432890791
5101101007	05101	10	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	604	0.0020360	05101	179949097
5101101008	05101	38	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2358	0.0079486	05101	702516507
5101101009	05101	113	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3502	0.0118050	05101	1043347247
5101111001	05101	142	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2120	0.0071463	05101	631609413
5101121001	05101	107	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1435	0.0048373	05101	427528069
5101121002	05101	79	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1941	0.0065430	05101	578280127
5101121003	05101	75	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1528	0.0051508	05101	455235463
5101131001	05101	49	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1170	0.0039440	05101	348576893
5101131002	05101	17	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	572	0.0019282	05101	170415370
5101131003	05101	20	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	638	0.0021506	05101	190078682
5101131004	05101	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	880	0.0029664	05101	262177492
5101131005	05101	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	664	0.0022383	05101	197824835
5101141001	05101	52	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1502	0.0050631	05101	447489310
5101141002	05101	27	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1122	0.0037822	05101	334276302
5101141003	05101	39	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1031	0.0034754	05101	307164766
5101141004	05101	9	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	638	0.0021506	05101	190078682
5101141005	05101	52	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	704	0.0023731	05101	209741994
5101141006	05101	53	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1265	0.0042642	05101	376880145
5101151001	05101	25	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	532	0.0017933	05101	158498211
5101151002	05101	35	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	455	0.0015338	05101	135557681
5101151003	05101	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	640	0.0021574	05101	190674540
5101151004	05101	20	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	543	0.0018304	05101	161775430
5101151005	05101	27	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	503	0.0016956	05101	149858271
5101151006	05101	28	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	774	0.0026091	05101	230597021
5101151007	05101	66	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1448	0.0048811	05101	431401146
5101161001	05101	17	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	625	0.0021068	05101	186205605
5101161002	05101	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	697	0.0023495	05101	207656491
5101161003	05101	8	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	496	0.0016720	05101	147772768
5101161004	05101	64	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	892	0.0030069	05101	265752640
5101161005	05101	13	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	518	0.0017461	05101	154327206
5101161006	05101	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1016	0.0034249	05101	302695832
5101161007	05101	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	950	0.0032024	05101	283032520
5101161008	05101	64	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1194	0.0040249	05101	355727188
5101161009	05101	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	856	0.0028855	05101	255027197
5101161010	05101	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1235	0.0041631	05101	367942276
5101161011	05101	40	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1840	0.0062025	05101	548189301
5101161012	05101	50	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2467	0.0083161	05101	734990765
5101171001	05101	79	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	932	0.0031417	05101	277669798
5101171002	05101	147	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1142	0.0038496	05101	340234882
5101171003	05101	114	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1447	0.0048777	05101	431103217
5101171004	05101	54	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1122	0.0037822	05101	334276302
5101171005	05101	60	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1452	0.0048946	05101	432592862

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -1.416e+09 -2.686e+08 -7.218e+07  2.170e+08  1.403e+09 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 436234119   18056200   24.16   <2e-16 ***
## Freq.x        1748840      75645   23.12   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 351500000 on 700 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.433,  Adjusted R-squared:  0.4322 
## F-statistic: 534.5 on 1 and 700 DF,  p-value: < 2.2e-16

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                
## [1,] "cuadrático" "0.43215253186823"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                
## [1,] "cúbico" "0.43215253186823"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.533612200036661"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.569081459848391"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.579500811315029"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.486132519844637"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.650802926871644"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.717763906072721"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 1    cuadrático  0.43215253186823
## 2        cúbico  0.43215253186823
## 6      log-raíz 0.486132519844637
## 3   logarítmico 0.533612200036661
## 4 raíz cuadrada 0.569081459848391
## 5     raíz-raíz 0.579500811315029
## 7      raíz-log 0.650802926871644
## 8       log-log 0.717763906072721
##                                                                     sintaxis
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 8
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.88052 -0.32795  0.01403  0.34394  1.72026 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.78162    0.08002  209.72   <2e-16 ***
## log(Freq.x)  0.72036    0.01706   42.23   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5804 on 700 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.7182, Adjusted R-squared:  0.7178 
## F-statistic:  1784 on 1 and 700 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    16.78162

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##    0.720362

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.7178 ).

9.1 Diagrama de dispersión sobre log-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.88052 -0.32795  0.01403  0.34394  1.72026 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.78162    0.08002  209.72   <2e-16 ***
## log(Freq.x)  0.72036    0.01706   42.23   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5804 on 700 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.7182, Adjusted R-squared:  0.7178 
## F-statistic:  1784 on 1 and 700 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = e^{16.78162+0.720362 \cdot ln{X}} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
5101011001	05101	200	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3280	0.0110566	05101	977207016	882553819
5101011002	05101	183	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3761	0.0126780	05101	1120510849	827847651
5101011003	05101	184	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2365	0.0079722	05101	704602010	831103910
5101011004	05101	49	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2690	0.0090678	05101	801428924	320420031
5101011005	05101	97	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2518	0.0084880	05101	750185142	524036442
5101011006	05101	193	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2848	0.0096004	05101	848501701	860191730
5101011007	05101	210	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3131	0.0105543	05101	932815599	914124112
5101021001	05101	5	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	273	0.0009203	05101	81334608	61898206
5101021002	05101	218	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1881	0.0063407	05101	560404389	939078344
5101021003	05101	140	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1696	0.0057171	05101	505287530	682583385
5101021004	05101	74	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1415	0.0047699	05101	421569490	431211296
5101031001	05101	86	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1402	0.0047260	05101	417696413	480513731
5101031002	05101	78	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1780	0.0060002	05101	530313563	447877950
5101031003	05101	81	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1071	0.0036103	05101	319081925	460221299
5101031004	05101	43	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	739	0.0024911	05101	220169507	291645469
5101031005	05101	29	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	457	0.0015405	05101	136153538	219595065
5101031006	05101	29	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1051	0.0035428	05101	313123346	219595065
5101031007	05101	84	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2154	0.0072610	05101	641738997	472437436
5101031008	05101	93	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2337	0.0078778	05101	696259999	508378228
5101031009	05101	29	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1059	0.0035698	05101	315506777	219595065
5101031010	05101	38	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1678	0.0056564	05101	499924809	266798080
5101031011	05101	104	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2610	0.0087981	05101	777594607	551011673
5101031012	05101	35	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	659	0.0022214	05101	196335190	251451699
5101041001	05101	10	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	177	0.0005967	05101	52733427	101983199
5101051001	05101	67	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1872	0.0063104	05101	557723028	401422329
5101051002	05101	49	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1714	0.0057778	05101	510650251	320420031
5101051003	05101	75	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2345	0.0079048	05101	698643430	435401089
5101051004	05101	65	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3014	0.0101600	05101	897957910	392753913
5101051005	05101	34	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1688	0.0056901	05101	502904098	246255447
5101051006	05101	53	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3387	0.0114173	05101	1009085415	339054446
5101051007	05101	31	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2233	0.0075273	05101	665275386	230402359
5101061001	05101	52	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1196	0.0040316	05101	356323046	334433851
5101061002	05101	36	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	878	0.0029597	05101	261581634	256606594
5101061003	05101	83	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1642	0.0055350	05101	489199366	468379158
5101061004	05101	71	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	862	0.0029057	05101	256814771	418545615
5101061005	05101	102	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1546	0.0052114	05101	460598185	543357750
5101071001	05101	152	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1265	0.0042642	05101	376880145	724242222
5101081001	05101	68	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	687	0.0023158	05101	204677201	405729340
5101081002	05101	50	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	634	0.0021372	05101	188886966	325117285
5101081003	05101	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	619	0.0020866	05101	184418031	185824776
5101081004	05101	65	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	620	0.0020900	05101	184715960	392753913
5101081005	05101	70	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	979	0.0033001	05101	291672460	414290661
5101081006	05101	39	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1435	0.0048373	05101	427528069	271837340
5101081007	05101	63	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1791	0.0060373	05101	533590782	384010577
5101081008	05101	41	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1274	0.0042946	05101	379561505	281808984
5101081009	05101	57	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1519	0.0051204	05101	452554103	357299289
5101081010	05101	79	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1320	0.0044496	05101	393266238	452006912
5101081011	05101	25	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1143	0.0038530	05101	340532811	197328353
5101091001	05101	87	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1689	0.0056935	05101	503202027	484532148
5101091002	05101	97	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1322	0.0044564	05101	393862096	524036442
5101091003	05101	79	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1275	0.0042979	05101	379859434	452006912
5101091004	05101	37	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	4201	0.0141612	05101	1251599595	261721596
5101101001	05101	77	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1044	0.0035192	05101	311037843	443734158
5101101002	05101	115	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	988	0.0033305	05101	294353821	592399996
5101101003	05101	75	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	849	0.0028619	05101	252941694	435401089
5101101004	05101	47	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	717	0.0024169	05101	213615070	310944147
5101101005	05101	46	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	936	0.0031552	05101	278861514	306164045
5101101006	05101	87	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1453	0.0048979	05101	432890791	484532148
5101101007	05101	10	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	604	0.0020360	05101	179949097	101983199
5101101008	05101	38	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2358	0.0079486	05101	702516507	266798080
5101101009	05101	113	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3502	0.0118050	05101	1043347247	584960206
5101111001	05101	142	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2120	0.0071463	05101	631609413	689593827
5101121001	05101	107	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1435	0.0048373	05101	427528069	562415895
5101121002	05101	79	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1941	0.0065430	05101	578280127	452006912
5101121003	05101	75	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1528	0.0051508	05101	455235463	435401089
5101131001	05101	49	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1170	0.0039440	05101	348576893	320420031
5101131002	05101	17	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	572	0.0019282	05101	170415370	149463544
5101131003	05101	20	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	638	0.0021506	05101	190078682	168027049
5101131004	05101	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	880	0.0029664	05101	262177492	185824776
5101131005	05101	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	664	0.0022383	05101	197824835	191610083
5101141001	05101	52	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1502	0.0050631	05101	447489310	334433851
5101141002	05101	27	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1122	0.0037822	05101	334276302	208577133
5101141003	05101	39	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1031	0.0034754	05101	307164766	271837340
5101141004	05101	9	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	638	0.0021506	05101	190078682	94529350
5101141005	05101	52	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	704	0.0023731	05101	209741994	334433851
5101141006	05101	53	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1265	0.0042642	05101	376880145	339054446
5101151001	05101	25	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	532	0.0017933	05101	158498211	197328353
5101151002	05101	35	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	455	0.0015338	05101	135557681	251451699
5101151003	05101	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	640	0.0021574	05101	190674540	185824776
5101151004	05101	20	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	543	0.0018304	05101	161775430	168027049
5101151005	05101	27	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	503	0.0016956	05101	149858271	208577133
5101151006	05101	28	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	774	0.0026091	05101	230597021	214113615
5101151007	05101	66	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1448	0.0048811	05101	431401146	397097303
5101161001	05101	17	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	625	0.0021068	05101	186205605	149463544
5101161002	05101	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	697	0.0023495	05101	207656491	185824776
5101161003	05101	8	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	496	0.0016720	05101	147772768	86839705
5101161004	05101	64	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	892	0.0030069	05101	265752640	388391796
5101161005	05101	13	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	518	0.0017461	05101	154327206	123199541
5101161006	05101	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1016	0.0034249	05101	302695832	191610083
5101161007	05101	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	950	0.0032024	05101	283032520	191610083
5101161008	05101	64	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1194	0.0040249	05101	355727188	388391796
5101161009	05101	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	856	0.0028855	05101	255027197	191610083
5101161010	05101	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1235	0.0041631	05101	367942276	185824776
5101161011	05101	40	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1840	0.0062025	05101	548189301	276840592
5101161012	05101	50	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2467	0.0083161	05101	734990765	325117285
5101171001	05101	79	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	932	0.0031417	05101	277669798	452006912
5101171002	05101	147	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1142	0.0038496	05101	340234882	707000431
5101171003	05101	114	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1447	0.0048777	05101	431103217	588684663
5101171004	05101	54	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1122	0.0037822	05101	334276302	343650724
5101171005	05101	60	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1452	0.0048946	05101	432592862	370748344

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
5101011001	05101	200	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3280	0.0110566	05101	977207016	882553819	269071.29
5101011002	05101	183	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3761	0.0126780	05101	1120510849	827847651	220113.71
5101011003	05101	184	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2365	0.0079722	05101	704602010	831103910	351418.14
5101011004	05101	49	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2690	0.0090678	05101	801428924	320420031	119115.25
5101011005	05101	97	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2518	0.0084880	05101	750185142	524036442	208116.14
5101011006	05101	193	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2848	0.0096004	05101	848501701	860191730	302033.61
5101011007	05101	210	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3131	0.0105543	05101	932815599	914124112	291959.15
5101021001	05101	5	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	273	0.0009203	05101	81334608	61898206	226733.35
5101021002	05101	218	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1881	0.0063407	05101	560404389	939078344	499244.20
5101021003	05101	140	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1696	0.0057171	05101	505287530	682583385	402466.62
5101021004	05101	74	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1415	0.0047699	05101	421569490	431211296	304742.97
5101031001	05101	86	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1402	0.0047260	05101	417696413	480513731	342734.47
5101031002	05101	78	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1780	0.0060002	05101	530313563	447877950	251616.83
5101031003	05101	81	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1071	0.0036103	05101	319081925	460221299	429711.76
5101031004	05101	43	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	739	0.0024911	05101	220169507	291645469	394648.81
5101031005	05101	29	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	457	0.0015405	05101	136153538	219595065	480514.37
5101031006	05101	29	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1051	0.0035428	05101	313123346	219595065	208939.17
5101031007	05101	84	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2154	0.0072610	05101	641738997	472437436	219330.29
5101031008	05101	93	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2337	0.0078778	05101	696259999	508378228	217534.54
5101031009	05101	29	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1059	0.0035698	05101	315506777	219595065	207360.78
5101031010	05101	38	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1678	0.0056564	05101	499924809	266798080	158997.66
5101031011	05101	104	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2610	0.0087981	05101	777594607	551011673	211115.58
5101031012	05101	35	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	659	0.0022214	05101	196335190	251451699	381565.55
5101041001	05101	10	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	177	0.0005967	05101	52733427	101983199	576176.26
5101051001	05101	67	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1872	0.0063104	05101	557723028	401422329	214435.00
5101051002	05101	49	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1714	0.0057778	05101	510650251	320420031	186942.84
5101051003	05101	75	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2345	0.0079048	05101	698643430	435401089	185672.11
5101051004	05101	65	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3014	0.0101600	05101	897957910	392753913	130309.86
5101051005	05101	34	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1688	0.0056901	05101	502904098	246255447	145885.93
5101051006	05101	53	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3387	0.0114173	05101	1009085415	339054446	100104.65
5101051007	05101	31	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2233	0.0075273	05101	665275386	230402359	103180.64
5101061001	05101	52	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1196	0.0040316	05101	356323046	334433851	279626.97
5101061002	05101	36	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	878	0.0029597	05101	261581634	256606594	292262.64
5101061003	05101	83	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1642	0.0055350	05101	489199366	468379158	285249.18
5101061004	05101	71	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	862	0.0029057	05101	256814771	418545615	485551.76
5101061005	05101	102	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1546	0.0052114	05101	460598185	543357750	351460.38
5101071001	05101	152	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1265	0.0042642	05101	376880145	724242222	572523.50
5101081001	05101	68	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	687	0.0023158	05101	204677201	405729340	590581.28
5101081002	05101	50	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	634	0.0021372	05101	188886966	325117285	512803.29
5101081003	05101	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	619	0.0020866	05101	184418031	185824776	300201.58
5101081004	05101	65	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	620	0.0020900	05101	184715960	392753913	633474.05
5101081005	05101	70	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	979	0.0033001	05101	291672460	414290661	423177.39
5101081006	05101	39	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1435	0.0048373	05101	427528069	271837340	189433.69
5101081007	05101	63	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1791	0.0060373	05101	533590782	384010577	214411.27
5101081008	05101	41	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1274	0.0042946	05101	379561505	281808984	221200.14
5101081009	05101	57	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1519	0.0051204	05101	452554103	357299289	235220.07
5101081010	05101	79	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1320	0.0044496	05101	393266238	452006912	342429.48
5101081011	05101	25	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1143	0.0038530	05101	340532811	197328353	172640.73
5101091001	05101	87	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1689	0.0056935	05101	503202027	484532148	286875.16
5101091002	05101	97	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1322	0.0044564	05101	393862096	524036442	396396.70
5101091003	05101	79	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1275	0.0042979	05101	379859434	452006912	354515.23
5101091004	05101	37	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	4201	0.0141612	05101	1251599595	261721596	62299.83
5101101001	05101	77	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1044	0.0035192	05101	311037843	443734158	425032.72
5101101002	05101	115	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	988	0.0033305	05101	294353821	592399996	599595.14
5101101003	05101	75	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	849	0.0028619	05101	252941694	435401089	512839.92
5101101004	05101	47	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	717	0.0024169	05101	213615070	310944147	433673.85
5101101005	05101	46	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	936	0.0031552	05101	278861514	306164045	327098.34
5101101006	05101	87	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1453	0.0048979	05101	432890791	484532148	333470.16
5101101007	05101	10	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	604	0.0020360	05101	179949097	101983199	168846.36
5101101008	05101	38	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2358	0.0079486	05101	702516507	266798080	113145.92
5101101009	05101	113	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	3502	0.0118050	05101	1043347247	584960206	167036.04
5101111001	05101	142	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2120	0.0071463	05101	631609413	689593827	325280.11
5101121001	05101	107	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1435	0.0048373	05101	427528069	562415895	391927.45
5101121002	05101	79	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1941	0.0065430	05101	578280127	452006912	232873.22
5101121003	05101	75	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1528	0.0051508	05101	455235463	435401089	284948.36
5101131001	05101	49	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1170	0.0039440	05101	348576893	320420031	273863.27
5101131002	05101	17	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	572	0.0019282	05101	170415370	149463544	261299.90
5101131003	05101	20	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	638	0.0021506	05101	190078682	168027049	263365.28
5101131004	05101	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	880	0.0029664	05101	262177492	185824776	211164.52
5101131005	05101	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	664	0.0022383	05101	197824835	191610083	288569.40
5101141001	05101	52	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1502	0.0050631	05101	447489310	334433851	222659.02
5101141002	05101	27	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1122	0.0037822	05101	334276302	208577133	185897.62
5101141003	05101	39	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1031	0.0034754	05101	307164766	271837340	263663.76
5101141004	05101	9	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	638	0.0021506	05101	190078682	94529350	148165.13
5101141005	05101	52	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	704	0.0023731	05101	209741994	334433851	475048.08
5101141006	05101	53	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1265	0.0042642	05101	376880145	339054446	268027.23
5101151001	05101	25	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	532	0.0017933	05101	158498211	197328353	370917.96
5101151002	05101	35	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	455	0.0015338	05101	135557681	251451699	552641.10
5101151003	05101	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	640	0.0021574	05101	190674540	185824776	290351.21
5101151004	05101	20	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	543	0.0018304	05101	161775430	168027049	309442.08
5101151005	05101	27	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	503	0.0016956	05101	149858271	208577133	414666.27
5101151006	05101	28	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	774	0.0026091	05101	230597021	214113615	276632.58
5101151007	05101	66	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1448	0.0048811	05101	431401146	397097303	274238.47
5101161001	05101	17	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	625	0.0021068	05101	186205605	149463544	239141.67
5101161002	05101	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	697	0.0023495	05101	207656491	185824776	266606.56
5101161003	05101	8	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	496	0.0016720	05101	147772768	86839705	175080.05
5101161004	05101	64	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	892	0.0030069	05101	265752640	388391796	435416.81
5101161005	05101	13	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	518	0.0017461	05101	154327206	123199541	237836.95
5101161006	05101	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1016	0.0034249	05101	302695832	191610083	188592.60
5101161007	05101	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	950	0.0032024	05101	283032520	191610083	201694.82
5101161008	05101	64	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1194	0.0040249	05101	355727188	388391796	325286.26
5101161009	05101	24	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	856	0.0028855	05101	255027197	191610083	223843.56
5101161010	05101	23	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1235	0.0041631	05101	367942276	185824776	150465.41
5101161011	05101	40	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1840	0.0062025	05101	548189301	276840592	150456.84
5101161012	05101	50	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	2467	0.0083161	05101	734990765	325117285	131786.50
5101171001	05101	79	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	932	0.0031417	05101	277669798	452006912	484985.96
5101171002	05101	147	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1142	0.0038496	05101	340234882	707000431	619089.69
5101171003	05101	114	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1447	0.0048777	05101	431103217	588684663	406831.14
5101171004	05101	54	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1122	0.0037822	05101	334276302	343650724	306284.07
5101171005	05101	60	2017	Valparaíso	297929	2017	5101	296655	88382118059	Urbano	1452	0.0048946	05101	432592862	370748344	255336.33

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_urbano_nivel_nacional_17_r05.rds")

---

Rural

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 2)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15202	1	2	6	13225	1	3	1	5	3	5	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	3	1	1	5	3	5	2	3	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	4	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	5	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	6	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	8	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	9	3	1	5	3	5	1	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	10	1	1	5	3	4	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	11	1	2	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	12	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	13	1	1	5	3	4	1	4	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	14	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	16	1	1	5	3	5	1	3	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	17	1	1	5	3	5	2	4	1	8	15	152	15202	15202012006
15	152	15202	1	2	6	13225	18	3	1	5	3	5	1	1	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	19	3	1	5	3	5	1	3	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	20	1	1	5	3	5	2	1	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	21	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	22	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	24	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	25	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	26	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	27	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	28	3	1	5	3	5	1	4	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	29	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	30	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	31	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	32	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	33	1	1	5	3	5	3	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	34	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	35	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	36	1	1	5	3	5	3	2	1	9	15	152	15202	15202012006
15	152	15202	1	2	6	13225	37	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	38	1	1	5	3	5	99	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	39	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	40	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	41	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	42	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	43	3	1	5	3	5	2	1	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	44	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	45	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	8	13910	1	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	3	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	4	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	5	3	1	5	3	5	2	3	1	3	15	152	15202	15202012008
15	152	15202	1	2	8	13910	6	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	7	1	1	5	99	5	2	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	8	3	1	5	3	5	3	3	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	9	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	10	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	11	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	12	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	13	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	14	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	16	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	18	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	20	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	21	1	1	5	3	5	1	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	22	3	1	5	3	5	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	23	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	24	3	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	25	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	26	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	27	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	28	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	29	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	30	1	1	5	1	4	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	12	8394	1	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	2	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	3	3	1	5	3	5	2	3	1	4	15	152	15202	15202012012
15	152	15202	1	2	12	8394	4	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	5	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	6	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	8	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	9	3	1	5	3	5	1	4	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	10	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	11	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	12	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	13	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	14	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	15	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	16	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	18	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	20	3	1	5	3	5	3	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	21	3	1	5	99	5	1	4	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	22	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	24	3	1	5	3	5	1	2	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	25	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Veamos los primeros 100 registros:

r3_100 <- d[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	unlist.c.	unlist.d.	Freq	anio
5101011001	1	5101	200	2017
5101011002	1	5101	183	2017
5101011003	1	5101	184	2017
5101011004	1	5101	49	2017
5101011005	1	5101	97	2017
5101011006	1	5101	193	2017
5101011007	1	5101	210	2017
5101021001	1	5101	5	2017
5101021002	1	5101	218	2017
5101021003	1	5101	140	2017
5101021004	1	5101	74	2017
5101031001	1	5101	86	2017
5101031002	1	5101	78	2017
5101031003	1	5101	81	2017
5101031004	1	5101	43	2017
5101031005	1	5101	29	2017
5101031006	1	5101	29	2017
5101031007	1	5101	84	2017
5101031008	1	5101	93	2017
5101031009	1	5101	29	2017
5101031010	1	5101	38	2017
5101031011	1	5101	104	2017
5101031012	1	5101	35	2017
5101041001	1	5101	10	2017
5101051001	1	5101	67	2017
5101051002	1	5101	49	2017
5101051003	1	5101	75	2017
5101051004	1	5101	65	2017
5101051005	1	5101	34	2017
5101051006	1	5101	53	2017
5101051007	1	5101	31	2017
5101061001	1	5101	52	2017
5101061002	1	5101	36	2017
5101061003	1	5101	83	2017
5101061004	1	5101	71	2017
5101061005	1	5101	102	2017
5101071001	1	5101	152	2017
5101081001	1	5101	68	2017
5101081002	1	5101	50	2017
5101081003	1	5101	23	2017
5101081004	1	5101	65	2017
5101081005	1	5101	70	2017
5101081006	1	5101	39	2017
5101081007	1	5101	63	2017
5101081008	1	5101	41	2017
5101081009	1	5101	57	2017
5101081010	1	5101	79	2017
5101081011	1	5101	25	2017
5101091001	1	5101	87	2017
5101091002	1	5101	97	2017
5101091003	1	5101	79	2017
5101091004	1	5101	37	2017
5101101001	1	5101	77	2017
5101101002	1	5101	115	2017
5101101003	1	5101	75	2017
5101101004	1	5101	47	2017
5101101005	1	5101	46	2017
5101101006	1	5101	87	2017
5101101007	1	5101	10	2017
5101101008	1	5101	38	2017
5101101009	1	5101	113	2017
5101111001	1	5101	142	2017
5101121001	1	5101	107	2017
5101121002	1	5101	79	2017
5101121003	1	5101	75	2017
5101131001	1	5101	49	2017
5101131002	1	5101	17	2017
5101131003	1	5101	20	2017
5101131004	1	5101	23	2017
5101131005	1	5101	24	2017
5101141001	1	5101	52	2017
5101141002	1	5101	27	2017
5101141003	1	5101	39	2017
5101141004	1	5101	9	2017
5101141005	1	5101	52	2017
5101141006	1	5101	53	2017
5101151001	1	5101	25	2017
5101151002	1	5101	35	2017
5101151003	1	5101	23	2017
5101151004	1	5101	20	2017
5101151005	1	5101	27	2017
5101151006	1	5101	28	2017
5101151007	1	5101	66	2017
5101161001	1	5101	17	2017
5101161002	1	5101	23	2017
5101161003	1	5101	8	2017
5101161004	1	5101	64	2017
5101161005	1	5101	13	2017
5101161006	1	5101	24	2017
5101161007	1	5101	24	2017
5101161008	1	5101	64	2017
5101161009	1	5101	24	2017
5101161010	1	5101	23	2017
5101161011	1	5101	40	2017
5101161012	1	5101	50	2017
5101171001	1	5101	79	2017
5101171002	1	5101	147	2017
5101171003	1	5101	114	2017
5101171004	1	5101	54	2017
5101171005	1	5101	60	2017

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	anio	código
5101011001	200	2017	05101
5101011002	183	2017	05101
5101011003	184	2017	05101
5101011004	49	2017	05101
5101011005	97	2017	05101
5101011006	193	2017	05101
5101011007	210	2017	05101
5101021001	5	2017	05101
5101021002	218	2017	05101
5101021003	140	2017	05101
5101021004	74	2017	05101
5101031001	86	2017	05101
5101031002	78	2017	05101
5101031003	81	2017	05101
5101031004	43	2017	05101
5101031005	29	2017	05101
5101031006	29	2017	05101
5101031007	84	2017	05101
5101031008	93	2017	05101
5101031009	29	2017	05101
5101031010	38	2017	05101
5101031011	104	2017	05101
5101031012	35	2017	05101
5101041001	10	2017	05101
5101051001	67	2017	05101
5101051002	49	2017	05101
5101051003	75	2017	05101
5101051004	65	2017	05101
5101051005	34	2017	05101
5101051006	53	2017	05101
5101051007	31	2017	05101
5101061001	52	2017	05101
5101061002	36	2017	05101
5101061003	83	2017	05101
5101061004	71	2017	05101
5101061005	102	2017	05101
5101071001	152	2017	05101
5101081001	68	2017	05101
5101081002	50	2017	05101
5101081003	23	2017	05101
5101081004	65	2017	05101
5101081005	70	2017	05101
5101081006	39	2017	05101
5101081007	63	2017	05101
5101081008	41	2017	05101
5101081009	57	2017	05101
5101081010	79	2017	05101
5101081011	25	2017	05101
5101091001	87	2017	05101
5101091002	97	2017	05101
5101091003	79	2017	05101
5101091004	37	2017	05101
5101101001	77	2017	05101
5101101002	115	2017	05101
5101101003	75	2017	05101
5101101004	47	2017	05101
5101101005	46	2017	05101
5101101006	87	2017	05101
5101101007	10	2017	05101
5101101008	38	2017	05101
5101101009	113	2017	05101
5101111001	142	2017	05101
5101121001	107	2017	05101
5101121002	79	2017	05101
5101121003	75	2017	05101
5101131001	49	2017	05101
5101131002	17	2017	05101
5101131003	20	2017	05101
5101131004	23	2017	05101
5101131005	24	2017	05101
5101141001	52	2017	05101
5101141002	27	2017	05101
5101141003	39	2017	05101
5101141004	9	2017	05101
5101141005	52	2017	05101
5101141006	53	2017	05101
5101151001	25	2017	05101
5101151002	35	2017	05101
5101151003	23	2017	05101
5101151004	20	2017	05101
5101151005	27	2017	05101
5101151006	28	2017	05101
5101151007	66	2017	05101
5101161001	17	2017	05101
5101161002	23	2017	05101
5101161003	8	2017	05101
5101161004	64	2017	05101
5101161005	13	2017	05101
5101161006	24	2017	05101
5101161007	24	2017	05101
5101161008	64	2017	05101
5101161009	24	2017	05101
5101161010	23	2017	05101
5101161011	40	2017	05101
5101161012	50	2017	05101
5101171001	79	2017	05101
5101171002	147	2017	05101
5101171003	114	2017	05101
5101171004	54	2017	05101
5101171005	60	2017	05101

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_rural_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	289375.3	2017	1101	191468	55406102543	Rural
01401	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
01402	Camiña	262850.3	2017	1402	1250	328562901	Rural
01404	Huara	253968.5	2017	1404	2730	693334131	Rural
01405	Pica	290496.7	2017	1405	9296	2700457509	Rural
02103	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
02104	Taltal	345494.0	2017	2104	13317	4600943086	Rural
02201	Calama	310025.0	2017	2201	165731	51380756402	Rural
02203	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
02301	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
03101	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03103	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03202	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural
03301	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03302	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03303	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03304	Huasco	227560.7	2017	3304	10149	2309513927	Rural
04101	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04102	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04103	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04104	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04105	Paiguano	205942.1	2017	4105	4497	926121774	Rural
04106	Vicuña	176130.6	2017	4106	27771	4891322768	Rural
04201	Illapel	191976.8	2017	4201	30848	5922099530	Rural
04202	Canela	171370.3	2017	4202	9093	1558270441	Rural
04203	Los Vilos	173238.5	2017	4203	21382	3704185607	Rural
04204	Salamanca	223234.2	2017	4204	29347	6551254640	Rural
04301	Ovalle	241393.7	2017	4301	111272	26860360045	Rural
04302	Combarbalá	179139.6	2017	4302	13322	2386498044	Rural
04303	Monte Patria	201205.8	2017	4303	30751	6187280931	Rural
04304	Punitaqui	171931.7	2017	4304	10956	1883683880	Rural
04305	Río Hurtado	182027.2	2017	4305	4278	778712384	Rural
05101	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05102	Casablanca	268917.1	2017	5102	26867	7224996933	Rural
05105	Puchuncaví	279614.4	2017	5105	18546	5185728335	Rural
05107	Quintero	334628.2	2017	5107	31923	10682335196	Rural
05301	Los Andes	324402.1	2017	5301	66708	21640215030	Rural
05302	Calle Larga	242743.8	2017	5302	14832	3600375502	Rural
05303	Rinconada	326532.5	2017	5303	10207	3332917471	Rural
05304	San Esteban	223168.6	2017	5304	18855	4207844130	Rural
05401	La Ligua	181468.0	2017	5401	35390	6422154059	Rural
05402	Cabildo	231277.8	2017	5402	19388	4484014285	Rural
05404	Petorca	298208.9	2017	5404	9826	2930200178	Rural
05405	Zapallar	292882.3	2017	5405	7339	2149463129	Rural
05501	Quillota	220926.8	2017	5501	90517	19997628209	Rural
05502	Calera	226906.2	2017	5502	50554	11471016698	Rural
05503	Hijuelas	253739.9	2017	5503	17988	4564273201	Rural
05504	La Cruz	291124.1	2017	5504	22098	6433259569	Rural
05506	Nogales	264475.3	2017	5506	22120	5850194593	Rural
05601	San Antonio	266331.2	2017	5601	91350	24329353815	Rural

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
05101	5101171002	147	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101051007	31	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101011001	200	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101011002	183	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101011003	184	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101061001	52	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101011005	97	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101061002	36	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101021004	74	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101011004	49	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031002	78	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031003	81	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031004	43	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031005	29	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101171003	114	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101171004	54	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101011006	193	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101011007	210	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101021001	5	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101021002	218	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101021003	140	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101041001	10	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031001	86	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101051002	49	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101051003	75	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101051004	65	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101051005	34	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101051006	53	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101141001	52	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101141002	27	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101141003	39	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101141004	9	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101141005	52	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101141006	53	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101151001	25	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101051001	67	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101151003	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101151004	20	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101151005	27	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101151006	28	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101151007	66	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161001	17	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161002	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161003	8	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161004	64	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161005	13	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161006	24	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161007	24	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161008	64	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161009	24	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161010	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161011	40	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161012	50	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101171001	79	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031006	29	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031007	84	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031008	93	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031009	29	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031010	38	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031011	104	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031012	35	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101171009	72	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101171010	49	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101181001	170	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101181002	122	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101181003	98	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101181004	125	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101191001	117	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101191002	121	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101191003	242	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101191004	104	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101191005	270	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101191006	100	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101191007	224	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101191008	177	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101151002	35	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101201002	112	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101201003	369	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101201004	179	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101201005	35	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101201006	17	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101201007	75	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101201008	109	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101211001	1412	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101211002	987	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101211003	112	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101211004	316	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101211005	549	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101211006	211	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101211007	1501	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101211008	88	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101221001	975	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101231001	135	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101231002	75	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101231003	144	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101231004	141	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101231005	145	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101171005	60	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101171006	113	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101171007	133	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
05101	5101171002	147	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101051007	31	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101011001	200	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101011002	183	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101011003	184	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101061001	52	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101011005	97	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101061002	36	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101021004	74	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101011004	49	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031002	78	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031003	81	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031004	43	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031005	29	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101171003	114	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101171004	54	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101011006	193	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101011007	210	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101021001	5	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101021002	218	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101021003	140	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101041001	10	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031001	86	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101051002	49	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101051003	75	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101051004	65	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101051005	34	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101051006	53	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101141001	52	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101141002	27	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101141003	39	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101141004	9	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101141005	52	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101141006	53	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101151001	25	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101051001	67	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101151003	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101151004	20	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101151005	27	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101151006	28	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101151007	66	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161001	17	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161002	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161003	8	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161004	64	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161005	13	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161006	24	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161007	24	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161008	64	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161009	24	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161010	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161011	40	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101161012	50	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101171001	79	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031006	29	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031007	84	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031008	93	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031009	29	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031010	38	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031011	104	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101031012	35	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101171009	72	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101171010	49	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101181001	170	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101181002	122	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101181003	98	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101181004	125	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101191001	117	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101191002	121	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101191003	242	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101191004	104	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101191005	270	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101191006	100	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101191007	224	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101191008	177	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101151002	35	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101201002	112	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101201003	369	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101201004	179	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101201005	35	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101201006	17	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101201007	75	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101201008	109	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101211001	1412	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101211002	987	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101211003	112	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101211004	316	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101211005	549	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101211006	211	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101211007	1501	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101211008	88	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101221001	975	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101231001	135	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101231002	75	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101231003	144	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101231004	141	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101231005	145	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101171005	60	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101171006	113	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05101	5101171007	133	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
5101011001	05101	200	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3280	0.0110566	05101
5101011002	05101	183	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3761	0.0126780	05101
5101011003	05101	184	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2365	0.0079722	05101
5101011004	05101	49	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2690	0.0090678	05101
5101011005	05101	97	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2518	0.0084880	05101
5101011006	05101	193	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2848	0.0096004	05101
5101011007	05101	210	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3131	0.0105543	05101
5101021001	05101	5	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	273	0.0009203	05101
5101021002	05101	218	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1881	0.0063407	05101
5101021003	05101	140	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1696	0.0057171	05101
5101021004	05101	74	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1415	0.0047699	05101
5101031001	05101	86	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1402	0.0047260	05101
5101031002	05101	78	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1780	0.0060002	05101
5101031003	05101	81	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1071	0.0036103	05101
5101031004	05101	43	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	739	0.0024911	05101
5101031005	05101	29	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	457	0.0015405	05101
5101031006	05101	29	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1051	0.0035428	05101
5101031007	05101	84	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2154	0.0072610	05101
5101031008	05101	93	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2337	0.0078778	05101
5101031009	05101	29	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1059	0.0035698	05101
5101031010	05101	38	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1678	0.0056564	05101
5101031011	05101	104	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2610	0.0087981	05101
5101031012	05101	35	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	659	0.0022214	05101
5101041001	05101	10	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	177	0.0005967	05101
5101051001	05101	67	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1872	0.0063104	05101
5101051002	05101	49	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1714	0.0057778	05101
5101051003	05101	75	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2345	0.0079048	05101
5101051004	05101	65	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3014	0.0101600	05101
5101051005	05101	34	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1688	0.0056901	05101
5101051006	05101	53	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3387	0.0114173	05101
5101051007	05101	31	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2233	0.0075273	05101
5101061001	05101	52	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1196	0.0040316	05101
5101061002	05101	36	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	878	0.0029597	05101
5101061003	05101	83	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1642	0.0055350	05101
5101061004	05101	71	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	862	0.0029057	05101
5101061005	05101	102	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1546	0.0052114	05101
5101071001	05101	152	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1265	0.0042642	05101
5101081001	05101	68	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	687	0.0023158	05101
5101081002	05101	50	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	634	0.0021372	05101
5101081003	05101	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	619	0.0020866	05101
5101081004	05101	65	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	620	0.0020900	05101
5101081005	05101	70	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	979	0.0033001	05101
5101081006	05101	39	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1435	0.0048373	05101
5101081007	05101	63	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1791	0.0060373	05101
5101081008	05101	41	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1274	0.0042946	05101
5101081009	05101	57	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1519	0.0051204	05101
5101081010	05101	79	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1320	0.0044496	05101
5101081011	05101	25	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1143	0.0038530	05101
5101091001	05101	87	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1689	0.0056935	05101
5101091002	05101	97	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1322	0.0044564	05101
5101091003	05101	79	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1275	0.0042979	05101
5101091004	05101	37	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	4201	0.0141612	05101
5101101001	05101	77	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1044	0.0035192	05101
5101101002	05101	115	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	988	0.0033305	05101
5101101003	05101	75	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	849	0.0028619	05101
5101101004	05101	47	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	717	0.0024169	05101
5101101005	05101	46	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	936	0.0031552	05101
5101101006	05101	87	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1453	0.0048979	05101
5101101007	05101	10	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	604	0.0020360	05101
5101101008	05101	38	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2358	0.0079486	05101
5101101009	05101	113	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3502	0.0118050	05101
5101111001	05101	142	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2120	0.0071463	05101
5101121001	05101	107	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1435	0.0048373	05101
5101121002	05101	79	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1941	0.0065430	05101
5101121003	05101	75	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1528	0.0051508	05101
5101131001	05101	49	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1170	0.0039440	05101
5101131002	05101	17	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	572	0.0019282	05101
5101131003	05101	20	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	638	0.0021506	05101
5101131004	05101	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	880	0.0029664	05101
5101131005	05101	24	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	664	0.0022383	05101
5101141001	05101	52	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1502	0.0050631	05101
5101141002	05101	27	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1122	0.0037822	05101
5101141003	05101	39	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1031	0.0034754	05101
5101141004	05101	9	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	638	0.0021506	05101
5101141005	05101	52	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	704	0.0023731	05101
5101141006	05101	53	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1265	0.0042642	05101
5101151001	05101	25	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	532	0.0017933	05101
5101151002	05101	35	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	455	0.0015338	05101
5101151003	05101	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	640	0.0021574	05101
5101151004	05101	20	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	543	0.0018304	05101
5101151005	05101	27	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	503	0.0016956	05101
5101151006	05101	28	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	774	0.0026091	05101
5101151007	05101	66	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1448	0.0048811	05101
5101161001	05101	17	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	625	0.0021068	05101
5101161002	05101	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	697	0.0023495	05101
5101161003	05101	8	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	496	0.0016720	05101
5101161004	05101	64	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	892	0.0030069	05101
5101161005	05101	13	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	518	0.0017461	05101
5101161006	05101	24	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1016	0.0034249	05101
5101161007	05101	24	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	950	0.0032024	05101
5101161008	05101	64	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1194	0.0040249	05101
5101161009	05101	24	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	856	0.0028855	05101
5101161010	05101	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1235	0.0041631	05101
5101161011	05101	40	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1840	0.0062025	05101
5101161012	05101	50	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2467	0.0083161	05101
5101171001	05101	79	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	932	0.0031417	05101
5101171002	05101	147	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1142	0.0038496	05101
5101171003	05101	114	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1447	0.0048777	05101
5101171004	05101	54	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1122	0.0037822	05101
5101171005	05101	60	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1452	0.0048946	05101

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
5101011001	05101	200	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3280	0.0110566	05101	1088028785
5101011002	05101	183	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3761	0.0126780	05101	1247584226
5101011003	05101	184	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2365	0.0079722	05101	784508560
5101011004	05101	49	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2690	0.0090678	05101	892316290
5101011005	05101	97	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2518	0.0084880	05101	835261122
5101011006	05101	193	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2848	0.0096004	05101	944727433
5101011007	05101	210	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3131	0.0105543	05101	1038603087
5101021001	05101	5	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	273	0.0009203	05101	90558493
5101021002	05101	218	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1881	0.0063407	05101	623957971
5101021003	05101	140	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1696	0.0057171	05101	562590494
5101021004	05101	74	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1415	0.0047699	05101	469378272
5101031001	05101	86	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1402	0.0047260	05101	465065962
5101031002	05101	78	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1780	0.0060002	05101	590454646
5101031003	05101	81	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1071	0.0036103	05101	355267936
5101031004	05101	43	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	739	0.0024911	05101	245138193
5101031005	05101	29	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	457	0.0015405	05101	151594255
5101031006	05101	29	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1051	0.0035428	05101	348633614
5101031007	05101	84	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2154	0.0072610	05101	714516464
5101031008	05101	93	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2337	0.0078778	05101	775220509
5101031009	05101	29	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1059	0.0035698	05101	351287343
5101031010	05101	38	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1678	0.0056564	05101	556619604
5101031011	05101	104	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2610	0.0087981	05101	865779003
5101031012	05101	35	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	659	0.0022214	05101	218600905
5101041001	05101	10	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	177	0.0005967	05101	58713748
5101051001	05101	67	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1872	0.0063104	05101	620972526
5101051002	05101	49	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1714	0.0057778	05101	568561383
5101051003	05101	75	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2345	0.0079048	05101	777874238
5101051004	05101	65	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3014	0.0101600	05101	999792304
5101051005	05101	34	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1688	0.0056901	05101	559936765
5101051006	05101	53	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3387	0.0114173	05101	1123522407
5101051007	05101	31	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2233	0.0075273	05101	740722036
5101061001	05101	52	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1196	0.0040316	05101	396732447
5101061002	05101	36	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	878	0.0029597	05101	291246730
5101061003	05101	83	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1642	0.0055350	05101	544677825
5101061004	05101	71	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	862	0.0029057	05101	285939272
5101061005	05101	102	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1546	0.0052114	05101	512833080
5101071001	05101	152	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1265	0.0042642	05101	419620858
5101081001	05101	68	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	687	0.0023158	05101	227888956
5101081002	05101	50	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	634	0.0021372	05101	210308003
5101081003	05101	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	619	0.0020866	05101	205332262
5101081004	05101	65	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	620	0.0020900	05101	205663978
5101081005	05101	70	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	979	0.0033001	05101	324750055
5101081006	05101	39	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1435	0.0048373	05101	476012593
5101081007	05101	63	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1791	0.0060373	05101	594103523
5101081008	05101	41	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1274	0.0042946	05101	422606303
5101081009	05101	57	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1519	0.0051204	05101	503876745
5101081010	05101	79	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1320	0.0044496	05101	437865243
5101081011	05101	25	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1143	0.0038530	05101	379151494
5101091001	05101	87	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1689	0.0056935	05101	560268481
5101091002	05101	97	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1322	0.0044564	05101	438528675
5101091003	05101	79	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1275	0.0042979	05101	422938019
5101091004	05101	37	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	4201	0.0141612	05101	1393539307
5101101001	05101	77	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1044	0.0035192	05101	346311601
5101101002	05101	115	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	988	0.0033305	05101	327735500
5101101003	05101	75	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	849	0.0028619	05101	281626963
5101101004	05101	47	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	717	0.0024169	05101	237840439
5101101005	05101	46	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	936	0.0031552	05101	310486263
5101101006	05101	87	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1453	0.0048979	05101	481983483
5101101007	05101	10	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	604	0.0020360	05101	200356520
5101101008	05101	38	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2358	0.0079486	05101	782186547
5101101009	05101	113	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3502	0.0118050	05101	1161669758
5101111001	05101	142	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2120	0.0071463	05101	703238117
5101121001	05101	107	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1435	0.0048373	05101	476012593
5101121002	05101	79	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1941	0.0065430	05101	643860937
5101121003	05101	75	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1528	0.0051508	05101	506862190
5101131001	05101	49	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1170	0.0039440	05101	388107829
5101131002	05101	17	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	572	0.0019282	05101	189741605
5101131003	05101	20	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	638	0.0021506	05101	211634867
5101131004	05101	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	880	0.0029664	05101	291910162
5101131005	05101	24	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	664	0.0022383	05101	220259486
5101141001	05101	52	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1502	0.0050631	05101	498237572
5101141002	05101	27	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1122	0.0037822	05101	372185456
5101141003	05101	39	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1031	0.0034754	05101	341999292
5101141004	05101	9	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	638	0.0021506	05101	211634867
5101141005	05101	52	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	704	0.0023731	05101	233528129
5101141006	05101	53	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1265	0.0042642	05101	419620858
5101151001	05101	25	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	532	0.0017933	05101	176472961
5101151002	05101	35	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	455	0.0015338	05101	150930822
5101151003	05101	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	640	0.0021574	05101	212298300
5101151004	05101	20	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	543	0.0018304	05101	180121839
5101151005	05101	27	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	503	0.0016956	05101	166853195
5101151006	05101	28	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	774	0.0026091	05101	256748256
5101151007	05101	66	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1448	0.0048811	05101	480324903
5101161001	05101	17	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	625	0.0021068	05101	207322558
5101161002	05101	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	697	0.0023495	05101	231206117
5101161003	05101	8	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	496	0.0016720	05101	164531182
5101161004	05101	64	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	892	0.0030069	05101	295890755
5101161005	05101	13	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	518	0.0017461	05101	171828936
5101161006	05101	24	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1016	0.0034249	05101	337023551
5101161007	05101	24	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	950	0.0032024	05101	315130288
5101161008	05101	64	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1194	0.0040249	05101	396069015
5101161009	05101	24	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	856	0.0028855	05101	283948976
5101161010	05101	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1235	0.0041631	05101	409669375
5101161011	05101	40	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1840	0.0062025	05101	610357611
5101161012	05101	50	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2467	0.0083161	05101	818343601
5101171001	05101	79	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	932	0.0031417	05101	309159399
5101171002	05101	147	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1142	0.0038496	05101	378819778
5101171003	05101	114	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1447	0.0048777	05101	479993187
5101171004	05101	54	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1122	0.0037822	05101	372185456
5101171005	05101	60	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1452	0.0048946	05101	481651767

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -1.031e+09 -2.421e+08 -5.676e+07  2.053e+08  1.085e+09 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 415191074   18435135   22.52   <2e-16 ***
## Freq.x        1610613      77395   20.81   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 328800000 on 540 degrees of freedom
##   (163 observations deleted due to missingness)
## Multiple R-squared:  0.4451, Adjusted R-squared:  0.444 
## F-statistic: 433.1 on 1 and 540 DF,  p-value: < 2.2e-16

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.444025903319356"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.444025903319356"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.562842033545985"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.599394134220532"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.592401626626796"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.471284092920308"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.679371502901814"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                
## [1,] "log-log" "0.72167651734697"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 1    cuadrático 0.444025903319356
## 2        cúbico 0.444025903319356
## 6      log-raíz 0.471284092920308
## 3   logarítmico 0.562842033545985
## 5     raíz-raíz 0.592401626626796
## 4 raíz cuadrada 0.599394134220532
## 7      raíz-log 0.679371502901814
## 8       log-log  0.72167651734697
##                                                                     sintaxis
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 8
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.84724 -0.30133  0.03854  0.40279  1.75665 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.73937    0.08882  188.47   <2e-16 ***
## log(Freq.x)  0.72485    0.01935   37.47   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6086 on 540 degrees of freedom
##   (163 observations deleted due to missingness)
## Multiple R-squared:  0.7222, Adjusted R-squared:  0.7217 
## F-statistic:  1404 on 1 and 540 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    16.73937

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##   0.7248503

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.7217 ).

9.1 Diagrama de dispersión sobre log-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.84724 -0.30133  0.03854  0.40279  1.75665 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.73937    0.08882  188.47   <2e-16 ***
## log(Freq.x)  0.72485    0.01935   37.47   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6086 on 540 degrees of freedom
##   (163 observations deleted due to missingness)
## Multiple R-squared:  0.7222, Adjusted R-squared:  0.7217 
## F-statistic:  1404 on 1 and 540 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = e^{16.73937+0.7248503 \cdot ln{X}} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
5101011001	05101	200	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3280	0.0110566	05101	1088028785	866397099
5101011002	05101	183	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3761	0.0126780	05101	1247584226	812368465
5101011003	05101	184	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2365	0.0079722	05101	784508560	815583786
5101011004	05101	49	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2690	0.0090678	05101	892316290	312574692
5101011005	05101	97	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2518	0.0084880	05101	835261122	512774924
5101011006	05101	193	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2848	0.0096004	05101	944727433	844309366
5101011007	05101	210	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3131	0.0105543	05101	1038603087	897585980
5101021001	05101	5	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	273	0.0009203	05101	90558493	59767246
5101021002	05101	218	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1881	0.0063407	05101	623957971	922243493
5101021003	05101	140	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1696	0.0057171	05101	562590494	669015608
5101021004	05101	74	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1415	0.0047699	05101	469378272	421432339
5101031001	05101	86	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1402	0.0047260	05101	465065962	469933571
5101031002	05101	78	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1780	0.0060002	05101	590454646	437824466
5101031003	05101	81	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1071	0.0036103	05101	355267936	449966960
5101031004	05101	43	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	739	0.0024911	05101	245138193	284337915
5101031005	05101	29	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	457	0.0015405	05101	151594255	213714650
5101031006	05101	29	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1051	0.0035428	05101	348633614	213714650
5101031007	05101	84	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2154	0.0072610	05101	714516464	461986310
5101031008	05101	93	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2337	0.0078778	05101	775220509	497359189
5101031009	05101	29	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1059	0.0035698	05101	351287343	213714650
5101031010	05101	38	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1678	0.0056564	05101	556619604	259968833
5101031011	05101	104	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2610	0.0087981	05101	865779003	539339110
5101031012	05101	35	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	659	0.0022214	05101	218600905	244924852
5101041001	05101	10	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	177	0.0005967	05101	58713748	98779072
5101051001	05101	67	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1872	0.0063104	05101	620972526	392143980
5101051002	05101	49	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1714	0.0057778	05101	568561383	312574692
5101051003	05101	75	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2345	0.0079048	05101	777874238	425552754
5101051004	05101	65	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3014	0.0101600	05101	999792304	383623738
5101051005	05101	34	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1688	0.0056901	05101	559936765	239832272
5101051006	05101	53	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3387	0.0114173	05101	1123522407	330869366
5101051007	05101	31	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2233	0.0075273	05101	740722036	224299672
5101061001	05101	52	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1196	0.0040316	05101	396732447	326332416
5101061002	05101	36	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	878	0.0029597	05101	291246730	249977549
5101061003	05101	83	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1642	0.0055350	05101	544677825	457993188
5101061004	05101	71	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	862	0.0029057	05101	285939272	408977913
5101061005	05101	102	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1546	0.0052114	05101	512833080	531800975
5101071001	05101	152	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1265	0.0042642	05101	419620858	710108449
5101081001	05101	68	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	687	0.0023158	05101	227888956	396377796
5101081002	05101	50	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	634	0.0021372	05101	210308003	317185696
5101081003	05101	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	619	0.0020866	05101	205332262	180660618
5101081004	05101	65	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	620	0.0020900	05101	205663978	383623738
5101081005	05101	70	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	979	0.0033001	05101	324750055	404794453
5101081006	05101	39	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1435	0.0048373	05101	476012593	264909986
5101081007	05101	63	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1791	0.0060373	05101	594103523	375031044
5101081008	05101	41	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1274	0.0042946	05101	422606303	274689169
5101081009	05101	57	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1519	0.0051204	05101	503876745	348787648
5101081010	05101	79	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1320	0.0044496	05101	437865243	441886011
5101081011	05101	25	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1143	0.0038530	05101	379151494	191916315
5101091001	05101	87	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1689	0.0056935	05101	560268481	473888098
5101091002	05101	97	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1322	0.0044564	05101	438528675	512774924
5101091003	05101	79	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1275	0.0042979	05101	422938019	441886011
5101091004	05101	37	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	4201	0.0141612	05101	1393539307	254991768
5101101001	05101	77	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1044	0.0035192	05101	346311601	433748569
5101101002	05101	115	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	988	0.0033305	05101	327735500	580112392
5101101003	05101	75	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	849	0.0028619	05101	281626963	425552754
5101101004	05101	47	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	717	0.0024169	05101	237840439	303274091
5101101005	05101	46	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	936	0.0031552	05101	310486263	298583076
5101101006	05101	87	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1453	0.0048979	05101	481983483	473888098
5101101007	05101	10	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	604	0.0020360	05101	200356520	98779072
5101101008	05101	38	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2358	0.0079486	05101	782186547	259968833
5101101009	05101	113	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3502	0.0118050	05101	1161669758	572781814
5101111001	05101	142	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2120	0.0071463	05101	703238117	675929734
5101121001	05101	107	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1435	0.0048373	05101	476012593	550572016
5101121002	05101	79	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1941	0.0065430	05101	643860937	441886011
5101121003	05101	75	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1528	0.0051508	05101	506862190	425552754
5101131001	05101	49	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1170	0.0039440	05101	388107829	312574692
5101131002	05101	17	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	572	0.0019282	05101	189741605	145112867
5101131003	05101	20	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	638	0.0021506	05101	211634867	163255056
5101131004	05101	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	880	0.0029664	05101	291910162	180660618
5101131005	05101	24	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	664	0.0022383	05101	220259486	186320737
5101141001	05101	52	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1502	0.0050631	05101	498237572	326332416
5101141002	05101	27	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1122	0.0037822	05101	372185456	202926665
5101141003	05101	39	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1031	0.0034754	05101	341999292	264909986
5101141004	05101	9	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	638	0.0021506	05101	211634867	91516122
5101141005	05101	52	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	704	0.0023731	05101	233528129	326332416
5101141006	05101	53	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1265	0.0042642	05101	419620858	330869366
5101151001	05101	25	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	532	0.0017933	05101	176472961	191916315
5101151002	05101	35	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	455	0.0015338	05101	150930822	244924852
5101151003	05101	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	640	0.0021574	05101	212298300	180660618
5101151004	05101	20	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	543	0.0018304	05101	180121839	163255056
5101151005	05101	27	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	503	0.0016956	05101	166853195	202926665
5101151006	05101	28	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	774	0.0026091	05101	256748256	208347166
5101151007	05101	66	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1448	0.0048811	05101	480324903	387892740
5101161001	05101	17	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	625	0.0021068	05101	207322558	145112867
5101161002	05101	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	697	0.0023495	05101	231206117	180660618
5101161003	05101	8	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	496	0.0016720	05101	164531182	84027160
5101161004	05101	64	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	892	0.0030069	05101	295890755	379336627
5101161005	05101	13	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	518	0.0017461	05101	171828936	119469438
5101161006	05101	24	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1016	0.0034249	05101	337023551	186320737
5101161007	05101	24	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	950	0.0032024	05101	315130288	186320737
5101161008	05101	64	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1194	0.0040249	05101	396069015	379336627
5101161009	05101	24	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	856	0.0028855	05101	283948976	186320737
5101161010	05101	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1235	0.0041631	05101	409669375	180660618
5101161011	05101	40	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1840	0.0062025	05101	610357611	269816396
5101161012	05101	50	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2467	0.0083161	05101	818343601	317185696
5101171001	05101	79	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	932	0.0031417	05101	309159399	441886011
5101171002	05101	147	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1142	0.0038496	05101	378819778	693099077
5101171003	05101	114	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1447	0.0048777	05101	479993187	576451526
5101171004	05101	54	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1122	0.0037822	05101	372185456	335382823
5101171005	05101	60	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1452	0.0048946	05101	481651767	361999648

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
5101011001	05101	200	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3280	0.0110566	05101	1088028785	866397099	264145.46
5101011002	05101	183	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3761	0.0126780	05101	1247584226	812368465	215998.00
5101011003	05101	184	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2365	0.0079722	05101	784508560	815583786	344855.72
5101011004	05101	49	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2690	0.0090678	05101	892316290	312574692	116198.77
5101011005	05101	97	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2518	0.0084880	05101	835261122	512774924	203643.73
5101011006	05101	193	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2848	0.0096004	05101	944727433	844309366	296456.94
5101011007	05101	210	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3131	0.0105543	05101	1038603087	897585980	286677.09
5101021001	05101	5	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	273	0.0009203	05101	90558493	59767246	218927.64
5101021002	05101	218	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1881	0.0063407	05101	623957971	922243493	490294.25
5101021003	05101	140	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1696	0.0057171	05101	562590494	669015608	394466.75
5101021004	05101	74	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1415	0.0047699	05101	469378272	421432339	297832.04
5101031001	05101	86	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1402	0.0047260	05101	465065962	469933571	335188.00
5101031002	05101	78	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1780	0.0060002	05101	590454646	437824466	245968.80
5101031003	05101	81	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1071	0.0036103	05101	355267936	449966960	420137.22
5101031004	05101	43	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	739	0.0024911	05101	245138193	284337915	384760.37
5101031005	05101	29	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	457	0.0015405	05101	151594255	213714650	467646.94
5101031006	05101	29	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1051	0.0035428	05101	348633614	213714650	203344.10
5101031007	05101	84	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2154	0.0072610	05101	714516464	461986310	214478.32
5101031008	05101	93	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2337	0.0078778	05101	775220509	497359189	212819.51
5101031009	05101	29	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1059	0.0035698	05101	351287343	213714650	201807.98
5101031010	05101	38	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1678	0.0056564	05101	556619604	259968833	154927.79
5101031011	05101	104	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2610	0.0087981	05101	865779003	539339110	206643.34
5101031012	05101	35	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	659	0.0022214	05101	218600905	244924852	371661.38
5101041001	05101	10	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	177	0.0005967	05101	58713748	98779072	558073.85
5101051001	05101	67	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1872	0.0063104	05101	620972526	392143980	209478.62
5101051002	05101	49	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1714	0.0057778	05101	568561383	312574692	182365.63
5101051003	05101	75	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2345	0.0079048	05101	777874238	425552754	181472.39
5101051004	05101	65	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3014	0.0101600	05101	999792304	383623738	127280.60
5101051005	05101	34	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1688	0.0056901	05101	559936765	239832272	142080.73
5101051006	05101	53	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3387	0.0114173	05101	1123522407	330869366	97688.03
5101051007	05101	31	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2233	0.0075273	05101	740722036	224299672	100447.68
5101061001	05101	52	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1196	0.0040316	05101	396732447	326332416	272853.19
5101061002	05101	36	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	878	0.0029597	05101	291246730	249977549	284712.47
5101061003	05101	83	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1642	0.0055350	05101	544677825	457993188	278923.99
5101061004	05101	71	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	862	0.0029057	05101	285939272	408977913	474452.33
5101061005	05101	102	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1546	0.0052114	05101	512833080	531800975	343985.11
5101071001	05101	152	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1265	0.0042642	05101	419620858	710108449	561350.55
5101081001	05101	68	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	687	0.0023158	05101	227888956	396377796	576969.14
5101081002	05101	50	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	634	0.0021372	05101	210308003	317185696	500292.90
5101081003	05101	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	619	0.0020866	05101	205332262	180660618	291858.83
5101081004	05101	65	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	620	0.0020900	05101	205663978	383623738	618747.97
5101081005	05101	70	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	979	0.0033001	05101	324750055	404794453	413477.48
5101081006	05101	39	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1435	0.0048373	05101	476012593	264909986	184606.26
5101081007	05101	63	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1791	0.0060373	05101	594103523	375031044	209397.57
5101081008	05101	41	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1274	0.0042946	05101	422606303	274689169	215611.59
5101081009	05101	57	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1519	0.0051204	05101	503876745	348787648	229616.62
5101081010	05101	79	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1320	0.0044496	05101	437865243	441886011	334762.13
5101081011	05101	25	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1143	0.0038530	05101	379151494	191916315	167905.79
5101091001	05101	87	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1689	0.0056935	05101	560268481	473888098	280573.18
5101091002	05101	97	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1322	0.0044564	05101	438528675	512774924	387878.16
5101091003	05101	79	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1275	0.0042979	05101	422938019	441886011	346577.26
5101091004	05101	37	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	4201	0.0141612	05101	1393539307	254991768	60697.87
5101101001	05101	77	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1044	0.0035192	05101	346311601	433748569	415467.98
5101101002	05101	115	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	988	0.0033305	05101	327735500	580112392	587158.29
5101101003	05101	75	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	849	0.0028619	05101	281626963	425552754	501239.99
5101101004	05101	47	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	717	0.0024169	05101	237840439	303274091	422976.42
5101101005	05101	46	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	936	0.0031552	05101	310486263	298583076	318999.01
5101101006	05101	87	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1453	0.0048979	05101	481983483	473888098	326144.60
5101101007	05101	10	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	604	0.0020360	05101	200356520	98779072	163541.51
5101101008	05101	38	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2358	0.0079486	05101	782186547	259968833	110249.72
5101101009	05101	113	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	3502	0.0118050	05101	1161669758	572781814	163558.48
5101111001	05101	142	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2120	0.0071463	05101	703238117	675929734	318834.78
5101121001	05101	107	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1435	0.0048373	05101	476012593	550572016	383673.88
5101121002	05101	79	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1941	0.0065430	05101	643860937	441886011	227658.94
5101121003	05101	75	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1528	0.0051508	05101	506862190	425552754	278503.11
5101131001	05101	49	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1170	0.0039440	05101	388107829	312574692	267157.86
5101131002	05101	17	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	572	0.0019282	05101	189741605	145112867	253693.82
5101131003	05101	20	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	638	0.0021506	05101	211634867	163255056	255885.67
5101131004	05101	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	880	0.0029664	05101	291910162	180660618	205296.16
5101131005	05101	24	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	664	0.0022383	05101	220259486	186320737	280603.52
5101141001	05101	52	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1502	0.0050631	05101	498237572	326332416	217265.26
5101141002	05101	27	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1122	0.0037822	05101	372185456	202926665	180861.56
5101141003	05101	39	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1031	0.0034754	05101	341999292	264909986	256944.70
5101141004	05101	9	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	638	0.0021506	05101	211634867	91516122	143442.20
5101141005	05101	52	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	704	0.0023731	05101	233528129	326332416	463540.36
5101141006	05101	53	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1265	0.0042642	05101	419620858	330869366	261556.81
5101151001	05101	25	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	532	0.0017933	05101	176472961	191916315	360744.95
5101151002	05101	35	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	455	0.0015338	05101	150930822	244924852	538296.38
5101151003	05101	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	640	0.0021574	05101	212298300	180660618	282282.22
5101151004	05101	20	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	543	0.0018304	05101	180121839	163255056	300653.88
5101151005	05101	27	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	503	0.0016956	05101	166853195	202926665	403432.73
5101151006	05101	28	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	774	0.0026091	05101	256748256	208347166	269182.39
5101151007	05101	66	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1448	0.0048811	05101	480324903	387892740	267881.73
5101161001	05101	17	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	625	0.0021068	05101	207322558	145112867	232180.59
5101161002	05101	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	697	0.0023495	05101	231206117	180660618	259197.44
5101161003	05101	8	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	496	0.0016720	05101	164531182	84027160	169409.60
5101161004	05101	64	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	892	0.0030069	05101	295890755	379336627	425265.28
5101161005	05101	13	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	518	0.0017461	05101	171828936	119469438	230635.98
5101161006	05101	24	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1016	0.0034249	05101	337023551	186320737	183386.55
5101161007	05101	24	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	950	0.0032024	05101	315130288	186320737	196127.09
5101161008	05101	64	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1194	0.0040249	05101	396069015	379336627	317702.37
5101161009	05101	24	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	856	0.0028855	05101	283948976	186320737	217664.41
5101161010	05101	23	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1235	0.0041631	05101	409669375	180660618	146283.90
5101161011	05101	40	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1840	0.0062025	05101	610357611	269816396	146639.35
5101161012	05101	50	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	2467	0.0083161	05101	818343601	317185696	128571.42
5101171001	05101	79	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	932	0.0031417	05101	309159399	441886011	474126.62
5101171002	05101	147	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1142	0.0038496	05101	378819778	693099077	606916.88
5101171003	05101	114	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1447	0.0048777	05101	479993187	576451526	398377.01
5101171004	05101	54	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1122	0.0037822	05101	372185456	335382823	298915.17
5101171005	05101	60	2017	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural	1452	0.0048946	05101	481651767	361999648	249311.05

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_rural_nivel_nacional_17_r05.rds")

---

R-06

Urbano

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 1)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15201	1	1	1	1767	1	1	1	2	1	3	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	2	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	3	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	4	1	1	2	3	1	99	1	2	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	5	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	6	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	7	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	8	1	1	3	3	1	99	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	9	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	10	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	11	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	12	1	1	4	3	3	3	4	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	13	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	14	1	1	2	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	15	1	1	2	1	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	16	1	1	5	1	5	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	17	1	1	5	3	1	3	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	18	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	19	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	20	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	21	4	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	22	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	23	1	1	5	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	24	1	1	3	1	1	6	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	25	1	1	5	6	1	1	4	2	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	26	4	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	27	1	1	5	99	3	3	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	28	1	1	6	3	2	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	29	1	1	2	3	1	99	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	30	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	31	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	32	1	1	3	1	1	2	1	1	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	33	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	34	3	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	35	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	36	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	37	1	1	5	3	3	1	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	38	1	1	1	1	4	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	39	1	1	5	3	4	3	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	40	1	1	5	3	2	1	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	41	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	42	1	1	2	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	43	1	1	1	1	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	44	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	45	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	46	1	1	5	6	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	47	1	1	5	3	4	0	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	48	1	1	3	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	49	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	50	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	51	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	52	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	53	1	1	1	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	54	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	55	1	1	2	2	1	4	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	56	8	1	98	98	98	98	98	98	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	57	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	58	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	59	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	60	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	61	1	1	2	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	62	1	1	5	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	63	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	64	1	1	5	3	2	4	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	65	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	66	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	67	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	68	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	69	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	70	1	1	1	3	3	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	71	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	72	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	73	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	74	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	75	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	76	1	1	5	3	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	77	1	1	3	6	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	78	5	1	3	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	79	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	80	1	1	2	1	1	3	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	81	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	82	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	83	1	1	5	3	2	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	84	4	1	5	3	2	0	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	85	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	86	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	87	1	1	3	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	88	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	89	5	1	4	3	2	1	2	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	90	1	1	5	3	3	2	1	3	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	91	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	92	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	93	5	1	4	3	5	1	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	94	1	1	5	3	3	99	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	95	1	1	5	3	2	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	96	1	1	2	3	4	6	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	97	1	1	5	1	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	98	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	99	1	1	2	7	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	100	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 6:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 6)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	6101011001	298	2017	06101
2	6101021001	140	2017	06101
3	6101031001	78	2017	06101
4	6101041001	128	2017	06101
5	6101041002	553	2017	06101
6	6101051001	118	2017	06101
7	6101051002	181	2017	06101
8	6101051003	133	2017	06101
9	6101051004	108	2017	06101
10	6101051005	77	2017	06101
11	6101061001	604	2017	06101
12	6101061002	101	2017	06101
13	6101061003	187	2017	06101
14	6101061004	136	2017	06101
15	6101061005	166	2017	06101
16	6101061006	72	2017	06101
17	6101061007	65	2017	06101
18	6101061008	100	2017	06101
19	6101061009	74	2017	06101
20	6101071001	75	2017	06101
21	6101071002	92	2017	06101
22	6101071003	61	2017	06101
23	6101071004	143	2017	06101
24	6101071005	118	2017	06101
25	6101071006	78	2017	06101
26	6101071007	41	2017	06101
27	6101081001	78	2017	06101
28	6101081002	208	2017	06101
29	6101081003	559	2017	06101
30	6101081004	598	2017	06101
31	6101081005	305	2017	06101
32	6101081006	235	2017	06101
33	6101081007	707	2017	06101
34	6101091001	31	2017	06101
35	6101091002	60	2017	06101
36	6101091003	357	2017	06101
37	6101091004	91	2017	06101
38	6101091005	104	2017	06101
39	6101091006	89	2017	06101
40	6101091007	426	2017	06101
41	6101091008	423	2017	06101
42	6101101001	336	2017	06101
43	6101101002	293	2017	06101
44	6101101003	213	2017	06101
45	6101101004	553	2017	06101
46	6101111001	427	2017	06101
47	6101111002	268	2017	06101
48	6101111003	84	2017	06101
49	6101111004	303	2017	06101
50	6101111005	367	2017	06101
51	6101111006	23	2017	06101
52	6101131001	5	2017	06101
53	6101141001	484	2017	06101
54	6101141002	11	2017	06101
55	6101141003	29	2017	06101
56	6101151001	1052	2017	06101
57	6101151002	52	2017	06101
58	6101151003	138	2017	06101
59	6101151004	294	2017	06101
60	6101151005	134	2017	06101
61	6101151006	135	2017	06101
62	6101151007	447	2017	06101
63	6101151008	334	2017	06101
64	6101151009	1673	2017	06101
65	6101151010	750	2017	06101
66	6101161001	297	2017	06101
67	6101161002	270	2017	06101
68	6101161003	52	2017	06101
69	6101161004	189	2017	06101
70	6101161005	157	2017	06101
71	6101161006	80	2017	06101
72	6101161007	126	2017	06101
73	6101171001	220	2017	06101
74	6101171002	99	2017	06101
75	6101171003	646	2017	06101
76	6101171004	159	2017	06101
77	6101171005	769	2017	06101
78	6101171006	86	2017	06101
79	6101991999	48	2017	06101
358	6102011001	81	2017	06102
359	6102011002	138	2017	06102
360	6102021001	12	2017	06102
639	6103011001	47	2017	06103
640	6103021001	135	2017	06103
641	6103031001	69	2017	06103
642	6103991999	2	2017	06103
921	6104011001	73	2017	06104
922	6104041001	16	2017	06104
923	6104061001	93	2017	06104
924	6104061002	1	2017	06104
925	6104071001	115	2017	06104
926	6104081001	92	2017	06104
927	6104081002	5	2017	06104
928	6104991999	8	2017	06104
1207	6105011001	315	2017	06105
1208	6105011002	157	2017	06105
1209	6105021001	4	2017	06105
1210	6105031001	161	2017	06105
1211	6105041001	192	2017	06105
1212	6105991999	3	2017	06105

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_urbano_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
01107	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
01401	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
01405	Pica	330061.1	2017	1405	9296	3068247619	Urbano
02101	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02102	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02104	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02201	Calama	416281.1	2017	2201	165731	68990679686	Urbano
02203	San Pedro de Atacama	437934.7	2017	2203	10996	4815529626	Urbano
02301	Tocopilla	271720.8	2017	2301	25186	6843559467	Urbano
02302	María Elena	466266.9	2017	2302	6457	3010685220	Urbano
03101	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
03102	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
03103	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
03201	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
03202	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
03301	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
03303	Freirina	289049.9	2017	3303	7041	2035200054	Urbano
03304	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
04101	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04102	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04103	Andacollo	251267.7	2017	4103	11044	2775000288	Urbano
04104	La Higuera	214257.0	2017	4104	4241	908664019	Urbano
04106	Vicuña	245957.4	2017	4106	27771	6830481918	Urbano
04201	Illapel	270316.5	2017	4201	30848	8338722128	Urbano
04202	Canela	233397.3	2017	4202	9093	2122281844	Urbano
04203	Los Vilos	282415.6	2017	4203	21382	6038609501	Urbano
04204	Salamanca	262056.9	2017	4204	29347	7690585032	Urbano
04301	Ovalle	274771.4	2017	4301	111272	30574361012	Urbano
04302	Combarbalá	228990.4	2017	4302	13322	3050610572	Urbano
04303	Monte Patria	225369.1	2017	4303	30751	6930326684	Urbano
04304	Punitaqui	212496.1	2017	4304	10956	2328107498	Urbano
05101	Valparaíso	297929.0	2017	5101	296655	88382118059	Urbano
05102	Casablanca	341641.8	2017	5102	26867	9178890241	Urbano
05103	Concón	318496.3	2017	5103	42152	13425257057	Urbano
05105	Puchuncaví	296035.5	2017	5105	18546	5490274928	Urbano
05107	Quintero	308224.7	2017	5107	31923	9839456903	Urbano
05109	Viña del Mar	337006.1	2017	5109	334248	112643604611	Urbano
05301	Los Andes	339720.2	2017	5301	66708	22662055502	Urbano
05302	Calle Larga	246387.3	2017	5302	14832	3654416747	Urbano
05303	Rinconada	273904.7	2017	5303	10207	2795744821	Urbano
05304	San Esteban	219571.6	2017	5304	18855	4140022481	Urbano
05401	La Ligua	250134.4	2017	5401	35390	8852256241	Urbano
05402	Cabildo	262745.9	2017	5402	19388	5094117762	Urbano
05403	Papudo	294355.2	2017	5403	6356	1870921373	Urbano
05404	Petorca	237510.8	2017	5404	9826	2333781007	Urbano
05405	Zapallar	294389.2	2017	5405	7339	2160521991	Urbano
05501	Quillota	286029.5	2017	5501	90517	25890529852	Urbano
05502	Calera	277181.9	2017	5502	50554	14012652087	Urbano
05503	Hijuelas	254094.0	2017	5503	17988	4570642363	Urbano

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
06101	6101011001	298	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101021001	140	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101031001	78	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101041001	128	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101041002	553	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101051001	118	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101051002	181	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101051003	133	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101051004	108	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101051005	77	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101061001	604	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101061002	101	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101061003	187	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101061004	136	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101061005	166	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101061006	72	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101061007	65	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101061008	100	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101061009	74	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101071001	75	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101071002	92	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101071003	61	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101071004	143	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101071005	118	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101071006	78	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101071007	41	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101081001	78	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101081002	208	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101081003	559	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101081004	598	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101081005	305	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101081006	235	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101081007	707	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101091001	31	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101091002	60	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101091003	357	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101091004	91	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101091005	104	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101091006	89	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101091007	426	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101091008	423	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101101001	336	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101101002	293	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101101003	213	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101101004	553	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101111001	427	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101111002	268	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101111003	84	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101111004	303	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101111005	367	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101111006	23	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101131001	5	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101141001	484	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101141002	11	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101141003	29	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101151001	1052	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101151002	52	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101151003	138	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101151004	294	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101151005	134	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101151006	135	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101151007	447	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101151008	334	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101151009	1673	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101151010	750	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101161001	297	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101161002	270	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101161003	52	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101161004	189	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101161005	157	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101161006	80	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101161007	126	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101171001	220	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101171002	99	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101171003	646	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101171004	159	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101171005	769	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101171006	86	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101991999	48	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06102	6102011001	81	2017	Codegua	280555.6	2017	6102	12988	3643855827	Urbano
06102	6102011002	138	2017	Codegua	280555.6	2017	6102	12988	3643855827	Urbano
06102	6102021001	12	2017	Codegua	280555.6	2017	6102	12988	3643855827	Urbano
06103	6103011001	47	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano
06103	6103021001	135	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano
06103	6103031001	69	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano
06103	6103991999	2	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano
06104	6104011001	73	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano
06104	6104041001	16	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano
06104	6104061001	93	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano
06104	6104061002	1	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano
06104	6104071001	115	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano
06104	6104081001	92	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano
06104	6104081002	5	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano
06104	6104991999	8	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano
06105	6105011001	315	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano
06105	6105011002	157	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano
06105	6105021001	4	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano
06105	6105031001	161	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano
06105	6105041001	192	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano
06105	6105991999	3	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
06101	6101011001	298	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101021001	140	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101031001	78	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101041001	128	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101041002	553	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101051001	118	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101051002	181	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101051003	133	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101051004	108	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101051005	77	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101061001	604	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101061002	101	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101061003	187	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101061004	136	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101061005	166	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101061006	72	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101061007	65	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101061008	100	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101061009	74	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101071001	75	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101071002	92	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101071003	61	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101071004	143	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101071005	118	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101071006	78	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101071007	41	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101081001	78	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101081002	208	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101081003	559	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101081004	598	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101081005	305	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101081006	235	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101081007	707	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101091001	31	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101091002	60	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101091003	357	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101091004	91	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101091005	104	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101091006	89	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101091007	426	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101091008	423	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101101001	336	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101101002	293	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101101003	213	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101101004	553	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101111001	427	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101111002	268	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101111003	84	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101111004	303	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101111005	367	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101111006	23	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101131001	5	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101141001	484	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101141002	11	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101141003	29	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101151001	1052	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101151002	52	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101151003	138	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101151004	294	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101151005	134	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101151006	135	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101151007	447	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101151008	334	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101151009	1673	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101151010	750	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101161001	297	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101161002	270	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101161003	52	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101161004	189	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101161005	157	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101161006	80	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101161007	126	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101171001	220	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101171002	99	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101171003	646	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101171004	159	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101171005	769	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101171006	86	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06101	6101991999	48	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano
06102	6102011001	81	2017	Codegua	280555.6	2017	6102	12988	3643855827	Urbano
06102	6102011002	138	2017	Codegua	280555.6	2017	6102	12988	3643855827	Urbano
06102	6102021001	12	2017	Codegua	280555.6	2017	6102	12988	3643855827	Urbano
06103	6103011001	47	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano
06103	6103021001	135	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano
06103	6103031001	69	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano
06103	6103991999	2	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano
06104	6104011001	73	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano
06104	6104041001	16	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano
06104	6104061001	93	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano
06104	6104061002	1	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano
06104	6104071001	115	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano
06104	6104081001	92	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano
06104	6104081002	5	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano
06104	6104991999	8	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano
06105	6105011001	315	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano
06105	6105011002	157	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano
06105	6105021001	4	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano
06105	6105031001	161	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano
06105	6105041001	192	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano
06105	6105991999	3	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
6101011001	06101	298	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2931	0.0121229	06101
6101021001	06101	140	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1326	0.0054845	06101
6101031001	06101	78	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	893	0.0036935	06101
6101041001	06101	128	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1599	0.0066136	06101
6101041002	06101	553	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3616	0.0149561	06101
6101051001	06101	118	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2105	0.0087065	06101
6101051002	06101	181	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1634	0.0067584	06101
6101051003	06101	133	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1810	0.0074863	06101
6101051004	06101	108	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1724	0.0071306	06101
6101051005	06101	77	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1297	0.0053645	06101
6101061001	06101	604	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3575	0.0147865	06101
6101061002	06101	101	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1868	0.0077262	06101
6101061003	06101	187	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1945	0.0080447	06101
6101061004	06101	136	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2013	0.0083260	06101
6101061005	06101	166	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1941	0.0080282	06101
6101061006	06101	72	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1440	0.0059560	06101
6101061007	06101	65	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1880	0.0077759	06101
6101061008	06101	100	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1255	0.0051908	06101
6101061009	06101	74	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1458	0.0060304	06101
6101071001	06101	75	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3085	0.0127599	06101
6101071002	06101	92	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1951	0.0080695	06101
6101071003	06101	61	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1805	0.0074656	06101
6101071004	06101	143	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2369	0.0097984	06101
6101071005	06101	118	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	411	0.0016999	06101
6101071006	06101	78	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1821	0.0075318	06101
6101071007	06101	41	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1281	0.0052983	06101
6101081001	06101	78	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1438	0.0059477	06101
6101081002	06101	208	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2053	0.0084914	06101
6101081003	06101	559	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2627	0.0108655	06101
6101081004	06101	598	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2379	0.0098398	06101
6101081005	06101	305	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2188	0.0090498	06101
6101081006	06101	235	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2502	0.0103485	06101
6101081007	06101	707	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2234	0.0092400	06101
6101091001	06101	31	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2303	0.0095254	06101
6101091002	06101	60	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1991	0.0082350	06101
6101091003	06101	357	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4641	0.0191956	06101
6101091004	06101	91	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1903	0.0078710	06101
6101091005	06101	104	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2187	0.0090456	06101
6101091006	06101	89	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1472	0.0060883	06101
6101091007	06101	426	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	8109	0.0335396	06101
6101091008	06101	423	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	11700	0.0483923	06101
6101101001	06101	336	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3965	0.0163996	06101
6101101002	06101	293	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4110	0.0169993	06101
6101101003	06101	213	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4224	0.0174709	06101
6101101004	06101	553	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	5803	0.0240018	06101
6101111001	06101	427	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1303	0.0053893	06101
6101111002	06101	268	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	5050	0.0208873	06101
6101111003	06101	84	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2031	0.0084004	06101
6101111004	06101	303	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3455	0.0142902	06101
6101111005	06101	367	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	5041	0.0208501	06101
6101111006	06101	23	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1279	0.0052901	06101
6101131001	06101	5	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	175	0.0007238	06101
6101141001	06101	484	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	7033	0.0290891	06101
6101141002	06101	11	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1110	0.0045911	06101
6101141003	06101	29	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1183	0.0048930	06101
6101151001	06101	1052	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	9988	0.0413113	06101
6101151002	06101	52	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1136	0.0046986	06101
6101151003	06101	138	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1480	0.0061214	06101
6101151004	06101	294	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	5748	0.0237743	06101
6101151005	06101	134	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3234	0.0133761	06101
6101151006	06101	135	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2829	0.0117010	06101
6101151007	06101	447	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2757	0.0114032	06101
6101151008	06101	334	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3771	0.0155972	06101
6101151009	06101	1673	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	6283	0.0259871	06101
6101151010	06101	750	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4858	0.0200931	06101
6101161001	06101	297	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	6794	0.0281006	06101
6101161002	06101	270	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	6040	0.0249820	06101
6101161003	06101	52	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2230	0.0092235	06101
6101161004	06101	189	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	6100	0.0252302	06101
6101161005	06101	157	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4455	0.0184263	06101
6101161006	06101	80	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1457	0.0060263	06101
6101161007	06101	126	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1821	0.0075318	06101
6101171001	06101	220	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1666	0.0068907	06101
6101171002	06101	99	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1913	0.0079123	06101
6101171003	06101	646	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3730	0.0154276	06101
6101171004	06101	159	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4000	0.0165444	06101
6101171005	06101	769	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	5138	0.0212513	06101
6101171006	06101	86	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1713	0.0070851	06101
6101991999	06101	48	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	520	0.0021508	06101
6102011001	06102	81	2017	Codegua	280555.6	2017	6102	12988	3643855827	Urbano	2483	0.1911765	06102
6102011002	06102	138	2017	Codegua	280555.6	2017	6102	12988	3643855827	Urbano	4176	0.3215276	06102
6102021001	06102	12	2017	Codegua	280555.6	2017	6102	12988	3643855827	Urbano	345	0.0265630	06102
6103011001	06103	47	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano	377	0.0512298	06103
6103021001	06103	135	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano	1770	0.2405218	06103
6103031001	06103	69	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano	1260	0.1712189	06103
6103991999	06103	2	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano	17	0.0023101	06103
6104011001	06104	73	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	1464	0.0747053	06104
6104041001	06104	16	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	1450	0.0739909	06104
6104061001	06104	93	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	4056	0.2069705	06104
6104061002	06104	1	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	37	0.0018880	06104
6104071001	06104	115	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	2247	0.1146604	06104
6104081001	06104	92	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	1713	0.0874113	06104
6104081002	06104	5	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	125	0.0063785	06104
6104991999	06104	8	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	71	0.0036230	06104
6105011001	06105	315	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	5036	0.2411069	06105
6105011002	06105	157	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	2266	0.1084885	06105
6105021001	06105	4	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	99	0.0047398	06105
6105031001	06105	161	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	3661	0.1752765	06105
6105041001	06105	192	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	5339	0.2556135	06105
6105991999	06105	3	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	23	0.0011012	06105

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
6101011001	06101	298	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2931	0.0121229	06101	894971115
6101021001	06101	140	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1326	0.0054845	06101	404889696
6101031001	06101	78	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	893	0.0036935	06101	272674584
6101041001	06101	128	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1599	0.0066136	06101	488249339
6101041002	06101	553	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3616	0.0149561	06101	1104133590
6101051001	06101	118	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2105	0.0087065	06101	642754759
6101051002	06101	181	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1634	0.0067584	06101	498936473
6101051003	06101	133	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1810	0.0074863	06101	552677489
6101051004	06101	108	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1724	0.0071306	06101	526417674
6101051005	06101	77	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1297	0.0053645	06101	396034642
6101061001	06101	604	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3575	0.0147865	06101	1091614377
6101061002	06101	101	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1868	0.0077262	06101	570387596
6101061003	06101	187	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1945	0.0080447	06101	593899290
6101061004	06101	136	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2013	0.0083260	06101	614662864
6101061005	06101	166	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1941	0.0080282	06101	592677903
6101061006	06101	72	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1440	0.0059560	06101	439699217
6101061007	06101	65	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1880	0.0077759	06101	574051756
6101061008	06101	100	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1255	0.0051908	06101	383210082
6101061009	06101	74	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1458	0.0060304	06101	445195458
6101071001	06101	75	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3085	0.0127599	06101	941994504
6101071002	06101	92	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1951	0.0080695	06101	595731370
6101071003	06101	61	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1805	0.0074656	06101	551150755
6101071004	06101	143	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2369	0.0097984	06101	723366282
6101071005	06101	118	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	411	0.0016999	06101	125497485
6101071006	06101	78	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1821	0.0075318	06101	556036302
6101071007	06101	41	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1281	0.0052983	06101	391149095
6101081001	06101	78	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1438	0.0059477	06101	439088524
6101081002	06101	208	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2053	0.0084914	06101	626876731
6101081003	06101	559	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2627	0.0108655	06101	802145725
6101081004	06101	598	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2379	0.0098398	06101	726419749
6101081005	06101	305	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2188	0.0090498	06101	668098533
6101081006	06101	235	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2502	0.0103485	06101	763977390
6101081007	06101	707	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2234	0.0092400	06101	682144480
6101091001	06101	31	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2303	0.0095254	06101	703213401
6101091002	06101	60	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1991	0.0082350	06101	607945237
6101091003	06101	357	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4641	0.0191956	06101	1417113936
6101091004	06101	91	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1903	0.0078710	06101	581074730
6101091005	06101	104	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2187	0.0090456	06101	667793186
6101091006	06101	89	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1472	0.0060883	06101	449470311
6101091007	06101	426	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	8109	0.0335396	06101	2476056218
6101091008	06101	423	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	11700	0.0483923	06101	3572556141
6101101001	06101	336	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3965	0.0163996	06101	1210699581
6101101002	06101	293	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4110	0.0169993	06101	1254974850
6101101003	06101	213	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4224	0.0174709	06101	1289784371
6101101004	06101	553	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	5803	0.0240018	06101	1771926777
6101111001	06101	427	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1303	0.0053893	06101	397866722
6101111002	06101	268	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	5050	0.0208873	06101	1542000728
6101111003	06101	84	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2031	0.0084004	06101	620159105
6101111004	06101	303	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3455	0.0142902	06101	1054972775
6101111005	06101	367	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	5041	0.0208501	06101	1539252608
6101111006	06101	23	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1279	0.0052901	06101	390538402
6101131001	06101	5	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	175	0.0007238	06101	53435669
6101141001	06101	484	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	7033	0.0290891	06101	2147503192
6101141002	06101	11	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1110	0.0045911	06101	338934813
6101141003	06101	29	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1183	0.0048930	06101	361225121
6101151001	06101	1052	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	9988	0.0413113	06101	3049802627
6101151002	06101	52	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1136	0.0046986	06101	346873827
6101151003	06101	138	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1480	0.0061214	06101	451913085
6101151004	06101	294	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	5748	0.0237743	06101	1755132709
6101151005	06101	134	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3234	0.0133761	06101	987491159
6101151006	06101	135	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2829	0.0117010	06101	863825754
6101151007	06101	447	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2757	0.0114032	06101	841840793
6101151008	06101	334	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3771	0.0155972	06101	1151462326
6101151009	06101	1673	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	6283	0.0259871	06101	1918493183
6101151010	06101	750	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4858	0.0200931	06101	1483374165
6101161001	06101	297	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	6794	0.0281006	06101	2074525335
6101161002	06101	270	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	6040	0.0249820	06101	1844293940
6101161003	06101	52	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2230	0.0092235	06101	680923094
6101161004	06101	189	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	6100	0.0252302	06101	1862614740
6101161005	06101	157	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4455	0.0184263	06101	1360319454
6101161006	06101	80	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1457	0.0060263	06101	444890111
6101161007	06101	126	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1821	0.0075318	06101	556036302
6101171001	06101	220	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1666	0.0068907	06101	508707567
6101171002	06101	99	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1913	0.0079123	06101	584128196
6101171003	06101	646	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3730	0.0154276	06101	1138943112
6101171004	06101	159	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4000	0.0165444	06101	1221386715
6101171005	06101	769	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	5138	0.0212513	06101	1568871235
6101171006	06101	86	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1713	0.0070851	06101	523058861
6101991999	06101	48	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	520	0.0021508	06101	158780273
6102011001	06102	81	2017	Codegua	280555.6	2017	6102	12988	3643855827	Urbano	2483	0.1911765	06102	696619496
6102011002	06102	138	2017	Codegua	280555.6	2017	6102	12988	3643855827	Urbano	4176	0.3215276	06102	1171600087
6102021001	06102	12	2017	Codegua	280555.6	2017	6102	12988	3643855827	Urbano	345	0.0265630	06102	96791674
6103011001	06103	47	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano	377	0.0512298	06103	81874291
6103021001	06103	135	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano	1770	0.2405218	06103	384396538
6103031001	06103	69	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano	1260	0.1712189	06103	273638213
6103991999	06103	2	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano	17	0.0023101	06103	3691944
6104011001	06104	73	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	1464	0.0747053	06104	399167926
6104041001	06104	16	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	1450	0.0739909	06104	395350746
6104061001	06104	93	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	4056	0.2069705	06104	1105891466
6104061002	06104	1	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	37	0.0018880	06104	10088260
6104071001	06104	115	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	2247	0.1146604	06104	612657329
6104081001	06104	92	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	1713	0.0874113	06104	467059192
6104081002	06104	5	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	125	0.0063785	06104	34081961
6104991999	06104	8	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	71	0.0036230	06104	19358554
6105011001	06105	315	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	5036	0.2411069	06105	1496691832
6105011002	06105	157	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	2266	0.1084885	06105	673451885
6105021001	06105	4	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	99	0.0047398	06105	29422655
6105031001	06105	161	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	3661	0.1752765	06105	1088043844
6105041001	06105	192	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	5339	0.2556135	06105	1586742989
6105991999	06105	3	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	23	0.0011012	06105	6835566

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -983330032 -292496336  -89398321  248643480 2506394772 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 464416128   33422697   13.89   <2e-16 ***
## Freq.x        1422566     118460   12.01   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 438800000 on 276 degrees of freedom
## Multiple R-squared:  0.3432, Adjusted R-squared:  0.3408 
## F-statistic: 144.2 on 1 and 276 DF,  p-value: < 2.2e-16

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.340809789143073"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.340809789143073"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.512284517467657"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.491596457534916"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.537568445722387"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.444949059948124"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.694918432920302"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.760165593348938"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 1    cuadrático 0.340809789143073
## 2        cúbico 0.340809789143073
## 6      log-raíz 0.444949059948124
## 4 raíz cuadrada 0.491596457534916
## 3   logarítmico 0.512284517467657
## 5     raíz-raíz 0.537568445722387
## 7      raíz-log 0.694918432920302
## 8       log-log 0.760165593348938
##                                                                     sintaxis
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 8
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2331 -0.3466  0.1331  0.4341  1.3827 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.41318    0.12346  132.94   <2e-16 ***
## log(Freq.x)  0.79089    0.02668   29.65   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6619 on 276 degrees of freedom
## Multiple R-squared:  0.761,  Adjusted R-squared:  0.7602 
## F-statistic:   879 on 1 and 276 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    16.41318

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##   0.7908946

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.7602 ).

9.1 Diagrama de dispersión sobre log-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2331 -0.3466  0.1331  0.4341  1.3827 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.41318    0.12346  132.94   <2e-16 ***
## log(Freq.x)  0.79089    0.02668   29.65   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6619 on 276 degrees of freedom
## Multiple R-squared:  0.761,  Adjusted R-squared:  0.7602 
## F-statistic:   879 on 1 and 276 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = e^{16.41318+0.7908946 \cdot ln{X}} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
6101011001	06101	298	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2931	0.0121229	06101	894971115	1216184701
6101021001	06101	140	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1326	0.0054845	06101	404889696	669138951
6101031001	06101	78	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	893	0.0036935	06101	272674584	421310835
6101041001	06101	128	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1599	0.0066136	06101	488249339	623356111
6101041002	06101	553	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3616	0.0149561	06101	1104133590	1983178304
6101051001	06101	118	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2105	0.0087065	06101	642754759	584514817
6101051002	06101	181	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1634	0.0067584	06101	498936473	819862526
6101051003	06101	133	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1810	0.0074863	06101	552677489	642536836
6101051004	06101	108	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1724	0.0071306	06101	526417674	544978161
6101051005	06101	77	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1297	0.0053645	06101	396034642	417033124
6101061001	06101	604	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3575	0.0147865	06101	1091614377	2126485208
6101061002	06101	101	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1868	0.0077262	06101	570387596	516847216
6101061003	06101	187	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1945	0.0080447	06101	593899290	841283753
6101061004	06101	136	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2013	0.0083260	06101	614662864	653972728
6101061005	06101	166	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1941	0.0080282	06101	592677903	765643745
6101061006	06101	72	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1440	0.0059560	06101	439699217	395466285
6101061007	06101	65	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1880	0.0077759	06101	574051756	364735978
6101061008	06101	100	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1255	0.0051908	06101	383210082	512795765
6101061009	06101	74	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1458	0.0060304	06101	445195458	404129447
6101071001	06101	75	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3085	0.0127599	06101	941994504	408442618
6101071002	06101	92	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1951	0.0080695	06101	595731370	480069833
6101071003	06101	61	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1805	0.0074656	06101	551150755	346866969
6101071004	06101	143	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2369	0.0097984	06101	723366282	680454156
6101071005	06101	118	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	411	0.0016999	06101	125497485	584514817
6101071006	06101	78	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1821	0.0075318	06101	556036302	421310835
6101071007	06101	41	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1281	0.0052983	06101	391149095	253336201
6101081001	06101	78	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1438	0.0059477	06101	439088524	421310835
6101081002	06101	208	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2053	0.0084914	06101	626876731	915164194
6101081003	06101	559	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2627	0.0108655	06101	802145725	2000177004
6101081004	06101	598	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2379	0.0098398	06101	726419749	2109760907
6101081005	06101	305	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2188	0.0090498	06101	668098533	1238724085
6101081006	06101	235	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2502	0.0103485	06101	763977390	1007905968
6101081007	06101	707	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2234	0.0092400	06101	682144480	2408494727
6101091001	06101	31	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2303	0.0095254	06101	703213401	203079046
6101091002	06101	60	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1991	0.0082350	06101	607945237	342361910
6101091003	06101	357	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4641	0.0191956	06101	1417113936	1402964769
6101091004	06101	91	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1903	0.0078710	06101	581074730	475938115
6101091005	06101	104	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2187	0.0090456	06101	667793186	528951687
6101091006	06101	89	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1472	0.0060883	06101	449470311	467646035
6101091007	06101	426	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	8109	0.0335396	06101	2476056218	1613396515
6101091008	06101	423	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	11700	0.0483923	06101	3572556141	1604403776
6101101001	06101	336	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3965	0.0163996	06101	1210699581	1337283076
6101101002	06101	293	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4110	0.0169993	06101	1254974850	1200017372
6101101003	06101	213	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4224	0.0174709	06101	1289784371	932519885
6101101004	06101	553	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	5803	0.0240018	06101	1771926777	1983178304
6101111001	06101	427	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1303	0.0053893	06101	397866722	1616391148
6101111002	06101	268	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	5050	0.0208873	06101	1542000728	1118288729
6101111003	06101	84	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2031	0.0084004	06101	620159105	446742554
6101111004	06101	303	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3455	0.0142902	06101	1054972775	1232295405
6101111005	06101	367	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	5041	0.0208501	06101	1539252608	1433955913
6101111006	06101	23	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1279	0.0052901	06101	390538402	160375635
6101131001	06101	5	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	175	0.0007238	06101	53435669	47969674
6101141001	06101	484	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	7033	0.0290891	06101	2147503192	1784781117
6101141002	06101	11	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1110	0.0045911	06101	338934813	89492581
6101141003	06101	29	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1183	0.0048930	06101	361225121	192645063
6101151001	06101	1052	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	9988	0.0413113	06101	3049802627	3298004247
6101151002	06101	52	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1136	0.0046986	06101	346873827	305726436
6101151003	06101	138	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1480	0.0061214	06101	451913085	661567331
6101151004	06101	294	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	5748	0.0237743	06101	1755132709	1203255423
6101151005	06101	134	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3234	0.0133761	06101	987491159	646354735
6101151006	06101	135	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2829	0.0117010	06101	863825754	650166682
6101151007	06101	447	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2757	0.0114032	06101	841840793	1675981308
6101151008	06101	334	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3771	0.0155972	06101	1151462326	1330983613
6101151009	06101	1673	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	6283	0.0259871	06101	1918493183	4759932825
6101151010	06101	750	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4858	0.0200931	06101	1483374165	2523630116
6101161001	06101	297	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	6794	0.0281006	06101	2074525335	1212955802
6101161002	06101	270	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	6040	0.0249820	06101	1844293940	1124883957
6101161003	06101	52	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2230	0.0092235	06101	680923094	305726436
6101161004	06101	189	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	6100	0.0252302	06101	1862614740	848392053
6101161005	06101	157	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4455	0.0184263	06101	1360319454	732622777
6101161006	06101	80	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1457	0.0060263	06101	444890111	429832074
6101161007	06101	126	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1821	0.0075318	06101	556036302	615640181
6101171001	06101	220	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1666	0.0068907	06101	508707567	956675597
6101171002	06101	99	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1913	0.0079123	06101	584128196	508735833
6101171003	06101	646	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3730	0.0154276	06101	1138943112	2242606071
6101171004	06101	159	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4000	0.0165444	06101	1221386715	739994238
6101171005	06101	769	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	5138	0.0212513	06101	1568871235	2574060986
6101171006	06101	86	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1713	0.0070851	06101	523058861	455134341
6101991999	06101	48	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	520	0.0021508	06101	158780273	286972202
6102011001	06102	81	2017	Codegua	280555.6	2017	6102	12988	3643855827	Urbano	2483	0.1911765	06102	696619496	434075946
6102011002	06102	138	2017	Codegua	280555.6	2017	6102	12988	3643855827	Urbano	4176	0.3215276	06102	1171600087	661567331
6102021001	06102	12	2017	Codegua	280555.6	2017	6102	12988	3643855827	Urbano	345	0.0265630	06102	96791674	95868030
6103011001	06103	47	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano	377	0.0512298	06103	81874291	282233382
6103021001	06103	135	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano	1770	0.2405218	06103	384396538	650166682
6103031001	06103	69	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano	1260	0.1712189	06103	273638213	382376363
6103991999	06103	2	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano	17	0.0023101	06103	3691944	23240108
6104011001	06104	73	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	1464	0.0747053	06104	399167926	399804070
6104041001	06104	16	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	1450	0.0739909	06104	395350746	120361387
6104061001	06104	93	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	4056	0.2069705	06104	1105891466	484192170
6104061002	06104	1	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	37	0.0018880	06104	10088260	13432447
6104071001	06104	115	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	2247	0.1146604	06104	612657329	572730125
6104081001	06104	92	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	1713	0.0874113	06104	467059192	480069833
6104081002	06104	5	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	125	0.0063785	06104	34081961	47969674
6104991999	06104	8	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	71	0.0036230	06104	19358554	69567143
6105011001	06105	315	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	5036	0.2411069	06105	1496691832	1270736723
6105011002	06105	157	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	2266	0.1084885	06105	673451885	732622777
6105021001	06105	4	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	99	0.0047398	06105	29422655	40208804
6105031001	06105	161	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	3661	0.1752765	06105	1088043844	747346334
6105041001	06105	192	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	5339	0.2556135	06105	1586742989	859025104
6105991999	06105	3	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	23	0.0011012	06105	6835566	32026374

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
6101011001	06101	298	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2931	0.0121229	06101	894971115	1216184701	414938.49
6101021001	06101	140	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1326	0.0054845	06101	404889696	669138951	504629.68
6101031001	06101	78	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	893	0.0036935	06101	272674584	421310835	471792.65
6101041001	06101	128	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1599	0.0066136	06101	488249339	623356111	389841.22
6101041002	06101	553	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3616	0.0149561	06101	1104133590	1983178304	548445.33
6101051001	06101	118	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2105	0.0087065	06101	642754759	584514817	277679.25
6101051002	06101	181	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1634	0.0067584	06101	498936473	819862526	501751.85
6101051003	06101	133	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1810	0.0074863	06101	552677489	642536836	354992.73
6101051004	06101	108	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1724	0.0071306	06101	526417674	544978161	316112.62
6101051005	06101	77	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1297	0.0053645	06101	396034642	417033124	321536.72
6101061001	06101	604	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3575	0.0147865	06101	1091614377	2126485208	594821.04
6101061002	06101	101	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1868	0.0077262	06101	570387596	516847216	276684.81
6101061003	06101	187	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1945	0.0080447	06101	593899290	841283753	432536.63
6101061004	06101	136	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2013	0.0083260	06101	614662864	653972728	324874.68
6101061005	06101	166	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1941	0.0080282	06101	592677903	765643745	394458.40
6101061006	06101	72	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1440	0.0059560	06101	439699217	395466285	274629.36
6101061007	06101	65	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1880	0.0077759	06101	574051756	364735978	194008.50
6101061008	06101	100	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1255	0.0051908	06101	383210082	512795765	408602.20
6101061009	06101	74	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1458	0.0060304	06101	445195458	404129447	277180.69
6101071001	06101	75	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3085	0.0127599	06101	941994504	408442618	132396.31
6101071002	06101	92	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1951	0.0080695	06101	595731370	480069833	246063.47
6101071003	06101	61	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1805	0.0074656	06101	551150755	346866969	192170.07
6101071004	06101	143	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2369	0.0097984	06101	723366282	680454156	287232.65
6101071005	06101	118	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	411	0.0016999	06101	125497485	584514817	1422177.17
6101071006	06101	78	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1821	0.0075318	06101	556036302	421310835	231362.35
6101071007	06101	41	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1281	0.0052983	06101	391149095	253336201	197764.40
6101081001	06101	78	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1438	0.0059477	06101	439088524	421310835	292983.89
6101081002	06101	208	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2053	0.0084914	06101	626876731	915164194	445769.21
6101081003	06101	559	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2627	0.0108655	06101	802145725	2000177004	761392.08
6101081004	06101	598	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2379	0.0098398	06101	726419749	2109760907	886826.78
6101081005	06101	305	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2188	0.0090498	06101	668098533	1238724085	566144.46
6101081006	06101	235	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2502	0.0103485	06101	763977390	1007905968	402840.12
6101081007	06101	707	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2234	0.0092400	06101	682144480	2408494727	1078108.65
6101091001	06101	31	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2303	0.0095254	06101	703213401	203079046	88180.22
6101091002	06101	60	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1991	0.0082350	06101	607945237	342361910	171954.75
6101091003	06101	357	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4641	0.0191956	06101	1417113936	1402964769	302297.95
6101091004	06101	91	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1903	0.0078710	06101	581074730	475938115	250098.85
6101091005	06101	104	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2187	0.0090456	06101	667793186	528951687	241861.77
6101091006	06101	89	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1472	0.0060883	06101	449470311	467646035	317694.32
6101091007	06101	426	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	8109	0.0335396	06101	2476056218	1613396515	198963.68
6101091008	06101	423	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	11700	0.0483923	06101	3572556141	1604403776	137128.53
6101101001	06101	336	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3965	0.0163996	06101	1210699581	1337283076	337271.90
6101101002	06101	293	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4110	0.0169993	06101	1254974850	1200017372	291975.03
6101101003	06101	213	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4224	0.0174709	06101	1289784371	932519885	220767.02
6101101004	06101	553	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	5803	0.0240018	06101	1771926777	1983178304	341750.53
6101111001	06101	427	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1303	0.0053893	06101	397866722	1616391148	1240515.08
6101111002	06101	268	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	5050	0.0208873	06101	1542000728	1118288729	221443.31
6101111003	06101	84	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2031	0.0084004	06101	620159105	446742554	219961.87
6101111004	06101	303	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3455	0.0142902	06101	1054972775	1232295405	356670.16
6101111005	06101	367	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	5041	0.0208501	06101	1539252608	1433955913	284458.62
6101111006	06101	23	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1279	0.0052901	06101	390538402	160375635	125391.43
6101131001	06101	5	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	175	0.0007238	06101	53435669	47969674	274112.42
6101141001	06101	484	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	7033	0.0290891	06101	2147503192	1784781117	253772.38
6101141002	06101	11	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1110	0.0045911	06101	338934813	89492581	80623.95
6101141003	06101	29	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1183	0.0048930	06101	361225121	192645063	162844.52
6101151001	06101	1052	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	9988	0.0413113	06101	3049802627	3298004247	330196.66
6101151002	06101	52	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1136	0.0046986	06101	346873827	305726436	269125.38
6101151003	06101	138	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1480	0.0061214	06101	451913085	661567331	447004.95
6101151004	06101	294	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	5748	0.0237743	06101	1755132709	1203255423	209334.62
6101151005	06101	134	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3234	0.0133761	06101	987491159	646354735	199862.32
6101151006	06101	135	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2829	0.0117010	06101	863825754	650166682	229822.09
6101151007	06101	447	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2757	0.0114032	06101	841840793	1675981308	607900.37
6101151008	06101	334	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3771	0.0155972	06101	1151462326	1330983613	352952.43
6101151009	06101	1673	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	6283	0.0259871	06101	1918493183	4759932825	757589.18
6101151010	06101	750	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4858	0.0200931	06101	1483374165	2523630116	519479.23
6101161001	06101	297	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	6794	0.0281006	06101	2074525335	1212955802	178533.38
6101161002	06101	270	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	6040	0.0249820	06101	1844293940	1124883957	186239.07
6101161003	06101	52	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	2230	0.0092235	06101	680923094	305726436	137097.06
6101161004	06101	189	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	6100	0.0252302	06101	1862614740	848392053	139080.66
6101161005	06101	157	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4455	0.0184263	06101	1360319454	732622777	164449.56
6101161006	06101	80	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1457	0.0060263	06101	444890111	429832074	295011.72
6101161007	06101	126	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1821	0.0075318	06101	556036302	615640181	338078.08
6101171001	06101	220	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1666	0.0068907	06101	508707567	956675597	574235.05
6101171002	06101	99	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1913	0.0079123	06101	584128196	508735833	265936.14
6101171003	06101	646	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	3730	0.0154276	06101	1138943112	2242606071	601234.87
6101171004	06101	159	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	4000	0.0165444	06101	1221386715	739994238	184998.56
6101171005	06101	769	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	5138	0.0212513	06101	1568871235	2574060986	500985.01
6101171006	06101	86	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	1713	0.0070851	06101	523058861	455134341	265694.30
6101991999	06101	48	2017	Rancagua	305346.7	2017	6101	241774	73824887908	Urbano	520	0.0021508	06101	158780273	286972202	551869.62
6102011001	06102	81	2017	Codegua	280555.6	2017	6102	12988	3643855827	Urbano	2483	0.1911765	06102	696619496	434075946	174819.15
6102011002	06102	138	2017	Codegua	280555.6	2017	6102	12988	3643855827	Urbano	4176	0.3215276	06102	1171600087	661567331	158421.30
6102021001	06102	12	2017	Codegua	280555.6	2017	6102	12988	3643855827	Urbano	345	0.0265630	06102	96791674	95868030	277878.35
6103011001	06103	47	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano	377	0.0512298	06103	81874291	282233382	748629.66
6103021001	06103	135	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano	1770	0.2405218	06103	384396538	650166682	367325.81
6103031001	06103	69	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano	1260	0.1712189	06103	273638213	382376363	303473.30
6103991999	06103	2	2017	Coinco	217173.2	2017	6103	7359	1598177470	Urbano	17	0.0023101	06103	3691944	23240108	1367065.18
6104011001	06104	73	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	1464	0.0747053	06104	399167926	399804070	273090.21
6104041001	06104	16	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	1450	0.0739909	06104	395350746	120361387	83007.85
6104061001	06104	93	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	4056	0.2069705	06104	1105891466	484192170	119376.77
6104061002	06104	1	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	37	0.0018880	06104	10088260	13432447	363039.10
6104071001	06104	115	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	2247	0.1146604	06104	612657329	572730125	254886.57
6104081001	06104	92	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	1713	0.0874113	06104	467059192	480069833	280250.92
6104081002	06104	5	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	125	0.0063785	06104	34081961	47969674	383757.39
6104991999	06104	8	2017	Coltauco	272655.7	2017	6104	19597	5343233497	Urbano	71	0.0036230	06104	19358554	69567143	979818.92
6105011001	06105	315	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	5036	0.2411069	06105	1496691832	1270736723	252330.56
6105011002	06105	157	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	2266	0.1084885	06105	673451885	732622777	323311.02
6105021001	06105	4	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	99	0.0047398	06105	29422655	40208804	406149.54
6105031001	06105	161	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	3661	0.1752765	06105	1088043844	747346334	204137.21
6105041001	06105	192	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	5339	0.2556135	06105	1586742989	859025104	160896.25
6105991999	06105	3	2017	Doñihue	297198.5	2017	6105	20887	6207585840	Urbano	23	0.0011012	06105	6835566	32026374	1392451.06

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_urbano_nivel_nacional_17_r06.rds")

---

Rural

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 2)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15202	1	2	6	13225	1	3	1	5	3	5	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	3	1	1	5	3	5	2	3	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	4	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	5	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	6	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	8	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	9	3	1	5	3	5	1	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	10	1	1	5	3	4	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	11	1	2	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	12	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	13	1	1	5	3	4	1	4	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	14	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	16	1	1	5	3	5	1	3	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	17	1	1	5	3	5	2	4	1	8	15	152	15202	15202012006
15	152	15202	1	2	6	13225	18	3	1	5	3	5	1	1	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	19	3	1	5	3	5	1	3	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	20	1	1	5	3	5	2	1	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	21	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	22	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	24	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	25	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	26	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	27	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	28	3	1	5	3	5	1	4	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	29	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	30	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	31	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	32	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	33	1	1	5	3	5	3	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	34	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	35	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	36	1	1	5	3	5	3	2	1	9	15	152	15202	15202012006
15	152	15202	1	2	6	13225	37	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	38	1	1	5	3	5	99	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	39	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	40	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	41	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	42	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	43	3	1	5	3	5	2	1	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	44	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	45	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	8	13910	1	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	3	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	4	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	5	3	1	5	3	5	2	3	1	3	15	152	15202	15202012008
15	152	15202	1	2	8	13910	6	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	7	1	1	5	99	5	2	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	8	3	1	5	3	5	3	3	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	9	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	10	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	11	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	12	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	13	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	14	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	16	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	18	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	20	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	21	1	1	5	3	5	1	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	22	3	1	5	3	5	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	23	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	24	3	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	25	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	26	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	27	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	28	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	29	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	30	1	1	5	1	4	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	12	8394	1	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	2	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	3	3	1	5	3	5	2	3	1	4	15	152	15202	15202012012
15	152	15202	1	2	12	8394	4	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	5	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	6	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	8	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	9	3	1	5	3	5	1	4	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	10	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	11	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	12	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	13	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	14	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	15	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	16	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	18	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	20	3	1	5	3	5	3	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	21	3	1	5	99	5	1	4	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	22	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	24	3	1	5	3	5	1	2	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	25	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Veamos los primeros 100 registros:

r3_100 <- d[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	unlist.c.	unlist.d.	Freq	anio
1	6101011001	1	6101	298	2017
2	6101021001	1	6101	140	2017
3	6101031001	1	6101	78	2017
4	6101041001	1	6101	128	2017
5	6101041002	1	6101	553	2017
6	6101051001	1	6101	118	2017
7	6101051002	1	6101	181	2017
8	6101051003	1	6101	133	2017
9	6101051004	1	6101	108	2017
10	6101051005	1	6101	77	2017
11	6101061001	1	6101	604	2017
12	6101061002	1	6101	101	2017
13	6101061003	1	6101	187	2017
14	6101061004	1	6101	136	2017
15	6101061005	1	6101	166	2017
16	6101061006	1	6101	72	2017
17	6101061007	1	6101	65	2017
18	6101061008	1	6101	100	2017
19	6101061009	1	6101	74	2017
20	6101071001	1	6101	75	2017
21	6101071002	1	6101	92	2017
22	6101071003	1	6101	61	2017
23	6101071004	1	6101	143	2017
24	6101071005	1	6101	118	2017
25	6101071006	1	6101	78	2017
26	6101071007	1	6101	41	2017
27	6101081001	1	6101	78	2017
28	6101081002	1	6101	208	2017
29	6101081003	1	6101	559	2017
30	6101081004	1	6101	598	2017
31	6101081005	1	6101	305	2017
32	6101081006	1	6101	235	2017
33	6101081007	1	6101	707	2017
34	6101091001	1	6101	31	2017
35	6101091002	1	6101	60	2017
36	6101091003	1	6101	357	2017
37	6101091004	1	6101	91	2017
38	6101091005	1	6101	104	2017
39	6101091006	1	6101	89	2017
40	6101091007	1	6101	426	2017
41	6101091008	1	6101	423	2017
42	6101101001	1	6101	336	2017
43	6101101002	1	6101	293	2017
44	6101101003	1	6101	213	2017
45	6101101004	1	6101	553	2017
46	6101111001	1	6101	427	2017
47	6101111002	1	6101	268	2017
48	6101111003	1	6101	84	2017
49	6101111004	1	6101	303	2017
50	6101111005	1	6101	367	2017
51	6101111006	1	6101	23	2017
52	6101131001	1	6101	5	2017
53	6101141001	1	6101	484	2017
54	6101141002	1	6101	11	2017
55	6101141003	1	6101	29	2017
56	6101151001	1	6101	1052	2017
57	6101151002	1	6101	52	2017
58	6101151003	1	6101	138	2017
59	6101151004	1	6101	294	2017
60	6101151005	1	6101	134	2017
61	6101151006	1	6101	135	2017
62	6101151007	1	6101	447	2017
63	6101151008	1	6101	334	2017
64	6101151009	1	6101	1673	2017
65	6101151010	1	6101	750	2017
66	6101161001	1	6101	297	2017
67	6101161002	1	6101	270	2017
68	6101161003	1	6101	52	2017
69	6101161004	1	6101	189	2017
70	6101161005	1	6101	157	2017
71	6101161006	1	6101	80	2017
72	6101161007	1	6101	126	2017
73	6101171001	1	6101	220	2017
74	6101171002	1	6101	99	2017
75	6101171003	1	6101	646	2017
76	6101171004	1	6101	159	2017
77	6101171005	1	6101	769	2017
78	6101171006	1	6101	86	2017
79	6101991999	1	6101	48	2017
358	6102011001	1	6102	81	2017
359	6102011002	1	6102	138	2017
360	6102021001	1	6102	12	2017
639	6103011001	1	6103	47	2017
640	6103021001	1	6103	135	2017
641	6103031001	1	6103	69	2017
642	6103991999	1	6103	2	2017
921	6104011001	1	6104	73	2017
922	6104041001	1	6104	16	2017
923	6104061001	1	6104	93	2017
924	6104061002	1	6104	1	2017
925	6104071001	1	6104	115	2017
926	6104081001	1	6104	92	2017
927	6104081002	1	6104	5	2017
928	6104991999	1	6104	8	2017
1207	6105011001	1	6105	315	2017
1208	6105011002	1	6105	157	2017
1209	6105021001	1	6105	4	2017
1210	6105031001	1	6105	161	2017
1211	6105041001	1	6105	192	2017
1212	6105991999	1	6105	3	2017

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	6101011001	298	2017	06101
2	6101021001	140	2017	06101
3	6101031001	78	2017	06101
4	6101041001	128	2017	06101
5	6101041002	553	2017	06101
6	6101051001	118	2017	06101
7	6101051002	181	2017	06101
8	6101051003	133	2017	06101
9	6101051004	108	2017	06101
10	6101051005	77	2017	06101
11	6101061001	604	2017	06101
12	6101061002	101	2017	06101
13	6101061003	187	2017	06101
14	6101061004	136	2017	06101
15	6101061005	166	2017	06101
16	6101061006	72	2017	06101
17	6101061007	65	2017	06101
18	6101061008	100	2017	06101
19	6101061009	74	2017	06101
20	6101071001	75	2017	06101
21	6101071002	92	2017	06101
22	6101071003	61	2017	06101
23	6101071004	143	2017	06101
24	6101071005	118	2017	06101
25	6101071006	78	2017	06101
26	6101071007	41	2017	06101
27	6101081001	78	2017	06101
28	6101081002	208	2017	06101
29	6101081003	559	2017	06101
30	6101081004	598	2017	06101
31	6101081005	305	2017	06101
32	6101081006	235	2017	06101
33	6101081007	707	2017	06101
34	6101091001	31	2017	06101
35	6101091002	60	2017	06101
36	6101091003	357	2017	06101
37	6101091004	91	2017	06101
38	6101091005	104	2017	06101
39	6101091006	89	2017	06101
40	6101091007	426	2017	06101
41	6101091008	423	2017	06101
42	6101101001	336	2017	06101
43	6101101002	293	2017	06101
44	6101101003	213	2017	06101
45	6101101004	553	2017	06101
46	6101111001	427	2017	06101
47	6101111002	268	2017	06101
48	6101111003	84	2017	06101
49	6101111004	303	2017	06101
50	6101111005	367	2017	06101
51	6101111006	23	2017	06101
52	6101131001	5	2017	06101
53	6101141001	484	2017	06101
54	6101141002	11	2017	06101
55	6101141003	29	2017	06101
56	6101151001	1052	2017	06101
57	6101151002	52	2017	06101
58	6101151003	138	2017	06101
59	6101151004	294	2017	06101
60	6101151005	134	2017	06101
61	6101151006	135	2017	06101
62	6101151007	447	2017	06101
63	6101151008	334	2017	06101
64	6101151009	1673	2017	06101
65	6101151010	750	2017	06101
66	6101161001	297	2017	06101
67	6101161002	270	2017	06101
68	6101161003	52	2017	06101
69	6101161004	189	2017	06101
70	6101161005	157	2017	06101
71	6101161006	80	2017	06101
72	6101161007	126	2017	06101
73	6101171001	220	2017	06101
74	6101171002	99	2017	06101
75	6101171003	646	2017	06101
76	6101171004	159	2017	06101
77	6101171005	769	2017	06101
78	6101171006	86	2017	06101
79	6101991999	48	2017	06101
358	6102011001	81	2017	06102
359	6102011002	138	2017	06102
360	6102021001	12	2017	06102
639	6103011001	47	2017	06103
640	6103021001	135	2017	06103
641	6103031001	69	2017	06103
642	6103991999	2	2017	06103
921	6104011001	73	2017	06104
922	6104041001	16	2017	06104
923	6104061001	93	2017	06104
924	6104061002	1	2017	06104
925	6104071001	115	2017	06104
926	6104081001	92	2017	06104
927	6104081002	5	2017	06104
928	6104991999	8	2017	06104
1207	6105011001	315	2017	06105
1208	6105011002	157	2017	06105
1209	6105021001	4	2017	06105
1210	6105031001	161	2017	06105
1211	6105041001	192	2017	06105
1212	6105991999	3	2017	06105

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_rural_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	289375.3	2017	1101	191468	55406102543	Rural
01401	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
01402	Camiña	262850.3	2017	1402	1250	328562901	Rural
01404	Huara	253968.5	2017	1404	2730	693334131	Rural
01405	Pica	290496.7	2017	1405	9296	2700457509	Rural
02103	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
02104	Taltal	345494.0	2017	2104	13317	4600943086	Rural
02201	Calama	310025.0	2017	2201	165731	51380756402	Rural
02203	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
02301	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
03101	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03103	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03202	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural
03301	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03302	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03303	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03304	Huasco	227560.7	2017	3304	10149	2309513927	Rural
04101	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04102	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04103	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04104	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04105	Paiguano	205942.1	2017	4105	4497	926121774	Rural
04106	Vicuña	176130.6	2017	4106	27771	4891322768	Rural
04201	Illapel	191976.8	2017	4201	30848	5922099530	Rural
04202	Canela	171370.3	2017	4202	9093	1558270441	Rural
04203	Los Vilos	173238.5	2017	4203	21382	3704185607	Rural
04204	Salamanca	223234.2	2017	4204	29347	6551254640	Rural
04301	Ovalle	241393.7	2017	4301	111272	26860360045	Rural
04302	Combarbalá	179139.6	2017	4302	13322	2386498044	Rural
04303	Monte Patria	201205.8	2017	4303	30751	6187280931	Rural
04304	Punitaqui	171931.7	2017	4304	10956	1883683880	Rural
04305	Río Hurtado	182027.2	2017	4305	4278	778712384	Rural
05101	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05102	Casablanca	268917.1	2017	5102	26867	7224996933	Rural
05105	Puchuncaví	279614.4	2017	5105	18546	5185728335	Rural
05107	Quintero	334628.2	2017	5107	31923	10682335196	Rural
05301	Los Andes	324402.1	2017	5301	66708	21640215030	Rural
05302	Calle Larga	242743.8	2017	5302	14832	3600375502	Rural
05303	Rinconada	326532.5	2017	5303	10207	3332917471	Rural
05304	San Esteban	223168.6	2017	5304	18855	4207844130	Rural
05401	La Ligua	181468.0	2017	5401	35390	6422154059	Rural
05402	Cabildo	231277.8	2017	5402	19388	4484014285	Rural
05404	Petorca	298208.9	2017	5404	9826	2930200178	Rural
05405	Zapallar	292882.3	2017	5405	7339	2149463129	Rural
05501	Quillota	220926.8	2017	5501	90517	19997628209	Rural
05502	Calera	226906.2	2017	5502	50554	11471016698	Rural
05503	Hijuelas	253739.9	2017	5503	17988	4564273201	Rural
05504	La Cruz	291124.1	2017	5504	22098	6433259569	Rural
05506	Nogales	264475.3	2017	5506	22120	5850194593	Rural
05601	San Antonio	266331.2	2017	5601	91350	24329353815	Rural

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
06101	6101011001	298	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101021001	140	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101031001	78	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101041001	128	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101041002	553	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101051001	118	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101051002	181	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101051003	133	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101051004	108	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101051005	77	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101061001	604	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101061002	101	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101061003	187	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101061004	136	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101061005	166	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101061006	72	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101061007	65	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101061008	100	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101061009	74	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101071001	75	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101071002	92	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101071003	61	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101071004	143	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101071005	118	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101071006	78	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101071007	41	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101081001	78	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101081002	208	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101081003	559	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101081004	598	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101081005	305	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101081006	235	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101081007	707	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101091001	31	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101091002	60	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101091003	357	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101091004	91	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101091005	104	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101091006	89	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101091007	426	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101091008	423	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101101001	336	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101101002	293	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101101003	213	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101101004	553	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101111001	427	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101111002	268	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101111003	84	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101111004	303	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101111005	367	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101111006	23	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101131001	5	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101141001	484	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101141002	11	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101141003	29	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101151001	1052	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101151002	52	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101151003	138	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101151004	294	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101151005	134	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101151006	135	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101151007	447	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101151008	334	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101151009	1673	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101151010	750	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101161001	297	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101161002	270	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101161003	52	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101161004	189	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101161005	157	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101161006	80	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101161007	126	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101171001	220	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101171002	99	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101171003	646	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101171004	159	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101171005	769	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101171006	86	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101991999	48	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06102	6102011001	81	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102011002	138	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102021001	12	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06103	6103011001	47	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural
06103	6103021001	135	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural
06103	6103031001	69	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural
06103	6103991999	2	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural
06104	6104011001	73	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104041001	16	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104061001	93	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104061002	1	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104071001	115	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104081001	92	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104081002	5	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104991999	8	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06105	6105011001	315	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06105	6105011002	157	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06105	6105021001	4	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06105	6105031001	161	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06105	6105041001	192	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06105	6105991999	3	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
06101	6101011001	298	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101021001	140	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101031001	78	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101041001	128	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101041002	553	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101051001	118	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101051002	181	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101051003	133	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101051004	108	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101051005	77	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101061001	604	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101061002	101	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101061003	187	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101061004	136	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101061005	166	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101061006	72	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101061007	65	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101061008	100	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101061009	74	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101071001	75	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101071002	92	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101071003	61	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101071004	143	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101071005	118	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101071006	78	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101071007	41	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101081001	78	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101081002	208	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101081003	559	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101081004	598	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101081005	305	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101081006	235	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101081007	707	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101091001	31	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101091002	60	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101091003	357	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101091004	91	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101091005	104	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101091006	89	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101091007	426	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101091008	423	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101101001	336	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101101002	293	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101101003	213	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101101004	553	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101111001	427	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101111002	268	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101111003	84	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101111004	303	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101111005	367	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101111006	23	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101131001	5	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101141001	484	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101141002	11	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101141003	29	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101151001	1052	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101151002	52	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101151003	138	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101151004	294	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101151005	134	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101151006	135	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101151007	447	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101151008	334	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101151009	1673	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101151010	750	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101161001	297	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101161002	270	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101161003	52	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101161004	189	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101161005	157	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101161006	80	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101161007	126	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101171001	220	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101171002	99	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101171003	646	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101171004	159	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101171005	769	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101171006	86	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101991999	48	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06102	6102011001	81	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102011002	138	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102021001	12	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06103	6103011001	47	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural
06103	6103021001	135	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural
06103	6103031001	69	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural
06103	6103991999	2	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural
06104	6104011001	73	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104041001	16	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104061001	93	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104061002	1	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104071001	115	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104081001	92	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104081002	5	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104991999	8	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06105	6105011001	315	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06105	6105011002	157	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06105	6105021001	4	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06105	6105031001	161	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06105	6105041001	192	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06105	6105991999	3	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
6101011001	06101	298	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2931	0.0121229	06101
6101021001	06101	140	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1326	0.0054845	06101
6101031001	06101	78	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	893	0.0036935	06101
6101041001	06101	128	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1599	0.0066136	06101
6101041002	06101	553	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3616	0.0149561	06101
6101051001	06101	118	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2105	0.0087065	06101
6101051002	06101	181	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1634	0.0067584	06101
6101051003	06101	133	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1810	0.0074863	06101
6101051004	06101	108	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1724	0.0071306	06101
6101051005	06101	77	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1297	0.0053645	06101
6101061001	06101	604	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3575	0.0147865	06101
6101061002	06101	101	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1868	0.0077262	06101
6101061003	06101	187	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1945	0.0080447	06101
6101061004	06101	136	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2013	0.0083260	06101
6101061005	06101	166	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1941	0.0080282	06101
6101061006	06101	72	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1440	0.0059560	06101
6101061007	06101	65	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1880	0.0077759	06101
6101061008	06101	100	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1255	0.0051908	06101
6101061009	06101	74	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1458	0.0060304	06101
6101071001	06101	75	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3085	0.0127599	06101
6101071002	06101	92	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1951	0.0080695	06101
6101071003	06101	61	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1805	0.0074656	06101
6101071004	06101	143	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2369	0.0097984	06101
6101071005	06101	118	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	411	0.0016999	06101
6101071006	06101	78	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1821	0.0075318	06101
6101071007	06101	41	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1281	0.0052983	06101
6101081001	06101	78	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1438	0.0059477	06101
6101081002	06101	208	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2053	0.0084914	06101
6101081003	06101	559	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2627	0.0108655	06101
6101081004	06101	598	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2379	0.0098398	06101
6101081005	06101	305	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2188	0.0090498	06101
6101081006	06101	235	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2502	0.0103485	06101
6101081007	06101	707	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2234	0.0092400	06101
6101091001	06101	31	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2303	0.0095254	06101
6101091002	06101	60	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1991	0.0082350	06101
6101091003	06101	357	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4641	0.0191956	06101
6101091004	06101	91	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1903	0.0078710	06101
6101091005	06101	104	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2187	0.0090456	06101
6101091006	06101	89	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1472	0.0060883	06101
6101091007	06101	426	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	8109	0.0335396	06101
6101091008	06101	423	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	11700	0.0483923	06101
6101101001	06101	336	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3965	0.0163996	06101
6101101002	06101	293	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4110	0.0169993	06101
6101101003	06101	213	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4224	0.0174709	06101
6101101004	06101	553	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	5803	0.0240018	06101
6101111001	06101	427	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1303	0.0053893	06101
6101111002	06101	268	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	5050	0.0208873	06101
6101111003	06101	84	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2031	0.0084004	06101
6101111004	06101	303	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3455	0.0142902	06101
6101111005	06101	367	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	5041	0.0208501	06101
6101111006	06101	23	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1279	0.0052901	06101
6101131001	06101	5	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	175	0.0007238	06101
6101141001	06101	484	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	7033	0.0290891	06101
6101141002	06101	11	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1110	0.0045911	06101
6101141003	06101	29	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1183	0.0048930	06101
6101151001	06101	1052	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	9988	0.0413113	06101
6101151002	06101	52	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1136	0.0046986	06101
6101151003	06101	138	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1480	0.0061214	06101
6101151004	06101	294	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	5748	0.0237743	06101
6101151005	06101	134	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3234	0.0133761	06101
6101151006	06101	135	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2829	0.0117010	06101
6101151007	06101	447	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2757	0.0114032	06101
6101151008	06101	334	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3771	0.0155972	06101
6101151009	06101	1673	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	6283	0.0259871	06101
6101151010	06101	750	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4858	0.0200931	06101
6101161001	06101	297	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	6794	0.0281006	06101
6101161002	06101	270	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	6040	0.0249820	06101
6101161003	06101	52	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2230	0.0092235	06101
6101161004	06101	189	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	6100	0.0252302	06101
6101161005	06101	157	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4455	0.0184263	06101
6101161006	06101	80	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1457	0.0060263	06101
6101161007	06101	126	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1821	0.0075318	06101
6101171001	06101	220	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1666	0.0068907	06101
6101171002	06101	99	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1913	0.0079123	06101
6101171003	06101	646	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3730	0.0154276	06101
6101171004	06101	159	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4000	0.0165444	06101
6101171005	06101	769	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	5138	0.0212513	06101
6101171006	06101	86	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1713	0.0070851	06101
6101991999	06101	48	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	520	0.0021508	06101
6102011001	06102	81	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	2483	0.1911765	06102
6102011002	06102	138	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	4176	0.3215276	06102
6102021001	06102	12	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	345	0.0265630	06102
6103011001	06103	47	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	377	0.0512298	06103
6103021001	06103	135	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	1770	0.2405218	06103
6103031001	06103	69	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	1260	0.1712189	06103
6103991999	06103	2	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	17	0.0023101	06103
6104011001	06104	73	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	1464	0.0747053	06104
6104041001	06104	16	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	1450	0.0739909	06104
6104061001	06104	93	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	4056	0.2069705	06104
6104061002	06104	1	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	37	0.0018880	06104
6104071001	06104	115	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	2247	0.1146604	06104
6104081001	06104	92	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	1713	0.0874113	06104
6104081002	06104	5	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	125	0.0063785	06104
6104991999	06104	8	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	71	0.0036230	06104
6105011001	06105	315	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	5036	0.2411069	06105
6105011002	06105	157	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	2266	0.1084885	06105
6105021001	06105	4	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	99	0.0047398	06105
6105031001	06105	161	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	3661	0.1752765	06105
6105041001	06105	192	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	5339	0.2556135	06105
6105991999	06105	3	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	23	0.0011012	06105

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
6101011001	06101	298	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2931	0.0121229	06101	714335714
6101021001	06101	140	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1326	0.0054845	06101	323169279
6101031001	06101	78	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	893	0.0036935	06101	217639643
6101041001	06101	128	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1599	0.0066136	06101	389704131
6101041002	06101	553	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3616	0.0149561	06101	881282136
6101051001	06101	118	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2105	0.0087065	06101	513025137
6101051002	06101	181	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1634	0.0067584	06101	398234240
6101051003	06101	133	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1810	0.0074863	06101	441128503
6101051004	06101	108	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1724	0.0071306	06101	420168806
6101051005	06101	77	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1297	0.0053645	06101	316101474
6101061001	06101	604	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3575	0.0147865	06101	871289723
6101061002	06101	101	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1868	0.0077262	06101	455264112
6101061003	06101	187	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1945	0.0080447	06101	474030353
6101061004	06101	136	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2013	0.0083260	06101	490603136
6101061005	06101	166	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1941	0.0080282	06101	473055483
6101061006	06101	72	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1440	0.0059560	06101	350953063
6101061007	06101	65	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1880	0.0077759	06101	458188721
6101061008	06101	100	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1255	0.0051908	06101	305865343
6101061009	06101	74	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1458	0.0060304	06101	355339976
6101071001	06101	75	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3085	0.0127599	06101	751868194
6101071002	06101	92	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1951	0.0080695	06101	475492657
6101071003	06101	61	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1805	0.0074656	06101	439909916
6101071004	06101	143	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2369	0.0097984	06101	577366532
6101071005	06101	118	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	411	0.0016999	06101	100167853
6101071006	06101	78	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1821	0.0075318	06101	443809394
6101071007	06101	41	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1281	0.0052983	06101	312201996
6101081001	06101	78	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1438	0.0059477	06101	350465628
6101081002	06101	208	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2053	0.0084914	06101	500351832
6101081003	06101	559	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2627	0.0108655	06101	640245623
6101081004	06101	598	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2379	0.0098398	06101	579803706
6101081005	06101	305	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2188	0.0090498	06101	533253682
6101081006	06101	235	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2502	0.0103485	06101	609780947
6101081007	06101	707	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2234	0.0092400	06101	544464683
6101091001	06101	31	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2303	0.0095254	06101	561281184
6101091002	06101	60	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1991	0.0082350	06101	485241353
6101091003	06101	357	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4641	0.0191956	06101	1131092476
6101091004	06101	91	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1903	0.0078710	06101	463794222
6101091005	06101	104	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2187	0.0090456	06101	533009965
6101091006	06101	89	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1472	0.0060883	06101	358752020
6101091007	06101	426	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	8109	0.0335396	06101	1976304437
6101091008	06101	423	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	11700	0.0483923	06101	2851493638
6101101001	06101	336	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3965	0.0163996	06101	966339511
6101101002	06101	293	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4110	0.0169993	06101	1001678534
6101101003	06101	213	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4224	0.0174709	06101	1029462319
6101101004	06101	553	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	5803	0.0240018	06101	1414292101
6101111001	06101	427	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1303	0.0053893	06101	317563779
6101111002	06101	268	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	5050	0.0208873	06101	1230772895
6101111003	06101	84	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2031	0.0084004	06101	494990049
6101111004	06101	303	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3455	0.0142902	06101	842043634
6101111005	06101	367	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	5041	0.0208501	06101	1228579438
6101111006	06101	23	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1279	0.0052901	06101	311714561
6101131001	06101	5	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	175	0.0007238	06101	42650546
6101141001	06101	484	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	7033	0.0290891	06101	1714064509
6101141002	06101	11	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1110	0.0045911	06101	270526320
6101141003	06101	29	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1183	0.0048930	06101	288317690
6101151001	06101	1052	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	9988	0.0413113	06101	2434249441
6101151002	06101	52	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1136	0.0046986	06101	276862972
6101151003	06101	138	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1480	0.0061214	06101	360701759
6101151004	06101	294	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	5748	0.0237743	06101	1400887644
6101151005	06101	134	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3234	0.0133761	06101	788182088
6101151006	06101	135	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2829	0.0117010	06101	689476539
6101151007	06101	447	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2757	0.0114032	06101	671928885
6101151008	06101	334	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3771	0.0155972	06101	919058334
6101151009	06101	1673	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	6283	0.0259871	06101	1531276455
6101151010	06101	750	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4858	0.0200931	06101	1183979153
6101161001	06101	297	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	6794	0.0281006	06101	1655816049
6101161002	06101	270	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	6040	0.0249820	06101	1472053126
6101161003	06101	52	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2230	0.0092235	06101	543489813
6101161004	06101	189	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	6100	0.0252302	06101	1486676170
6101161005	06101	157	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4455	0.0184263	06101	1085761039
6101161006	06101	80	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1457	0.0060263	06101	355096259
6101161007	06101	126	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1821	0.0075318	06101	443809394
6101171001	06101	220	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1666	0.0068907	06101	406033197
6101171002	06101	99	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1913	0.0079123	06101	466231396
6101171003	06101	646	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3730	0.0154276	06101	909065920
6101171004	06101	159	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4000	0.0165444	06101	974869620
6101171005	06101	769	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	5138	0.0212513	06101	1252220027
6101171006	06101	86	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1713	0.0070851	06101	417487915
6101991999	06101	48	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	520	0.0021508	06101	126733051
6102011001	06102	81	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	2483	0.1911765	06102	686235278
6102011002	06102	138	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	4176	0.3215276	06102	1154135529
6102021001	06102	12	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	345	0.0265630	06102	95348840
6103011001	06103	47	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	377	0.0512298	06103	73485540
6103021001	06103	135	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	1770	0.2405218	06103	345011687
6103031001	06103	69	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	1260	0.1712189	06103	245601540
6103991999	06103	2	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	17	0.0023101	06103	3313672
6104011001	06104	73	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	1464	0.0747053	06104	429538039
6104041001	06104	16	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	1450	0.0739909	06104	425430435
6104061001	06104	93	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	4056	0.2069705	06104	1190031617
6104061002	06104	1	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	37	0.0018880	06104	10855811
6104071001	06104	115	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	2247	0.1146604	06104	659270474
6104081001	06104	92	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	1713	0.0874113	06104	502594714
6104081002	06104	5	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	125	0.0063785	06104	36675038
6104991999	06104	8	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	71	0.0036230	06104	20831421
6105011001	06105	315	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	5036	0.2411069	06105	1224432817
6105011002	06105	157	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	2266	0.1084885	06105	550946141
6105021001	06105	4	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	99	0.0047398	06105	24070462
6105031001	06105	161	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	3661	0.1752765	06105	890120839
6105041001	06105	192	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	5339	0.2556135	06105	1298103021
6105991999	06105	3	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	23	0.0011012	06105	5592128

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -875129100 -266199862  -70086224  223416065 1935486759 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 411655968   28704385   14.34   <2e-16 ***
## Freq.x        1192319     101737   11.72   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 376800000 on 276 degrees of freedom
## Multiple R-squared:  0.3323, Adjusted R-squared:  0.3299 
## F-statistic: 137.4 on 1 and 276 DF,  p-value: < 2.2e-16

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.329866330186185"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.329866330186185"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.510087204089427"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.480188375627984"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.522876200247738"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.437034576861668"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.682396834854948"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.746418130343469"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 1    cuadrático 0.329866330186185
## 2        cúbico 0.329866330186185
## 6      log-raíz 0.437034576861668
## 4 raíz cuadrada 0.480188375627984
## 3   logarítmico 0.510087204089427
## 5     raíz-raíz 0.522876200247738
## 7      raíz-log 0.682396834854948
## 8       log-log 0.746418130343469
##                                                                     sintaxis
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 8
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2761 -0.3837  0.1349  0.4473  1.7169 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.36948    0.12501  130.94   <2e-16 ***
## log(Freq.x)  0.77179    0.02701   28.57   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6702 on 276 degrees of freedom
## Multiple R-squared:  0.7473, Adjusted R-squared:  0.7464 
## F-statistic: 816.3 on 1 and 276 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    16.36948

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##   0.7717882

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.7464 ).

9.1 Diagrama de dispersión sobre log-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2761 -0.3837  0.1349  0.4473  1.7169 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.36948    0.12501  130.94   <2e-16 ***
## log(Freq.x)  0.77179    0.02701   28.57   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6702 on 276 degrees of freedom
## Multiple R-squared:  0.7473, Adjusted R-squared:  0.7464 
## F-statistic: 816.3 on 1 and 276 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = e^{16.36948+0.7717882 \cdot ln{X}} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
6101011001	06101	298	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2931	0.0121229	06101	714335714	1044109144
6101021001	06101	140	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1326	0.0054845	06101	323169279	582815752
6101031001	06101	78	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	893	0.0036935	06101	217639643	371083188
6101041001	06101	128	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1599	0.0066136	06101	389704131	543869592
6101041002	06101	553	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3616	0.0149561	06101	881282136	1682588323
6101051001	06101	118	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2105	0.0087065	06101	513025137	510774343
6101051002	06101	181	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1634	0.0067584	06101	398234240	710599126
6101051003	06101	133	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1810	0.0074863	06101	441128503	560194219
6101051004	06101	108	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1724	0.0071306	06101	420168806	477031930
6101051005	06101	77	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1297	0.0053645	06101	316101474	367406024
6101061001	06101	604	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3575	0.0147865	06101	871289723	1801135851
6101061002	06101	101	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1868	0.0077262	06101	455264112	452987872
6101061003	06101	187	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1945	0.0080447	06101	474030353	728711343
6101061004	06101	136	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2013	0.0083260	06101	490603136	569921632
6101061005	06101	166	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1941	0.0080282	06101	473055483	664703876
6101061006	06101	72	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1440	0.0059560	06101	350953063	348852867
6101061007	06101	65	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1880	0.0077759	06101	458188721	322374090
6101061008	06101	100	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1255	0.0051908	06101	305865343	449522454
6101061009	06101	74	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1458	0.0060304	06101	355339976	356308330
6101071001	06101	75	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3085	0.0127599	06101	751868194	360018774
6101071002	06101	92	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1951	0.0080695	06101	475492657	421505516
6101071003	06101	61	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1805	0.0074656	06101	439909916	306952725
6101071004	06101	143	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2369	0.0097984	06101	577366532	592431181
6101071005	06101	118	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	411	0.0016999	06101	100167853	510774343
6101071006	06101	78	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1821	0.0075318	06101	443809394	371083188
6101071007	06101	41	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1281	0.0052983	06101	312201996	225892881
6101081001	06101	78	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1438	0.0059477	06101	350465628	371083188
6101081002	06101	208	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2053	0.0084914	06101	500351832	791095499
6101081003	06101	559	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2627	0.0108655	06101	640245623	1696660668
6101081004	06101	598	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2379	0.0098398	06101	579803706	1787311240
6101081005	06101	305	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2188	0.0090498	06101	533253682	1062987808
6101081006	06101	235	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2502	0.0103485	06101	609780947	869234953
6101081007	06101	707	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2234	0.0092400	06101	544464683	2033870312
6101091001	06101	31	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2303	0.0095254	06101	561281184	182049862
6101091002	06101	60	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1991	0.0082350	06101	485241353	303061763
6101091003	06101	357	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4641	0.0191956	06101	1131092476	1200312113
6101091004	06101	91	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1903	0.0078710	06101	463794222	417965100
6101091005	06101	104	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2187	0.0090456	06101	533009965	463337573
6101091006	06101	89	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1472	0.0060883	06101	358752020	410857477
6101091007	06101	426	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	8109	0.0335396	06101	1976304437	1375695389
6101091008	06101	423	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	11700	0.0483923	06101	2851493638	1368212283
6101101001	06101	336	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3965	0.0163996	06101	966339511	1145443904
6101101002	06101	293	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4110	0.0169993	06101	1001678534	1030562423
6101101003	06101	213	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4224	0.0174709	06101	1029462319	805732511
6101101004	06101	553	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	5803	0.0240018	06101	1414292101	1682588323
6101111001	06101	427	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1303	0.0053893	06101	317563779	1378187082
6101111002	06101	268	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	5050	0.0208873	06101	1230772895	962012611
6101111003	06101	84	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2031	0.0084004	06101	494990049	392926244
6101111004	06101	303	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3455	0.0142902	06101	842043634	1057604091
6101111005	06101	367	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	5041	0.0208501	06101	1228579438	1226179317
6101111006	06101	23	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1279	0.0052901	06101	311714561	144590738
6101131001	06101	5	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	175	0.0007238	06101	42650546	44527859
6101141001	06101	484	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	7033	0.0290891	06101	1714064509	1518122959
6101141002	06101	11	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1110	0.0045911	06101	270526320	81829442
6101141003	06101	29	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1183	0.0048930	06101	288317690	172916532
6101151001	06101	1052	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	9988	0.0413113	06101	2434249441	2763955600
6101151002	06101	52	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1136	0.0046986	06101	276862972	271372681
6101151003	06101	138	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1480	0.0061214	06101	360701759	576379354
6101151004	06101	294	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	5748	0.0237743	06101	1400887644	1033275961
6101151005	06101	134	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3234	0.0133761	06101	788182088	563442200
6101151006	06101	135	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2829	0.0117010	06101	689476539	566684654
6101151007	06101	447	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2757	0.0114032	06101	671928885	1427746330
6101151008	06101	334	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3771	0.0155972	06101	919058334	1140178178
6101151009	06101	1673	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	6283	0.0259871	06101	1531276455	3953949344
6101151010	06101	750	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4858	0.0200931	06101	1183979153	2128694479
6101161001	06101	297	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	6794	0.0281006	06101	1655816049	1041403976
6101161002	06101	270	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	6040	0.0249820	06101	1472053126	967548728
6101161003	06101	52	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2230	0.0092235	06101	543489813	271372681
6101161004	06101	189	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	6100	0.0252302	06101	1486676170	734719123
6101161005	06101	157	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4455	0.0184263	06101	1085761039	636714040
6101161006	06101	80	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1457	0.0060263	06101	355096259	378405453
6101161007	06101	126	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1821	0.0075318	06101	443809394	537299196
6101171001	06101	220	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1666	0.0068907	06101	406033197	826093431
6101171002	06101	99	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1913	0.0079123	06101	466231396	446049117
6101171003	06101	646	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3730	0.0154276	06101	909065920	1897052168
6101171004	06101	159	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4000	0.0165444	06101	974869620	642964967
6101171005	06101	769	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	5138	0.0212513	06101	1252220027	2170195563
6101171006	06101	86	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1713	0.0070851	06101	417487915	400127194
6101991999	06101	48	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	520	0.0021508	06101	126733051	255115675
6102011001	06102	81	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	2483	0.1911765	06102	686235278	382050884
6102011002	06102	138	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	4176	0.3215276	06102	1154135529	576379354
6102021001	06102	12	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	345	0.0265630	06102	95348840	87513359
6103011001	06103	47	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	377	0.0512298	06103	73485540	251003854
6103021001	06103	135	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	1770	0.2405218	06103	345011687	566684654
6103031001	06103	69	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	1260	0.1712189	06103	245601540	337580243
6103991999	06103	2	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	17	0.0023101	06103	3313672	21953634
6104011001	06104	73	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	1464	0.0747053	06104	429538039	352586425
6104041001	06104	16	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	1450	0.0739909	06104	425430435	109269913
6104061001	06104	93	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	4056	0.2069705	06104	1190031617	425037161
6104061002	06104	1	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	37	0.0018880	06104	10855811	12858047
6104071001	06104	115	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	2247	0.1146604	06104	659270474	500722683
6104081001	06104	92	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	1713	0.0874113	06104	502594714	421505516
6104081002	06104	5	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	125	0.0063785	06104	36675038	44527859
6104991999	06104	8	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	71	0.0036230	06104	20831421	63998413
6105011001	06105	315	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	5036	0.2411069	06105	1224432817	1089786911
6105011002	06105	157	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	2266	0.1084885	06105	550946141	636714040
6105021001	06105	4	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	99	0.0047398	06105	24070462	37483299
6105031001	06105	161	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	3661	0.1752765	06105	890120839	649197976
6105041001	06105	192	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	5339	0.2556135	06105	1298103021	743703680
6105991999	06105	3	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	23	0.0011012	06105	5592128	30020061

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
6101011001	06101	298	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2931	0.0121229	06101	714335714	1044109144	356229.66
6101021001	06101	140	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1326	0.0054845	06101	323169279	582815752	439529.22
6101031001	06101	78	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	893	0.0036935	06101	217639643	371083188	415546.68
6101041001	06101	128	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1599	0.0066136	06101	389704131	543869592	340131.08
6101041002	06101	553	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3616	0.0149561	06101	881282136	1682588323	465317.57
6101051001	06101	118	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2105	0.0087065	06101	513025137	510774343	242648.14
6101051002	06101	181	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1634	0.0067584	06101	398234240	710599126	434883.19
6101051003	06101	133	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1810	0.0074863	06101	441128503	560194219	309499.57
6101051004	06101	108	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1724	0.0071306	06101	420168806	477031930	276700.66
6101051005	06101	77	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1297	0.0053645	06101	316101474	367406024	283273.73
6101061001	06101	604	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3575	0.0147865	06101	871289723	1801135851	503814.22
6101061002	06101	101	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1868	0.0077262	06101	455264112	452987872	242498.86
6101061003	06101	187	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1945	0.0080447	06101	474030353	728711343	374658.79
6101061004	06101	136	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2013	0.0083260	06101	490603136	569921632	283120.53
6101061005	06101	166	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1941	0.0080282	06101	473055483	664703876	342454.34
6101061006	06101	72	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1440	0.0059560	06101	350953063	348852867	242258.94
6101061007	06101	65	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1880	0.0077759	06101	458188721	322374090	171475.58
6101061008	06101	100	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1255	0.0051908	06101	305865343	449522454	358185.22
6101061009	06101	74	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1458	0.0060304	06101	355339976	356308330	244381.57
6101071001	06101	75	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3085	0.0127599	06101	751868194	360018774	116699.76
6101071002	06101	92	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1951	0.0080695	06101	475492657	421505516	216045.88
6101071003	06101	61	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1805	0.0074656	06101	439909916	306952725	170056.91
6101071004	06101	143	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2369	0.0097984	06101	577366532	592431181	250076.48
6101071005	06101	118	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	411	0.0016999	06101	100167853	510774343	1242759.96
6101071006	06101	78	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1821	0.0075318	06101	443809394	371083188	203779.89
6101071007	06101	41	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1281	0.0052983	06101	312201996	225892881	176341.05
6101081001	06101	78	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1438	0.0059477	06101	350465628	371083188	258055.07
6101081002	06101	208	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2053	0.0084914	06101	500351832	791095499	385336.34
6101081003	06101	559	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2627	0.0108655	06101	640245623	1696660668	645854.84
6101081004	06101	598	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2379	0.0098398	06101	579803706	1787311240	751286.78
6101081005	06101	305	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2188	0.0090498	06101	533253682	1062987808	485826.24
6101081006	06101	235	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2502	0.0103485	06101	609780947	869234953	347416.05
6101081007	06101	707	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2234	0.0092400	06101	544464683	2033870312	910416.43
6101091001	06101	31	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2303	0.0095254	06101	561281184	182049862	79049.01
6101091002	06101	60	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1991	0.0082350	06101	485241353	303061763	152215.85
6101091003	06101	357	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4641	0.0191956	06101	1131092476	1200312113	258632.22
6101091004	06101	91	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1903	0.0078710	06101	463794222	417965100	219634.84
6101091005	06101	104	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2187	0.0090456	06101	533009965	463337573	211859.89
6101091006	06101	89	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1472	0.0060883	06101	358752020	410857477	279115.13
6101091007	06101	426	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	8109	0.0335396	06101	1976304437	1375695389	169650.44
6101091008	06101	423	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	11700	0.0483923	06101	2851493638	1368212283	116941.22
6101101001	06101	336	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3965	0.0163996	06101	966339511	1145443904	288888.75
6101101002	06101	293	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4110	0.0169993	06101	1001678534	1030562423	250745.12
6101101003	06101	213	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4224	0.0174709	06101	1029462319	805732511	190751.07
6101101004	06101	553	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	5803	0.0240018	06101	1414292101	1682588323	289951.46
6101111001	06101	427	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1303	0.0053893	06101	317563779	1378187082	1057703.06
6101111002	06101	268	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	5050	0.0208873	06101	1230772895	962012611	190497.55
6101111003	06101	84	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2031	0.0084004	06101	494990049	392926244	193464.42
6101111004	06101	303	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3455	0.0142902	06101	842043634	1057604091	306108.28
6101111005	06101	367	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	5041	0.0208501	06101	1228579438	1226179317	243241.28
6101111006	06101	23	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1279	0.0052901	06101	311714561	144590738	113049.83
6101131001	06101	5	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	175	0.0007238	06101	42650546	44527859	254444.91
6101141001	06101	484	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	7033	0.0290891	06101	1714064509	1518122959	215857.10
6101141002	06101	11	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1110	0.0045911	06101	270526320	81829442	73720.22
6101141003	06101	29	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1183	0.0048930	06101	288317690	172916532	146167.82
6101151001	06101	1052	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	9988	0.0413113	06101	2434249441	2763955600	276727.63
6101151002	06101	52	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1136	0.0046986	06101	276862972	271372681	238884.40
6101151003	06101	138	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1480	0.0061214	06101	360701759	576379354	389445.51
6101151004	06101	294	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	5748	0.0237743	06101	1400887644	1033275961	179762.69
6101151005	06101	134	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3234	0.0133761	06101	788182088	563442200	174224.55
6101151006	06101	135	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2829	0.0117010	06101	689476539	566684654	200312.71
6101151007	06101	447	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2757	0.0114032	06101	671928885	1427746330	517862.29
6101151008	06101	334	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3771	0.0155972	06101	919058334	1140178178	302354.33
6101151009	06101	1673	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	6283	0.0259871	06101	1531276455	3953949344	629309.14
6101151010	06101	750	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4858	0.0200931	06101	1183979153	2128694479	438183.30
6101161001	06101	297	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	6794	0.0281006	06101	1655816049	1041403976	153282.89
6101161002	06101	270	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	6040	0.0249820	06101	1472053126	967548728	160190.19
6101161003	06101	52	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2230	0.0092235	06101	543489813	271372681	121691.79
6101161004	06101	189	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	6100	0.0252302	06101	1486676170	734719123	120445.76
6101161005	06101	157	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4455	0.0184263	06101	1085761039	636714040	142921.22
6101161006	06101	80	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1457	0.0060263	06101	355096259	378405453	259715.48
6101161007	06101	126	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1821	0.0075318	06101	443809394	537299196	295057.22
6101171001	06101	220	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1666	0.0068907	06101	406033197	826093431	495854.40
6101171002	06101	99	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1913	0.0079123	06101	466231396	446049117	233167.34
6101171003	06101	646	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	3730	0.0154276	06101	909065920	1897052168	508593.07
6101171004	06101	159	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	4000	0.0165444	06101	974869620	642964967	160741.24
6101171005	06101	769	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	5138	0.0212513	06101	1252220027	2170195563	422381.39
6101171006	06101	86	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1713	0.0070851	06101	417487915	400127194	233582.72
6101991999	06101	48	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	520	0.0021508	06101	126733051	255115675	490607.07
6102011001	06102	81	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	2483	0.1911765	06102	686235278	382050884	153866.65
6102011002	06102	138	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	4176	0.3215276	06102	1154135529	576379354	138021.88
6102021001	06102	12	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	345	0.0265630	06102	95348840	87513359	253661.91
6103011001	06103	47	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	377	0.0512298	06103	73485540	251003854	665792.72
6103021001	06103	135	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	1770	0.2405218	06103	345011687	566684654	320160.82
6103031001	06103	69	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	1260	0.1712189	06103	245601540	337580243	267920.83
6103991999	06103	2	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	17	0.0023101	06103	3313672	21953634	1291390.22
6104011001	06104	73	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	1464	0.0747053	06104	429538039	352586425	240837.72
6104041001	06104	16	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	1450	0.0739909	06104	425430435	109269913	75358.56
6104061001	06104	93	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	4056	0.2069705	06104	1190031617	425037161	104792.20
6104061002	06104	1	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	37	0.0018880	06104	10855811	12858047	347514.79
6104071001	06104	115	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	2247	0.1146604	06104	659270474	500722683	222840.54
6104081001	06104	92	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	1713	0.0874113	06104	502594714	421505516	246062.76
6104081002	06104	5	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	125	0.0063785	06104	36675038	44527859	356222.87
6104991999	06104	8	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	71	0.0036230	06104	20831421	63998413	901386.09
6105011001	06105	315	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	5036	0.2411069	06105	1224432817	1089786911	216399.31
6105011002	06105	157	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	2266	0.1084885	06105	550946141	636714040	280985.90
6105021001	06105	4	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	99	0.0047398	06105	24070462	37483299	378619.18
6105031001	06105	161	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	3661	0.1752765	06105	890120839	649197976	177328.05
6105041001	06105	192	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	5339	0.2556135	06105	1298103021	743703680	139296.44
6105991999	06105	3	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	23	0.0011012	06105	5592128	30020061	1305220.03

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_rural_nivel_nacional_17_r06.rds")

---

R-07

Urbano

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 1)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15201	1	1	1	1767	1	1	1	2	1	3	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	2	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	3	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	4	1	1	2	3	1	99	1	2	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	5	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	6	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	7	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	8	1	1	3	3	1	99	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	9	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	10	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	11	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	12	1	1	4	3	3	3	4	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	13	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	14	1	1	2	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	15	1	1	2	1	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	16	1	1	5	1	5	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	17	1	1	5	3	1	3	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	18	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	19	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	20	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	21	4	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	22	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	23	1	1	5	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	24	1	1	3	1	1	6	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	25	1	1	5	6	1	1	4	2	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	26	4	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	27	1	1	5	99	3	3	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	28	1	1	6	3	2	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	29	1	1	2	3	1	99	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	30	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	31	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	32	1	1	3	1	1	2	1	1	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	33	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	34	3	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	35	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	36	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	37	1	1	5	3	3	1	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	38	1	1	1	1	4	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	39	1	1	5	3	4	3	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	40	1	1	5	3	2	1	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	41	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	42	1	1	2	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	43	1	1	1	1	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	44	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	45	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	46	1	1	5	6	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	47	1	1	5	3	4	0	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	48	1	1	3	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	49	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	50	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	51	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	52	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	53	1	1	1	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	54	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	55	1	1	2	2	1	4	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	56	8	1	98	98	98	98	98	98	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	57	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	58	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	59	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	60	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	61	1	1	2	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	62	1	1	5	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	63	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	64	1	1	5	3	2	4	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	65	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	66	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	67	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	68	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	69	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	70	1	1	1	3	3	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	71	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	72	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	73	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	74	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	75	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	76	1	1	5	3	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	77	1	1	3	6	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	78	5	1	3	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	79	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	80	1	1	2	1	1	3	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	81	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	82	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	83	1	1	5	3	2	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	84	4	1	5	3	2	0	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	85	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	86	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	87	1	1	3	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	88	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	89	5	1	4	3	2	1	2	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	90	1	1	5	3	3	2	1	3	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	91	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	92	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	93	5	1	4	3	5	1	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	94	1	1	5	3	3	99	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	95	1	1	5	3	2	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	96	1	1	2	3	4	6	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	97	1	1	5	1	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	98	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	99	1	1	2	7	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	100	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 7:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 7)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	7101011001	70	2017	07101
2	7101011002	156	2017	07101
3	7101021001	59	2017	07101
4	7101021002	67	2017	07101
5	7101021003	31	2017	07101
6	7101031001	145	2017	07101
7	7101031002	52	2017	07101
8	7101031003	63	2017	07101
9	7101041001	45	2017	07101
10	7101041002	102	2017	07101
11	7101041003	64	2017	07101
12	7101041004	92	2017	07101
13	7101041005	50	2017	07101
14	7101051001	140	2017	07101
15	7101051002	66	2017	07101
16	7101061001	55	2017	07101
17	7101061002	39	2017	07101
18	7101061003	82	2017	07101
19	7101061004	58	2017	07101
20	7101061005	28	2017	07101
21	7101061006	129	2017	07101
22	7101071001	236	2017	07101
23	7101071002	304	2017	07101
24	7101071003	74	2017	07101
25	7101071004	75	2017	07101
26	7101071005	117	2017	07101
27	7101071006	153	2017	07101
28	7101071007	196	2017	07101
29	7101071008	122	2017	07101
30	7101071009	121	2017	07101
31	7101071010	116	2017	07101
32	7101081001	18	2017	07101
33	7101081002	79	2017	07101
34	7101081003	99	2017	07101
35	7101081004	141	2017	07101
36	7101081005	361	2017	07101
37	7101081006	43	2017	07101
38	7101091001	9	2017	07101
39	7101101001	37	2017	07101
40	7101111001	133	2017	07101
41	7101111002	608	2017	07101
42	7101111003	164	2017	07101
43	7101111004	7	2017	07101
44	7101111005	820	2017	07101
45	7101111006	402	2017	07101
46	7101111007	47	2017	07101
47	7101111008	1428	2017	07101
48	7101111009	399	2017	07101
49	7101111010	543	2017	07101
50	7101121001	107	2017	07101
51	7101121002	73	2017	07101
52	7101121003	127	2017	07101
53	7101121004	84	2017	07101
54	7101121005	20	2017	07101
55	7101121006	429	2017	07101
56	7101121007	41	2017	07101
57	7101121008	51	2017	07101
58	7101121009	86	2017	07101
59	7101121010	41	2017	07101
60	7101131001	80	2017	07101
61	7101131002	191	2017	07101
62	7101131003	461	2017	07101
63	7101131004	229	2017	07101
64	7101131005	169	2017	07101
65	7101131006	103	2017	07101
66	7101131007	112	2017	07101
67	7101131008	104	2017	07101
68	7101141001	45	2017	07101
69	7101141002	67	2017	07101
70	7101141003	121	2017	07101
71	7101141004	76	2017	07101
72	7101141005	88	2017	07101
73	7101141006	434	2017	07101
74	7101141007	89	2017	07101
75	7101141008	223	2017	07101
76	7101141009	89	2017	07101
77	7101141010	71	2017	07101
78	7101151001	4	2017	07101
79	7101161001	69	2017	07101
80	7101991999	55	2017	07101
391	7102011001	141	2017	07102
392	7102011002	162	2017	07102
393	7102021001	271	2017	07102
394	7102021002	171	2017	07102
395	7102021003	305	2017	07102
396	7102021004	148	2017	07102
397	7102021005	91	2017	07102
398	7102031001	215	2017	07102
399	7102031002	131	2017	07102
400	7102041001	53	2017	07102
401	7102071001	22	2017	07102
712	7103011001	157	2017	07103
1023	7104011001	86	2017	07104
1024	7104021001	1	2017	07104
1025	7104991999	1	2017	07104
1336	7105011001	85	2017	07105
1337	7105051001	47	2017	07105
1338	7105051002	82	2017	07105
1339	7105051003	14	2017	07105
1340	7105051004	244	2017	07105

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_urbano_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
01107	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
01401	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
01405	Pica	330061.1	2017	1405	9296	3068247619	Urbano
02101	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02102	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02104	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02201	Calama	416281.1	2017	2201	165731	68990679686	Urbano
02203	San Pedro de Atacama	437934.7	2017	2203	10996	4815529626	Urbano
02301	Tocopilla	271720.8	2017	2301	25186	6843559467	Urbano
02302	María Elena	466266.9	2017	2302	6457	3010685220	Urbano
03101	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
03102	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
03103	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
03201	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
03202	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
03301	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
03303	Freirina	289049.9	2017	3303	7041	2035200054	Urbano
03304	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
04101	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04102	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04103	Andacollo	251267.7	2017	4103	11044	2775000288	Urbano
04104	La Higuera	214257.0	2017	4104	4241	908664019	Urbano
04106	Vicuña	245957.4	2017	4106	27771	6830481918	Urbano
04201	Illapel	270316.5	2017	4201	30848	8338722128	Urbano
04202	Canela	233397.3	2017	4202	9093	2122281844	Urbano
04203	Los Vilos	282415.6	2017	4203	21382	6038609501	Urbano
04204	Salamanca	262056.9	2017	4204	29347	7690585032	Urbano
04301	Ovalle	274771.4	2017	4301	111272	30574361012	Urbano
04302	Combarbalá	228990.4	2017	4302	13322	3050610572	Urbano
04303	Monte Patria	225369.1	2017	4303	30751	6930326684	Urbano
04304	Punitaqui	212496.1	2017	4304	10956	2328107498	Urbano
05101	Valparaíso	297929.0	2017	5101	296655	88382118059	Urbano
05102	Casablanca	341641.8	2017	5102	26867	9178890241	Urbano
05103	Concón	318496.3	2017	5103	42152	13425257057	Urbano
05105	Puchuncaví	296035.5	2017	5105	18546	5490274928	Urbano
05107	Quintero	308224.7	2017	5107	31923	9839456903	Urbano
05109	Viña del Mar	337006.1	2017	5109	334248	112643604611	Urbano
05301	Los Andes	339720.2	2017	5301	66708	22662055502	Urbano
05302	Calle Larga	246387.3	2017	5302	14832	3654416747	Urbano
05303	Rinconada	273904.7	2017	5303	10207	2795744821	Urbano
05304	San Esteban	219571.6	2017	5304	18855	4140022481	Urbano
05401	La Ligua	250134.4	2017	5401	35390	8852256241	Urbano
05402	Cabildo	262745.9	2017	5402	19388	5094117762	Urbano
05403	Papudo	294355.2	2017	5403	6356	1870921373	Urbano
05404	Petorca	237510.8	2017	5404	9826	2333781007	Urbano
05405	Zapallar	294389.2	2017	5405	7339	2160521991	Urbano
05501	Quillota	286029.5	2017	5501	90517	25890529852	Urbano
05502	Calera	277181.9	2017	5502	50554	14012652087	Urbano
05503	Hijuelas	254094.0	2017	5503	17988	4570642363	Urbano

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
07101	7101011001	70	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101011002	156	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101021001	59	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101021002	67	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101021003	31	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101031001	145	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101031002	52	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101031003	63	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101041001	45	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101041002	102	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101041003	64	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101041004	92	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101041005	50	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101051001	140	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101051002	66	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101061001	55	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101061002	39	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101061003	82	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101061004	58	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101061005	28	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101061006	129	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101071001	236	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101071002	304	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101071003	74	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101071004	75	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101071005	117	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101071006	153	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101071007	196	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101071008	122	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101071009	121	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101071010	116	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101081001	18	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101081002	79	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101081003	99	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101081004	141	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101081005	361	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101081006	43	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101091001	9	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101101001	37	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101111001	133	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101111002	608	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101111003	164	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101111004	7	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101111005	820	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101111006	402	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101111007	47	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101111008	1428	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101111009	399	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101111010	543	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101121001	107	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101121002	73	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101121003	127	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101121004	84	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101121005	20	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101121006	429	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101121007	41	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101121008	51	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101121009	86	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101121010	41	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101131001	80	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101131002	191	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101131003	461	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101131004	229	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101131005	169	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101131006	103	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101131007	112	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101131008	104	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101141001	45	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101141002	67	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101141003	121	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101141004	76	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101141005	88	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101141006	434	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101141007	89	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101141008	223	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101141009	89	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101141010	71	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101151001	4	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101161001	69	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101991999	55	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07102	7102011001	141	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano
07102	7102011002	162	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano
07102	7102021001	271	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano
07102	7102021002	171	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano
07102	7102021003	305	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano
07102	7102021004	148	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano
07102	7102021005	91	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano
07102	7102031001	215	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano
07102	7102031002	131	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano
07102	7102041001	53	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano
07102	7102071001	22	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano
07103	7103011001	157	2017	Curepto	281855.5	2017	7103	9448	2662971120	Urbano
07104	7104011001	86	2017	Empedrado	209235.2	2017	7104	4142	866652110	Urbano
07104	7104021001	1	2017	Empedrado	209235.2	2017	7104	4142	866652110	Urbano
07104	7104991999	1	2017	Empedrado	209235.2	2017	7104	4142	866652110	Urbano
07105	7105011001	85	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano
07105	7105051001	47	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano
07105	7105051002	82	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano
07105	7105051003	14	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano
07105	7105051004	244	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
07101	7101011001	70	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101011002	156	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101021001	59	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101021002	67	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101021003	31	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101031001	145	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101031002	52	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101031003	63	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101041001	45	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101041002	102	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101041003	64	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101041004	92	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101041005	50	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101051001	140	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101051002	66	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101061001	55	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101061002	39	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101061003	82	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101061004	58	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101061005	28	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101061006	129	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101071001	236	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101071002	304	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101071003	74	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101071004	75	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101071005	117	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101071006	153	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101071007	196	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101071008	122	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101071009	121	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101071010	116	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101081001	18	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101081002	79	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101081003	99	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101081004	141	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101081005	361	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101081006	43	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101091001	9	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101101001	37	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101111001	133	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101111002	608	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101111003	164	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101111004	7	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101111005	820	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101111006	402	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101111007	47	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101111008	1428	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101111009	399	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101111010	543	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101121001	107	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101121002	73	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101121003	127	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101121004	84	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101121005	20	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101121006	429	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101121007	41	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101121008	51	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101121009	86	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101121010	41	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101131001	80	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101131002	191	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101131003	461	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101131004	229	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101131005	169	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101131006	103	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101131007	112	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101131008	104	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101141001	45	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101141002	67	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101141003	121	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101141004	76	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101141005	88	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101141006	434	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101141007	89	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101141008	223	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101141009	89	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101141010	71	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101151001	4	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101161001	69	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07101	7101991999	55	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano
07102	7102011001	141	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano
07102	7102011002	162	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano
07102	7102021001	271	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano
07102	7102021002	171	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano
07102	7102021003	305	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano
07102	7102021004	148	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano
07102	7102021005	91	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano
07102	7102031001	215	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano
07102	7102031002	131	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano
07102	7102041001	53	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano
07102	7102071001	22	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano
07103	7103011001	157	2017	Curepto	281855.5	2017	7103	9448	2662971120	Urbano
07104	7104011001	86	2017	Empedrado	209235.2	2017	7104	4142	866652110	Urbano
07104	7104021001	1	2017	Empedrado	209235.2	2017	7104	4142	866652110	Urbano
07104	7104991999	1	2017	Empedrado	209235.2	2017	7104	4142	866652110	Urbano
07105	7105011001	85	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano
07105	7105051001	47	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano
07105	7105051002	82	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano
07105	7105051003	14	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano
07105	7105051004	244	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
7101011001	07101	70	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1693	0.0076830	07101
7101011002	07101	156	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1889	0.0085725	07101
7101021001	07101	59	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	951	0.0043157	07101
7101021002	07101	67	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	912	0.0041387	07101
7101021003	07101	31	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	857	0.0038891	07101
7101031001	07101	145	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1465	0.0066483	07101
7101031002	07101	52	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1428	0.0064804	07101
7101031003	07101	63	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1037	0.0047060	07101
7101041001	07101	45	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1306	0.0059267	07101
7101041002	07101	102	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1682	0.0076331	07101
7101041003	07101	64	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1627	0.0073835	07101
7101041004	07101	92	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1684	0.0076421	07101
7101041005	07101	50	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1196	0.0054276	07101
7101051001	07101	140	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2870	0.0130243	07101
7101051002	07101	66	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1049	0.0047605	07101
7101061001	07101	55	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1756	0.0079689	07101
7101061002	07101	39	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1371	0.0062217	07101
7101061003	07101	82	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3032	0.0137595	07101
7101061004	07101	58	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3215	0.0145900	07101
7101061005	07101	28	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2884	0.0130879	07101
7101061006	07101	129	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3367	0.0152798	07101
7101071001	07101	236	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2136	0.0096934	07101
7101071002	07101	304	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3210	0.0145673	07101
7101071003	07101	74	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1773	0.0080460	07101
7101071004	07101	75	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1769	0.0080279	07101
7101071005	07101	117	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1956	0.0088765	07101
7101071006	07101	153	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3097	0.0140545	07101
7101071007	07101	196	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3759	0.0170587	07101
7101071008	07101	122	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2586	0.0117355	07101
7101071009	07101	121	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3775	0.0171313	07101
7101071010	07101	116	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2262	0.0102652	07101
7101081001	07101	18	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1170	0.0053096	07101
7101081002	07101	79	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1841	0.0083546	07101
7101081003	07101	99	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1281	0.0058133	07101
7101081004	07101	141	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1562	0.0070885	07101
7101081005	07101	361	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3349	0.0151981	07101
7101081006	07101	43	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1555	0.0070567	07101
7101091001	07101	9	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	179	0.0008123	07101
7101101001	07101	37	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1347	0.0061128	07101
7101111001	07101	133	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	5385	0.0244376	07101
7101111002	07101	608	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3446	0.0156383	07101
7101111003	07101	164	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3435	0.0155883	07101
7101111004	07101	7	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	347	0.0015747	07101
7101111005	07101	820	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	5190	0.0235527	07101
7101111006	07101	402	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	7789	0.0353472	07101
7101111007	07101	47	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1912	0.0086768	07101
7101111008	07101	1428	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	5035	0.0228493	07101
7101111009	07101	399	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3737	0.0169588	07101
7101111010	07101	543	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1695	0.0076921	07101
7101121001	07101	107	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4067	0.0184564	07101
7101121002	07101	73	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1855	0.0084182	07101
7101121003	07101	127	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3773	0.0171222	07101
7101121004	07101	84	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4426	0.0200856	07101
7101121005	07101	20	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	344	0.0015611	07101
7101121006	07101	429	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	6821	0.0309543	07101
7101121007	07101	41	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2524	0.0114541	07101
7101121008	07101	51	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2216	0.0100564	07101
7101121009	07101	86	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2511	0.0113951	07101
7101121010	07101	41	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2406	0.0109186	07101
7101131001	07101	80	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1494	0.0067799	07101
7101131002	07101	191	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4280	0.0194230	07101
7101131003	07101	461	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3547	0.0160966	07101
7101131004	07101	229	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3431	0.0155702	07101
7101131005	07101	169	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2875	0.0130470	07101
7101131006	07101	103	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2089	0.0094801	07101
7101131007	07101	112	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2855	0.0129562	07101
7101131008	07101	104	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3485	0.0158152	07101
7101141001	07101	45	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3226	0.0146399	07101
7101141002	07101	67	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2620	0.0118898	07101
7101141003	07101	121	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2174	0.0098658	07101
7101141004	07101	76	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	5692	0.0258308	07101
7101141005	07101	88	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4770	0.0216467	07101
7101141006	07101	434	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4860	0.0220551	07101
7101141007	07101	89	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4908	0.0222729	07101
7101141008	07101	223	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4971	0.0225588	07101
7101141009	07101	89	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3856	0.0174989	07101
7101141010	07101	71	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2182	0.0099021	07101
7101151001	07101	4	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	282	0.0012797	07101
7101161001	07101	69	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1644	0.0074606	07101
7101991999	07101	55	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	883	0.0040071	07101
7102011001	07102	141	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	1744	0.0378571	07102
7102011002	07102	162	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	2069	0.0449119	07102
7102021001	07102	271	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	4504	0.0977685	07102
7102021002	07102	171	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	3767	0.0817704	07102
7102021003	07102	305	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	5586	0.1212555	07102
7102021004	07102	148	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	5245	0.1138534	07102
7102021005	07102	91	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	2544	0.0552227	07102
7102031001	07102	215	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	4021	0.0872840	07102
7102031002	07102	131	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	4542	0.0985934	07102
7102041001	07102	53	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	2083	0.0452158	07102
7102071001	07102	22	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	1093	0.0237258	07102
7103011001	07103	157	2017	Curepto	281855.5	2017	7103	9448	2662971120	Urbano	3368	0.3564776	07103
7104011001	07104	86	2017	Empedrado	209235.2	2017	7104	4142	866652110	Urbano	2983	0.7201835	07104
7104021001	07104	1	2017	Empedrado	209235.2	2017	7104	4142	866652110	Urbano	22	0.0053114	07104
7104991999	07104	1	2017	Empedrado	209235.2	2017	7104	4142	866652110	Urbano	3	0.0007243	07104
7105011001	07105	85	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano	4782	0.0961767	07105
7105051001	07105	47	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano	620	0.0124696	07105
7105051002	07105	82	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano	3942	0.0792824	07105
7105051003	07105	14	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano	2173	0.0437039	07105
7105051004	07105	244	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano	5904	0.1187426	07105

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
7101011001	07101	70	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1693	0.0076830	07101	497158880.9
7101011002	07101	156	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1889	0.0085725	07101	554715372.7
7101021001	07101	59	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	951	0.0043157	07101	279266447.5
7101021002	07101	67	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	912	0.0041387	07101	267813880.3
7101021003	07101	31	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	857	0.0038891	07101	251662823.9
7101031001	07101	145	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1465	0.0066483	07101	430205410.8
7101031002	07101	52	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1428	0.0064804	07101	419340154.7
7101031003	07101	63	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1037	0.0047060	07101	304520826.6
7101041001	07101	45	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1306	0.0059267	07101	383514175.1
7101041002	07101	102	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1682	0.0076331	07101	493928669.6
7101041003	07101	64	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1627	0.0073835	07101	477777613.2
7101041004	07101	92	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1684	0.0076421	07101	494515980.7
7101041005	07101	50	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1196	0.0054276	07101	351212062.3
7101051001	07101	140	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2870	0.0130243	07101	842791487.3
7101051002	07101	66	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1049	0.0047605	07101	308044693.5
7101061001	07101	55	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1756	0.0079689	07101	515659181.8
7101061002	07101	39	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1371	0.0062217	07101	402601787.2
7101061003	07101	82	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3032	0.0137595	07101	890363689.8
7101061004	07101	58	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3215	0.0145900	07101	944102659.2
7101061005	07101	28	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2884	0.0130879	07101	846902665.3
7101061006	07101	129	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3367	0.0152798	07101	988738305.9
7101071001	07101	236	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2136	0.0096934	07101	627248298.6
7101071002	07101	304	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3210	0.0145673	07101	942634381.3
7101071003	07101	74	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1773	0.0080460	07101	520651326.5
7101071004	07101	75	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1769	0.0080279	07101	519476704.2
7101071005	07101	117	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1956	0.0088765	07101	574390295.9
7101071006	07101	153	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3097	0.0140545	07101	909451301.8
7101071007	07101	196	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3759	0.0170587	07101	1103851289.5
7101071008	07101	122	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2586	0.0117355	07101	759393305.3
7101071009	07101	121	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3775	0.0171313	07101	1108549778.6
7101071010	07101	116	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2262	0.0102652	07101	664248900.5
7101081001	07101	18	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1170	0.0053096	07101	343577017.5
7101081002	07101	79	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1841	0.0083546	07101	540619905.3
7101081003	07101	99	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1281	0.0058133	07101	376172785.8
7101081004	07101	141	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1562	0.0070885	07101	458690001.1
7101081005	07101	361	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3349	0.0151981	07101	983452505.6
7101081006	07101	43	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1555	0.0070567	07101	456634412.1
7101091001	07101	9	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	179	0.0008123	07101	52564347.1
7101101001	07101	37	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1347	0.0061128	07101	395554053.5
7101111001	07101	133	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	5385	0.0244376	07101	1581335247.2
7101111002	07101	608	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3446	0.0156383	07101	1011937096.0
7101111003	07101	164	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3435	0.0155883	07101	1008706884.7
7101111004	07101	7	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	347	0.0015747	07101	101898483.0
7101111005	07101	820	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	5190	0.0235527	07101	1524072410.9
7101111006	07101	402	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	7789	0.0353472	07101	2287283238.6
7101111007	07101	47	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1912	0.0086768	07101	561469450.8
7101111008	07101	1428	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	5035	0.0228493	07101	1478555797.5
7101111009	07101	399	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3737	0.0169588	07101	1097390867.0
7101111010	07101	543	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1695	0.0076921	07101	497746192.0
7101121001	07101	107	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4067	0.0184564	07101	1194297205.2
7101121002	07101	73	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1855	0.0084182	07101	544731083.3
7101121003	07101	127	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3773	0.0171222	07101	1107962467.5
7101121004	07101	84	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4426	0.0200856	07101	1299719555.0
7101121005	07101	20	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	344	0.0015611	07101	101017516.3
7101121006	07101	429	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	6821	0.0309543	07101	2003024646.4
7101121007	07101	41	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2524	0.0114541	07101	741186659.9
7101121008	07101	51	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2216	0.0100564	07101	650740744.2
7101121009	07101	86	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2511	0.0113951	07101	737369137.5
7101121010	07101	41	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2406	0.0109186	07101	706535302.6
7101131001	07101	80	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1494	0.0067799	07101	438721422.3
7101131002	07101	191	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4280	0.0194230	07101	1256845841.8
7101131003	07101	461	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3547	0.0160966	07101	1041596308.6
7101131004	07101	229	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3431	0.0155702	07101	1007532262.4
7101131005	07101	169	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2875	0.0130470	07101	844259765.2
7101131006	07101	103	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2089	0.0094801	07101	613446486.8
7101131007	07101	112	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2855	0.0129562	07101	838386653.8
7101131008	07101	104	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3485	0.0158152	07101	1023389663.2
7101141001	07101	45	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3226	0.0146399	07101	947332870.4
7101141002	07101	67	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2620	0.0118898	07101	769377594.7
7101141003	07101	121	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2174	0.0098658	07101	638407210.3
7101141004	07101	76	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	5692	0.0258308	07101	1671487507.3
7101141005	07101	88	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4770	0.0216467	07101	1400737071.3
7101141006	07101	434	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4860	0.0220551	07101	1427166072.6
7101141007	07101	89	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4908	0.0222729	07101	1441261540.0
7101141008	07101	223	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4971	0.0225588	07101	1459761841.0
7101141009	07101	89	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3856	0.0174989	07101	1132335879.9
7101141010	07101	71	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2182	0.0099021	07101	640756454.8
7101151001	07101	4	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	282	0.0012797	07101	82810870.9
7101161001	07101	69	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1644	0.0074606	07101	482769757.9
7101991999	07101	55	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	883	0.0040071	07101	259297868.8
7102011001	07102	141	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	1744	0.0378571	07102	476235959.6
7102011002	07102	162	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	2069	0.0449119	07102	564984059.8
7102021001	07102	271	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	4504	0.0977685	07102	1229912134.1
7102021002	07102	171	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	3767	0.0817704	07102	1028658749.8
7102021003	07102	305	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	5586	0.1212555	07102	1525375040.2
7102021004	07102	148	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	5245	0.1138534	07102	1432257802.7
7102021005	07102	91	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	2544	0.0552227	07102	694692821.8
7102031001	07102	215	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	4021	0.0872840	07102	1098018803.6
7102031002	07102	131	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	4542	0.0985934	07102	1240288835.1
7102041001	07102	53	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	2083	0.0452158	07102	568807054.9
7102071001	07102	22	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	1093	0.0237258	07102	298466688.0
7103011001	07103	157	2017	Curepto	281855.5	2017	7103	9448	2662971120	Urbano	3368	0.3564776	07103	949289450.8
7104011001	07104	86	2017	Empedrado	209235.2	2017	7104	4142	866652110	Urbano	2983	0.7201835	07104	624148537.7
7104021001	07104	1	2017	Empedrado	209235.2	2017	7104	4142	866652110	Urbano	22	0.0053114	07104	4603173.9
7104991999	07104	1	2017	Empedrado	209235.2	2017	7104	4142	866652110	Urbano	3	0.0007243	07104	627705.5
7105011001	07105	85	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano	4782	0.0961767	07105	1171684175.2
7105051001	07105	47	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano	620	0.0124696	07105	151912210.1
7105051002	07105	82	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano	3942	0.0792824	07105	965867632.5
7105051003	07105	14	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano	2173	0.0437039	07105	532427794.4
7105051004	07105	244	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano	5904	0.1187426	07105	1446596271.6

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -1.395e+09 -2.589e+08 -3.896e+07  2.284e+08  1.339e+09 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 455161183   27931573   16.30   <2e-16 ***
## Freq.x        1693776     150188   11.28   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 374200000 on 308 degrees of freedom
## Multiple R-squared:  0.2923, Adjusted R-squared:   0.29 
## F-statistic: 127.2 on 1 and 308 DF,  p-value: < 2.2e-16

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.289959921251943"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.289959921251943"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.499488567555027"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.471764243532637"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.518013444018378"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.440757458472172"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.670522696748873"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.753280102675438"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 1    cuadrático 0.289959921251943
## 2        cúbico 0.289959921251943
## 6      log-raíz 0.440757458472172
## 4 raíz cuadrada 0.471764243532637
## 3   logarítmico 0.499488567555027
## 5     raíz-raíz 0.518013444018378
## 7      raíz-log 0.670522696748873
## 8       log-log 0.753280102675438
##                                                                     sintaxis
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 8
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6340 -0.4053  0.0682  0.4760  1.8978 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.07886    0.12791  125.70   <2e-16 ***
## log(Freq.x)  0.89561    0.02914   30.73   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7088 on 308 degrees of freedom
## Multiple R-squared:  0.7541, Adjusted R-squared:  0.7533 
## F-statistic: 944.4 on 1 and 308 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    16.07886

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##   0.8956098

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.7533 ).

9.1 Diagrama de dispersión sobre log-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6340 -0.4053  0.0682  0.4760  1.8978 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.07886    0.12791  125.70   <2e-16 ***
## log(Freq.x)  0.89561    0.02914   30.73   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7088 on 308 degrees of freedom
## Multiple R-squared:  0.7541, Adjusted R-squared:  0.7533 
## F-statistic: 944.4 on 1 and 308 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = e^{16.07886+0.8956098 \cdot ln{X}} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
7101011001	07101	70	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1693	0.0076830	07101	497158880.9	431965933
7101011002	07101	156	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1889	0.0085725	07101	554715372.7	885412214
7101021001	07101	59	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	951	0.0043157	07101	279266447.5	370641482
7101021002	07101	67	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	912	0.0041387	07101	267813880.3	415347977
7101021003	07101	31	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	857	0.0038891	07101	251662823.9	208276248
7101031001	07101	145	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1465	0.0066483	07101	430205410.8	829285341
7101031002	07101	52	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1428	0.0064804	07101	419340154.7	331002305
7101031003	07101	63	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1037	0.0047060	07101	304520826.6	393068860
7101041001	07101	45	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1306	0.0059267	07101	383514175.1	290800355
7101041002	07101	102	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1682	0.0076331	07101	493928669.6	605178675
7101041003	07101	64	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1627	0.0073835	07101	477777613.2	398652136
7101041004	07101	92	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1684	0.0076421	07101	494515980.7	551758763
7101041005	07101	50	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1196	0.0054276	07101	351212062.3	319577203
7101051001	07101	140	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2870	0.0130243	07101	842791487.3	803627749
7101051002	07101	66	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1049	0.0047605	07101	308044693.5	409791542
7101061001	07101	55	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1756	0.0079689	07101	515659181.8	348054688
7101061002	07101	39	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1371	0.0062217	07101	402601787.2	255820095
7101061003	07101	82	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3032	0.0137595	07101	890363689.8	497727980
7101061004	07101	58	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3215	0.0145900	07101	944102659.2	365010200
7101061005	07101	28	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2884	0.0130879	07101	846902665.3	190129940
7101061006	07101	129	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3367	0.0152798	07101	988738305.9	746838074
7101071001	07101	236	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2136	0.0096934	07101	627248298.6	1282817504
7101071002	07101	304	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3210	0.0145673	07101	942634381.3	1609339056
7101071003	07101	74	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1773	0.0080460	07101	520651326.5	454008369
7101071004	07101	75	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1769	0.0080279	07101	519476704.2	459499302
7101071005	07101	117	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1956	0.0088765	07101	574390295.9	684304107
7101071006	07101	153	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3097	0.0140545	07101	909451301.8	870147107
7101071007	07101	196	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3759	0.0170587	07101	1103851289.5	1086247103
7101071008	07101	122	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2586	0.0117355	07101	759393305.3	710437589
7101071009	07101	121	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3775	0.0171313	07101	1108549778.6	705219983
7101071010	07101	116	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2262	0.0102652	07101	664248900.5	679063562
7101081001	07101	18	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1170	0.0053096	07101	343577017.5	127995866
7101081002	07101	79	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1841	0.0083546	07101	540619905.3	481387749
7101081003	07101	99	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1281	0.0058133	07101	376172785.8	589212640
7101081004	07101	141	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1562	0.0070885	07101	458690001.1	808766815
7101081005	07101	361	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3349	0.0151981	07101	983452505.6	1877111861
7101081006	07101	43	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1555	0.0070567	07101	456634412.1	279197779
7101091001	07101	9	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	179	0.0008123	07101	52564347.1	68800329
7101101001	07101	37	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1347	0.0061128	07101	395554053.5	244038548
7101111001	07101	133	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	5385	0.0244376	07101	1581335247.2	767545211
7101111002	07101	608	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3446	0.0156383	07101	1011937096.0	2994008145
7101111003	07101	164	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3435	0.0155883	07101	1008706884.7	925971206
7101111004	07101	7	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	347	0.0015747	07101	101898483.0	54933802
7101111005	07101	820	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	5190	0.0235527	07101	1524072410.9	3913829447
7101111006	07101	402	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	7789	0.0353472	07101	2287283238.6	2066959737
7101111007	07101	47	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1912	0.0086768	07101	561469450.8	302349206
7101111008	07101	1428	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	5035	0.0228493	07101	1478555797.5	6432312917
7101111009	07101	399	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3737	0.0169588	07101	1097390867.0	2053139494
7101111010	07101	543	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1695	0.0076921	07101	497746192.0	2705672179
7101121001	07101	107	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4067	0.0184564	07101	1194297205.2	631680715
7101121002	07101	73	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1855	0.0084182	07101	544731083.3	448509686
7101121003	07101	127	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3773	0.0171222	07101	1107962467.5	736459471
7101121004	07101	84	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4426	0.0200856	07101	1299719555.0	508586703
7101121005	07101	20	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	344	0.0015611	07101	101017516.3	140662005
7101121006	07101	429	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	6821	0.0309543	07101	2003024646.4	2190867901
7101121007	07101	41	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2524	0.0114541	07101	741186659.9	267538710
7101121008	07101	51	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2216	0.0100564	07101	650740744.2	325295600
7101121009	07101	86	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2511	0.0113951	07101	737369137.5	519418467
7101121010	07101	41	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2406	0.0109186	07101	706535302.6	267538710
7101131001	07101	80	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1494	0.0067799	07101	438721422.3	486841572
7101131002	07101	191	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4280	0.0194230	07101	1256845841.8	1061396050
7101131003	07101	461	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3547	0.0160966	07101	1041596308.6	2336674923
7101131004	07101	229	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3431	0.0155702	07101	1007532262.4	1248686501
7101131005	07101	169	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2875	0.0130470	07101	844259765.2	951215225
7101131006	07101	103	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2089	0.0094801	07101	613446486.8	610489730
7101131007	07101	112	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2855	0.0129562	07101	838386653.8	658053742
7101131008	07101	104	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3485	0.0158152	07101	1023389663.2	615795405
7101141001	07101	45	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3226	0.0146399	07101	947332870.4	290800355
7101141002	07101	67	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2620	0.0118898	07101	769377594.7	415347977
7101141003	07101	121	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2174	0.0098658	07101	638407210.3	705219983
7101141004	07101	76	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	5692	0.0258308	07101	1671487507.3	464982596
7101141005	07101	88	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4770	0.0216467	07101	1400737071.3	530223964
7101141006	07101	434	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4860	0.0220551	07101	1427166072.6	2213723079
7101141007	07101	89	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4908	0.0222729	07101	1441261540.0	535617070
7101141008	07101	223	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4971	0.0225588	07101	1459761841.0	1219344660
7101141009	07101	89	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3856	0.0174989	07101	1132335879.9	535617070
7101141010	07101	71	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2182	0.0099021	07101	640756454.8	437488590
7101151001	07101	4	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	282	0.0012797	07101	82810870.9	33279163
7101161001	07101	69	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1644	0.0074606	07101	482769757.9	426435034
7101991999	07101	55	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	883	0.0040071	07101	259297868.8	348054688
7102011001	07102	141	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	1744	0.0378571	07102	476235959.6	808766815
7102011002	07102	162	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	2069	0.0449119	07102	564984059.8	915851216
7102021001	07102	271	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	4504	0.0977685	07102	1229912134.1	1451953725
7102021002	07102	171	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	3767	0.0817704	07102	1028658749.8	961290891
7102021003	07102	305	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	5586	0.1212555	07102	1525375040.2	1614079493
7102021004	07102	148	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	5245	0.1138534	07102	1432257802.7	844635412
7102021005	07102	91	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	2544	0.0552227	07102	694692821.8	546384393
7102031001	07102	215	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	4021	0.0872840	07102	1098018803.6	1180093379
7102031002	07102	131	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	4542	0.0985934	07102	1240288835.1	757199893
7102041001	07102	53	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	2083	0.0452158	07102	568807054.9	336697563
7102071001	07102	22	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	1093	0.0237258	07102	298466688.0	153196379
7103011001	07103	157	2017	Curepto	281855.5	2017	7103	9448	2662971120	Urbano	3368	0.3564776	07103	949289450.8	890493747
7104011001	07104	86	2017	Empedrado	209235.2	2017	7104	4142	866652110	Urbano	2983	0.7201835	07104	624148537.7	519418467
7104021001	07104	1	2017	Empedrado	209235.2	2017	7104	4142	866652110	Urbano	22	0.0053114	07104	4603173.9	9615271
7104991999	07104	1	2017	Empedrado	209235.2	2017	7104	4142	866652110	Urbano	3	0.0007243	07104	627705.5	9615271
7105011001	07105	85	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano	4782	0.0961767	07105	1171684175.2	514005911
7105051001	07105	47	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano	620	0.0124696	07105	151912210.1	302349206
7105051002	07105	82	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano	3942	0.0792824	07105	965867632.5	497727980
7105051003	07105	14	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano	2173	0.0437039	07105	532427794.4	102198632
7105051004	07105	244	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano	5904	0.1187426	07105	1446596271.6	1321695339

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
7101011001	07101	70	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1693	0.0076830	07101	497158880.9	431965933	255148.22
7101011002	07101	156	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1889	0.0085725	07101	554715372.7	885412214	468720.07
7101021001	07101	59	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	951	0.0043157	07101	279266447.5	370641482	389738.68
7101021002	07101	67	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	912	0.0041387	07101	267813880.3	415347977	455425.41
7101021003	07101	31	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	857	0.0038891	07101	251662823.9	208276248	243029.46
7101031001	07101	145	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1465	0.0066483	07101	430205410.8	829285341	566065.08
7101031002	07101	52	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1428	0.0064804	07101	419340154.7	331002305	231794.33
7101031003	07101	63	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1037	0.0047060	07101	304520826.6	393068860	379044.22
7101041001	07101	45	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1306	0.0059267	07101	383514175.1	290800355	222664.90
7101041002	07101	102	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1682	0.0076331	07101	493928669.6	605178675	359797.07
7101041003	07101	64	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1627	0.0073835	07101	477777613.2	398652136	245022.82
7101041004	07101	92	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1684	0.0076421	07101	494515980.7	551758763	327647.72
7101041005	07101	50	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1196	0.0054276	07101	351212062.3	319577203	267205.02
7101051001	07101	140	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2870	0.0130243	07101	842791487.3	803627749	280009.67
7101051002	07101	66	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1049	0.0047605	07101	308044693.5	409791542	390649.71
7101061001	07101	55	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1756	0.0079689	07101	515659181.8	348054688	198208.82
7101061002	07101	39	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1371	0.0062217	07101	402601787.2	255820095	186593.80
7101061003	07101	82	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3032	0.0137595	07101	890363689.8	497727980	164158.30
7101061004	07101	58	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3215	0.0145900	07101	944102659.2	365010200	113533.50
7101061005	07101	28	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2884	0.0130879	07101	846902665.3	190129940	65925.78
7101061006	07101	129	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3367	0.0152798	07101	988738305.9	746838074	221811.13
7101071001	07101	236	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2136	0.0096934	07101	627248298.6	1282817504	600569.99
7101071002	07101	304	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3210	0.0145673	07101	942634381.3	1609339056	501351.73
7101071003	07101	74	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1773	0.0080460	07101	520651326.5	454008369	256067.89
7101071004	07101	75	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1769	0.0080279	07101	519476704.2	459499302	259750.88
7101071005	07101	117	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1956	0.0088765	07101	574390295.9	684304107	349848.73
7101071006	07101	153	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3097	0.0140545	07101	909451301.8	870147107	280964.52
7101071007	07101	196	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3759	0.0170587	07101	1103851289.5	1086247103	288972.36
7101071008	07101	122	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2586	0.0117355	07101	759393305.3	710437589	274724.51
7101071009	07101	121	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3775	0.0171313	07101	1108549778.6	705219983	186813.24
7101071010	07101	116	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2262	0.0102652	07101	664248900.5	679063562	300204.93
7101081001	07101	18	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1170	0.0053096	07101	343577017.5	127995866	109398.18
7101081002	07101	79	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1841	0.0083546	07101	540619905.3	481387749	261481.67
7101081003	07101	99	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1281	0.0058133	07101	376172785.8	589212640	459963.03
7101081004	07101	141	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1562	0.0070885	07101	458690001.1	808766815	517776.45
7101081005	07101	361	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3349	0.0151981	07101	983452505.6	1877111861	560499.21
7101081006	07101	43	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1555	0.0070567	07101	456634412.1	279197779	179548.41
7101091001	07101	9	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	179	0.0008123	07101	52564347.1	68800329	384359.38
7101101001	07101	37	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1347	0.0061128	07101	395554053.5	244038548	181171.90
7101111001	07101	133	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	5385	0.0244376	07101	1581335247.2	767545211	142533.93
7101111002	07101	608	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3446	0.0156383	07101	1011937096.0	2994008145	868835.79
7101111003	07101	164	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3435	0.0155883	07101	1008706884.7	925971206	269569.49
7101111004	07101	7	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	347	0.0015747	07101	101898483.0	54933802	158310.67
7101111005	07101	820	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	5190	0.0235527	07101	1524072410.9	3913829447	754109.72
7101111006	07101	402	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	7789	0.0353472	07101	2287283238.6	2066959737	265369.08
7101111007	07101	47	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1912	0.0086768	07101	561469450.8	302349206	158132.43
7101111008	07101	1428	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	5035	0.0228493	07101	1478555797.5	6432312917	1277519.94
7101111009	07101	399	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3737	0.0169588	07101	1097390867.0	2053139494	549408.48
7101111010	07101	543	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1695	0.0076921	07101	497746192.0	2705672179	1596266.77
7101121001	07101	107	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4067	0.0184564	07101	1194297205.2	631680715	155318.59
7101121002	07101	73	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1855	0.0084182	07101	544731083.3	448509686	241784.20
7101121003	07101	127	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3773	0.0171222	07101	1107962467.5	736459471	195192.01
7101121004	07101	84	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4426	0.0200856	07101	1299719555.0	508586703	114908.88
7101121005	07101	20	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	344	0.0015611	07101	101017516.3	140662005	408901.18
7101121006	07101	429	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	6821	0.0309543	07101	2003024646.4	2190867901	321194.53
7101121007	07101	41	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2524	0.0114541	07101	741186659.9	267538710	105997.90
7101121008	07101	51	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2216	0.0100564	07101	650740744.2	325295600	146794.04
7101121009	07101	86	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2511	0.0113951	07101	737369137.5	519418467	206857.21
7101121010	07101	41	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2406	0.0109186	07101	706535302.6	267538710	111196.47
7101131001	07101	80	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1494	0.0067799	07101	438721422.3	486841572	325864.51
7101131002	07101	191	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4280	0.0194230	07101	1256845841.8	1061396050	247989.73
7101131003	07101	461	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3547	0.0160966	07101	1041596308.6	2336674923	658775.00
7101131004	07101	229	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3431	0.0155702	07101	1007532262.4	1248686501	363942.44
7101131005	07101	169	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2875	0.0130470	07101	844259765.2	951215225	330857.47
7101131006	07101	103	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2089	0.0094801	07101	613446486.8	610489730	292240.18
7101131007	07101	112	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2855	0.0129562	07101	838386653.8	658053742	230491.68
7101131008	07101	104	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3485	0.0158152	07101	1023389663.2	615795405	176698.82
7101141001	07101	45	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3226	0.0146399	07101	947332870.4	290800355	90142.70
7101141002	07101	67	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2620	0.0118898	07101	769377594.7	415347977	158529.76
7101141003	07101	121	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2174	0.0098658	07101	638407210.3	705219983	324388.22
7101141004	07101	76	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	5692	0.0258308	07101	1671487507.3	464982596	81690.55
7101141005	07101	88	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4770	0.0216467	07101	1400737071.3	530223964	111158.06
7101141006	07101	434	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4860	0.0220551	07101	1427166072.6	2213723079	455498.58
7101141007	07101	89	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4908	0.0222729	07101	1441261540.0	535617070	109131.43
7101141008	07101	223	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	4971	0.0225588	07101	1459761841.0	1219344660	245291.62
7101141009	07101	89	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	3856	0.0174989	07101	1132335879.9	535617070	138904.84
7101141010	07101	71	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	2182	0.0099021	07101	640756454.8	437488590	200498.90
7101151001	07101	4	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	282	0.0012797	07101	82810870.9	33279163	118011.21
7101161001	07101	69	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	1644	0.0074606	07101	482769757.9	426435034	259388.71
7101991999	07101	55	2017	Talca	293655.6	2017	7101	220357	64709060549	Urbano	883	0.0040071	07101	259297868.8	348054688	394172.92
7102011001	07102	141	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	1744	0.0378571	07102	476235959.6	808766815	463742.44
7102011002	07102	162	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	2069	0.0449119	07102	564984059.8	915851216	442654.04
7102021001	07102	271	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	4504	0.0977685	07102	1229912134.1	1451953725	322369.83
7102021002	07102	171	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	3767	0.0817704	07102	1028658749.8	961290891	255187.39
7102021003	07102	305	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	5586	0.1212555	07102	1525375040.2	1614079493	288950.86
7102021004	07102	148	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	5245	0.1138534	07102	1432257802.7	844635412	161036.30
7102021005	07102	91	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	2544	0.0552227	07102	694692821.8	546384393	214773.74
7102031001	07102	215	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	4021	0.0872840	07102	1098018803.6	1180093379	293482.56
7102031002	07102	131	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	4542	0.0985934	07102	1240288835.1	757199893	166710.68
7102041001	07102	53	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	2083	0.0452158	07102	568807054.9	336697563	161640.69
7102071001	07102	22	2017	Constitución	273071.1	2017	7102	46068	12579838409	Urbano	1093	0.0237258	07102	298466688.0	153196379	140161.37
7103011001	07103	157	2017	Curepto	281855.5	2017	7103	9448	2662971120	Urbano	3368	0.3564776	07103	949289450.8	890493747	264398.38
7104011001	07104	86	2017	Empedrado	209235.2	2017	7104	4142	866652110	Urbano	2983	0.7201835	07104	624148537.7	519418467	174126.20
7104021001	07104	1	2017	Empedrado	209235.2	2017	7104	4142	866652110	Urbano	22	0.0053114	07104	4603173.9	9615271	437057.78
7104991999	07104	1	2017	Empedrado	209235.2	2017	7104	4142	866652110	Urbano	3	0.0007243	07104	627705.5	9615271	3205090.39
7105011001	07105	85	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano	4782	0.0961767	07105	1171684175.2	514005911	107487.64
7105051001	07105	47	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano	620	0.0124696	07105	151912210.1	302349206	487660.01
7105051002	07105	82	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano	3942	0.0792824	07105	965867632.5	497727980	126262.81
7105051003	07105	14	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano	2173	0.0437039	07105	532427794.4	102198632	47031.12
7105051004	07105	244	2017	Maule	245019.7	2017	7105	49721	12182624190	Urbano	5904	0.1187426	07105	1446596271.6	1321695339	223864.39

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_urbano_nivel_nacional_17_r07.rds")

---

Rural

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 2)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15202	1	2	6	13225	1	3	1	5	3	5	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	3	1	1	5	3	5	2	3	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	4	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	5	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	6	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	8	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	9	3	1	5	3	5	1	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	10	1	1	5	3	4	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	11	1	2	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	12	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	13	1	1	5	3	4	1	4	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	14	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	16	1	1	5	3	5	1	3	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	17	1	1	5	3	5	2	4	1	8	15	152	15202	15202012006
15	152	15202	1	2	6	13225	18	3	1	5	3	5	1	1	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	19	3	1	5	3	5	1	3	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	20	1	1	5	3	5	2	1	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	21	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	22	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	24	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	25	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	26	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	27	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	28	3	1	5	3	5	1	4	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	29	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	30	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	31	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	32	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	33	1	1	5	3	5	3	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	34	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	35	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	36	1	1	5	3	5	3	2	1	9	15	152	15202	15202012006
15	152	15202	1	2	6	13225	37	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	38	1	1	5	3	5	99	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	39	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	40	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	41	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	42	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	43	3	1	5	3	5	2	1	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	44	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	45	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	8	13910	1	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	3	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	4	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	5	3	1	5	3	5	2	3	1	3	15	152	15202	15202012008
15	152	15202	1	2	8	13910	6	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	7	1	1	5	99	5	2	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	8	3	1	5	3	5	3	3	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	9	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	10	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	11	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	12	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	13	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	14	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	16	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	18	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	20	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	21	1	1	5	3	5	1	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	22	3	1	5	3	5	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	23	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	24	3	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	25	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	26	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	27	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	28	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	29	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	30	1	1	5	1	4	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	12	8394	1	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	2	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	3	3	1	5	3	5	2	3	1	4	15	152	15202	15202012012
15	152	15202	1	2	12	8394	4	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	5	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	6	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	8	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	9	3	1	5	3	5	1	4	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	10	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	11	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	12	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	13	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	14	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	15	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	16	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	18	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	20	3	1	5	3	5	3	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	21	3	1	5	99	5	1	4	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	22	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	24	3	1	5	3	5	1	2	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	25	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 6:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 6)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Veamos los primeros 100 registros:

r3_100 <- d[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	unlist.c.	unlist.d.	Freq	anio
1	6101092004	1	6101	3	2017
2	6101102003	1	6101	1	2017
3	6101102007	1	6101	18	2017
4	6101102009	1	6101	5	2017
5	6101102015	1	6101	6	2017
6	6101112015	1	6101	16	2017
7	6101122002	1	6101	4	2017
8	6101122008	1	6101	5	2017
9	6101122901	1	6101	10	2017
10	6101132005	1	6101	7	2017
11	6101132008	1	6101	3	2017
12	6101132013	1	6101	31	2017
13	6101132014	1	6101	74	2017
14	6101142010	1	6101	2	2017
15	6101142011	1	6101	19	2017
16	6101142013	1	6101	10	2017
17	6101182001	1	6101	42	2017
18	6101182002	1	6101	9	2017
19	6101182005	1	6101	20	2017
20	6101182006	1	6101	4	2017
21	6101182015	1	6101	29	2017
763	6102012001	1	6102	16	2017
764	6102012002	1	6102	9	2017
765	6102012004	1	6102	5	2017
766	6102012006	1	6102	43	2017
767	6102012009	1	6102	2	2017
768	6102012014	1	6102	6	2017
769	6102012018	1	6102	3	2017
770	6102022002	1	6102	2	2017
771	6102022003	1	6102	25	2017
772	6102022005	1	6102	19	2017
773	6102022007	1	6102	15	2017
774	6102022012	1	6102	9	2017
775	6102022013	1	6102	20	2017
776	6102022016	1	6102	4	2017
777	6102022017	1	6102	29	2017
778	6102022019	1	6102	23	2017
779	6102022020	1	6102	5	2017
780	6102022901	1	6102	8	2017
781	6102032011	1	6102	9	2017
1523	6103012003	1	6103	97	2017
1524	6103012004	1	6103	59	2017
1525	6103022002	1	6103	40	2017
1526	6103032001	1	6103	20	2017
1527	6103042005	1	6103	66	2017
2269	6104012002	1	6104	41	2017
2270	6104012006	1	6104	12	2017
2271	6104022006	1	6104	70	2017
2272	6104032006	1	6104	83	2017
2273	6104032008	1	6104	28	2017
2274	6104042013	1	6104	20	2017
2275	6104042015	1	6104	2	2017
2276	6104052004	1	6104	11	2017
2277	6104052005	1	6104	40	2017
2278	6104052007	1	6104	57	2017
2279	6104052010	1	6104	13	2017
2280	6104052014	1	6104	4	2017
2281	6104052016	1	6104	8	2017
2282	6104062011	1	6104	39	2017
2283	6104062016	1	6104	4	2017
2284	6104062901	1	6104	16	2017
2285	6104072011	1	6104	20	2017
2286	6104082001	1	6104	8	2017
2287	6104082009	1	6104	1	2017
2288	6104082901	1	6104	2	2017
3030	6105012001	1	6105	10	2017
3031	6105012002	1	6105	4	2017
3032	6105012003	1	6105	21	2017
3033	6105022003	1	6105	88	2017
3034	6105022005	1	6105	47	2017
3035	6105032004	1	6105	69	2017
3036	6105042004	1	6105	59	2017
3778	6106012001	1	6106	3	2017
3779	6106012004	1	6106	13	2017
3780	6106012012	1	6106	3	2017
3781	6106022003	1	6106	11	2017
3782	6106022004	1	6106	11	2017
3783	6106022006	1	6106	4	2017
3784	6106022010	1	6106	65	2017
3785	6106032005	1	6106	4	2017
3786	6106032007	1	6106	7	2017
3787	6106032009	1	6106	11	2017
3788	6106032011	1	6106	5	2017
3789	6106042001	1	6106	23	2017
3790	6106042002	1	6106	17	2017
3791	6106042008	1	6106	3	2017
3792	6106042011	1	6106	5	2017
3793	6106042012	1	6106	15	2017
4535	6107012001	1	6107	9	2017
4536	6107012008	1	6107	4	2017
4537	6107012021	1	6107	6	2017
4538	6107022003	1	6107	34	2017
4539	6107022007	1	6107	35	2017
4540	6107022009	1	6107	76	2017
4541	6107032005	1	6107	66	2017
4542	6107032006	1	6107	28	2017
4543	6107042002	1	6107	28	2017
4544	6107042016	1	6107	40	2017
4545	6107042019	1	6107	30	2017
4546	6107042023	1	6107	2	2017

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	6101092004	3	2017	06101
2	6101102003	1	2017	06101
3	6101102007	18	2017	06101
4	6101102009	5	2017	06101
5	6101102015	6	2017	06101
6	6101112015	16	2017	06101
7	6101122002	4	2017	06101
8	6101122008	5	2017	06101
9	6101122901	10	2017	06101
10	6101132005	7	2017	06101
11	6101132008	3	2017	06101
12	6101132013	31	2017	06101
13	6101132014	74	2017	06101
14	6101142010	2	2017	06101
15	6101142011	19	2017	06101
16	6101142013	10	2017	06101
17	6101182001	42	2017	06101
18	6101182002	9	2017	06101
19	6101182005	20	2017	06101
20	6101182006	4	2017	06101
21	6101182015	29	2017	06101
763	6102012001	16	2017	06102
764	6102012002	9	2017	06102
765	6102012004	5	2017	06102
766	6102012006	43	2017	06102
767	6102012009	2	2017	06102
768	6102012014	6	2017	06102
769	6102012018	3	2017	06102
770	6102022002	2	2017	06102
771	6102022003	25	2017	06102
772	6102022005	19	2017	06102
773	6102022007	15	2017	06102
774	6102022012	9	2017	06102
775	6102022013	20	2017	06102
776	6102022016	4	2017	06102
777	6102022017	29	2017	06102
778	6102022019	23	2017	06102
779	6102022020	5	2017	06102
780	6102022901	8	2017	06102
781	6102032011	9	2017	06102
1523	6103012003	97	2017	06103
1524	6103012004	59	2017	06103
1525	6103022002	40	2017	06103
1526	6103032001	20	2017	06103
1527	6103042005	66	2017	06103
2269	6104012002	41	2017	06104
2270	6104012006	12	2017	06104
2271	6104022006	70	2017	06104
2272	6104032006	83	2017	06104
2273	6104032008	28	2017	06104
2274	6104042013	20	2017	06104
2275	6104042015	2	2017	06104
2276	6104052004	11	2017	06104
2277	6104052005	40	2017	06104
2278	6104052007	57	2017	06104
2279	6104052010	13	2017	06104
2280	6104052014	4	2017	06104
2281	6104052016	8	2017	06104
2282	6104062011	39	2017	06104
2283	6104062016	4	2017	06104
2284	6104062901	16	2017	06104
2285	6104072011	20	2017	06104
2286	6104082001	8	2017	06104
2287	6104082009	1	2017	06104
2288	6104082901	2	2017	06104
3030	6105012001	10	2017	06105
3031	6105012002	4	2017	06105
3032	6105012003	21	2017	06105
3033	6105022003	88	2017	06105
3034	6105022005	47	2017	06105
3035	6105032004	69	2017	06105
3036	6105042004	59	2017	06105
3778	6106012001	3	2017	06106
3779	6106012004	13	2017	06106
3780	6106012012	3	2017	06106
3781	6106022003	11	2017	06106
3782	6106022004	11	2017	06106
3783	6106022006	4	2017	06106
3784	6106022010	65	2017	06106
3785	6106032005	4	2017	06106
3786	6106032007	7	2017	06106
3787	6106032009	11	2017	06106
3788	6106032011	5	2017	06106
3789	6106042001	23	2017	06106
3790	6106042002	17	2017	06106
3791	6106042008	3	2017	06106
3792	6106042011	5	2017	06106
3793	6106042012	15	2017	06106
4535	6107012001	9	2017	06107
4536	6107012008	4	2017	06107
4537	6107012021	6	2017	06107
4538	6107022003	34	2017	06107
4539	6107022007	35	2017	06107
4540	6107022009	76	2017	06107
4541	6107032005	66	2017	06107
4542	6107032006	28	2017	06107
4543	6107042002	28	2017	06107
4544	6107042016	40	2017	06107
4545	6107042019	30	2017	06107
4546	6107042023	2	2017	06107

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_rural_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	289375.3	2017	1101	191468	55406102543	Rural
01401	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
01402	Camiña	262850.3	2017	1402	1250	328562901	Rural
01404	Huara	253968.5	2017	1404	2730	693334131	Rural
01405	Pica	290496.7	2017	1405	9296	2700457509	Rural
02103	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
02104	Taltal	345494.0	2017	2104	13317	4600943086	Rural
02201	Calama	310025.0	2017	2201	165731	51380756402	Rural
02203	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
02301	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
03101	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03103	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03202	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural
03301	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03302	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03303	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03304	Huasco	227560.7	2017	3304	10149	2309513927	Rural
04101	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04102	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04103	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04104	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04105	Paiguano	205942.1	2017	4105	4497	926121774	Rural
04106	Vicuña	176130.6	2017	4106	27771	4891322768	Rural
04201	Illapel	191976.8	2017	4201	30848	5922099530	Rural
04202	Canela	171370.3	2017	4202	9093	1558270441	Rural
04203	Los Vilos	173238.5	2017	4203	21382	3704185607	Rural
04204	Salamanca	223234.2	2017	4204	29347	6551254640	Rural
04301	Ovalle	241393.7	2017	4301	111272	26860360045	Rural
04302	Combarbalá	179139.6	2017	4302	13322	2386498044	Rural
04303	Monte Patria	201205.8	2017	4303	30751	6187280931	Rural
04304	Punitaqui	171931.7	2017	4304	10956	1883683880	Rural
04305	Río Hurtado	182027.2	2017	4305	4278	778712384	Rural
05101	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05102	Casablanca	268917.1	2017	5102	26867	7224996933	Rural
05105	Puchuncaví	279614.4	2017	5105	18546	5185728335	Rural
05107	Quintero	334628.2	2017	5107	31923	10682335196	Rural
05301	Los Andes	324402.1	2017	5301	66708	21640215030	Rural
05302	Calle Larga	242743.8	2017	5302	14832	3600375502	Rural
05303	Rinconada	326532.5	2017	5303	10207	3332917471	Rural
05304	San Esteban	223168.6	2017	5304	18855	4207844130	Rural
05401	La Ligua	181468.0	2017	5401	35390	6422154059	Rural
05402	Cabildo	231277.8	2017	5402	19388	4484014285	Rural
05404	Petorca	298208.9	2017	5404	9826	2930200178	Rural
05405	Zapallar	292882.3	2017	5405	7339	2149463129	Rural
05501	Quillota	220926.8	2017	5501	90517	19997628209	Rural
05502	Calera	226906.2	2017	5502	50554	11471016698	Rural
05503	Hijuelas	253739.9	2017	5503	17988	4564273201	Rural
05504	La Cruz	291124.1	2017	5504	22098	6433259569	Rural
05506	Nogales	264475.3	2017	5506	22120	5850194593	Rural
05601	San Antonio	266331.2	2017	5601	91350	24329353815	Rural

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
06101	6101092004	3	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101102003	1	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101102007	18	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101102009	5	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101102015	6	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101112015	16	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101122002	4	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101122008	5	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101122901	10	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101132005	7	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101132008	3	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101132013	31	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101132014	74	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101142010	2	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101142011	19	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101142013	10	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101182001	42	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101182002	9	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101182005	20	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101182006	4	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101182015	29	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06102	6102012001	16	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102012002	9	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102012004	5	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102012006	43	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102012009	2	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102012014	6	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102012018	3	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102022002	2	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102022003	25	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102022005	19	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102022007	15	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102022012	9	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102022013	20	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102022016	4	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102022017	29	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102022019	23	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102022020	5	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102022901	8	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102032011	9	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06103	6103012003	97	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural
06103	6103012004	59	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural
06103	6103022002	40	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural
06103	6103032001	20	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural
06103	6103042005	66	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural
06104	6104012002	41	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104012006	12	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104022006	70	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104032006	83	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104032008	28	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104042013	20	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104042015	2	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104052004	11	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104052005	40	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104052007	57	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104052010	13	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104052014	4	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104052016	8	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104062011	39	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104062016	4	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104062901	16	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104072011	20	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104082001	8	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104082009	1	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104082901	2	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06105	6105012001	10	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06105	6105012002	4	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06105	6105012003	21	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06105	6105022003	88	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06105	6105022005	47	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06105	6105032004	69	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06105	6105042004	59	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06106	6106012001	3	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106012004	13	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106012012	3	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106022003	11	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106022004	11	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106022006	4	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106022010	65	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106032005	4	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106032007	7	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106032009	11	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106032011	5	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106042001	23	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106042002	17	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106042008	3	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106042011	5	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106042012	15	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06107	6107012001	9	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural
06107	6107012008	4	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural
06107	6107012021	6	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural
06107	6107022003	34	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural
06107	6107022007	35	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural
06107	6107022009	76	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural
06107	6107032005	66	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural
06107	6107032006	28	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural
06107	6107042002	28	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural
06107	6107042016	40	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural
06107	6107042019	30	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural
06107	6107042023	2	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
06101	6101092004	3	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101102003	1	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101102007	18	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101102009	5	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101102015	6	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101112015	16	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101122002	4	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101122008	5	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101122901	10	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101132005	7	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101132008	3	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101132013	31	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101132014	74	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101142010	2	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101142011	19	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101142013	10	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101182001	42	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101182002	9	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101182005	20	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101182006	4	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06101	6101182015	29	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural
06102	6102012001	16	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102012002	9	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102012004	5	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102012006	43	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102012009	2	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102012014	6	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102012018	3	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102022002	2	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102022003	25	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102022005	19	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102022007	15	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102022012	9	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102022013	20	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102022016	4	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102022017	29	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102022019	23	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102022020	5	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102022901	8	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06102	6102032011	9	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural
06103	6103012003	97	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural
06103	6103012004	59	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural
06103	6103022002	40	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural
06103	6103032001	20	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural
06103	6103042005	66	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural
06104	6104012002	41	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104012006	12	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104022006	70	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104032006	83	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104032008	28	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104042013	20	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104042015	2	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104052004	11	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104052005	40	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104052007	57	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104052010	13	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104052014	4	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104052016	8	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104062011	39	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104062016	4	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104062901	16	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104072011	20	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104082001	8	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104082009	1	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06104	6104082901	2	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural
06105	6105012001	10	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06105	6105012002	4	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06105	6105012003	21	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06105	6105022003	88	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06105	6105022005	47	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06105	6105032004	69	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06105	6105042004	59	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural
06106	6106012001	3	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106012004	13	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106012012	3	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106022003	11	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106022004	11	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106022006	4	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106022010	65	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106032005	4	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106032007	7	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106032009	11	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106032011	5	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106042001	23	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106042002	17	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106042008	3	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106042011	5	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06106	6106042012	15	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural
06107	6107012001	9	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural
06107	6107012008	4	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural
06107	6107012021	6	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural
06107	6107022003	34	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural
06107	6107022007	35	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural
06107	6107022009	76	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural
06107	6107032005	66	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural
06107	6107032006	28	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural
06107	6107042002	28	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural
06107	6107042016	40	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural
06107	6107042019	30	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural
06107	6107042023	2	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
6101092004	06101	3	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	128	0.0005294	06101
6101102003	06101	1	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	37	0.0001530	06101
6101102007	06101	18	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	481	0.0019895	06101
6101102009	06101	5	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	88	0.0003640	06101
6101102015	06101	6	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	271	0.0011209	06101
6101112015	06101	16	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	74	0.0003061	06101
6101122002	06101	4	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	84	0.0003474	06101
6101122008	06101	5	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	88	0.0003640	06101
6101122901	06101	10	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	45	0.0001861	06101
6101132005	06101	7	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	345	0.0014270	06101
6101132008	06101	3	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	13	0.0000538	06101
6101132013	06101	31	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	432	0.0017868	06101
6101132014	06101	74	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	825	0.0034123	06101
6101142010	06101	2	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	43	0.0001779	06101
6101142011	06101	19	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	253	0.0010464	06101
6101142013	06101	10	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	189	0.0007817	06101
6101182001	06101	42	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1112	0.0045993	06101
6101182002	06101	9	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	397	0.0016420	06101
6101182005	06101	20	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2002	0.0082805	06101
6101182006	06101	4	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	115	0.0004757	06101
6101182015	06101	29	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	569	0.0023534	06101
6102012001	06102	16	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	575	0.0442716	06102
6102012002	06102	9	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	157	0.0120881	06102
6102012004	06102	5	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	183	0.0140899	06102
6102012006	06102	43	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	631	0.0485833	06102
6102012009	06102	2	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	99	0.0076224	06102
6102012014	06102	6	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	83	0.0063905	06102
6102012018	06102	3	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	90	0.0069295	06102
6102022002	06102	2	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	32	0.0024638	06102
6102022003	06102	25	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	811	0.0624423	06102
6102022005	06102	19	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	388	0.0298737	06102
6102022007	06102	15	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	606	0.0466585	06102
6102022012	06102	9	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	462	0.0355713	06102
6102022013	06102	20	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	219	0.0168617	06102
6102022016	06102	4	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	265	0.0204034	06102
6102022017	06102	29	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	600	0.0461965	06102
6102022019	06102	23	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	269	0.0207114	06102
6102022020	06102	5	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	91	0.0070065	06102
6102022901	06102	8	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	37	0.0028488	06102
6102032011	06102	9	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	284	0.0218663	06102
6103012003	06103	97	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	1211	0.1645604	06103
6103012004	06103	59	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	970	0.1318114	06103
6103022002	06103	40	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	276	0.0375051	06103
6103032001	06103	20	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	666	0.0905014	06103
6103042005	06103	66	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	812	0.1103411	06103
6104012002	06104	41	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	721	0.0367913	06104
6104012006	06104	12	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	221	0.0112772	06104
6104022006	06104	70	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	1313	0.0670001	06104
6104032006	06104	83	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	1279	0.0652651	06104
6104032008	06104	28	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	803	0.0409757	06104
6104042013	06104	20	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	620	0.0316375	06104
6104042015	06104	2	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	84	0.0042864	06104
6104052004	06104	11	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	69	0.0035209	06104
6104052005	06104	40	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	672	0.0342910	06104
6104052007	06104	57	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	907	0.0462826	06104
6104052010	06104	13	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	294	0.0150023	06104
6104052014	06104	4	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	77	0.0039292	06104
6104052016	06104	8	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	64	0.0032658	06104
6104062011	06104	39	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	653	0.0333214	06104
6104062016	06104	4	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	18	0.0009185	06104
6104062901	06104	16	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	171	0.0087258	06104
6104072011	06104	20	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	278	0.0141858	06104
6104082001	06104	8	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	109	0.0055621	06104
6104082009	06104	1	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	19	0.0009695	06104
6104082901	06104	2	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	14	0.0007144	06104
6105012001	06105	10	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	335	0.0160387	06105
6105012002	06105	4	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	52	0.0024896	06105
6105012003	06105	21	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	210	0.0100541	06105
6105022003	06105	88	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	1037	0.0496481	06105
6105022005	06105	47	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	757	0.0362426	06105
6105032004	06105	69	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	1254	0.0600373	06105
6105042004	06105	59	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	818	0.0391631	06105
6106012001	06106	3	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	21	0.0006280	06106
6106012004	06106	13	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	265	0.0079254	06106
6106012012	06106	3	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	50	0.0014953	06106
6106022003	06106	11	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	411	0.0122918	06106
6106022004	06106	11	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	221	0.0066094	06106
6106022006	06106	4	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	71	0.0021234	06106
6106022010	06106	65	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	592	0.0177049	06106
6106032005	06106	4	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	25	0.0007477	06106
6106032007	06106	7	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	279	0.0083441	06106
6106032009	06106	11	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	241	0.0072076	06106
6106032011	06106	5	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	128	0.0038281	06106
6106042001	06106	23	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	324	0.0096899	06106
6106042002	06106	17	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	258	0.0077160	06106
6106042008	06106	3	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	282	0.0084338	06106
6106042011	06106	5	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	64	0.0019140	06106
6106042012	06106	15	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	384	0.0114843	06106
6107012001	06107	9	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	253	0.0102679	06107
6107012008	06107	4	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	66	0.0026786	06107
6107012021	06107	6	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	152	0.0061688	06107
6107022003	06107	34	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	735	0.0298295	06107
6107022007	06107	35	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	525	0.0213068	06107
6107022009	06107	76	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	1472	0.0597403	06107
6107032005	06107	66	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	1243	0.0504464	06107
6107032006	06107	28	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	309	0.0125406	06107
6107042002	06107	28	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	862	0.0349838	06107
6107042016	06107	40	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	570	0.0231331	06107
6107042019	06107	30	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	426	0.0172890	06107
6107042023	06107	2	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	57	0.0023133	06107

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
6101092004	06101	3	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	128	0.0005294	06101	31195828
6101102003	06101	1	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	37	0.0001530	06101	9017544
6101102007	06101	18	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	481	0.0019895	06101	117228072
6101102009	06101	5	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	88	0.0003640	06101	21447132
6101102015	06101	6	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	271	0.0011209	06101	66047417
6101112015	06101	16	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	74	0.0003061	06101	18035088
6101122002	06101	4	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	84	0.0003474	06101	20472262
6101122008	06101	5	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	88	0.0003640	06101	21447132
6101122901	06101	10	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	45	0.0001861	06101	10967283
6101132005	06101	7	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	345	0.0014270	06101	84082505
6101132008	06101	3	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	13	0.0000538	06101	3168326
6101132013	06101	31	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	432	0.0017868	06101	105285919
6101132014	06101	74	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	825	0.0034123	06101	201066859
6101142010	06101	2	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	43	0.0001779	06101	10479848
6101142011	06101	19	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	253	0.0010464	06101	61660503
6101142013	06101	10	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	189	0.0007817	06101	46062590
6101182001	06101	42	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1112	0.0045993	06101	271013754
6101182002	06101	9	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	397	0.0016420	06101	96755810
6101182005	06101	20	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2002	0.0082805	06101	487922245
6101182006	06101	4	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	115	0.0004757	06101	28027502
6101182015	06101	29	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	569	0.0023534	06101	138675203
6102012001	06102	16	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	575	0.0442716	06102	158914734
6102012002	06102	9	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	157	0.0120881	06102	43390632
6102012004	06102	5	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	183	0.0140899	06102	50576341
6102012006	06102	43	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	631	0.0485833	06102	174391647
6102012009	06102	2	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	99	0.0076224	06102	27360972
6102012014	06102	6	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	83	0.0063905	06102	22938996
6102012018	06102	3	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	90	0.0069295	06102	24873611
6102022002	06102	2	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	32	0.0024638	06102	8843950
6102022003	06102	25	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	811	0.0624423	06102	224138868
6102022005	06102	19	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	388	0.0298737	06102	107232899
6102022007	06102	15	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	606	0.0466585	06102	167482311
6102022012	06102	9	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	462	0.0355713	06102	127684534
6102022013	06102	20	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	219	0.0168617	06102	60525786
6102022016	06102	4	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	265	0.0204034	06102	73238964
6102022017	06102	29	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	600	0.0461965	06102	165824070
6102022019	06102	23	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	269	0.0207114	06102	74344458
6102022020	06102	5	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	91	0.0070065	06102	25149984
6102022901	06102	8	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	37	0.0028488	06102	10225818
6102032011	06102	9	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	284	0.0218663	06102	78490060
6103012003	06103	97	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	1211	0.1645604	06103	236050369
6103012004	06103	59	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	970	0.1318114	06103	189074201
6103022002	06103	40	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	276	0.0375051	06103	53798433
6103032001	06103	20	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	666	0.0905014	06103	129817957
6103042005	06103	66	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	812	0.1103411	06103	158276548
6104012002	06104	41	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	721	0.0367913	06104	211541616
6104012006	06104	12	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	221	0.0112772	06104	64841466
6104022006	06104	70	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	1313	0.0670001	06104	385234594
6104032006	06104	83	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	1279	0.0652651	06104	375258984
6104032008	06104	28	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	803	0.0409757	06104	235600441
6104042013	06104	20	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	620	0.0316375	06104	181908186
6104042015	06104	2	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	84	0.0042864	06104	24645625
6104052004	06104	11	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	69	0.0035209	06104	20244621
6104052005	06104	40	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	672	0.0342910	06104	197165002
6104052007	06104	57	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	907	0.0462826	06104	266114072
6104052010	06104	13	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	294	0.0150023	06104	86259688
6104052014	06104	4	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	77	0.0039292	06104	22591823
6104052016	06104	8	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	64	0.0032658	06104	18777619
6104062011	06104	39	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	653	0.0333214	06104	191590396
6104062016	06104	4	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	18	0.0009185	06104	5281205
6104062901	06104	16	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	171	0.0087258	06104	50171451
6104072011	06104	20	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	278	0.0141858	06104	81565283
6104082001	06104	8	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	109	0.0055621	06104	31980633
6104082009	06104	1	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	19	0.0009695	06104	5574606
6104082901	06104	2	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	14	0.0007144	06104	4107604
6105012001	06105	10	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	335	0.0160387	06105	81450555
6105012002	06105	4	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	52	0.0024896	06105	12643071
6105012003	06105	21	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	210	0.0100541	06105	51058557
6105022003	06105	88	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	1037	0.0496481	06105	252132016
6105022005	06105	47	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	757	0.0362426	06105	184053940
6105032004	06105	69	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	1254	0.0600373	06105	304892524
6105042004	06105	59	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	818	0.0391631	06105	198885235
6106012001	06106	3	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	21	0.0006280	06106	5612395
6106012004	06106	13	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	265	0.0079254	06106	70823074
6106012012	06106	3	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	50	0.0014953	06106	13362844
6106022003	06106	11	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	411	0.0122918	06106	109842579
6106022004	06106	11	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	221	0.0066094	06106	59063771
6106022006	06106	4	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	71	0.0021234	06106	18975239
6106022010	06106	65	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	592	0.0177049	06106	158216074
6106032005	06106	4	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	25	0.0007477	06106	6681422
6106032007	06106	7	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	279	0.0083441	06106	74564670
6106032009	06106	11	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	241	0.0072076	06106	64408909
6106032011	06106	5	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	128	0.0038281	06106	34208881
6106042001	06106	23	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	324	0.0096899	06106	86591230
6106042002	06106	17	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	258	0.0077160	06106	68952276
6106042008	06106	3	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	282	0.0084338	06106	75366441
6106042011	06106	5	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	64	0.0019140	06106	17104440
6106042012	06106	15	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	384	0.0114843	06106	102626643
6107012001	06107	9	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	253	0.0102679	06107	51048349
6107012008	06107	4	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	66	0.0026786	06107	13316960
6107012021	06107	6	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	152	0.0061688	06107	30669364
6107022003	06107	34	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	735	0.0298295	06107	148302515
6107022007	06107	35	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	525	0.0213068	06107	105930368
6107022009	06107	76	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	1472	0.0597403	06107	297008573
6107032005	06107	66	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	1243	0.0504464	06107	250802756
6107032006	06107	28	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	309	0.0125406	06107	62347588
6107042002	06107	28	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	862	0.0349838	06107	173927575
6107042016	06107	40	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	570	0.0231331	06107	115010113
6107042019	06107	30	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	426	0.0172890	06107	85954927
6107042023	06107	2	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	57	0.0023133	06107	11501011

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -226752157  -25505810  -12493990   15789315  426933806 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 23491902    2397255    9.80   <2e-16 ***
## Freq.x       3528369      97295   36.27   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 51700000 on 739 degrees of freedom
## Multiple R-squared:  0.6402, Adjusted R-squared:  0.6398 
## F-statistic:  1315 on 1 and 739 DF,  p-value: < 2.2e-16

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                
## [1,] "cuadrático" "0.63975128805717"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                
## [1,] "cúbico" "0.63975128805717"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.539858136531161"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.662565758675859"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.691022048864001"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                
## [1,] "log-raíz" "0.58824987826823"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.652352257261554"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.649495435307698"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 3   logarítmico 0.539858136531161
## 6      log-raíz  0.58824987826823
## 1    cuadrático  0.63975128805717
## 2        cúbico  0.63975128805717
## 8       log-log 0.649495435307698
## 7      raíz-log 0.652352257261554
## 4 raíz cuadrada 0.662565758675859
## 5     raíz-raíz 0.691022048864001
##                                                                     sintaxis
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 5
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = sqrt(multi_pob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6268.6 -1633.4  -243.2  1372.2 12419.4 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1457.31     174.97   8.329 3.94e-16 ***
## sqrt(Freq.x)  1836.32      45.13  40.694  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2422 on 739 degrees of freedom
## Multiple R-squared:  0.6914, Adjusted R-squared:  0.691 
## F-statistic:  1656 on 1 and 739 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##     1457.31

bb <- linearMod$coefficients[2]
bb

## sqrt(Freq.x) 
##     1836.322

9 Modelo raíz-raíz (raíz-raíz)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.691 ).

9.1 Diagrama de dispersión sobre raíz-raíz

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=sqrt(h_y_m_comuna_corr_01$Freq.x), y=sqrt(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo raíz-raíz

Observemos nuevamente el resultado sobre raíz-raíz.

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = sqrt(multi_pob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6268.6 -1633.4  -243.2  1372.2 12419.4 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1457.31     174.97   8.329 3.94e-16 ***
## sqrt(Freq.x)  1836.32      45.13  40.694  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2422 on 739 degrees of freedom
## Multiple R-squared:  0.6914, Adjusted R-squared:  0.691 
## F-statistic:  1656 on 1 and 739 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = sqrt(Freq.x) , y = sqrt(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = {1457.31}^2 + 2 1457.31 1836.322 \sqrt{X}+ 1836.322^2 X \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- ((aa^2)+(2*(aa*bb)) * sqrt(h_y_m_comuna_corr_01$Freq.x)+((bb^2)*h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
6101092004	06101	3	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	128	0.0005294	06101	31195828	21510242
6101102003	06101	1	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	37	0.0001530	06101	9017544	10848015
6101102007	06101	18	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	481	0.0019895	06101	117228072	85528564
6101102009	06101	5	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	88	0.0003640	06101	21447132	30951993
6101102015	06101	6	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	271	0.0011209	06101	66047417	35466344
6101112015	06101	16	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	74	0.0003061	06101	18035088	77485749
6101122002	06101	4	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	84	0.0003474	06101	20472262	26316435
6101122008	06101	5	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	88	0.0003640	06101	21447132	30951993
6101122901	06101	10	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	45	0.0001861	06101	10967283	52769631
6101132005	06101	7	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	345	0.0014270	06101	84082505	39888851
6101132008	06101	3	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	13	0.0000538	06101	3168326	21510242
6101132013	06101	31	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	432	0.0017868	06101	105285919	136457897
6101132014	06101	74	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	825	0.0034123	06101	201066859	297698822
6101142010	06101	2	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	43	0.0001779	06101	10479848	16437040
6101142011	06101	19	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	253	0.0010464	06101	61660503	89522879
6101142013	06101	10	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	189	0.0007817	06101	46062590	52769631
6101182001	06101	42	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1112	0.0045993	06101	271013754	178437183
6101182002	06101	9	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	397	0.0016420	06101	96755810	48529013
6101182005	06101	20	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2002	0.0082805	06101	487922245	93501023
6101182006	06101	4	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	115	0.0004757	06101	28027502	26316435
6101182015	06101	29	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	569	0.0023534	06101	138675203	128736432
6102012001	06102	16	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	575	0.0442716	06102	158914734	77485749
6102012002	06102	9	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	157	0.0120881	06102	43390632	48529013
6102012004	06102	5	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	183	0.0140899	06102	50576341	30951993
6102012006	06102	43	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	631	0.0485833	06102	174391647	182219763
6102012009	06102	2	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	99	0.0076224	06102	27360972	16437040
6102012014	06102	6	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	83	0.0063905	06102	22938996	35466344
6102012018	06102	3	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	90	0.0069295	06102	24873611	21510242
6102022002	06102	2	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	32	0.0024638	06102	8843950	16437040
6102022003	06102	25	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	811	0.0624423	06102	224138868	113186643
6102022005	06102	19	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	388	0.0298737	06102	107232899	89522879
6102022007	06102	15	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	606	0.0466585	06102	167482311	73433853
6102022012	06102	9	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	462	0.0355713	06102	127684534	48529013
6102022013	06102	20	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	219	0.0168617	06102	60525786	93501023
6102022016	06102	4	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	265	0.0204034	06102	73238964	26316435
6102022017	06102	29	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	600	0.0461965	06102	165824070	128736432
6102022019	06102	23	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	269	0.0207114	06102	74344458	105349738
6102022020	06102	5	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	91	0.0070065	06102	25149984	30951993
6102022901	06102	8	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	37	0.0028488	06102	10225818	44238644
6102032011	06102	9	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	284	0.0218663	06102	78490060	48529013
6103012003	06103	97	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	1211	0.1645604	06103	236050369	381928312
6103012004	06103	59	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	970	0.1318114	06103	189074201	242187315
6103022002	06103	40	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	276	0.0375051	06103	53798433	170857092
6103032001	06103	20	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	666	0.0905014	06103	129817957	93501023
6103042005	06103	66	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	812	0.1103411	06103	158276548	268162311
6104012002	06104	41	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	721	0.0367913	06104	211541616	174649686
6104012006	06104	12	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	221	0.0112772	06104	64841466	61129206
6104022006	06104	70	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	1313	0.0670001	06104	385234594	282948863
6104032006	06104	83	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	1279	0.0652651	06104	375258984	330767023
6104032008	06104	28	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	803	0.0409757	06104	235600441	124863056
6104042013	06104	20	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	620	0.0316375	06104	181908186	93501023
6104042015	06104	2	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	84	0.0042864	06104	24645625	16437040
6104052004	06104	11	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	69	0.0035209	06104	20244621	56967804
6104052005	06104	40	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	672	0.0342910	06104	197165002	170857092
6104052007	06104	57	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	907	0.0462826	06104	266114072	234740354
6104052010	06104	13	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	294	0.0150023	06104	86259688	65258350
6104052014	06104	4	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	77	0.0039292	06104	22591823	26316435
6104052016	06104	8	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	64	0.0032658	06104	18777619	44238644
6104062011	06104	39	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	653	0.0333214	06104	191590396	167059207
6104062016	06104	4	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	18	0.0009185	06104	5281205	26316435
6104062901	06104	16	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	171	0.0087258	06104	50171451	77485749
6104072011	06104	20	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	278	0.0141858	06104	81565283	93501023
6104082001	06104	8	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	109	0.0055621	06104	31980633	44238644
6104082009	06104	1	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	19	0.0009695	06104	5574606	10848015
6104082901	06104	2	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	14	0.0007144	06104	4107604	16437040
6105012001	06105	10	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	335	0.0160387	06105	81450555	52769631
6105012002	06105	4	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	52	0.0024896	06105	12643071	26316435
6105012003	06105	21	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	210	0.0100541	06105	51058557	97464196
6105022003	06105	88	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	1037	0.0496481	06105	252132016	349074638
6105022005	06105	47	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	757	0.0362426	06105	184053940	197304186
6105032004	06105	69	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	1254	0.0600373	06105	304892524	279255779
6105042004	06105	59	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	818	0.0391631	06105	198885235	242187315
6106012001	06106	3	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	21	0.0006280	06106	5612395	21510242
6106012004	06106	13	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	265	0.0079254	06106	70823074	65258350
6106012012	06106	3	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	50	0.0014953	06106	13362844	21510242
6106022003	06106	11	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	411	0.0122918	06106	109842579	56967804
6106022004	06106	11	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	221	0.0066094	06106	59063771	56967804
6106022006	06106	4	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	71	0.0021234	06106	18975239	26316435
6106022010	06106	65	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	592	0.0177049	06106	158216074	264459570
6106032005	06106	4	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	25	0.0007477	06106	6681422	26316435
6106032007	06106	7	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	279	0.0083441	06106	74564670	39888851
6106032009	06106	11	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	241	0.0072076	06106	64408909	56967804
6106032011	06106	5	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	128	0.0038281	06106	34208881	30951993
6106042001	06106	23	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	324	0.0096899	06106	86591230	105349738
6106042002	06106	17	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	258	0.0077160	06106	68952276	81516712
6106042008	06106	3	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	282	0.0084338	06106	75366441	21510242
6106042011	06106	5	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	64	0.0019140	06106	17104440	30951993
6106042012	06106	15	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	384	0.0114843	06106	102626643	73433853
6107012001	06107	9	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	253	0.0102679	06107	51048349	48529013
6107012008	06107	4	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	66	0.0026786	06107	13316960	26316435
6107012021	06107	6	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	152	0.0061688	06107	30669364	35466344
6107022003	06107	34	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	735	0.0298295	06107	148302515	147982762
6107022007	06107	35	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	525	0.0213068	06107	105930368	151810461
6107022009	06107	76	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	1472	0.0597403	06107	297008573	305061011
6107032005	06107	66	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	1243	0.0504464	06107	250802756	268162311
6107032006	06107	28	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	309	0.0125406	06107	62347588	124863056
6107042002	06107	28	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	862	0.0349838	06107	173927575	124863056
6107042016	06107	40	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	570	0.0231331	06107	115010113	170857092
6107042019	06107	30	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	426	0.0172890	06107	85954927	132601237
6107042023	06107	2	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	57	0.0023133	06107	11501011	16437040

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
6101092004	06101	3	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	128	0.0005294	06101	31195828	21510242	168048.77
6101102003	06101	1	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	37	0.0001530	06101	9017544	10848015	293189.59
6101102007	06101	18	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	481	0.0019895	06101	117228072	85528564	177814.06
6101102009	06101	5	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	88	0.0003640	06101	21447132	30951993	351727.19
6101102015	06101	6	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	271	0.0011209	06101	66047417	35466344	130872.12
6101112015	06101	16	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	74	0.0003061	06101	18035088	77485749	1047104.71
6101122002	06101	4	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	84	0.0003474	06101	20472262	26316435	313290.89
6101122008	06101	5	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	88	0.0003640	06101	21447132	30951993	351727.19
6101122901	06101	10	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	45	0.0001861	06101	10967283	52769631	1172658.47
6101132005	06101	7	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	345	0.0014270	06101	84082505	39888851	115619.86
6101132008	06101	3	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	13	0.0000538	06101	3168326	21510242	1654634.04
6101132013	06101	31	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	432	0.0017868	06101	105285919	136457897	315874.76
6101132014	06101	74	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	825	0.0034123	06101	201066859	297698822	360847.06
6101142010	06101	2	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	43	0.0001779	06101	10479848	16437040	382256.75
6101142011	06101	19	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	253	0.0010464	06101	61660503	89522879	353845.37
6101142013	06101	10	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	189	0.0007817	06101	46062590	52769631	279204.40
6101182001	06101	42	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	1112	0.0045993	06101	271013754	178437183	160465.09
6101182002	06101	9	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	397	0.0016420	06101	96755810	48529013	122239.33
6101182005	06101	20	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	2002	0.0082805	06101	487922245	93501023	46703.81
6101182006	06101	4	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	115	0.0004757	06101	28027502	26316435	228838.56
6101182015	06101	29	2017	Rancagua	243717.4	2017	6101	241774	58924531866	Rural	569	0.0023534	06101	138675203	128736432	226250.32
6102012001	06102	16	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	575	0.0442716	06102	158914734	77485749	134757.82
6102012002	06102	9	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	157	0.0120881	06102	43390632	48529013	309101.99
6102012004	06102	5	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	183	0.0140899	06102	50576341	30951993	169136.57
6102012006	06102	43	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	631	0.0485833	06102	174391647	182219763	288779.34
6102012009	06102	2	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	99	0.0076224	06102	27360972	16437040	166030.71
6102012014	06102	6	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	83	0.0063905	06102	22938996	35466344	427305.35
6102012018	06102	3	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	90	0.0069295	06102	24873611	21510242	239002.69
6102022002	06102	2	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	32	0.0024638	06102	8843950	16437040	513657.51
6102022003	06102	25	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	811	0.0624423	06102	224138868	113186643	139564.30
6102022005	06102	19	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	388	0.0298737	06102	107232899	89522879	230729.07
6102022007	06102	15	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	606	0.0466585	06102	167482311	73433853	121177.98
6102022012	06102	9	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	462	0.0355713	06102	127684534	48529013	105041.15
6102022013	06102	20	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	219	0.0168617	06102	60525786	93501023	426945.31
6102022016	06102	4	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	265	0.0204034	06102	73238964	26316435	99307.30
6102022017	06102	29	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	600	0.0461965	06102	165824070	128736432	214560.72
6102022019	06102	23	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	269	0.0207114	06102	74344458	105349738	391634.71
6102022020	06102	5	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	91	0.0070065	06102	25149984	30951993	340131.79
6102022901	06102	8	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	37	0.0028488	06102	10225818	44238644	1195639.03
6102032011	06102	9	2017	Codegua	276373.5	2017	6102	12988	3589538376	Rural	284	0.0218663	06102	78490060	48529013	170876.80
6103012003	06103	97	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	1211	0.1645604	06103	236050369	381928312	315382.59
6103012004	06103	59	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	970	0.1318114	06103	189074201	242187315	249677.64
6103022002	06103	40	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	276	0.0375051	06103	53798433	170857092	619047.43
6103032001	06103	20	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	666	0.0905014	06103	129817957	93501023	140391.93
6103042005	06103	66	2017	Coinco	194921.9	2017	6103	7359	1434429947	Rural	812	0.1103411	06103	158276548	268162311	330249.15
6104012002	06104	41	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	721	0.0367913	06104	211541616	174649686	242232.57
6104012006	06104	12	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	221	0.0112772	06104	64841466	61129206	276602.74
6104022006	06104	70	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	1313	0.0670001	06104	385234594	282948863	215497.99
6104032006	06104	83	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	1279	0.0652651	06104	375258984	330767023	258613.78
6104032008	06104	28	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	803	0.0409757	06104	235600441	124863056	155495.71
6104042013	06104	20	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	620	0.0316375	06104	181908186	93501023	150808.10
6104042015	06104	2	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	84	0.0042864	06104	24645625	16437040	195679.05
6104052004	06104	11	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	69	0.0035209	06104	20244621	56967804	825620.35
6104052005	06104	40	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	672	0.0342910	06104	197165002	170857092	254251.62
6104052007	06104	57	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	907	0.0462826	06104	266114072	234740354	258809.65
6104052010	06104	13	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	294	0.0150023	06104	86259688	65258350	221967.18
6104052014	06104	4	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	77	0.0039292	06104	22591823	26316435	341771.88
6104052016	06104	8	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	64	0.0032658	06104	18777619	44238644	691228.81
6104062011	06104	39	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	653	0.0333214	06104	191590396	167059207	255833.40
6104062016	06104	4	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	18	0.0009185	06104	5281205	26316435	1462024.14
6104062901	06104	16	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	171	0.0087258	06104	50171451	77485749	453133.03
6104072011	06104	20	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	278	0.0141858	06104	81565283	93501023	336334.62
6104082001	06104	8	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	109	0.0055621	06104	31980633	44238644	405859.12
6104082009	06104	1	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	19	0.0009695	06104	5574606	10848015	570948.15
6104082901	06104	2	2017	Coltauco	293400.3	2017	6104	19597	5749765679	Rural	14	0.0007144	06104	4107604	16437040	1174074.32
6105012001	06105	10	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	335	0.0160387	06105	81450555	52769631	157521.29
6105012002	06105	4	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	52	0.0024896	06105	12643071	26316435	506085.28
6105012003	06105	21	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	210	0.0100541	06105	51058557	97464196	464115.22
6105022003	06105	88	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	1037	0.0496481	06105	252132016	349074638	336619.71
6105022005	06105	47	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	757	0.0362426	06105	184053940	197304186	260639.61
6105032004	06105	69	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	1254	0.0600373	06105	304892524	279255779	222692.01
6105042004	06105	59	2017	Doñihue	243136.0	2017	6105	20887	5078381306	Rural	818	0.0391631	06105	198885235	242187315	296072.51
6106012001	06106	3	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	21	0.0006280	06106	5612395	21510242	1024297.26
6106012004	06106	13	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	265	0.0079254	06106	70823074	65258350	246257.92
6106012012	06106	3	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	50	0.0014953	06106	13362844	21510242	430204.85
6106022003	06106	11	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	411	0.0122918	06106	109842579	56967804	138607.80
6106022004	06106	11	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	221	0.0066094	06106	59063771	56967804	257772.87
6106022006	06106	4	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	71	0.0021234	06106	18975239	26316435	370654.01
6106022010	06106	65	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	592	0.0177049	06106	158216074	264459570	446722.25
6106032005	06106	4	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	25	0.0007477	06106	6681422	26316435	1052657.38
6106032007	06106	7	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	279	0.0083441	06106	74564670	39888851	142970.79
6106032009	06106	11	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	241	0.0072076	06106	64408909	56967804	236380.93
6106032011	06106	5	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	128	0.0038281	06106	34208881	30951993	241812.44
6106042001	06106	23	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	324	0.0096899	06106	86591230	105349738	325153.51
6106042002	06106	17	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	258	0.0077160	06106	68952276	81516712	315956.25
6106042008	06106	3	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	282	0.0084338	06106	75366441	21510242	76277.46
6106042011	06106	5	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	64	0.0019140	06106	17104440	30951993	483624.88
6106042012	06106	15	2017	Graneros	267256.9	2017	6106	33437	8936268375	Rural	384	0.0114843	06106	102626643	73433853	191233.99
6107012001	06107	9	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	253	0.0102679	06107	51048349	48529013	191814.28
6107012008	06107	4	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	66	0.0026786	06107	13316960	26316435	398733.86
6107012021	06107	6	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	152	0.0061688	06107	30669364	35466344	233331.21
6107022003	06107	34	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	735	0.0298295	06107	148302515	147982762	201337.09
6107022007	06107	35	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	525	0.0213068	06107	105930368	151810461	289162.78
6107022009	06107	76	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	1472	0.0597403	06107	297008573	305061011	207242.53
6107032005	06107	66	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	1243	0.0504464	06107	250802756	268162311	215737.98
6107032006	06107	28	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	309	0.0125406	06107	62347588	124863056	404087.56
6107042002	06107	28	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	862	0.0349838	06107	173927575	124863056	144852.73
6107042016	06107	40	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	570	0.0231331	06107	115010113	170857092	299749.28
6107042019	06107	30	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	426	0.0172890	06107	85954927	132601237	311270.51
6107042023	06107	2	2017	Las Cabras	201772.1	2017	6107	24640	4971665251	Rural	57	0.0023133	06107	11501011	16437040	288369.13

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_rural_nivel_nacional_17_r07.rds")

---

R-08

Urbano

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 1)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15201	1	1	1	1767	1	1	1	2	1	3	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	2	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	3	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	4	1	1	2	3	1	99	1	2	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	5	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	6	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	7	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	8	1	1	3	3	1	99	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	9	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	10	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	11	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	12	1	1	4	3	3	3	4	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	13	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	14	1	1	2	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	15	1	1	2	1	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	16	1	1	5	1	5	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	17	1	1	5	3	1	3	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	18	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	19	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	20	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	21	4	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	22	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	23	1	1	5	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	24	1	1	3	1	1	6	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	25	1	1	5	6	1	1	4	2	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	26	4	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	27	1	1	5	99	3	3	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	28	1	1	6	3	2	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	29	1	1	2	3	1	99	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	30	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	31	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	32	1	1	3	1	1	2	1	1	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	33	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	34	3	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	35	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	36	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	37	1	1	5	3	3	1	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	38	1	1	1	1	4	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	39	1	1	5	3	4	3	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	40	1	1	5	3	2	1	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	41	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	42	1	1	2	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	43	1	1	1	1	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	44	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	45	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	46	1	1	5	6	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	47	1	1	5	3	4	0	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	48	1	1	3	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	49	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	50	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	51	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	52	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	53	1	1	1	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	54	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	55	1	1	2	2	1	4	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	56	8	1	98	98	98	98	98	98	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	57	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	58	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	59	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	60	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	61	1	1	2	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	62	1	1	5	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	63	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	64	1	1	5	3	2	4	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	65	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	66	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	67	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	68	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	69	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	70	1	1	1	3	3	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	71	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	72	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	73	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	74	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	75	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	76	1	1	5	3	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	77	1	1	3	6	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	78	5	1	3	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	79	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	80	1	1	2	1	1	3	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	81	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	82	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	83	1	1	5	3	2	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	84	4	1	5	3	2	0	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	85	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	86	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	87	1	1	3	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	88	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	89	5	1	4	3	2	1	2	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	90	1	1	5	3	3	2	1	3	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	91	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	92	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	93	5	1	4	3	5	1	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	94	1	1	5	3	3	99	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	95	1	1	5	3	2	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	96	1	1	2	3	4	6	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	97	1	1	5	1	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	98	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	99	1	1	2	7	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	100	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 8:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 8)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	8101011001	132	2017	08101
2	8101021001	120	2017	08101
3	8101021002	337	2017	08101
4	8101021003	137	2017	08101
5	8101031001	221	2017	08101
6	8101041001	86	2017	08101
7	8101051001	46	2017	08101
8	8101051002	79	2017	08101
9	8101051003	115	2017	08101
10	8101061001	217	2017	08101
11	8101071001	68	2017	08101
12	8101071002	111	2017	08101
13	8101081001	49	2017	08101
14	8101081002	8	2017	08101
15	8101081003	85	2017	08101
16	8101081004	81	2017	08101
17	8101081005	52	2017	08101
18	8101091001	318	2017	08101
19	8101101001	187	2017	08101
20	8101101002	322	2017	08101
21	8101101003	71	2017	08101
22	8101101004	48	2017	08101
23	8101111001	222	2017	08101
24	8101121001	218	2017	08101
25	8101131001	360	2017	08101
26	8101141001	98	2017	08101
27	8101151001	47	2017	08101
28	8101151002	32	2017	08101
29	8101151003	70	2017	08101
30	8101151004	36	2017	08101
31	8101151005	49	2017	08101
32	8101151006	43	2017	08101
33	8101161001	508	2017	08101
34	8101161002	97	2017	08101
35	8101161003	57	2017	08101
36	8101161004	148	2017	08101
37	8101161005	173	2017	08101
38	8101161006	448	2017	08101
39	8101161007	47	2017	08101
40	8101161008	483	2017	08101
41	8101161009	475	2017	08101
42	8101161010	531	2017	08101
43	8101161011	57	2017	08101
44	8101161012	820	2017	08101
45	8101161013	406	2017	08101
46	8101161014	554	2017	08101
47	8101171001	210	2017	08101
48	8101171002	121	2017	08101
49	8101171003	57	2017	08101
50	8101181001	229	2017	08101
51	8101191001	208	2017	08101
52	8101191002	157	2017	08101
53	8101201001	286	2017	08101
54	8101211001	383	2017	08101
55	8101221001	197	2017	08101
56	8101221002	122	2017	08101
57	8101231001	185	2017	08101
58	8101241001	97	2017	08101
59	8101241002	156	2017	08101
60	8101241003	252	2017	08101
61	8101251001	520	2017	08101
62	8101251002	43	2017	08101
63	8101251003	131	2017	08101
64	8101251004	45	2017	08101
65	8101251005	166	2017	08101
66	8101261001	53	2017	08101
67	8101261002	67	2017	08101
68	8101261003	29	2017	08101
69	8101271001	84	2017	08101
70	8101281001	233	2017	08101
71	8101281002	142	2017	08101
72	8101321001	375	2017	08101
73	8101321002	226	2017	08101
74	8101321003	142	2017	08101
75	8101321004	180	2017	08101
76	8101321005	171	2017	08101
77	8101991999	21	2017	08101
550	8102011001	21	2017	08102
551	8102011002	60	2017	08102
552	8102021001	187	2017	08102
553	8102021002	96	2017	08102
554	8102021003	192	2017	08102
555	8102021004	88	2017	08102
556	8102021005	54	2017	08102
557	8102031001	100	2017	08102
558	8102031002	83	2017	08102
559	8102031003	154	2017	08102
560	8102031004	138	2017	08102
561	8102031005	61	2017	08102
562	8102041001	137	2017	08102
563	8102041002	140	2017	08102
564	8102041003	190	2017	08102
565	8102041004	94	2017	08102
566	8102051001	7	2017	08102
567	8102081001	121	2017	08102
568	8102081002	45	2017	08102
569	8102081003	76	2017	08102
570	8102111001	127	2017	08102
571	8102111002	98	2017	08102
572	8102111003	63	2017	08102

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_urbano_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
01107	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
01401	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
01405	Pica	330061.1	2017	1405	9296	3068247619	Urbano
02101	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02102	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02104	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02201	Calama	416281.1	2017	2201	165731	68990679686	Urbano
02203	San Pedro de Atacama	437934.7	2017	2203	10996	4815529626	Urbano
02301	Tocopilla	271720.8	2017	2301	25186	6843559467	Urbano
02302	María Elena	466266.9	2017	2302	6457	3010685220	Urbano
03101	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
03102	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
03103	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
03201	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
03202	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
03301	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
03303	Freirina	289049.9	2017	3303	7041	2035200054	Urbano
03304	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
04101	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04102	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04103	Andacollo	251267.7	2017	4103	11044	2775000288	Urbano
04104	La Higuera	214257.0	2017	4104	4241	908664019	Urbano
04106	Vicuña	245957.4	2017	4106	27771	6830481918	Urbano
04201	Illapel	270316.5	2017	4201	30848	8338722128	Urbano
04202	Canela	233397.3	2017	4202	9093	2122281844	Urbano
04203	Los Vilos	282415.6	2017	4203	21382	6038609501	Urbano
04204	Salamanca	262056.9	2017	4204	29347	7690585032	Urbano
04301	Ovalle	274771.4	2017	4301	111272	30574361012	Urbano
04302	Combarbalá	228990.4	2017	4302	13322	3050610572	Urbano
04303	Monte Patria	225369.1	2017	4303	30751	6930326684	Urbano
04304	Punitaqui	212496.1	2017	4304	10956	2328107498	Urbano
05101	Valparaíso	297929.0	2017	5101	296655	88382118059	Urbano
05102	Casablanca	341641.8	2017	5102	26867	9178890241	Urbano
05103	Concón	318496.3	2017	5103	42152	13425257057	Urbano
05105	Puchuncaví	296035.5	2017	5105	18546	5490274928	Urbano
05107	Quintero	308224.7	2017	5107	31923	9839456903	Urbano
05109	Viña del Mar	337006.1	2017	5109	334248	112643604611	Urbano
05301	Los Andes	339720.2	2017	5301	66708	22662055502	Urbano
05302	Calle Larga	246387.3	2017	5302	14832	3654416747	Urbano
05303	Rinconada	273904.7	2017	5303	10207	2795744821	Urbano
05304	San Esteban	219571.6	2017	5304	18855	4140022481	Urbano
05401	La Ligua	250134.4	2017	5401	35390	8852256241	Urbano
05402	Cabildo	262745.9	2017	5402	19388	5094117762	Urbano
05403	Papudo	294355.2	2017	5403	6356	1870921373	Urbano
05404	Petorca	237510.8	2017	5404	9826	2333781007	Urbano
05405	Zapallar	294389.2	2017	5405	7339	2160521991	Urbano
05501	Quillota	286029.5	2017	5501	90517	25890529852	Urbano
05502	Calera	277181.9	2017	5502	50554	14012652087	Urbano
05503	Hijuelas	254094.0	2017	5503	17988	4570642363	Urbano

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
08101	8101011001	132	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101021001	120	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101021002	337	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101021003	137	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101031001	221	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101041001	86	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101051001	46	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101051002	79	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101051003	115	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101061001	217	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101071001	68	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101071002	111	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101081001	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101081002	8	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101081003	85	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101081004	81	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101081005	52	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101091001	318	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101101001	187	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101101002	322	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101101003	71	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101101004	48	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101111001	222	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101121001	218	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101131001	360	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101141001	98	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151001	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151002	32	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151003	70	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151004	36	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151005	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151006	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161001	508	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161002	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161003	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161004	148	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161005	173	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161006	448	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161007	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161008	483	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161009	475	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161010	531	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161011	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161012	820	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161013	406	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161014	554	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101171001	210	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101171002	121	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101171003	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101181001	229	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101191001	208	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101191002	157	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101201001	286	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101211001	383	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101221001	197	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101221002	122	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101231001	185	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101241001	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101241002	156	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101241003	252	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101251001	520	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101251002	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101251003	131	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101251004	45	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101251005	166	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101261001	53	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101261002	67	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101261003	29	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101271001	84	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101281001	233	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101281002	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101321001	375	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101321002	226	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101321003	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101321004	180	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101321005	171	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101991999	21	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08102	8102011001	21	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102011002	60	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102021001	187	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102021002	96	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102021003	192	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102021004	88	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102021005	54	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102031001	100	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102031002	83	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102031003	154	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102031004	138	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102031005	61	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102041001	137	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102041002	140	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102041003	190	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102041004	94	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102051001	7	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102081001	121	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102081002	45	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102081003	76	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102111001	127	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102111002	98	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102111003	63	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
08101	8101011001	132	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101021001	120	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101021002	337	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101021003	137	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101031001	221	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101041001	86	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101051001	46	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101051002	79	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101051003	115	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101061001	217	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101071001	68	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101071002	111	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101081001	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101081002	8	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101081003	85	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101081004	81	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101081005	52	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101091001	318	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101101001	187	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101101002	322	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101101003	71	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101101004	48	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101111001	222	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101121001	218	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101131001	360	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101141001	98	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151001	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151002	32	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151003	70	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151004	36	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151005	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151006	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161001	508	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161002	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161003	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161004	148	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161005	173	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161006	448	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161007	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161008	483	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161009	475	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161010	531	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161011	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161012	820	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161013	406	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161014	554	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101171001	210	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101171002	121	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101171003	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101181001	229	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101191001	208	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101191002	157	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101201001	286	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101211001	383	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101221001	197	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101221002	122	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101231001	185	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101241001	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101241002	156	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101241003	252	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101251001	520	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101251002	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101251003	131	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101251004	45	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101251005	166	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101261001	53	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101261002	67	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101261003	29	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101271001	84	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101281001	233	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101281002	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101321001	375	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101321002	226	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101321003	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101321004	180	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101321005	171	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101991999	21	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08102	8102011001	21	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102011002	60	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102021001	187	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102021002	96	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102021003	192	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102021004	88	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102021005	54	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102031001	100	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102031002	83	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102031003	154	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102031004	138	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102031005	61	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102041001	137	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102041002	140	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102041003	190	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102041004	94	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102051001	7	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102081001	121	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102081002	45	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102081003	76	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102111001	127	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102111002	98	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102111003	63	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
8101011001	08101	132	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1455	0.0065079	08101
8101021001	08101	120	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1516	0.0067808	08101
8101021002	08101	337	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1874	0.0083820	08101
8101021003	08101	137	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1349	0.0060338	08101
8101031001	08101	221	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2590	0.0115845	08101
8101041001	08101	86	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1373	0.0061411	08101
8101051001	08101	46	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1825	0.0081628	08101
8101051002	08101	79	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2383	0.0106587	08101
8101051003	08101	115	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2945	0.0131724	08101
8101061001	08101	217	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2976	0.0133110	08101
8101071001	08101	68	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1523	0.0068121	08101
8101071002	08101	111	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2187	0.0097820	08101
8101081001	08101	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1852	0.0082836	08101
8101081002	08101	8	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	836	0.0037393	08101
8101081003	08101	85	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2570	0.0114951	08101
8101081004	08101	81	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1420	0.0063514	08101
8101081005	08101	52	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3105	0.0138880	08101
8101091001	08101	318	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3351	0.0149883	08101
8101101001	08101	187	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1345	0.0060159	08101
8101101002	08101	322	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4045	0.0180924	08101
8101101003	08101	71	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2968	0.0132752	08101
8101101004	08101	48	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2134	0.0095449	08101
8101111001	08101	222	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3916	0.0175155	08101
8101121001	08101	218	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2733	0.0122241	08101
8101131001	08101	360	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3498	0.0156458	08101
8101141001	08101	98	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2701	0.0120810	08101
8101151001	08101	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2271	0.0101577	08101
8101151002	08101	32	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2231	0.0099788	08101
8101151003	08101	70	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3075	0.0137538	08101
8101151004	08101	36	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1854	0.0082926	08101
8101151005	08101	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2355	0.0105334	08101
8101151006	08101	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2089	0.0093437	08101
8101161001	08101	508	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3863	0.0172784	08101
8101161002	08101	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3464	0.0154938	08101
8101161003	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3640	0.0162810	08101
8101161004	08101	148	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3612	0.0161557	08101
8101161005	08101	173	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4756	0.0212726	08101
8101161006	08101	448	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3401	0.0152120	08101
8101161007	08101	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2944	0.0131679	08101
8101161008	08101	483	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	5013	0.0224221	08101
8101161009	08101	475	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3072	0.0137404	08101
8101161010	08101	531	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2380	0.0106452	08101
8101161011	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2320	0.0103769	08101
8101161012	08101	820	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3886	0.0173813	08101
8101161013	08101	406	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2922	0.0130695	08101
8101161014	08101	554	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3814	0.0170592	08101
8101171001	08101	210	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3284	0.0146886	08101
8101171002	08101	121	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3682	0.0164688	08101
8101171003	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1315	0.0058817	08101
8101181001	08101	229	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4508	0.0201633	08101
8101191001	08101	208	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1629	0.0072862	08101
8101191002	08101	157	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3336	0.0149212	08101
8101201001	08101	286	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3154	0.0141072	08101
8101211001	08101	383	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4901	0.0219212	08101
8101221001	08101	197	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2688	0.0120229	08101
8101221002	08101	122	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1855	0.0082970	08101
8101231001	08101	185	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2503	0.0111954	08101
8101241001	08101	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2763	0.0123583	08101
8101241002	08101	156	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1864	0.0083373	08101
8101241003	08101	252	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1888	0.0084446	08101
8101251001	08101	520	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3790	0.0169519	08101
8101251002	08101	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3121	0.0139596	08101
8101251003	08101	131	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4587	0.0205167	08101
8101251004	08101	45	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2288	0.0102337	08101
8101251005	08101	166	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4334	0.0193851	08101
8101261001	08101	53	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1200	0.0053674	08101
8101261002	08101	67	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2084	0.0093213	08101
8101261003	08101	29	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2696	0.0120586	08101
8101271001	08101	84	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2727	0.0121973	08101
8101281001	08101	233	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3513	0.0157129	08101
8101281002	08101	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3737	0.0167148	08101
8101321001	08101	375	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4131	0.0184771	08101
8101321002	08101	226	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4089	0.0182892	08101
8101321003	08101	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2991	0.0133781	08101
8101321004	08101	180	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	5956	0.0266399	08101
8101321005	08101	171	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3491	0.0156145	08101
8101991999	08101	21	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1520	0.0067986	08101
8102011001	08102	21	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	231	0.0019869	08102
8102011002	08102	60	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2650	0.0227933	08102
8102021001	08102	187	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4404	0.0378800	08102
8102021002	08102	96	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2895	0.0249007	08102
8102021003	08102	192	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4041	0.0347577	08102
8102021004	08102	88	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2507	0.0215634	08102
8102021005	08102	54	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2879	0.0247630	08102
8102031001	08102	100	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3680	0.0316526	08102
8102031002	08102	83	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1697	0.0145963	08102
8102031003	08102	154	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	5338	0.0459135	08102
8102031004	08102	138	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1446	0.0124374	08102
8102031005	08102	61	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1870	0.0160844	08102
8102041001	08102	137	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3438	0.0295711	08102
8102041002	08102	140	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4279	0.0368048	08102
8102041003	08102	190	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4627	0.0397980	08102
8102041004	08102	94	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2601	0.0223719	08102
8102051001	08102	7	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	77	0.0006623	08102
8102081001	08102	121	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3302	0.0284014	08102
8102081002	08102	45	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2248	0.0193356	08102
8102081003	08102	76	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2303	0.0198087	08102
8102111001	08102	127	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3295	0.0283412	08102
8102111002	08102	98	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3487	0.0299926	08102
8102111003	08102	63	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2783	0.0239373	08102

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
8101011001	08101	132	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1455	0.0065079	08101	455161522
8101021001	08101	120	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1516	0.0067808	08101	474243895
8101021002	08101	337	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1874	0.0083820	08101	586235527
8101021003	08101	137	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1349	0.0060338	08101	422001988
8101031001	08101	221	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2590	0.0115845	08101	810218791
8101041001	08101	86	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1373	0.0061411	08101	429509807
8101051001	08101	46	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1825	0.0081628	08101	570907063
8101051002	08101	79	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2383	0.0106587	08101	745463853
8101051003	08101	115	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2945	0.0131724	08101	921271946
8101061001	08101	217	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2976	0.0133110	08101	930969545
8101071001	08101	68	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1523	0.0068121	08101	476433675
8101071002	08101	111	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2187	0.0097820	08101	684149999
8101081001	08101	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1852	0.0082836	08101	579353360
8101081002	08101	8	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	836	0.0037393	08101	261522359
8101081003	08101	85	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2570	0.0114951	08101	803962275
8101081004	08101	81	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1420	0.0063514	08101	444212619
8101081005	08101	52	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3105	0.0138880	08101	971324072
8101091001	08101	318	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3351	0.0149883	08101	1048279216
8101101001	08101	187	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1345	0.0060159	08101	420750685
8101101002	08101	322	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4045	0.0180924	08101	1265380313
8101101003	08101	71	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2968	0.0132752	08101	928466939
8101101004	08101	48	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2134	0.0095449	08101	667570232
8101111001	08101	222	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3916	0.0175155	08101	1225025786
8101121001	08101	218	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2733	0.0122241	08101	854952879
8101131001	08101	360	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3498	0.0156458	08101	1094264607
8101141001	08101	98	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2701	0.0120810	08101	844942454
8101151001	08101	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2271	0.0101577	08101	710427365
8101151002	08101	32	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2231	0.0099788	08101	697914333
8101151003	08101	70	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3075	0.0137538	08101	961939298
8101151004	08101	36	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1854	0.0082926	08101	579979011
8101151005	08101	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2355	0.0105334	08101	736704731
8101151006	08101	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2089	0.0093437	08101	653493071
8101161001	08101	508	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3863	0.0172784	08101	1208446019
8101161002	08101	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3464	0.0154938	08101	1083628530
8101161003	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3640	0.0162810	08101	1138685869
8101161004	08101	148	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3612	0.0161557	08101	1129926747
8101161005	08101	173	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4756	0.0212726	08101	1487799448
8101161006	08101	448	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3401	0.0152120	08101	1063920505
8101161007	08101	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2944	0.0131679	08101	920959120
8101161008	08101	483	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	5013	0.0224221	08101	1568195676
8101161009	08101	475	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3072	0.0137404	08101	961000821
8101161010	08101	531	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2380	0.0106452	08101	744525376
8101161011	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2320	0.0103769	08101	725755828
8101161012	08101	820	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3886	0.0173813	08101	1215641012
8101161013	08101	406	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2922	0.0130695	08101	914076953
8101161014	08101	554	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3814	0.0170592	08101	1193117556
8101171001	08101	210	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3284	0.0146886	08101	1027319888
8101171002	08101	121	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3682	0.0164688	08101	1151824552
8101171003	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1315	0.0058817	08101	411365911
8101181001	08101	229	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4508	0.0201633	08101	1410218653
8101191001	08101	208	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1629	0.0072862	08101	509593209
8101191002	08101	157	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3336	0.0149212	08101	1043586829
8101201001	08101	286	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3154	0.0141072	08101	986652536
8101211001	08101	383	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4901	0.0219212	08101	1533159187
8101221001	08101	197	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2688	0.0120229	08101	840875718
8101221002	08101	122	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1855	0.0082970	08101	580291837
8101231001	08101	185	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2503	0.0111954	08101	783002948
8101241001	08101	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2763	0.0123583	08101	864337652
8101241002	08101	156	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1864	0.0083373	08101	583107269
8101241003	08101	252	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1888	0.0084446	08101	590615088
8101251001	08101	520	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3790	0.0169519	08101	1185609737
8101251002	08101	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3121	0.0139596	08101	976329285
8101251003	08101	131	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4587	0.0205167	08101	1434931890
8101251004	08101	45	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2288	0.0102337	08101	715745403
8101251005	08101	166	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4334	0.0193851	08101	1355786966
8101261001	08101	53	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1200	0.0053674	08101	375390946
8101261002	08101	67	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2084	0.0093213	08101	651928942
8101261003	08101	29	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2696	0.0120586	08101	843378325
8101271001	08101	84	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2727	0.0121973	08101	853075924
8101281001	08101	233	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3513	0.0157129	08101	1098956994
8101281002	08101	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3737	0.0167148	08101	1169029970
8101321001	08101	375	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4131	0.0184771	08101	1292283331
8101321002	08101	226	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4089	0.0182892	08101	1279144647
8101321003	08101	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2991	0.0133781	08101	935661932
8101321004	08101	180	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	5956	0.0266399	08101	1863190394
8101321005	08101	171	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3491	0.0156145	08101	1092074826
8101991999	08101	21	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1520	0.0067986	08101	475495198
8102011001	08102	21	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	231	0.0019869	08102	61716800
8102011002	08102	60	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2650	0.0227933	08102	708006586
8102021001	08102	187	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4404	0.0378800	08102	1176626794
8102021002	08102	96	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2895	0.0249007	08102	773463798
8102021003	08102	192	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4041	0.0347577	08102	1079643250
8102021004	08102	88	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2507	0.0215634	08102	669800947
8102021005	08102	54	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2879	0.0247630	08102	769189042
8102031001	08102	100	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3680	0.0316526	08102	983194051
8102031002	08102	83	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1697	0.0145963	08102	453391387
8102031003	08102	154	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	5338	0.0459135	08102	1426165719
8102031004	08102	138	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1446	0.0124374	08102	386331141
8102031005	08102	61	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1870	0.0160844	08102	499612194
8102041001	08102	137	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3438	0.0295711	08102	918538355
8102041002	08102	140	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4279	0.0368048	08102	1143230257
8102041003	08102	190	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4627	0.0397980	08102	1236206216
8102041004	08102	94	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2601	0.0223719	08102	694915143
8102051001	08102	7	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	77	0.0006623	08102	20572267
8102081001	08102	121	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3302	0.0284014	08102	882202923
8102081002	08102	45	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2248	0.0193356	08102	600603322
8102081003	08102	76	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2303	0.0198087	08102	615297799
8102111001	08102	127	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3295	0.0283412	08102	880332717
8102111002	08102	98	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3487	0.0299926	08102	931629798
8102111003	08102	63	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2783	0.0239373	08102	743540501

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -789139518 -229998481  -17716814  218589556 1341310477 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 663261697   20914997   31.71   <2e-16 ***
## Freq.x        1104251      99359   11.11   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 350900000 on 470 degrees of freedom
## Multiple R-squared:  0.2081, Adjusted R-squared:  0.2064 
## F-statistic: 123.5 on 1 and 470 DF,  p-value: < 2.2e-16

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.206422068260736"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.206422068260736"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.429357928472113"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.325507963000801"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.331109627578258"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.267591932013513"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.532295344305123"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                
## [1,] "log-log" "0.56853136470591"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 1    cuadrático 0.206422068260736
## 2        cúbico 0.206422068260736
## 6      log-raíz 0.267591932013513
## 4 raíz cuadrada 0.325507963000801
## 5     raíz-raíz 0.331109627578258
## 3   logarítmico 0.429357928472113
## 7      raíz-log 0.532295344305123
## 8       log-log  0.56853136470591
##                                                                     sintaxis
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 8
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5258 -0.3083  0.0999  0.3854  1.4623 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  17.6993     0.1073  164.96   <2e-16 ***
## log(Freq.x)   0.5958     0.0239   24.93   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6139 on 470 degrees of freedom
## Multiple R-squared:  0.5694, Adjusted R-squared:  0.5685 
## F-statistic: 621.6 on 1 and 470 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    17.69933

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##    0.595802

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.5685 ).

9.1 Diagrama de dispersión sobre log-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5258 -0.3083  0.0999  0.3854  1.4623 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  17.6993     0.1073  164.96   <2e-16 ***
## log(Freq.x)   0.5958     0.0239   24.93   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6139 on 470 degrees of freedom
## Multiple R-squared:  0.5694, Adjusted R-squared:  0.5685 
## F-statistic: 621.6 on 1 and 470 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = e^{17.69933+0.595802 \cdot ln{X}} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
8101011001	08101	132	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1455	0.0065079	08101	455161522	891583213
8101021001	08101	120	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1516	0.0067808	08101	474243895	842364466
8101021002	08101	337	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1874	0.0083820	08101	586235527	1558427640
8101021003	08101	137	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1349	0.0060338	08101	422001988	911553328
8101031001	08101	221	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2590	0.0115845	08101	810218791	1212029801
8101041001	08101	86	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1373	0.0061411	08101	429509807	690713176
8101051001	08101	46	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1825	0.0081628	08101	570907063	475766805
8101051002	08101	79	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2383	0.0106587	08101	745463853	656643593
8101051003	08101	115	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2945	0.0131724	08101	921271946	821273082
8101061001	08101	217	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2976	0.0133110	08101	930969545	1198911359
8101071001	08101	68	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1523	0.0068121	08101	476433675	600526491
8101071002	08101	111	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2187	0.0097820	08101	684149999	804131760
8101081001	08101	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1852	0.0082836	08101	579353360	494017007
8101081002	08101	8	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	836	0.0037393	08101	261522359	167796354
8101081003	08101	85	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2570	0.0114951	08101	803962275	685916657
8101081004	08101	81	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1420	0.0063514	08101	444212619	666498054
8101081005	08101	52	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3105	0.0138880	08101	971324072	511820786
8101091001	08101	318	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3351	0.0149883	08101	1048279216	1505465387
8101101001	08101	187	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1345	0.0060159	08101	420750685	1097204211
8101101002	08101	322	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4045	0.0180924	08101	1265380313	1516719380
8101101003	08101	71	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2968	0.0132752	08101	928466939	616173649
8101101004	08101	48	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2134	0.0095449	08101	667570232	487985129
8101111001	08101	222	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3916	0.0175155	08101	1225025786	1215294374
8101121001	08101	218	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2733	0.0122241	08101	854952879	1202200069
8101131001	08101	360	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3498	0.0156458	08101	1094264607	1620950804
8101141001	08101	98	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2701	0.0120810	08101	844942454	746614075
8101151001	08101	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2271	0.0101577	08101	710427365	481902238
8101151002	08101	32	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2231	0.0099788	08101	697914333	383257854
8101151003	08101	70	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3075	0.0137538	08101	961939298	610988164
8101151004	08101	36	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1854	0.0082926	08101	579979011	411119275
8101151005	08101	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2355	0.0105334	08101	736704731	494017007
8101151006	08101	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2089	0.0093437	08101	653493071	457028695
8101161001	08101	508	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3863	0.0172784	08101	1208446019	1990117426
8101161002	08101	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3464	0.0154938	08101	1083628530	742065545
8101161003	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3640	0.0162810	08101	1138685869	540596769
8101161004	08101	148	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3612	0.0161557	08101	1129926747	954478091
8101161005	08101	173	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4756	0.0212726	08101	1487799448	1047495190
8101161006	08101	448	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3401	0.0152120	08101	1063920505	1846530284
8101161007	08101	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2944	0.0131679	08101	920959120	481902238
8101161008	08101	483	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	5013	0.0224221	08101	1568195676	1931171126
8101161009	08101	475	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3072	0.0137404	08101	961000821	1912049350
8101161010	08101	531	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2380	0.0106452	08101	744525376	2043320334
8101161011	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2320	0.0103769	08101	725755828	540596769
8101161012	08101	820	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3886	0.0173813	08101	1215641012	2647133298
8101161013	08101	406	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2922	0.0130695	08101	914076953	1741344583
8101161014	08101	554	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3814	0.0170592	08101	1193117556	2095599546
8101171001	08101	210	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3284	0.0146886	08101	1027319888	1175716446
8101171002	08101	121	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3682	0.0164688	08101	1151824552	846539804
8101171003	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1315	0.0058817	08101	411365911	540596769
8101181001	08101	229	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4508	0.0201633	08101	1410218653	1237982178
8101191001	08101	208	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1629	0.0072862	08101	509593209	1169032174
8101191002	08101	157	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3336	0.0149212	08101	1043586829	988646648
8101201001	08101	286	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3154	0.0141072	08101	986652536	1413277639
8101211001	08101	383	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4901	0.0219212	08101	1533159187	1681878766
8101221001	08101	197	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2688	0.0120229	08101	840875718	1131793667
8101221002	08101	122	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1855	0.0082970	08101	580291837	850701216
8101231001	08101	185	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2503	0.0111954	08101	783002948	1090197403
8101241001	08101	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2763	0.0123583	08101	864337652	742065545
8101241002	08101	156	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1864	0.0083373	08101	583107269	984889971
8101241003	08101	252	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1888	0.0084446	08101	590615088	1310626478
8101251001	08101	520	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3790	0.0169519	08101	1185609737	2017994207
8101251002	08101	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3121	0.0139596	08101	976329285	457028695
8101251003	08101	131	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4587	0.0205167	08101	1434931890	887552734
8101251004	08101	45	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2288	0.0102337	08101	715745403	469577217
8101251005	08101	166	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4334	0.0193851	08101	1355786966	1022032090
8101261001	08101	53	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1200	0.0053674	08101	375390946	517662502
8101261002	08101	67	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2084	0.0093213	08101	651928942	595249055
8101261003	08101	29	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2696	0.0120586	08101	843378325	361425964
8101271001	08101	84	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2727	0.0121973	08101	853075924	681097273
8101281001	08101	233	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3513	0.0157129	08101	1098956994	1250820773
8101281002	08101	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3737	0.0167148	08101	1169029970	931230933
8101321001	08101	375	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4131	0.0184771	08101	1292283331	1660858636
8101321002	08101	226	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4089	0.0182892	08101	1279144647	1228293656
8101321003	08101	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2991	0.0133781	08101	935661932	931230933
8101321004	08101	180	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	5956	0.0266399	08101	1863190394	1072545098
8101321005	08101	171	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3491	0.0156145	08101	1092074826	1040263215
8101991999	08101	21	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1520	0.0067986	08101	475495198	298195165
8102011001	08102	21	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	231	0.0019869	08102	61716800	298195165
8102011002	08102	60	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2650	0.0227933	08102	708006586	557372794
8102021001	08102	187	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4404	0.0378800	08102	1176626794	1097204211
8102021002	08102	96	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2895	0.0249007	08102	773463798	737498021
8102021003	08102	192	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4041	0.0347577	08102	1079643250	1114589973
8102021004	08102	88	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2507	0.0215634	08102	669800947	700239104
8102021005	08102	54	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2879	0.0247630	08102	769189042	523459833
8102031001	08102	100	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3680	0.0316526	08102	983194051	755655233
8102031002	08102	83	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1697	0.0145963	08102	453391387	676254643
8102031003	08102	154	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	5338	0.0459135	08102	1426165719	977347292
8102031004	08102	138	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1446	0.0124374	08102	386331141	915511772
8102031005	08102	61	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1870	0.0160844	08102	499612194	562889026
8102041001	08102	137	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3438	0.0295711	08102	918538355	911553328
8102041002	08102	140	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4279	0.0368048	08102	1143230257	923394045
8102041003	08102	190	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4627	0.0397980	08102	1236206216	1107657891
8102041004	08102	94	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2601	0.0223719	08102	694915143	728304873
8102051001	08102	7	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	77	0.0006623	08102	20572267	154963994
8102081001	08102	121	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3302	0.0284014	08102	882202923	846539804
8102081002	08102	45	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2248	0.0193356	08102	600603322	469577217
8102081003	08102	76	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2303	0.0198087	08102	615297799	641670679
8102111001	08102	127	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3295	0.0283412	08102	880332717	871304904
8102111002	08102	98	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3487	0.0299926	08102	931629798	746614075
8102111003	08102	63	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2783	0.0239373	08102	743540501	573813013

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
8101011001	08101	132	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1455	0.0065079	08101	455161522	891583213	612772.0
8101021001	08101	120	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1516	0.0067808	08101	474243895	842364466	555649.4
8101021002	08101	337	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1874	0.0083820	08101	586235527	1558427640	831604.9
8101021003	08101	137	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1349	0.0060338	08101	422001988	911553328	675725.2
8101031001	08101	221	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2590	0.0115845	08101	810218791	1212029801	467965.2
8101041001	08101	86	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1373	0.0061411	08101	429509807	690713176	503068.6
8101051001	08101	46	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1825	0.0081628	08101	570907063	475766805	260694.1
8101051002	08101	79	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2383	0.0106587	08101	745463853	656643593	275553.3
8101051003	08101	115	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2945	0.0131724	08101	921271946	821273082	278870.3
8101061001	08101	217	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2976	0.0133110	08101	930969545	1198911359	402860.0
8101071001	08101	68	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1523	0.0068121	08101	476433675	600526491	394305.0
8101071002	08101	111	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2187	0.0097820	08101	684149999	804131760	367687.1
8101081001	08101	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1852	0.0082836	08101	579353360	494017007	266747.8
8101081002	08101	8	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	836	0.0037393	08101	261522359	167796354	200713.3
8101081003	08101	85	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2570	0.0114951	08101	803962275	685916657	266893.6
8101081004	08101	81	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1420	0.0063514	08101	444212619	666498054	469364.8
8101081005	08101	52	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3105	0.0138880	08101	971324072	511820786	164837.6
8101091001	08101	318	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3351	0.0149883	08101	1048279216	1505465387	449258.5
8101101001	08101	187	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1345	0.0060159	08101	420750685	1097204211	815765.2
8101101002	08101	322	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4045	0.0180924	08101	1265380313	1516719380	374961.5
8101101003	08101	71	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2968	0.0132752	08101	928466939	616173649	207605.7
8101101004	08101	48	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2134	0.0095449	08101	667570232	487985129	228671.6
8101111001	08101	222	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3916	0.0175155	08101	1225025786	1215294374	310340.7
8101121001	08101	218	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2733	0.0122241	08101	854952879	1202200069	439882.9
8101131001	08101	360	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3498	0.0156458	08101	1094264607	1620950804	463393.6
8101141001	08101	98	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2701	0.0120810	08101	844942454	746614075	276421.4
8101151001	08101	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2271	0.0101577	08101	710427365	481902238	212198.3
8101151002	08101	32	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2231	0.0099788	08101	697914333	383257854	171787.5
8101151003	08101	70	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3075	0.0137538	08101	961939298	610988164	198695.3
8101151004	08101	36	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1854	0.0082926	08101	579979011	411119275	221747.2
8101151005	08101	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2355	0.0105334	08101	736704731	494017007	209773.7
8101151006	08101	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2089	0.0093437	08101	653493071	457028695	218778.7
8101161001	08101	508	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3863	0.0172784	08101	1208446019	1990117426	515174.1
8101161002	08101	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3464	0.0154938	08101	1083628530	742065545	214222.2
8101161003	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3640	0.0162810	08101	1138685869	540596769	148515.6
8101161004	08101	148	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3612	0.0161557	08101	1129926747	954478091	264252.0
8101161005	08101	173	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4756	0.0212726	08101	1487799448	1047495190	220247.1
8101161006	08101	448	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3401	0.0152120	08101	1063920505	1846530284	542937.5
8101161007	08101	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2944	0.0131679	08101	920959120	481902238	163689.6
8101161008	08101	483	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	5013	0.0224221	08101	1568195676	1931171126	385232.6
8101161009	08101	475	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3072	0.0137404	08101	961000821	1912049350	622411.9
8101161010	08101	531	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2380	0.0106452	08101	744525376	2043320334	858538.0
8101161011	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2320	0.0103769	08101	725755828	540596769	233015.8
8101161012	08101	820	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3886	0.0173813	08101	1215641012	2647133298	681197.5
8101161013	08101	406	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2922	0.0130695	08101	914076953	1741344583	595942.7
8101161014	08101	554	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3814	0.0170592	08101	1193117556	2095599546	549449.3
8101171001	08101	210	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3284	0.0146886	08101	1027319888	1175716446	358013.5
8101171002	08101	121	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3682	0.0164688	08101	1151824552	846539804	229913.0
8101171003	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1315	0.0058817	08101	411365911	540596769	411100.2
8101181001	08101	229	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4508	0.0201633	08101	1410218653	1237982178	274618.9
8101191001	08101	208	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1629	0.0072862	08101	509593209	1169032174	717637.9
8101191002	08101	157	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3336	0.0149212	08101	1043586829	988646648	296356.9
8101201001	08101	286	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3154	0.0141072	08101	986652536	1413277639	448090.6
8101211001	08101	383	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4901	0.0219212	08101	1533159187	1681878766	343170.5
8101221001	08101	197	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2688	0.0120229	08101	840875718	1131793667	421054.2
8101221002	08101	122	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1855	0.0082970	08101	580291837	850701216	458599.0
8101231001	08101	185	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2503	0.0111954	08101	783002948	1090197403	435556.3
8101241001	08101	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2763	0.0123583	08101	864337652	742065545	268572.4
8101241002	08101	156	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1864	0.0083373	08101	583107269	984889971	528374.4
8101241003	08101	252	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1888	0.0084446	08101	590615088	1310626478	694187.8
8101251001	08101	520	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3790	0.0169519	08101	1185609737	2017994207	532452.3
8101251002	08101	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3121	0.0139596	08101	976329285	457028695	146436.6
8101251003	08101	131	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4587	0.0205167	08101	1434931890	887552734	193493.1
8101251004	08101	45	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2288	0.0102337	08101	715745403	469577217	205234.8
8101251005	08101	166	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4334	0.0193851	08101	1355786966	1022032090	235817.3
8101261001	08101	53	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1200	0.0053674	08101	375390946	517662502	431385.4
8101261002	08101	67	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2084	0.0093213	08101	651928942	595249055	285628.1
8101261003	08101	29	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2696	0.0120586	08101	843378325	361425964	134060.1
8101271001	08101	84	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2727	0.0121973	08101	853075924	681097273	249760.6
8101281001	08101	233	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3513	0.0157129	08101	1098956994	1250820773	356054.9
8101281002	08101	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3737	0.0167148	08101	1169029970	931230933	249192.1
8101321001	08101	375	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4131	0.0184771	08101	1292283331	1660858636	402047.6
8101321002	08101	226	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4089	0.0182892	08101	1279144647	1228293656	300389.7
8101321003	08101	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2991	0.0133781	08101	935661932	931230933	311344.3
8101321004	08101	180	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	5956	0.0266399	08101	1863190394	1072545098	180078.1
8101321005	08101	171	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3491	0.0156145	08101	1092074826	1040263215	297984.3
8101991999	08101	21	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1520	0.0067986	08101	475495198	298195165	196181.0
8102011001	08102	21	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	231	0.0019869	08102	61716800	298195165	1290888.2
8102011002	08102	60	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2650	0.0227933	08102	708006586	557372794	210329.4
8102021001	08102	187	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4404	0.0378800	08102	1176626794	1097204211	249138.1
8102021002	08102	96	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2895	0.0249007	08102	773463798	737498021	254748.9
8102021003	08102	192	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4041	0.0347577	08102	1079643250	1114589973	275820.3
8102021004	08102	88	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2507	0.0215634	08102	669800947	700239104	279313.6
8102021005	08102	54	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2879	0.0247630	08102	769189042	523459833	181820.0
8102031001	08102	100	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3680	0.0316526	08102	983194051	755655233	205341.1
8102031002	08102	83	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1697	0.0145963	08102	453391387	676254643	398500.1
8102031003	08102	154	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	5338	0.0459135	08102	1426165719	977347292	183092.4
8102031004	08102	138	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1446	0.0124374	08102	386331141	915511772	633134.0
8102031005	08102	61	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1870	0.0160844	08102	499612194	562889026	301010.2
8102041001	08102	137	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3438	0.0295711	08102	918538355	911553328	265140.6
8102041002	08102	140	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4279	0.0368048	08102	1143230257	923394045	215796.7
8102041003	08102	190	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4627	0.0397980	08102	1236206216	1107657891	239390.1
8102041004	08102	94	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2601	0.0223719	08102	694915143	728304873	280009.6
8102051001	08102	7	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	77	0.0006623	08102	20572267	154963994	2012519.4
8102081001	08102	121	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3302	0.0284014	08102	882202923	846539804	256371.8
8102081002	08102	45	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2248	0.0193356	08102	600603322	469577217	208886.7
8102081003	08102	76	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2303	0.0198087	08102	615297799	641670679	278623.8
8102111001	08102	127	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3295	0.0283412	08102	880332717	871304904	264432.4
8102111002	08102	98	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3487	0.0299926	08102	931629798	746614075	214113.6
8102111003	08102	63	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2783	0.0239373	08102	743540501	573813013	206185.1

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_urbano_nivel_nacional_17_r08.rds")

---

Rural

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 2)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15202	1	2	6	13225	1	3	1	5	3	5	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	3	1	1	5	3	5	2	3	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	4	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	5	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	6	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	8	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	9	3	1	5	3	5	1	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	10	1	1	5	3	4	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	11	1	2	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	12	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	13	1	1	5	3	4	1	4	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	14	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	16	1	1	5	3	5	1	3	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	17	1	1	5	3	5	2	4	1	8	15	152	15202	15202012006
15	152	15202	1	2	6	13225	18	3	1	5	3	5	1	1	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	19	3	1	5	3	5	1	3	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	20	1	1	5	3	5	2	1	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	21	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	22	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	24	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	25	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	26	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	27	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	28	3	1	5	3	5	1	4	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	29	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	30	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	31	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	32	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	33	1	1	5	3	5	3	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	34	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	35	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	36	1	1	5	3	5	3	2	1	9	15	152	15202	15202012006
15	152	15202	1	2	6	13225	37	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	38	1	1	5	3	5	99	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	39	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	40	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	41	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	42	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	43	3	1	5	3	5	2	1	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	44	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	45	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	8	13910	1	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	3	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	4	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	5	3	1	5	3	5	2	3	1	3	15	152	15202	15202012008
15	152	15202	1	2	8	13910	6	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	7	1	1	5	99	5	2	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	8	3	1	5	3	5	3	3	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	9	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	10	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	11	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	12	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	13	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	14	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	16	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	18	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	20	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	21	1	1	5	3	5	1	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	22	3	1	5	3	5	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	23	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	24	3	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	25	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	26	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	27	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	28	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	29	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	30	1	1	5	1	4	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	12	8394	1	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	2	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	3	3	1	5	3	5	2	3	1	4	15	152	15202	15202012012
15	152	15202	1	2	12	8394	4	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	5	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	6	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	8	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	9	3	1	5	3	5	1	4	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	10	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	11	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	12	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	13	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	14	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	15	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	16	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	18	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	20	3	1	5	3	5	3	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	21	3	1	5	99	5	1	4	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	22	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	24	3	1	5	3	5	1	2	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	25	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 8:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 8)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Veamos los primeros 100 registros:

r3_100 <- d[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	unlist.c.	unlist.d.	Freq	anio
1	8101252025	1	8101	6	2017
2	8101282901	1	8101	3	2017
3	8101292012	1	8101	4	2017
4	8101292020	1	8101	3	2017
5	8101292901	1	8101	2	2017
6	8101302001	1	8101	8	2017
7	8101302005	1	8101	10	2017
8	8101302008	1	8101	3	2017
9	8101302011	1	8101	3	2017
10	8101302019	1	8101	1	2017
11	8101302021	1	8101	12	2017
12	8101302024	1	8101	1	2017
13	8101302901	1	8101	2	2017
14	8101312004	1	8101	6	2017
15	8101312013	1	8101	16	2017
16	8101312014	1	8101	30	2017
17	8101312017	1	8101	2	2017
18	8101322025	1	8101	2	2017
773	8102052001	1	8102	4	2017
774	8102062004	1	8102	2	2017
775	8102062005	1	8102	44	2017
776	8102062009	1	8102	52	2017
777	8102072005	1	8102	46	2017
778	8102092010	1	8102	13	2017
779	8102102007	1	8102	12	2017
780	8102102011	1	8102	15	2017
781	8102112008	1	8102	6	2017
1536	8103062006	1	8103	1	2017
1537	8103062901	1	8103	3	2017
2292	8104012005	1	8104	2	2017
2293	8104012023	1	8104	3	2017
2294	8104012029	1	8104	1	2017
2295	8104012037	1	8104	5	2017
2296	8104012043	1	8104	1	2017
2297	8104012044	1	8104	12	2017
2298	8104012052	1	8104	2	2017
2299	8104012054	1	8104	5	2017
2300	8104012901	1	8104	2	2017
2301	8104022001	1	8104	1	2017
2302	8104022036	1	8104	1	2017
2303	8104022040	1	8104	5	2017
2304	8104022901	1	8104	1	2017
2305	8104032004	1	8104	9	2017
2306	8104032012	1	8104	2	2017
2307	8104032017	1	8104	2	2017
2308	8104032019	1	8104	1	2017
2309	8104032028	1	8104	3	2017
2310	8104032045	1	8104	4	2017
2311	8104032050	1	8104	1	2017
2312	8104032053	1	8104	2	2017
2313	8104042008	1	8104	1	2017
2314	8104042012	1	8104	18	2017
2315	8104042014	1	8104	4	2017
2316	8104042027	1	8104	3	2017
2317	8104042030	1	8104	1	2017
2318	8104042042	1	8104	2	2017
2319	8104042047	1	8104	1	2017
2320	8104052003	1	8104	2	2017
2321	8104052010	1	8104	1	2017
2322	8104052020	1	8104	1	2017
2323	8104052025	1	8104	2	2017
2324	8104052027	1	8104	1	2017
2325	8104052031	1	8104	3	2017
2326	8104052032	1	8104	1	2017
2327	8104052039	1	8104	6	2017
2328	8104052043	1	8104	7	2017
2329	8104052054	1	8104	8	2017
2330	8104052059	1	8104	11	2017
2331	8104052901	1	8104	8	2017
2332	8104062001	1	8104	2	2017
2333	8104062002	1	8104	1	2017
2334	8104062003	1	8104	1	2017
2335	8104062013	1	8104	2	2017
2336	8104062024	1	8104	2	2017
2337	8104062036	1	8104	3	2017
2338	8104062049	1	8104	6	2017
2339	8104062051	1	8104	8	2017
2340	8104062901	1	8104	6	2017
3095	8105012014	1	8105	3	2017
3096	8105012028	1	8105	3	2017
3097	8105012034	1	8105	1	2017
3098	8105022024	1	8105	3	2017
3099	8105022034	1	8105	1	2017
3100	8105032001	1	8105	2	2017
3101	8105032003	1	8105	2	2017
3102	8105032009	1	8105	1	2017
3103	8105032034	1	8105	2	2017
3104	8105032039	1	8105	6	2017
3105	8105042003	1	8105	2	2017
3106	8105042005	1	8105	7	2017
3107	8105042007	1	8105	5	2017
3108	8105042023	1	8105	5	2017
3109	8105042047	1	8105	2	2017
3110	8105042050	1	8105	3	2017
3111	8105062017	1	8105	7	2017
3112	8105062037	1	8105	3	2017
3113	8105072004	1	8105	2	2017
3114	8105072048	1	8105	4	2017
3115	8105072053	1	8105	2	2017
3116	8105072901	1	8105	3	2017

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	8101252025	6	2017	08101
2	8101282901	3	2017	08101
3	8101292012	4	2017	08101
4	8101292020	3	2017	08101
5	8101292901	2	2017	08101
6	8101302001	8	2017	08101
7	8101302005	10	2017	08101
8	8101302008	3	2017	08101
9	8101302011	3	2017	08101
10	8101302019	1	2017	08101
11	8101302021	12	2017	08101
12	8101302024	1	2017	08101
13	8101302901	2	2017	08101
14	8101312004	6	2017	08101
15	8101312013	16	2017	08101
16	8101312014	30	2017	08101
17	8101312017	2	2017	08101
18	8101322025	2	2017	08101
773	8102052001	4	2017	08102
774	8102062004	2	2017	08102
775	8102062005	44	2017	08102
776	8102062009	52	2017	08102
777	8102072005	46	2017	08102
778	8102092010	13	2017	08102
779	8102102007	12	2017	08102
780	8102102011	15	2017	08102
781	8102112008	6	2017	08102
1536	8103062006	1	2017	08103
1537	8103062901	3	2017	08103
2292	8104012005	2	2017	08104
2293	8104012023	3	2017	08104
2294	8104012029	1	2017	08104
2295	8104012037	5	2017	08104
2296	8104012043	1	2017	08104
2297	8104012044	12	2017	08104
2298	8104012052	2	2017	08104
2299	8104012054	5	2017	08104
2300	8104012901	2	2017	08104
2301	8104022001	1	2017	08104
2302	8104022036	1	2017	08104
2303	8104022040	5	2017	08104
2304	8104022901	1	2017	08104
2305	8104032004	9	2017	08104
2306	8104032012	2	2017	08104
2307	8104032017	2	2017	08104
2308	8104032019	1	2017	08104
2309	8104032028	3	2017	08104
2310	8104032045	4	2017	08104
2311	8104032050	1	2017	08104
2312	8104032053	2	2017	08104
2313	8104042008	1	2017	08104
2314	8104042012	18	2017	08104
2315	8104042014	4	2017	08104
2316	8104042027	3	2017	08104
2317	8104042030	1	2017	08104
2318	8104042042	2	2017	08104
2319	8104042047	1	2017	08104
2320	8104052003	2	2017	08104
2321	8104052010	1	2017	08104
2322	8104052020	1	2017	08104
2323	8104052025	2	2017	08104
2324	8104052027	1	2017	08104
2325	8104052031	3	2017	08104
2326	8104052032	1	2017	08104
2327	8104052039	6	2017	08104
2328	8104052043	7	2017	08104
2329	8104052054	8	2017	08104
2330	8104052059	11	2017	08104
2331	8104052901	8	2017	08104
2332	8104062001	2	2017	08104
2333	8104062002	1	2017	08104
2334	8104062003	1	2017	08104
2335	8104062013	2	2017	08104
2336	8104062024	2	2017	08104
2337	8104062036	3	2017	08104
2338	8104062049	6	2017	08104
2339	8104062051	8	2017	08104
2340	8104062901	6	2017	08104
3095	8105012014	3	2017	08105
3096	8105012028	3	2017	08105
3097	8105012034	1	2017	08105
3098	8105022024	3	2017	08105
3099	8105022034	1	2017	08105
3100	8105032001	2	2017	08105
3101	8105032003	2	2017	08105
3102	8105032009	1	2017	08105
3103	8105032034	2	2017	08105
3104	8105032039	6	2017	08105
3105	8105042003	2	2017	08105
3106	8105042005	7	2017	08105
3107	8105042007	5	2017	08105
3108	8105042023	5	2017	08105
3109	8105042047	2	2017	08105
3110	8105042050	3	2017	08105
3111	8105062017	7	2017	08105
3112	8105062037	3	2017	08105
3113	8105072004	2	2017	08105
3114	8105072048	4	2017	08105
3115	8105072053	2	2017	08105
3116	8105072901	3	2017	08105

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_rural_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	289375.3	2017	1101	191468	55406102543	Rural
01401	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
01402	Camiña	262850.3	2017	1402	1250	328562901	Rural
01404	Huara	253968.5	2017	1404	2730	693334131	Rural
01405	Pica	290496.7	2017	1405	9296	2700457509	Rural
02103	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
02104	Taltal	345494.0	2017	2104	13317	4600943086	Rural
02201	Calama	310025.0	2017	2201	165731	51380756402	Rural
02203	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
02301	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
03101	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03103	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03202	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural
03301	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03302	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03303	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03304	Huasco	227560.7	2017	3304	10149	2309513927	Rural
04101	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04102	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04103	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04104	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04105	Paiguano	205942.1	2017	4105	4497	926121774	Rural
04106	Vicuña	176130.6	2017	4106	27771	4891322768	Rural
04201	Illapel	191976.8	2017	4201	30848	5922099530	Rural
04202	Canela	171370.3	2017	4202	9093	1558270441	Rural
04203	Los Vilos	173238.5	2017	4203	21382	3704185607	Rural
04204	Salamanca	223234.2	2017	4204	29347	6551254640	Rural
04301	Ovalle	241393.7	2017	4301	111272	26860360045	Rural
04302	Combarbalá	179139.6	2017	4302	13322	2386498044	Rural
04303	Monte Patria	201205.8	2017	4303	30751	6187280931	Rural
04304	Punitaqui	171931.7	2017	4304	10956	1883683880	Rural
04305	Río Hurtado	182027.2	2017	4305	4278	778712384	Rural
05101	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05102	Casablanca	268917.1	2017	5102	26867	7224996933	Rural
05105	Puchuncaví	279614.4	2017	5105	18546	5185728335	Rural
05107	Quintero	334628.2	2017	5107	31923	10682335196	Rural
05301	Los Andes	324402.1	2017	5301	66708	21640215030	Rural
05302	Calle Larga	242743.8	2017	5302	14832	3600375502	Rural
05303	Rinconada	326532.5	2017	5303	10207	3332917471	Rural
05304	San Esteban	223168.6	2017	5304	18855	4207844130	Rural
05401	La Ligua	181468.0	2017	5401	35390	6422154059	Rural
05402	Cabildo	231277.8	2017	5402	19388	4484014285	Rural
05404	Petorca	298208.9	2017	5404	9826	2930200178	Rural
05405	Zapallar	292882.3	2017	5405	7339	2149463129	Rural
05501	Quillota	220926.8	2017	5501	90517	19997628209	Rural
05502	Calera	226906.2	2017	5502	50554	11471016698	Rural
05503	Hijuelas	253739.9	2017	5503	17988	4564273201	Rural
05504	La Cruz	291124.1	2017	5504	22098	6433259569	Rural
05506	Nogales	264475.3	2017	5506	22120	5850194593	Rural
05601	San Antonio	266331.2	2017	5601	91350	24329353815	Rural

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
08101	8101292020	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101252025	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302005	10	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302008	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101292901	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302001	8	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101282901	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101292012	4	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101312017	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101312004	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302019	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302021	12	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302024	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101322025	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302901	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101312013	16	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101312014	30	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302011	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08102	8102052001	4	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102062004	2	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102062005	44	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102092010	13	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102112008	6	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102102011	15	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102072005	46	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102062009	52	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102102007	12	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08103	8103062006	1	2017	NA	NA	NA	NA	NA	NA	NA
08103	8103062901	3	2017	NA	NA	NA	NA	NA	NA	NA
08104	8104012005	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012037	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012043	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012023	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012029	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104022040	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104022901	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032004	9	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012052	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032017	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032019	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032028	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032012	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032050	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012054	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012901	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032045	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104022036	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042027	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042030	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042042	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042047	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052003	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052010	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052020	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052025	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052027	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032053	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042008	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042012	18	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042014	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052054	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052059	11	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052901	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062001	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062002	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062003	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062013	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062024	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012044	12	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062049	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062051	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062901	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104022001	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052039	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052043	7	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052031	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052032	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062036	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08105	8105022024	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105032009	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105022034	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105032001	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105032003	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105042005	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105032034	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105032039	6	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105012028	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105012034	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105062017	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105062037	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105072004	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105072048	4	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105072053	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105072901	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105082010	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105042003	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105082026	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105042007	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105042023	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105042047	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
08101	8101292020	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101252025	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302005	10	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302008	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101292901	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302001	8	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101282901	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101292012	4	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101312017	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101312004	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302019	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302021	12	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302024	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101322025	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302901	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101312013	16	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101312014	30	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302011	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08102	8102052001	4	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102062004	2	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102062005	44	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102092010	13	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102112008	6	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102102011	15	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102072005	46	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102062009	52	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102102007	12	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08103	8103062006	1	2017	NA	NA	NA	NA	NA	NA	NA
08103	8103062901	3	2017	NA	NA	NA	NA	NA	NA	NA
08104	8104012005	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012037	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012043	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012023	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012029	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104022040	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104022901	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032004	9	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012052	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032017	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032019	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032028	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032012	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032050	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012054	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012901	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032045	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104022036	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042027	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042030	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042042	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042047	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052003	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052010	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052020	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052025	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052027	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032053	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042008	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042012	18	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042014	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052054	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052059	11	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052901	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062001	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062002	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062003	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062013	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062024	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012044	12	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062049	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062051	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062901	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104022001	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052039	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052043	7	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052031	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052032	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062036	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08105	8105022024	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105032009	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105022034	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105032001	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105032003	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105042005	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105032034	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105032039	6	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105012028	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105012034	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105062017	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105062037	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105072004	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105072048	4	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105072053	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105072901	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105082010	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105042003	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105082026	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105042007	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105042023	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105042047	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
8101252025	08101	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	60	0.0002684	08101
8101282901	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	31	0.0001387	08101
8101292012	08101	4	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	86	0.0003847	08101
8101292020	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	61	0.0002728	08101
8101292901	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	21	0.0000939	08101
8101302001	08101	8	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	280	0.0012524	08101
8101302005	08101	10	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	688	0.0030773	08101
8101302008	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	98	0.0004383	08101
8101302011	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	469	0.0020977	08101
8101302019	08101	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	198	0.0008856	08101
8101302021	08101	12	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	401	0.0017936	08101
8101302024	08101	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	88	0.0003936	08101
8101302901	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	16	0.0000716	08101
8101312004	08101	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	63	0.0002818	08101
8101312013	08101	16	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	1045	0.0046741	08101
8101312014	08101	30	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	534	0.0023885	08101
8101312017	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	47	0.0002102	08101
8101322025	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	100	0.0004473	08101
8102052001	08102	4	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	62	0.0005333	08102
8102062004	08102	2	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	29	0.0002494	08102
8102062005	08102	44	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	350	0.0030104	08102
8102062009	08102	52	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	382	0.0032857	08102
8102072005	08102	46	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	624	0.0053672	08102
8102092010	08102	13	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	298	0.0025632	08102
8102102007	08102	12	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	744	0.0063993	08102
8102102011	08102	15	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	503	0.0043264	08102
8102112008	08102	6	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	104	0.0008945	08102
8103062006	08103	1	2017	NA	NA	NA	NA	NA	NA	NA	65	0.0007564	08103
8103062901	08103	3	2017	NA	NA	NA	NA	NA	NA	NA	10	0.0001164	08103
8104012005	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	27	0.0025414	08104
8104012023	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	163	0.0153426	08104
8104012029	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	45	0.0042357	08104
8104012037	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	162	0.0152485	08104
8104012043	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	68	0.0064006	08104
8104012044	08104	12	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	214	0.0201431	08104
8104012052	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	93	0.0087538	08104
8104012054	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	43	0.0040474	08104
8104012901	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	14	0.0013178	08104
8104022001	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	42	0.0039533	08104
8104022036	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	60	0.0056476	08104
8104022040	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	70	0.0065889	08104
8104022901	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	11	0.0010354	08104
8104032004	08104	9	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	175	0.0164721	08104
8104032012	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	26	0.0024473	08104
8104032017	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	19	0.0017884	08104
8104032019	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	21	0.0019767	08104
8104032028	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	174	0.0163780	08104
8104032045	08104	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	244	0.0229669	08104
8104032050	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	67	0.0063065	08104
8104032053	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	58	0.0054593	08104
8104042008	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	21	0.0019767	08104
8104042012	08104	18	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	745	0.0701242	08104
8104042014	08104	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	118	0.0111069	08104
8104042027	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	18	0.0016943	08104
8104042030	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	42	0.0039533	08104
8104042042	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	89	0.0083773	08104
8104042047	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	59	0.0055535	08104
8104052003	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	34	0.0032003	08104
8104052010	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	61	0.0057417	08104
8104052020	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	16	0.0015060	08104
8104052025	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	27	0.0025414	08104
8104052027	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	15	0.0014119	08104
8104052031	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	61	0.0057417	08104
8104052032	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	47	0.0044239	08104
8104052039	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	181	0.0170369	08104
8104052043	08104	7	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	291	0.0273908	08104
8104052054	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	124	0.0116717	08104
8104052059	08104	11	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	115	0.0108245	08104
8104052901	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	87	0.0081890	08104
8104062001	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	50	0.0047063	08104
8104062002	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	22	0.0020708	08104
8104062003	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	17	0.0016002	08104
8104062013	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	282	0.0265437	08104
8104062024	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	141	0.0132718	08104
8104062036	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	36	0.0033886	08104
8104062049	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	499	0.0469691	08104
8104062051	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	70	0.0065889	08104
8104062901	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	123	0.0115776	08104
8105012014	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	25	0.0010274	08105
8105012028	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	137	0.0056302	08105
8105012034	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	50	0.0020548	08105
8105022024	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	10	0.0004110	08105
8105022034	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	38	0.0015617	08105
8105032001	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	18	0.0007397	08105
8105032003	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	14	0.0005754	08105
8105032009	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	26	0.0010685	08105
8105032034	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	32	0.0013151	08105
8105032039	08105	6	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	94	0.0038631	08105
8105042003	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	23	0.0009452	08105
8105042005	08105	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	76	0.0031233	08105
8105042007	08105	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	70	0.0028768	08105
8105042023	08105	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	65	0.0026713	08105
8105042047	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	7	0.0002877	08105
8105042050	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	19	0.0007808	08105
8105062017	08105	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	96	0.0039453	08105
8105062037	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	38	0.0015617	08105
8105072004	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	42	0.0017261	08105
8105072048	08105	4	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	49	0.0020137	08105
8105072053	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	91	0.0037398	08105
8105072901	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	56	0.0023014	08105

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
8101252025	08101	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	60	0.0002684	08101	13210846
8101282901	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	31	0.0001387	08101	6825604
8101292012	08101	4	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	86	0.0003847	08101	18935547
8101292020	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	61	0.0002728	08101	13431027
8101292901	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	21	0.0000939	08101	4623796
8101302001	08101	8	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	280	0.0012524	08101	61650617
8101302005	08101	10	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	688	0.0030773	08101	151484373
8101302008	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	98	0.0004383	08101	21577716
8101302011	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	469	0.0020977	08101	103264783
8101302019	08101	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	198	0.0008856	08101	43595793
8101302021	08101	12	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	401	0.0017936	08101	88292491
8101302024	08101	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	88	0.0003936	08101	19375908
8101302901	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	16	0.0000716	08101	3522892
8101312004	08101	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	63	0.0002818	08101	13871389
8101312013	08101	16	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	1045	0.0046741	08101	230088909
8101312014	08101	30	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	534	0.0023885	08101	117576534
8101312017	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	47	0.0002102	08101	10348496
8101322025	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	100	0.0004473	08101	22018077
8102052001	08102	4	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	62	0.0005333	08102	16514777
8102062004	08102	2	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	29	0.0002494	08102	7724654
8102062005	08102	44	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	350	0.0030104	08102	93228582
8102062009	08102	52	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	382	0.0032857	08102	101752338
8102072005	08102	46	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	624	0.0053672	08102	166213243
8102092010	08102	13	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	298	0.0025632	08102	79377478
8102102007	08102	12	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	744	0.0063993	08102	198177329
8102102011	08102	15	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	503	0.0043264	08102	133982791
8102112008	08102	6	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	104	0.0008945	08102	27702207
8103062006	08103	1	2017	NA	NA	NA	NA	NA	NA	NA	65	0.0007564	08103	NA
8103062901	08103	3	2017	NA	NA	NA	NA	NA	NA	NA	10	0.0001164	08103	NA
8104012005	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	27	0.0025414	08104	4275227
8104012023	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	163	0.0153426	08104	25809703
8104012029	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	45	0.0042357	08104	7125378
8104012037	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	162	0.0152485	08104	25651361
8104012043	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	68	0.0064006	08104	10767238
8104012044	08104	12	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	214	0.0201431	08104	33885131
8104012052	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	93	0.0087538	08104	14725781
8104012054	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	43	0.0040474	08104	6808695
8104012901	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	14	0.0013178	08104	2216784
8104022001	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	42	0.0039533	08104	6650353
8104022036	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	60	0.0056476	08104	9500504
8104022040	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	70	0.0065889	08104	11083921
8104022901	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	11	0.0010354	08104	1741759
8104032004	08104	9	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	175	0.0164721	08104	27709804
8104032012	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	26	0.0024473	08104	4116885
8104032017	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	19	0.0017884	08104	3008493
8104032019	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	21	0.0019767	08104	3325176
8104032028	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	174	0.0163780	08104	27551462
8104032045	08104	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	244	0.0229669	08104	38635383
8104032050	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	67	0.0063065	08104	10608896
8104032053	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	58	0.0054593	08104	9183821
8104042008	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	21	0.0019767	08104	3325176
8104042012	08104	18	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	745	0.0701242	08104	117964593
8104042014	08104	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	118	0.0111069	08104	18684325
8104042027	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	18	0.0016943	08104	2850151
8104042030	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	42	0.0039533	08104	6650353
8104042042	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	89	0.0083773	08104	14092414
8104042047	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	59	0.0055535	08104	9342162
8104052003	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	34	0.0032003	08104	5383619
8104052010	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	61	0.0057417	08104	9658846
8104052020	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	16	0.0015060	08104	2533468
8104052025	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	27	0.0025414	08104	4275227
8104052027	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	15	0.0014119	08104	2375126
8104052031	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	61	0.0057417	08104	9658846
8104052032	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	47	0.0044239	08104	7442062
8104052039	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	181	0.0170369	08104	28659854
8104052043	08104	7	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	291	0.0273908	08104	46077445
8104052054	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	124	0.0116717	08104	19634375
8104052059	08104	11	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	115	0.0108245	08104	18209300
8104052901	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	87	0.0081890	08104	13775731
8104062001	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	50	0.0047063	08104	7917087
8104062002	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	22	0.0020708	08104	3483518
8104062003	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	17	0.0016002	08104	2691810
8104062013	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	282	0.0265437	08104	44652369
8104062024	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	141	0.0132718	08104	22326185
8104062036	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	36	0.0033886	08104	5700302
8104062049	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	499	0.0469691	08104	79012526
8104062051	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	70	0.0065889	08104	11083921
8104062901	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	123	0.0115776	08104	19476033
8105012014	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	25	0.0010274	08105	5242978
8105012028	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	137	0.0056302	08105	28731520
8105012034	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	50	0.0020548	08105	10485956
8105022024	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	10	0.0004110	08105	2097191
8105022034	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	38	0.0015617	08105	7969327
8105032001	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	18	0.0007397	08105	3774944
8105032003	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	14	0.0005754	08105	2936068
8105032009	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	26	0.0010685	08105	5452697
8105032034	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	32	0.0013151	08105	6711012
8105032039	08105	6	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	94	0.0038631	08105	19713598
8105042003	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	23	0.0009452	08105	4823540
8105042005	08105	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	76	0.0031233	08105	15938654
8105042007	08105	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	70	0.0028768	08105	14680339
8105042023	08105	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	65	0.0026713	08105	13631743
8105042047	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	7	0.0002877	08105	1468034
8105042050	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	19	0.0007808	08105	3984663
8105062017	08105	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	96	0.0039453	08105	20133036
8105062037	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	38	0.0015617	08105	7969327
8105072004	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	42	0.0017261	08105	8808203
8105072048	08105	4	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	49	0.0020137	08105	10276237
8105072053	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	91	0.0037398	08105	19084441
8105072901	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	56	0.0023014	08105	11744271

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -412526429  -21327794  -14153247    4434759  531550255 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 21602452    2105504   10.26   <2e-16 ***
## Freq.x       2815265     117698   23.92   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 51950000 on 744 degrees of freedom
##   (8 observations deleted due to missingness)
## Multiple R-squared:  0.4347, Adjusted R-squared:  0.434 
## F-statistic: 572.1 on 1 and 744 DF,  p-value: < 2.2e-16

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.433950454967059"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.433950454967059"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.443988432346371"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.548791391143826"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.584518160773268"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.448903258907332"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.563618666620665"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.521653695901717"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 1    cuadrático 0.433950454967059
## 2        cúbico 0.433950454967059
## 3   logarítmico 0.443988432346371
## 6      log-raíz 0.448903258907332
## 8       log-log 0.521653695901717
## 4 raíz cuadrada 0.548791391143826
## 7      raíz-log 0.563618666620665
## 5     raíz-raíz 0.584518160773268
##                                                                     sintaxis
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 5
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = sqrt(multi_pob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12639.7  -1544.2   -427.6   1160.5  11644.4 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1404.66     151.92   9.246   <2e-16 ***
## sqrt(Freq.x)  1776.46      54.85  32.390   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2398 on 744 degrees of freedom
##   (8 observations deleted due to missingness)
## Multiple R-squared:  0.5851, Adjusted R-squared:  0.5845 
## F-statistic:  1049 on 1 and 744 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    1404.663

bb <- linearMod$coefficients[2]
bb

## sqrt(Freq.x) 
##     1776.456

9 Modelo raíz-raíz (raíz-raíz)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.5845 ).

9.1 Diagrama de dispersión sobre raíz-raíz

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=sqrt(h_y_m_comuna_corr_01$Freq.x), y=sqrt(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo raíz-raíz

Observemos nuevamente el resultado sobre raíz-raíz.

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = sqrt(multi_pob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12639.7  -1544.2   -427.6   1160.5  11644.4 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1404.66     151.92   9.246   <2e-16 ***
## sqrt(Freq.x)  1776.46      54.85  32.390   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2398 on 744 degrees of freedom
##   (8 observations deleted due to missingness)
## Multiple R-squared:  0.5851, Adjusted R-squared:  0.5845 
## F-statistic:  1049 on 1 and 744 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = sqrt(Freq.x) , y = sqrt(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

(Intercept) 1404.663 sqrt(Freq.x) 1776.456 \[ \hat Y = {1404.663}^2 + 2 1404.663 1776.456 \sqrt{X}+ 1776.456^2 X \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- ((aa^2)+(2*(aa*bb)) * sqrt(h_y_m_comuna_corr_01$Freq.x)+((bb^2)*h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
8101252025	08101	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	60	0.0002684	08101	13210846	33132395
8101282901	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	31	0.0001387	08101	6825604	20084521
8101292012	08101	4	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	86	0.0003847	08101	18935547	24577557
8101292020	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	61	0.0002728	08101	13431027	20084521
8101292901	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	21	0.0000939	08101	4623796	15342511
8101302001	08101	8	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	280	0.0012524	08101	61650617	41335130
8101302005	08101	10	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	688	0.0030773	08101	151484373	49312854
8101302008	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	98	0.0004383	08101	21577716	20084521
8101302011	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	469	0.0020977	08101	103264783	20084521
8101302019	08101	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	198	0.0008856	08101	43595793	10119521
8101302021	08101	12	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	401	0.0017936	08101	88292491	57130744
8101302024	08101	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	88	0.0003936	08101	19375908	10119521
8101302901	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	16	0.0000716	08101	3522892	15342511
8101312004	08101	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	63	0.0002818	08101	13871389	33132395
8101312013	08101	16	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	1045	0.0046741	08101	230088909	72428410
8101312014	08101	30	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	534	0.0023885	08101	117576534	123981874
8101312017	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	47	0.0002102	08101	10348496	15342511
8101322025	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	100	0.0004473	08101	22018077	15342511
8102052001	08102	4	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	62	0.0005333	08102	16514777	24577557
8102062004	08102	2	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	29	0.0002494	08102	7724654	15342511
8102062005	08102	44	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	350	0.0030104	08102	93228582	173932334
8102062009	08102	52	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	382	0.0032857	08102	101752338	202062568
8102072005	08102	46	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	624	0.0053672	08102	166213243	180987935
8102092010	08102	13	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	298	0.0025632	08102	79377478	60992466
8102102007	08102	12	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	744	0.0063993	08102	198177329	57130744
8102102011	08102	15	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	503	0.0043264	08102	133982791	68638718
8102112008	08102	6	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	104	0.0008945	08102	27702207	33132395
8103062006	08103	1	2017	NA	NA	NA	NA	NA	NA	NA	65	0.0007564	08103	NA	10119521
8103062901	08103	3	2017	NA	NA	NA	NA	NA	NA	NA	10	0.0001164	08103	NA	20084521
8104012005	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	27	0.0025414	08104	4275227	15342511
8104012023	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	163	0.0153426	08104	25809703	20084521
8104012029	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	45	0.0042357	08104	7125378	10119521
8104012037	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	162	0.0152485	08104	25651361	28911486
8104012043	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	68	0.0064006	08104	10767238	10119521
8104012044	08104	12	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	214	0.0201431	08104	33885131	57130744
8104012052	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	93	0.0087538	08104	14725781	15342511
8104012054	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	43	0.0040474	08104	6808695	28911486
8104012901	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	14	0.0013178	08104	2216784	15342511
8104022001	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	42	0.0039533	08104	6650353	10119521
8104022036	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	60	0.0056476	08104	9500504	10119521
8104022040	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	70	0.0065889	08104	11083921	28911486
8104022901	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	11	0.0010354	08104	1741759	10119521
8104032004	08104	9	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	175	0.0164721	08104	27709804	45347187
8104032012	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	26	0.0024473	08104	4116885	15342511
8104032017	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	19	0.0017884	08104	3008493	15342511
8104032019	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	21	0.0019767	08104	3325176	10119521
8104032028	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	174	0.0163780	08104	27551462	20084521
8104032045	08104	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	244	0.0229669	08104	38635383	24577557
8104032050	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	67	0.0063065	08104	10608896	10119521
8104032053	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	58	0.0054593	08104	9183821	15342511
8104042008	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	21	0.0019767	08104	3325176	10119521
8104042012	08104	18	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	745	0.0701242	08104	117964593	79950937
8104042014	08104	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	118	0.0111069	08104	18684325	24577557
8104042027	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	18	0.0016943	08104	2850151	20084521
8104042030	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	42	0.0039533	08104	6650353	10119521
8104042042	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	89	0.0083773	08104	14092414	15342511
8104042047	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	59	0.0055535	08104	9342162	10119521
8104052003	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	34	0.0032003	08104	5383619	15342511
8104052010	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	61	0.0057417	08104	9658846	10119521
8104052020	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	16	0.0015060	08104	2533468	10119521
8104052025	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	27	0.0025414	08104	4275227	15342511
8104052027	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	15	0.0014119	08104	2375126	10119521
8104052031	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	61	0.0057417	08104	9658846	20084521
8104052032	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	47	0.0044239	08104	7442062	10119521
8104052039	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	181	0.0170369	08104	28659854	33132395
8104052043	08104	7	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	291	0.0273908	08104	46077445	37267663
8104052054	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	124	0.0116717	08104	19634375	41335130
8104052059	08104	11	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	115	0.0108245	08104	18209300	53238942
8104052901	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	87	0.0081890	08104	13775731	41335130
8104062001	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	50	0.0047063	08104	7917087	15342511
8104062002	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	22	0.0020708	08104	3483518	10119521
8104062003	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	17	0.0016002	08104	2691810	10119521
8104062013	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	282	0.0265437	08104	44652369	15342511
8104062024	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	141	0.0132718	08104	22326185	15342511
8104062036	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	36	0.0033886	08104	5700302	20084521
8104062049	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	499	0.0469691	08104	79012526	33132395
8104062051	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	70	0.0065889	08104	11083921	41335130
8104062901	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	123	0.0115776	08104	19476033	33132395
8105012014	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	25	0.0010274	08105	5242978	20084521
8105012028	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	137	0.0056302	08105	28731520	20084521
8105012034	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	50	0.0020548	08105	10485956	10119521
8105022024	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	10	0.0004110	08105	2097191	20084521
8105022034	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	38	0.0015617	08105	7969327	10119521
8105032001	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	18	0.0007397	08105	3774944	15342511
8105032003	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	14	0.0005754	08105	2936068	15342511
8105032009	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	26	0.0010685	08105	5452697	10119521
8105032034	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	32	0.0013151	08105	6711012	15342511
8105032039	08105	6	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	94	0.0038631	08105	19713598	33132395
8105042003	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	23	0.0009452	08105	4823540	15342511
8105042005	08105	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	76	0.0031233	08105	15938654	37267663
8105042007	08105	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	70	0.0028768	08105	14680339	28911486
8105042023	08105	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	65	0.0026713	08105	13631743	28911486
8105042047	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	7	0.0002877	08105	1468034	15342511
8105042050	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	19	0.0007808	08105	3984663	20084521
8105062017	08105	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	96	0.0039453	08105	20133036	37267663
8105062037	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	38	0.0015617	08105	7969327	20084521
8105072004	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	42	0.0017261	08105	8808203	15342511
8105072048	08105	4	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	49	0.0020137	08105	10276237	24577557
8105072053	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	91	0.0037398	08105	19084441	15342511
8105072901	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	56	0.0023014	08105	11744271	20084521

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
8101252025	08101	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	60	0.0002684	08101	13210846	33132395	552206.58
8101282901	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	31	0.0001387	08101	6825604	20084521	647887.77
8101292012	08101	4	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	86	0.0003847	08101	18935547	24577557	285785.55
8101292020	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	61	0.0002728	08101	13431027	20084521	329254.44
8101292901	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	21	0.0000939	08101	4623796	15342511	730595.77
8101302001	08101	8	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	280	0.0012524	08101	61650617	41335130	147625.47
8101302005	08101	10	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	688	0.0030773	08101	151484373	49312854	71675.66
8101302008	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	98	0.0004383	08101	21577716	20084521	204944.09
8101302011	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	469	0.0020977	08101	103264783	20084521	42824.14
8101302019	08101	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	198	0.0008856	08101	43595793	10119521	51108.69
8101302021	08101	12	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	401	0.0017936	08101	88292491	57130744	142470.68
8101302024	08101	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	88	0.0003936	08101	19375908	10119521	114994.56
8101302901	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	16	0.0000716	08101	3522892	15342511	958906.95
8101312004	08101	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	63	0.0002818	08101	13871389	33132395	525911.03
8101312013	08101	16	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	1045	0.0046741	08101	230088909	72428410	69309.48
8101312014	08101	30	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	534	0.0023885	08101	117576534	123981874	232175.79
8101312017	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	47	0.0002102	08101	10348496	15342511	326436.41
8101322025	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	100	0.0004473	08101	22018077	15342511	153425.11
8102052001	08102	4	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	62	0.0005333	08102	16514777	24577557	396412.21
8102062004	08102	2	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	29	0.0002494	08102	7724654	15342511	529052.11
8102062005	08102	44	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	350	0.0030104	08102	93228582	173932334	496949.53
8102062009	08102	52	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	382	0.0032857	08102	101752338	202062568	528959.60
8102072005	08102	46	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	624	0.0053672	08102	166213243	180987935	290044.77
8102092010	08102	13	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	298	0.0025632	08102	79377478	60992466	204672.70
8102102007	08102	12	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	744	0.0063993	08102	198177329	57130744	76788.63
8102102011	08102	15	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	503	0.0043264	08102	133982791	68638718	136458.68
8102112008	08102	6	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	104	0.0008945	08102	27702207	33132395	318580.72
8103062006	08103	1	2017	NA	NA	NA	NA	NA	NA	NA	65	0.0007564	08103	NA	10119521	NA
8103062901	08103	3	2017	NA	NA	NA	NA	NA	NA	NA	10	0.0001164	08103	NA	20084521	NA
8104012005	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	27	0.0025414	08104	4275227	15342511	568241.16
8104012023	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	163	0.0153426	08104	25809703	20084521	123217.92
8104012029	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	45	0.0042357	08104	7125378	10119521	224878.25
8104012037	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	162	0.0152485	08104	25651361	28911486	178465.96
8104012043	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	68	0.0064006	08104	10767238	10119521	148816.49
8104012044	08104	12	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	214	0.0201431	08104	33885131	57130744	266966.09
8104012052	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	93	0.0087538	08104	14725781	15342511	164973.24
8104012054	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	43	0.0040474	08104	6808695	28911486	672360.13
8104012901	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	14	0.0013178	08104	2216784	15342511	1095893.66
8104022001	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	42	0.0039533	08104	6650353	10119521	240940.98
8104022036	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	60	0.0056476	08104	9500504	10119521	168658.69
8104022040	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	70	0.0065889	08104	11083921	28911486	413021.22
8104022901	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	11	0.0010354	08104	1741759	10119521	919956.48
8104032004	08104	9	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	175	0.0164721	08104	27709804	45347187	259126.78
8104032012	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	26	0.0024473	08104	4116885	15342511	590096.58
8104032017	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	19	0.0017884	08104	3008493	15342511	807500.59
8104032019	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	21	0.0019767	08104	3325176	10119521	481881.97
8104032028	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	174	0.0163780	08104	27551462	20084521	115428.28
8104032045	08104	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	244	0.0229669	08104	38635383	24577557	100727.69
8104032050	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	67	0.0063065	08104	10608896	10119521	151037.63
8104032053	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	58	0.0054593	08104	9183821	15342511	264526.05
8104042008	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	21	0.0019767	08104	3325176	10119521	481881.97
8104042012	08104	18	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	745	0.0701242	08104	117964593	79950937	107316.69
8104042014	08104	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	118	0.0111069	08104	18684325	24577557	208284.38
8104042027	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	18	0.0016943	08104	2850151	20084521	1115806.72
8104042030	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	42	0.0039533	08104	6650353	10119521	240940.98
8104042042	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	89	0.0083773	08104	14092414	15342511	172387.77
8104042047	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	59	0.0055535	08104	9342162	10119521	171517.31
8104052003	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	34	0.0032003	08104	5383619	15342511	451250.33
8104052010	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	61	0.0057417	08104	9658846	10119521	165893.79
8104052020	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	16	0.0015060	08104	2533468	10119521	632470.08
8104052025	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	27	0.0025414	08104	4275227	15342511	568241.16
8104052027	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	15	0.0014119	08104	2375126	10119521	674634.75
8104052031	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	61	0.0057417	08104	9658846	20084521	329254.44
8104052032	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	47	0.0044239	08104	7442062	10119521	215308.96
8104052039	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	181	0.0170369	08104	28659854	33132395	183051.90
8104052043	08104	7	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	291	0.0273908	08104	46077445	37267663	128067.57
8104052054	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	124	0.0116717	08104	19634375	41335130	333347.83
8104052059	08104	11	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	115	0.0108245	08104	18209300	53238942	462947.32
8104052901	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	87	0.0081890	08104	13775731	41335130	475116.44
8104062001	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	50	0.0047063	08104	7917087	15342511	306850.22
8104062002	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	22	0.0020708	08104	3483518	10119521	459978.24
8104062003	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	17	0.0016002	08104	2691810	10119521	595265.96
8104062013	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	282	0.0265437	08104	44652369	15342511	54406.07
8104062024	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	141	0.0132718	08104	22326185	15342511	108812.14
8104062036	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	36	0.0033886	08104	5700302	20084521	557903.36
8104062049	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	499	0.0469691	08104	79012526	33132395	66397.58
8104062051	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	70	0.0065889	08104	11083921	41335130	590501.86
8104062901	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	123	0.0115776	08104	19476033	33132395	269369.06
8105012014	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	25	0.0010274	08105	5242978	20084521	803380.84
8105012028	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	137	0.0056302	08105	28731520	20084521	146602.34
8105012034	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	50	0.0020548	08105	10485956	10119521	202390.43
8105022024	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	10	0.0004110	08105	2097191	20084521	2008452.10
8105022034	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	38	0.0015617	08105	7969327	10119521	266303.19
8105032001	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	18	0.0007397	08105	3774944	15342511	852361.73
8105032003	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	14	0.0005754	08105	2936068	15342511	1095893.66
8105032009	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	26	0.0010685	08105	5452697	10119521	389212.36
8105032034	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	32	0.0013151	08105	6711012	15342511	479453.47
8105032039	08105	6	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	94	0.0038631	08105	19713598	33132395	352472.28
8105042003	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	23	0.0009452	08105	4823540	15342511	667065.70
8105042005	08105	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	76	0.0031233	08105	15938654	37267663	490363.99
8105042007	08105	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	70	0.0028768	08105	14680339	28911486	413021.22
8105042023	08105	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	65	0.0026713	08105	13631743	28911486	444792.09
8105042047	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	7	0.0002877	08105	1468034	15342511	2191787.31
8105042050	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	19	0.0007808	08105	3984663	20084521	1057080.05
8105062017	08105	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	96	0.0039453	08105	20133036	37267663	388204.83
8105062037	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	38	0.0015617	08105	7969327	20084521	528540.03
8105072004	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	42	0.0017261	08105	8808203	15342511	365297.89
8105072048	08105	4	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	49	0.0020137	08105	10276237	24577557	501582.80
8105072053	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	91	0.0037398	08105	19084441	15342511	168599.02
8105072901	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	56	0.0023014	08105	11744271	20084521	358652.16

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_rural_nivel_nacional_17_r08.rds")

---

R-08

Urbano

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 1)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15201	1	1	1	1767	1	1	1	2	1	3	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	2	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	3	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	4	1	1	2	3	1	99	1	2	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	5	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	6	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	7	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	8	1	1	3	3	1	99	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	9	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	10	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	11	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	12	1	1	4	3	3	3	4	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	13	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	14	1	1	2	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	15	1	1	2	1	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	16	1	1	5	1	5	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	17	1	1	5	3	1	3	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	18	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	19	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	20	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	21	4	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	22	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	23	1	1	5	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	24	1	1	3	1	1	6	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	25	1	1	5	6	1	1	4	2	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	26	4	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	27	1	1	5	99	3	3	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	28	1	1	6	3	2	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	29	1	1	2	3	1	99	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	30	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	31	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	32	1	1	3	1	1	2	1	1	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	33	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	34	3	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	35	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	36	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	37	1	1	5	3	3	1	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	38	1	1	1	1	4	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	39	1	1	5	3	4	3	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	40	1	1	5	3	2	1	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	41	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	42	1	1	2	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	43	1	1	1	1	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	44	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	45	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	46	1	1	5	6	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	47	1	1	5	3	4	0	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	48	1	1	3	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	49	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	50	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	51	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	52	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	53	1	1	1	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	54	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	55	1	1	2	2	1	4	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	56	8	1	98	98	98	98	98	98	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	57	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	58	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	59	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	60	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	61	1	1	2	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	62	1	1	5	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	63	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	64	1	1	5	3	2	4	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	65	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	66	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	67	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	68	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	69	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	70	1	1	1	3	3	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	71	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	72	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	73	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	74	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	75	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	76	1	1	5	3	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	77	1	1	3	6	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	78	5	1	3	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	79	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	80	1	1	2	1	1	3	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	81	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	82	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	83	1	1	5	3	2	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	84	4	1	5	3	2	0	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	85	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	86	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	87	1	1	3	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	88	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	89	5	1	4	3	2	1	2	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	90	1	1	5	3	3	2	1	3	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	91	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	92	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	93	5	1	4	3	5	1	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	94	1	1	5	3	3	99	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	95	1	1	5	3	2	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	96	1	1	2	3	4	6	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	97	1	1	5	1	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	98	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	99	1	1	2	7	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	100	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 8:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 8)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	8101011001	132	2017	08101
2	8101021001	120	2017	08101
3	8101021002	337	2017	08101
4	8101021003	137	2017	08101
5	8101031001	221	2017	08101
6	8101041001	86	2017	08101
7	8101051001	46	2017	08101
8	8101051002	79	2017	08101
9	8101051003	115	2017	08101
10	8101061001	217	2017	08101
11	8101071001	68	2017	08101
12	8101071002	111	2017	08101
13	8101081001	49	2017	08101
14	8101081002	8	2017	08101
15	8101081003	85	2017	08101
16	8101081004	81	2017	08101
17	8101081005	52	2017	08101
18	8101091001	318	2017	08101
19	8101101001	187	2017	08101
20	8101101002	322	2017	08101
21	8101101003	71	2017	08101
22	8101101004	48	2017	08101
23	8101111001	222	2017	08101
24	8101121001	218	2017	08101
25	8101131001	360	2017	08101
26	8101141001	98	2017	08101
27	8101151001	47	2017	08101
28	8101151002	32	2017	08101
29	8101151003	70	2017	08101
30	8101151004	36	2017	08101
31	8101151005	49	2017	08101
32	8101151006	43	2017	08101
33	8101161001	508	2017	08101
34	8101161002	97	2017	08101
35	8101161003	57	2017	08101
36	8101161004	148	2017	08101
37	8101161005	173	2017	08101
38	8101161006	448	2017	08101
39	8101161007	47	2017	08101
40	8101161008	483	2017	08101
41	8101161009	475	2017	08101
42	8101161010	531	2017	08101
43	8101161011	57	2017	08101
44	8101161012	820	2017	08101
45	8101161013	406	2017	08101
46	8101161014	554	2017	08101
47	8101171001	210	2017	08101
48	8101171002	121	2017	08101
49	8101171003	57	2017	08101
50	8101181001	229	2017	08101
51	8101191001	208	2017	08101
52	8101191002	157	2017	08101
53	8101201001	286	2017	08101
54	8101211001	383	2017	08101
55	8101221001	197	2017	08101
56	8101221002	122	2017	08101
57	8101231001	185	2017	08101
58	8101241001	97	2017	08101
59	8101241002	156	2017	08101
60	8101241003	252	2017	08101
61	8101251001	520	2017	08101
62	8101251002	43	2017	08101
63	8101251003	131	2017	08101
64	8101251004	45	2017	08101
65	8101251005	166	2017	08101
66	8101261001	53	2017	08101
67	8101261002	67	2017	08101
68	8101261003	29	2017	08101
69	8101271001	84	2017	08101
70	8101281001	233	2017	08101
71	8101281002	142	2017	08101
72	8101321001	375	2017	08101
73	8101321002	226	2017	08101
74	8101321003	142	2017	08101
75	8101321004	180	2017	08101
76	8101321005	171	2017	08101
77	8101991999	21	2017	08101
550	8102011001	21	2017	08102
551	8102011002	60	2017	08102
552	8102021001	187	2017	08102
553	8102021002	96	2017	08102
554	8102021003	192	2017	08102
555	8102021004	88	2017	08102
556	8102021005	54	2017	08102
557	8102031001	100	2017	08102
558	8102031002	83	2017	08102
559	8102031003	154	2017	08102
560	8102031004	138	2017	08102
561	8102031005	61	2017	08102
562	8102041001	137	2017	08102
563	8102041002	140	2017	08102
564	8102041003	190	2017	08102
565	8102041004	94	2017	08102
566	8102051001	7	2017	08102
567	8102081001	121	2017	08102
568	8102081002	45	2017	08102
569	8102081003	76	2017	08102
570	8102111001	127	2017	08102
571	8102111002	98	2017	08102
572	8102111003	63	2017	08102

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_urbano_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
01107	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
01401	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
01405	Pica	330061.1	2017	1405	9296	3068247619	Urbano
02101	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02102	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02104	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02201	Calama	416281.1	2017	2201	165731	68990679686	Urbano
02203	San Pedro de Atacama	437934.7	2017	2203	10996	4815529626	Urbano
02301	Tocopilla	271720.8	2017	2301	25186	6843559467	Urbano
02302	María Elena	466266.9	2017	2302	6457	3010685220	Urbano
03101	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
03102	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
03103	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
03201	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
03202	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
03301	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
03303	Freirina	289049.9	2017	3303	7041	2035200054	Urbano
03304	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
04101	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04102	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04103	Andacollo	251267.7	2017	4103	11044	2775000288	Urbano
04104	La Higuera	214257.0	2017	4104	4241	908664019	Urbano
04106	Vicuña	245957.4	2017	4106	27771	6830481918	Urbano
04201	Illapel	270316.5	2017	4201	30848	8338722128	Urbano
04202	Canela	233397.3	2017	4202	9093	2122281844	Urbano
04203	Los Vilos	282415.6	2017	4203	21382	6038609501	Urbano
04204	Salamanca	262056.9	2017	4204	29347	7690585032	Urbano
04301	Ovalle	274771.4	2017	4301	111272	30574361012	Urbano
04302	Combarbalá	228990.4	2017	4302	13322	3050610572	Urbano
04303	Monte Patria	225369.1	2017	4303	30751	6930326684	Urbano
04304	Punitaqui	212496.1	2017	4304	10956	2328107498	Urbano
05101	Valparaíso	297929.0	2017	5101	296655	88382118059	Urbano
05102	Casablanca	341641.8	2017	5102	26867	9178890241	Urbano
05103	Concón	318496.3	2017	5103	42152	13425257057	Urbano
05105	Puchuncaví	296035.5	2017	5105	18546	5490274928	Urbano
05107	Quintero	308224.7	2017	5107	31923	9839456903	Urbano
05109	Viña del Mar	337006.1	2017	5109	334248	112643604611	Urbano
05301	Los Andes	339720.2	2017	5301	66708	22662055502	Urbano
05302	Calle Larga	246387.3	2017	5302	14832	3654416747	Urbano
05303	Rinconada	273904.7	2017	5303	10207	2795744821	Urbano
05304	San Esteban	219571.6	2017	5304	18855	4140022481	Urbano
05401	La Ligua	250134.4	2017	5401	35390	8852256241	Urbano
05402	Cabildo	262745.9	2017	5402	19388	5094117762	Urbano
05403	Papudo	294355.2	2017	5403	6356	1870921373	Urbano
05404	Petorca	237510.8	2017	5404	9826	2333781007	Urbano
05405	Zapallar	294389.2	2017	5405	7339	2160521991	Urbano
05501	Quillota	286029.5	2017	5501	90517	25890529852	Urbano
05502	Calera	277181.9	2017	5502	50554	14012652087	Urbano
05503	Hijuelas	254094.0	2017	5503	17988	4570642363	Urbano

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
08101	8101011001	132	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101021001	120	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101021002	337	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101021003	137	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101031001	221	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101041001	86	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101051001	46	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101051002	79	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101051003	115	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101061001	217	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101071001	68	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101071002	111	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101081001	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101081002	8	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101081003	85	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101081004	81	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101081005	52	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101091001	318	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101101001	187	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101101002	322	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101101003	71	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101101004	48	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101111001	222	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101121001	218	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101131001	360	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101141001	98	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151001	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151002	32	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151003	70	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151004	36	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151005	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151006	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161001	508	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161002	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161003	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161004	148	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161005	173	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161006	448	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161007	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161008	483	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161009	475	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161010	531	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161011	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161012	820	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161013	406	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161014	554	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101171001	210	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101171002	121	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101171003	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101181001	229	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101191001	208	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101191002	157	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101201001	286	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101211001	383	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101221001	197	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101221002	122	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101231001	185	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101241001	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101241002	156	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101241003	252	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101251001	520	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101251002	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101251003	131	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101251004	45	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101251005	166	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101261001	53	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101261002	67	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101261003	29	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101271001	84	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101281001	233	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101281002	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101321001	375	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101321002	226	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101321003	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101321004	180	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101321005	171	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101991999	21	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08102	8102011001	21	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102011002	60	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102021001	187	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102021002	96	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102021003	192	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102021004	88	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102021005	54	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102031001	100	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102031002	83	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102031003	154	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102031004	138	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102031005	61	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102041001	137	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102041002	140	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102041003	190	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102041004	94	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102051001	7	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102081001	121	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102081002	45	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102081003	76	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102111001	127	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102111002	98	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102111003	63	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
08101	8101011001	132	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101021001	120	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101021002	337	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101021003	137	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101031001	221	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101041001	86	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101051001	46	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101051002	79	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101051003	115	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101061001	217	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101071001	68	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101071002	111	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101081001	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101081002	8	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101081003	85	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101081004	81	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101081005	52	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101091001	318	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101101001	187	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101101002	322	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101101003	71	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101101004	48	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101111001	222	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101121001	218	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101131001	360	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101141001	98	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151001	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151002	32	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151003	70	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151004	36	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151005	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101151006	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161001	508	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161002	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161003	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161004	148	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161005	173	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161006	448	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161007	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161008	483	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161009	475	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161010	531	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161011	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161012	820	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161013	406	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101161014	554	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101171001	210	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101171002	121	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101171003	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101181001	229	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101191001	208	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101191002	157	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101201001	286	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101211001	383	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101221001	197	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101221002	122	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101231001	185	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101241001	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101241002	156	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101241003	252	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101251001	520	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101251002	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101251003	131	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101251004	45	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101251005	166	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101261001	53	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101261002	67	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101261003	29	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101271001	84	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101281001	233	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101281002	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101321001	375	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101321002	226	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101321003	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101321004	180	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101321005	171	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08101	8101991999	21	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano
08102	8102011001	21	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102011002	60	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102021001	187	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102021002	96	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102021003	192	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102021004	88	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102021005	54	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102031001	100	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102031002	83	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102031003	154	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102031004	138	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102031005	61	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102041001	137	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102041002	140	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102041003	190	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102041004	94	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102051001	7	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102081001	121	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102081002	45	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102081003	76	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102111001	127	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102111002	98	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano
08102	8102111003	63	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
8101011001	08101	132	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1455	0.0065079	08101
8101021001	08101	120	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1516	0.0067808	08101
8101021002	08101	337	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1874	0.0083820	08101
8101021003	08101	137	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1349	0.0060338	08101
8101031001	08101	221	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2590	0.0115845	08101
8101041001	08101	86	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1373	0.0061411	08101
8101051001	08101	46	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1825	0.0081628	08101
8101051002	08101	79	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2383	0.0106587	08101
8101051003	08101	115	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2945	0.0131724	08101
8101061001	08101	217	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2976	0.0133110	08101
8101071001	08101	68	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1523	0.0068121	08101
8101071002	08101	111	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2187	0.0097820	08101
8101081001	08101	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1852	0.0082836	08101
8101081002	08101	8	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	836	0.0037393	08101
8101081003	08101	85	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2570	0.0114951	08101
8101081004	08101	81	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1420	0.0063514	08101
8101081005	08101	52	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3105	0.0138880	08101
8101091001	08101	318	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3351	0.0149883	08101
8101101001	08101	187	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1345	0.0060159	08101
8101101002	08101	322	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4045	0.0180924	08101
8101101003	08101	71	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2968	0.0132752	08101
8101101004	08101	48	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2134	0.0095449	08101
8101111001	08101	222	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3916	0.0175155	08101
8101121001	08101	218	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2733	0.0122241	08101
8101131001	08101	360	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3498	0.0156458	08101
8101141001	08101	98	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2701	0.0120810	08101
8101151001	08101	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2271	0.0101577	08101
8101151002	08101	32	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2231	0.0099788	08101
8101151003	08101	70	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3075	0.0137538	08101
8101151004	08101	36	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1854	0.0082926	08101
8101151005	08101	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2355	0.0105334	08101
8101151006	08101	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2089	0.0093437	08101
8101161001	08101	508	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3863	0.0172784	08101
8101161002	08101	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3464	0.0154938	08101
8101161003	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3640	0.0162810	08101
8101161004	08101	148	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3612	0.0161557	08101
8101161005	08101	173	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4756	0.0212726	08101
8101161006	08101	448	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3401	0.0152120	08101
8101161007	08101	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2944	0.0131679	08101
8101161008	08101	483	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	5013	0.0224221	08101
8101161009	08101	475	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3072	0.0137404	08101
8101161010	08101	531	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2380	0.0106452	08101
8101161011	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2320	0.0103769	08101
8101161012	08101	820	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3886	0.0173813	08101
8101161013	08101	406	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2922	0.0130695	08101
8101161014	08101	554	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3814	0.0170592	08101
8101171001	08101	210	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3284	0.0146886	08101
8101171002	08101	121	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3682	0.0164688	08101
8101171003	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1315	0.0058817	08101
8101181001	08101	229	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4508	0.0201633	08101
8101191001	08101	208	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1629	0.0072862	08101
8101191002	08101	157	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3336	0.0149212	08101
8101201001	08101	286	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3154	0.0141072	08101
8101211001	08101	383	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4901	0.0219212	08101
8101221001	08101	197	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2688	0.0120229	08101
8101221002	08101	122	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1855	0.0082970	08101
8101231001	08101	185	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2503	0.0111954	08101
8101241001	08101	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2763	0.0123583	08101
8101241002	08101	156	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1864	0.0083373	08101
8101241003	08101	252	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1888	0.0084446	08101
8101251001	08101	520	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3790	0.0169519	08101
8101251002	08101	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3121	0.0139596	08101
8101251003	08101	131	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4587	0.0205167	08101
8101251004	08101	45	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2288	0.0102337	08101
8101251005	08101	166	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4334	0.0193851	08101
8101261001	08101	53	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1200	0.0053674	08101
8101261002	08101	67	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2084	0.0093213	08101
8101261003	08101	29	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2696	0.0120586	08101
8101271001	08101	84	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2727	0.0121973	08101
8101281001	08101	233	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3513	0.0157129	08101
8101281002	08101	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3737	0.0167148	08101
8101321001	08101	375	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4131	0.0184771	08101
8101321002	08101	226	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4089	0.0182892	08101
8101321003	08101	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2991	0.0133781	08101
8101321004	08101	180	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	5956	0.0266399	08101
8101321005	08101	171	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3491	0.0156145	08101
8101991999	08101	21	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1520	0.0067986	08101
8102011001	08102	21	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	231	0.0019869	08102
8102011002	08102	60	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2650	0.0227933	08102
8102021001	08102	187	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4404	0.0378800	08102
8102021002	08102	96	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2895	0.0249007	08102
8102021003	08102	192	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4041	0.0347577	08102
8102021004	08102	88	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2507	0.0215634	08102
8102021005	08102	54	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2879	0.0247630	08102
8102031001	08102	100	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3680	0.0316526	08102
8102031002	08102	83	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1697	0.0145963	08102
8102031003	08102	154	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	5338	0.0459135	08102
8102031004	08102	138	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1446	0.0124374	08102
8102031005	08102	61	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1870	0.0160844	08102
8102041001	08102	137	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3438	0.0295711	08102
8102041002	08102	140	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4279	0.0368048	08102
8102041003	08102	190	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4627	0.0397980	08102
8102041004	08102	94	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2601	0.0223719	08102
8102051001	08102	7	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	77	0.0006623	08102
8102081001	08102	121	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3302	0.0284014	08102
8102081002	08102	45	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2248	0.0193356	08102
8102081003	08102	76	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2303	0.0198087	08102
8102111001	08102	127	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3295	0.0283412	08102
8102111002	08102	98	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3487	0.0299926	08102
8102111003	08102	63	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2783	0.0239373	08102

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
8101011001	08101	132	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1455	0.0065079	08101	455161522
8101021001	08101	120	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1516	0.0067808	08101	474243895
8101021002	08101	337	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1874	0.0083820	08101	586235527
8101021003	08101	137	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1349	0.0060338	08101	422001988
8101031001	08101	221	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2590	0.0115845	08101	810218791
8101041001	08101	86	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1373	0.0061411	08101	429509807
8101051001	08101	46	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1825	0.0081628	08101	570907063
8101051002	08101	79	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2383	0.0106587	08101	745463853
8101051003	08101	115	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2945	0.0131724	08101	921271946
8101061001	08101	217	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2976	0.0133110	08101	930969545
8101071001	08101	68	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1523	0.0068121	08101	476433675
8101071002	08101	111	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2187	0.0097820	08101	684149999
8101081001	08101	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1852	0.0082836	08101	579353360
8101081002	08101	8	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	836	0.0037393	08101	261522359
8101081003	08101	85	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2570	0.0114951	08101	803962275
8101081004	08101	81	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1420	0.0063514	08101	444212619
8101081005	08101	52	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3105	0.0138880	08101	971324072
8101091001	08101	318	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3351	0.0149883	08101	1048279216
8101101001	08101	187	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1345	0.0060159	08101	420750685
8101101002	08101	322	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4045	0.0180924	08101	1265380313
8101101003	08101	71	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2968	0.0132752	08101	928466939
8101101004	08101	48	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2134	0.0095449	08101	667570232
8101111001	08101	222	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3916	0.0175155	08101	1225025786
8101121001	08101	218	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2733	0.0122241	08101	854952879
8101131001	08101	360	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3498	0.0156458	08101	1094264607
8101141001	08101	98	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2701	0.0120810	08101	844942454
8101151001	08101	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2271	0.0101577	08101	710427365
8101151002	08101	32	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2231	0.0099788	08101	697914333
8101151003	08101	70	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3075	0.0137538	08101	961939298
8101151004	08101	36	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1854	0.0082926	08101	579979011
8101151005	08101	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2355	0.0105334	08101	736704731
8101151006	08101	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2089	0.0093437	08101	653493071
8101161001	08101	508	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3863	0.0172784	08101	1208446019
8101161002	08101	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3464	0.0154938	08101	1083628530
8101161003	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3640	0.0162810	08101	1138685869
8101161004	08101	148	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3612	0.0161557	08101	1129926747
8101161005	08101	173	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4756	0.0212726	08101	1487799448
8101161006	08101	448	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3401	0.0152120	08101	1063920505
8101161007	08101	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2944	0.0131679	08101	920959120
8101161008	08101	483	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	5013	0.0224221	08101	1568195676
8101161009	08101	475	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3072	0.0137404	08101	961000821
8101161010	08101	531	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2380	0.0106452	08101	744525376
8101161011	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2320	0.0103769	08101	725755828
8101161012	08101	820	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3886	0.0173813	08101	1215641012
8101161013	08101	406	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2922	0.0130695	08101	914076953
8101161014	08101	554	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3814	0.0170592	08101	1193117556
8101171001	08101	210	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3284	0.0146886	08101	1027319888
8101171002	08101	121	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3682	0.0164688	08101	1151824552
8101171003	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1315	0.0058817	08101	411365911
8101181001	08101	229	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4508	0.0201633	08101	1410218653
8101191001	08101	208	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1629	0.0072862	08101	509593209
8101191002	08101	157	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3336	0.0149212	08101	1043586829
8101201001	08101	286	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3154	0.0141072	08101	986652536
8101211001	08101	383	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4901	0.0219212	08101	1533159187
8101221001	08101	197	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2688	0.0120229	08101	840875718
8101221002	08101	122	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1855	0.0082970	08101	580291837
8101231001	08101	185	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2503	0.0111954	08101	783002948
8101241001	08101	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2763	0.0123583	08101	864337652
8101241002	08101	156	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1864	0.0083373	08101	583107269
8101241003	08101	252	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1888	0.0084446	08101	590615088
8101251001	08101	520	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3790	0.0169519	08101	1185609737
8101251002	08101	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3121	0.0139596	08101	976329285
8101251003	08101	131	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4587	0.0205167	08101	1434931890
8101251004	08101	45	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2288	0.0102337	08101	715745403
8101251005	08101	166	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4334	0.0193851	08101	1355786966
8101261001	08101	53	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1200	0.0053674	08101	375390946
8101261002	08101	67	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2084	0.0093213	08101	651928942
8101261003	08101	29	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2696	0.0120586	08101	843378325
8101271001	08101	84	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2727	0.0121973	08101	853075924
8101281001	08101	233	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3513	0.0157129	08101	1098956994
8101281002	08101	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3737	0.0167148	08101	1169029970
8101321001	08101	375	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4131	0.0184771	08101	1292283331
8101321002	08101	226	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4089	0.0182892	08101	1279144647
8101321003	08101	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2991	0.0133781	08101	935661932
8101321004	08101	180	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	5956	0.0266399	08101	1863190394
8101321005	08101	171	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3491	0.0156145	08101	1092074826
8101991999	08101	21	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1520	0.0067986	08101	475495198
8102011001	08102	21	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	231	0.0019869	08102	61716800
8102011002	08102	60	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2650	0.0227933	08102	708006586
8102021001	08102	187	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4404	0.0378800	08102	1176626794
8102021002	08102	96	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2895	0.0249007	08102	773463798
8102021003	08102	192	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4041	0.0347577	08102	1079643250
8102021004	08102	88	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2507	0.0215634	08102	669800947
8102021005	08102	54	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2879	0.0247630	08102	769189042
8102031001	08102	100	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3680	0.0316526	08102	983194051
8102031002	08102	83	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1697	0.0145963	08102	453391387
8102031003	08102	154	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	5338	0.0459135	08102	1426165719
8102031004	08102	138	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1446	0.0124374	08102	386331141
8102031005	08102	61	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1870	0.0160844	08102	499612194
8102041001	08102	137	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3438	0.0295711	08102	918538355
8102041002	08102	140	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4279	0.0368048	08102	1143230257
8102041003	08102	190	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4627	0.0397980	08102	1236206216
8102041004	08102	94	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2601	0.0223719	08102	694915143
8102051001	08102	7	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	77	0.0006623	08102	20572267
8102081001	08102	121	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3302	0.0284014	08102	882202923
8102081002	08102	45	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2248	0.0193356	08102	600603322
8102081003	08102	76	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2303	0.0198087	08102	615297799
8102111001	08102	127	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3295	0.0283412	08102	880332717
8102111002	08102	98	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3487	0.0299926	08102	931629798
8102111003	08102	63	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2783	0.0239373	08102	743540501

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -789139518 -229998481  -17716814  218589556 1341310477 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 663261697   20914997   31.71   <2e-16 ***
## Freq.x        1104251      99359   11.11   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 350900000 on 470 degrees of freedom
## Multiple R-squared:  0.2081, Adjusted R-squared:  0.2064 
## F-statistic: 123.5 on 1 and 470 DF,  p-value: < 2.2e-16

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.206422068260736"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.206422068260736"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.429357928472113"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.325507963000801"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.331109627578258"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.267591932013513"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.532295344305123"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                
## [1,] "log-log" "0.56853136470591"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 1    cuadrático 0.206422068260736
## 2        cúbico 0.206422068260736
## 6      log-raíz 0.267591932013513
## 4 raíz cuadrada 0.325507963000801
## 5     raíz-raíz 0.331109627578258
## 3   logarítmico 0.429357928472113
## 7      raíz-log 0.532295344305123
## 8       log-log  0.56853136470591
##                                                                     sintaxis
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 8
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5258 -0.3083  0.0999  0.3854  1.4623 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  17.6993     0.1073  164.96   <2e-16 ***
## log(Freq.x)   0.5958     0.0239   24.93   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6139 on 470 degrees of freedom
## Multiple R-squared:  0.5694, Adjusted R-squared:  0.5685 
## F-statistic: 621.6 on 1 and 470 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    17.69933

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##    0.595802

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.5685 ).

9.1 Diagrama de dispersión sobre log-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5258 -0.3083  0.0999  0.3854  1.4623 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  17.6993     0.1073  164.96   <2e-16 ***
## log(Freq.x)   0.5958     0.0239   24.93   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6139 on 470 degrees of freedom
## Multiple R-squared:  0.5694, Adjusted R-squared:  0.5685 
## F-statistic: 621.6 on 1 and 470 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = e^{17.69933+0.595802 \cdot ln{X}} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
8101011001	08101	132	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1455	0.0065079	08101	455161522	891583213
8101021001	08101	120	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1516	0.0067808	08101	474243895	842364466
8101021002	08101	337	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1874	0.0083820	08101	586235527	1558427640
8101021003	08101	137	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1349	0.0060338	08101	422001988	911553328
8101031001	08101	221	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2590	0.0115845	08101	810218791	1212029801
8101041001	08101	86	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1373	0.0061411	08101	429509807	690713176
8101051001	08101	46	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1825	0.0081628	08101	570907063	475766805
8101051002	08101	79	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2383	0.0106587	08101	745463853	656643593
8101051003	08101	115	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2945	0.0131724	08101	921271946	821273082
8101061001	08101	217	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2976	0.0133110	08101	930969545	1198911359
8101071001	08101	68	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1523	0.0068121	08101	476433675	600526491
8101071002	08101	111	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2187	0.0097820	08101	684149999	804131760
8101081001	08101	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1852	0.0082836	08101	579353360	494017007
8101081002	08101	8	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	836	0.0037393	08101	261522359	167796354
8101081003	08101	85	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2570	0.0114951	08101	803962275	685916657
8101081004	08101	81	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1420	0.0063514	08101	444212619	666498054
8101081005	08101	52	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3105	0.0138880	08101	971324072	511820786
8101091001	08101	318	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3351	0.0149883	08101	1048279216	1505465387
8101101001	08101	187	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1345	0.0060159	08101	420750685	1097204211
8101101002	08101	322	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4045	0.0180924	08101	1265380313	1516719380
8101101003	08101	71	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2968	0.0132752	08101	928466939	616173649
8101101004	08101	48	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2134	0.0095449	08101	667570232	487985129
8101111001	08101	222	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3916	0.0175155	08101	1225025786	1215294374
8101121001	08101	218	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2733	0.0122241	08101	854952879	1202200069
8101131001	08101	360	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3498	0.0156458	08101	1094264607	1620950804
8101141001	08101	98	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2701	0.0120810	08101	844942454	746614075
8101151001	08101	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2271	0.0101577	08101	710427365	481902238
8101151002	08101	32	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2231	0.0099788	08101	697914333	383257854
8101151003	08101	70	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3075	0.0137538	08101	961939298	610988164
8101151004	08101	36	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1854	0.0082926	08101	579979011	411119275
8101151005	08101	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2355	0.0105334	08101	736704731	494017007
8101151006	08101	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2089	0.0093437	08101	653493071	457028695
8101161001	08101	508	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3863	0.0172784	08101	1208446019	1990117426
8101161002	08101	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3464	0.0154938	08101	1083628530	742065545
8101161003	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3640	0.0162810	08101	1138685869	540596769
8101161004	08101	148	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3612	0.0161557	08101	1129926747	954478091
8101161005	08101	173	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4756	0.0212726	08101	1487799448	1047495190
8101161006	08101	448	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3401	0.0152120	08101	1063920505	1846530284
8101161007	08101	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2944	0.0131679	08101	920959120	481902238
8101161008	08101	483	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	5013	0.0224221	08101	1568195676	1931171126
8101161009	08101	475	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3072	0.0137404	08101	961000821	1912049350
8101161010	08101	531	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2380	0.0106452	08101	744525376	2043320334
8101161011	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2320	0.0103769	08101	725755828	540596769
8101161012	08101	820	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3886	0.0173813	08101	1215641012	2647133298
8101161013	08101	406	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2922	0.0130695	08101	914076953	1741344583
8101161014	08101	554	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3814	0.0170592	08101	1193117556	2095599546
8101171001	08101	210	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3284	0.0146886	08101	1027319888	1175716446
8101171002	08101	121	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3682	0.0164688	08101	1151824552	846539804
8101171003	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1315	0.0058817	08101	411365911	540596769
8101181001	08101	229	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4508	0.0201633	08101	1410218653	1237982178
8101191001	08101	208	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1629	0.0072862	08101	509593209	1169032174
8101191002	08101	157	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3336	0.0149212	08101	1043586829	988646648
8101201001	08101	286	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3154	0.0141072	08101	986652536	1413277639
8101211001	08101	383	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4901	0.0219212	08101	1533159187	1681878766
8101221001	08101	197	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2688	0.0120229	08101	840875718	1131793667
8101221002	08101	122	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1855	0.0082970	08101	580291837	850701216
8101231001	08101	185	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2503	0.0111954	08101	783002948	1090197403
8101241001	08101	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2763	0.0123583	08101	864337652	742065545
8101241002	08101	156	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1864	0.0083373	08101	583107269	984889971
8101241003	08101	252	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1888	0.0084446	08101	590615088	1310626478
8101251001	08101	520	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3790	0.0169519	08101	1185609737	2017994207
8101251002	08101	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3121	0.0139596	08101	976329285	457028695
8101251003	08101	131	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4587	0.0205167	08101	1434931890	887552734
8101251004	08101	45	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2288	0.0102337	08101	715745403	469577217
8101251005	08101	166	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4334	0.0193851	08101	1355786966	1022032090
8101261001	08101	53	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1200	0.0053674	08101	375390946	517662502
8101261002	08101	67	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2084	0.0093213	08101	651928942	595249055
8101261003	08101	29	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2696	0.0120586	08101	843378325	361425964
8101271001	08101	84	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2727	0.0121973	08101	853075924	681097273
8101281001	08101	233	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3513	0.0157129	08101	1098956994	1250820773
8101281002	08101	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3737	0.0167148	08101	1169029970	931230933
8101321001	08101	375	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4131	0.0184771	08101	1292283331	1660858636
8101321002	08101	226	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4089	0.0182892	08101	1279144647	1228293656
8101321003	08101	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2991	0.0133781	08101	935661932	931230933
8101321004	08101	180	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	5956	0.0266399	08101	1863190394	1072545098
8101321005	08101	171	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3491	0.0156145	08101	1092074826	1040263215
8101991999	08101	21	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1520	0.0067986	08101	475495198	298195165
8102011001	08102	21	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	231	0.0019869	08102	61716800	298195165
8102011002	08102	60	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2650	0.0227933	08102	708006586	557372794
8102021001	08102	187	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4404	0.0378800	08102	1176626794	1097204211
8102021002	08102	96	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2895	0.0249007	08102	773463798	737498021
8102021003	08102	192	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4041	0.0347577	08102	1079643250	1114589973
8102021004	08102	88	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2507	0.0215634	08102	669800947	700239104
8102021005	08102	54	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2879	0.0247630	08102	769189042	523459833
8102031001	08102	100	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3680	0.0316526	08102	983194051	755655233
8102031002	08102	83	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1697	0.0145963	08102	453391387	676254643
8102031003	08102	154	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	5338	0.0459135	08102	1426165719	977347292
8102031004	08102	138	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1446	0.0124374	08102	386331141	915511772
8102031005	08102	61	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1870	0.0160844	08102	499612194	562889026
8102041001	08102	137	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3438	0.0295711	08102	918538355	911553328
8102041002	08102	140	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4279	0.0368048	08102	1143230257	923394045
8102041003	08102	190	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4627	0.0397980	08102	1236206216	1107657891
8102041004	08102	94	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2601	0.0223719	08102	694915143	728304873
8102051001	08102	7	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	77	0.0006623	08102	20572267	154963994
8102081001	08102	121	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3302	0.0284014	08102	882202923	846539804
8102081002	08102	45	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2248	0.0193356	08102	600603322	469577217
8102081003	08102	76	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2303	0.0198087	08102	615297799	641670679
8102111001	08102	127	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3295	0.0283412	08102	880332717	871304904
8102111002	08102	98	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3487	0.0299926	08102	931629798	746614075
8102111003	08102	63	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2783	0.0239373	08102	743540501	573813013

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
8101011001	08101	132	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1455	0.0065079	08101	455161522	891583213	612772.0
8101021001	08101	120	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1516	0.0067808	08101	474243895	842364466	555649.4
8101021002	08101	337	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1874	0.0083820	08101	586235527	1558427640	831604.9
8101021003	08101	137	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1349	0.0060338	08101	422001988	911553328	675725.2
8101031001	08101	221	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2590	0.0115845	08101	810218791	1212029801	467965.2
8101041001	08101	86	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1373	0.0061411	08101	429509807	690713176	503068.6
8101051001	08101	46	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1825	0.0081628	08101	570907063	475766805	260694.1
8101051002	08101	79	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2383	0.0106587	08101	745463853	656643593	275553.3
8101051003	08101	115	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2945	0.0131724	08101	921271946	821273082	278870.3
8101061001	08101	217	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2976	0.0133110	08101	930969545	1198911359	402860.0
8101071001	08101	68	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1523	0.0068121	08101	476433675	600526491	394305.0
8101071002	08101	111	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2187	0.0097820	08101	684149999	804131760	367687.1
8101081001	08101	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1852	0.0082836	08101	579353360	494017007	266747.8
8101081002	08101	8	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	836	0.0037393	08101	261522359	167796354	200713.3
8101081003	08101	85	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2570	0.0114951	08101	803962275	685916657	266893.6
8101081004	08101	81	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1420	0.0063514	08101	444212619	666498054	469364.8
8101081005	08101	52	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3105	0.0138880	08101	971324072	511820786	164837.6
8101091001	08101	318	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3351	0.0149883	08101	1048279216	1505465387	449258.5
8101101001	08101	187	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1345	0.0060159	08101	420750685	1097204211	815765.2
8101101002	08101	322	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4045	0.0180924	08101	1265380313	1516719380	374961.5
8101101003	08101	71	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2968	0.0132752	08101	928466939	616173649	207605.7
8101101004	08101	48	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2134	0.0095449	08101	667570232	487985129	228671.6
8101111001	08101	222	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3916	0.0175155	08101	1225025786	1215294374	310340.7
8101121001	08101	218	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2733	0.0122241	08101	854952879	1202200069	439882.9
8101131001	08101	360	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3498	0.0156458	08101	1094264607	1620950804	463393.6
8101141001	08101	98	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2701	0.0120810	08101	844942454	746614075	276421.4
8101151001	08101	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2271	0.0101577	08101	710427365	481902238	212198.3
8101151002	08101	32	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2231	0.0099788	08101	697914333	383257854	171787.5
8101151003	08101	70	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3075	0.0137538	08101	961939298	610988164	198695.3
8101151004	08101	36	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1854	0.0082926	08101	579979011	411119275	221747.2
8101151005	08101	49	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2355	0.0105334	08101	736704731	494017007	209773.7
8101151006	08101	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2089	0.0093437	08101	653493071	457028695	218778.7
8101161001	08101	508	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3863	0.0172784	08101	1208446019	1990117426	515174.1
8101161002	08101	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3464	0.0154938	08101	1083628530	742065545	214222.2
8101161003	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3640	0.0162810	08101	1138685869	540596769	148515.6
8101161004	08101	148	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3612	0.0161557	08101	1129926747	954478091	264252.0
8101161005	08101	173	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4756	0.0212726	08101	1487799448	1047495190	220247.1
8101161006	08101	448	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3401	0.0152120	08101	1063920505	1846530284	542937.5
8101161007	08101	47	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2944	0.0131679	08101	920959120	481902238	163689.6
8101161008	08101	483	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	5013	0.0224221	08101	1568195676	1931171126	385232.6
8101161009	08101	475	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3072	0.0137404	08101	961000821	1912049350	622411.9
8101161010	08101	531	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2380	0.0106452	08101	744525376	2043320334	858538.0
8101161011	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2320	0.0103769	08101	725755828	540596769	233015.8
8101161012	08101	820	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3886	0.0173813	08101	1215641012	2647133298	681197.5
8101161013	08101	406	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2922	0.0130695	08101	914076953	1741344583	595942.7
8101161014	08101	554	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3814	0.0170592	08101	1193117556	2095599546	549449.3
8101171001	08101	210	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3284	0.0146886	08101	1027319888	1175716446	358013.5
8101171002	08101	121	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3682	0.0164688	08101	1151824552	846539804	229913.0
8101171003	08101	57	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1315	0.0058817	08101	411365911	540596769	411100.2
8101181001	08101	229	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4508	0.0201633	08101	1410218653	1237982178	274618.9
8101191001	08101	208	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1629	0.0072862	08101	509593209	1169032174	717637.9
8101191002	08101	157	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3336	0.0149212	08101	1043586829	988646648	296356.9
8101201001	08101	286	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3154	0.0141072	08101	986652536	1413277639	448090.6
8101211001	08101	383	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4901	0.0219212	08101	1533159187	1681878766	343170.5
8101221001	08101	197	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2688	0.0120229	08101	840875718	1131793667	421054.2
8101221002	08101	122	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1855	0.0082970	08101	580291837	850701216	458599.0
8101231001	08101	185	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2503	0.0111954	08101	783002948	1090197403	435556.3
8101241001	08101	97	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2763	0.0123583	08101	864337652	742065545	268572.4
8101241002	08101	156	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1864	0.0083373	08101	583107269	984889971	528374.4
8101241003	08101	252	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1888	0.0084446	08101	590615088	1310626478	694187.8
8101251001	08101	520	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3790	0.0169519	08101	1185609737	2017994207	532452.3
8101251002	08101	43	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3121	0.0139596	08101	976329285	457028695	146436.6
8101251003	08101	131	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4587	0.0205167	08101	1434931890	887552734	193493.1
8101251004	08101	45	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2288	0.0102337	08101	715745403	469577217	205234.8
8101251005	08101	166	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4334	0.0193851	08101	1355786966	1022032090	235817.3
8101261001	08101	53	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1200	0.0053674	08101	375390946	517662502	431385.4
8101261002	08101	67	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2084	0.0093213	08101	651928942	595249055	285628.1
8101261003	08101	29	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2696	0.0120586	08101	843378325	361425964	134060.1
8101271001	08101	84	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2727	0.0121973	08101	853075924	681097273	249760.6
8101281001	08101	233	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3513	0.0157129	08101	1098956994	1250820773	356054.9
8101281002	08101	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3737	0.0167148	08101	1169029970	931230933	249192.1
8101321001	08101	375	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4131	0.0184771	08101	1292283331	1660858636	402047.6
8101321002	08101	226	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	4089	0.0182892	08101	1279144647	1228293656	300389.7
8101321003	08101	142	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	2991	0.0133781	08101	935661932	931230933	311344.3
8101321004	08101	180	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	5956	0.0266399	08101	1863190394	1072545098	180078.1
8101321005	08101	171	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	3491	0.0156145	08101	1092074826	1040263215	297984.3
8101991999	08101	21	2017	Concepción	312825.8	2017	8101	223574	69939712743	Urbano	1520	0.0067986	08101	475495198	298195165	196181.0
8102011001	08102	21	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	231	0.0019869	08102	61716800	298195165	1290888.2
8102011002	08102	60	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2650	0.0227933	08102	708006586	557372794	210329.4
8102021001	08102	187	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4404	0.0378800	08102	1176626794	1097204211	249138.1
8102021002	08102	96	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2895	0.0249007	08102	773463798	737498021	254748.9
8102021003	08102	192	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4041	0.0347577	08102	1079643250	1114589973	275820.3
8102021004	08102	88	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2507	0.0215634	08102	669800947	700239104	279313.6
8102021005	08102	54	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2879	0.0247630	08102	769189042	523459833	181820.0
8102031001	08102	100	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3680	0.0316526	08102	983194051	755655233	205341.1
8102031002	08102	83	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1697	0.0145963	08102	453391387	676254643	398500.1
8102031003	08102	154	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	5338	0.0459135	08102	1426165719	977347292	183092.4
8102031004	08102	138	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1446	0.0124374	08102	386331141	915511772	633134.0
8102031005	08102	61	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	1870	0.0160844	08102	499612194	562889026	301010.2
8102041001	08102	137	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3438	0.0295711	08102	918538355	911553328	265140.6
8102041002	08102	140	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4279	0.0368048	08102	1143230257	923394045	215796.7
8102041003	08102	190	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	4627	0.0397980	08102	1236206216	1107657891	239390.1
8102041004	08102	94	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2601	0.0223719	08102	694915143	728304873	280009.6
8102051001	08102	7	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	77	0.0006623	08102	20572267	154963994	2012519.4
8102081001	08102	121	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3302	0.0284014	08102	882202923	846539804	256371.8
8102081002	08102	45	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2248	0.0193356	08102	600603322	469577217	208886.7
8102081003	08102	76	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2303	0.0198087	08102	615297799	641670679	278623.8
8102111001	08102	127	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3295	0.0283412	08102	880332717	871304904	264432.4
8102111002	08102	98	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	3487	0.0299926	08102	931629798	746614075	214113.6
8102111003	08102	63	2017	Coronel	267172.3	2017	8102	116262	31061985532	Urbano	2783	0.0239373	08102	743540501	573813013	206185.1

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_urbano_nivel_nacional_17_r08.rds")

---

Rural

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 2)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15202	1	2	6	13225	1	3	1	5	3	5	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	3	1	1	5	3	5	2	3	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	4	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	5	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	6	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	8	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	9	3	1	5	3	5	1	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	10	1	1	5	3	4	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	11	1	2	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	12	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	13	1	1	5	3	4	1	4	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	14	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	16	1	1	5	3	5	1	3	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	17	1	1	5	3	5	2	4	1	8	15	152	15202	15202012006
15	152	15202	1	2	6	13225	18	3	1	5	3	5	1	1	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	19	3	1	5	3	5	1	3	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	20	1	1	5	3	5	2	1	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	21	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	22	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	24	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	25	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	26	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	27	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	28	3	1	5	3	5	1	4	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	29	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	30	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	31	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	32	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	33	1	1	5	3	5	3	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	34	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	35	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	36	1	1	5	3	5	3	2	1	9	15	152	15202	15202012006
15	152	15202	1	2	6	13225	37	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	38	1	1	5	3	5	99	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	39	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	40	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	41	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	42	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	43	3	1	5	3	5	2	1	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	44	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	45	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	8	13910	1	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	3	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	4	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	5	3	1	5	3	5	2	3	1	3	15	152	15202	15202012008
15	152	15202	1	2	8	13910	6	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	7	1	1	5	99	5	2	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	8	3	1	5	3	5	3	3	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	9	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	10	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	11	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	12	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	13	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	14	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	16	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	18	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	20	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	21	1	1	5	3	5	1	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	22	3	1	5	3	5	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	23	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	24	3	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	25	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	26	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	27	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	28	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	29	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	30	1	1	5	1	4	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	12	8394	1	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	2	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	3	3	1	5	3	5	2	3	1	4	15	152	15202	15202012012
15	152	15202	1	2	12	8394	4	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	5	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	6	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	8	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	9	3	1	5	3	5	1	4	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	10	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	11	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	12	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	13	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	14	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	15	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	16	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	18	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	20	3	1	5	3	5	3	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	21	3	1	5	99	5	1	4	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	22	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	24	3	1	5	3	5	1	2	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	25	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 8:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 8)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Veamos los primeros 100 registros:

r3_100 <- d[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	unlist.c.	unlist.d.	Freq	anio
1	8101252025	1	8101	6	2017
2	8101282901	1	8101	3	2017
3	8101292012	1	8101	4	2017
4	8101292020	1	8101	3	2017
5	8101292901	1	8101	2	2017
6	8101302001	1	8101	8	2017
7	8101302005	1	8101	10	2017
8	8101302008	1	8101	3	2017
9	8101302011	1	8101	3	2017
10	8101302019	1	8101	1	2017
11	8101302021	1	8101	12	2017
12	8101302024	1	8101	1	2017
13	8101302901	1	8101	2	2017
14	8101312004	1	8101	6	2017
15	8101312013	1	8101	16	2017
16	8101312014	1	8101	30	2017
17	8101312017	1	8101	2	2017
18	8101322025	1	8101	2	2017
773	8102052001	1	8102	4	2017
774	8102062004	1	8102	2	2017
775	8102062005	1	8102	44	2017
776	8102062009	1	8102	52	2017
777	8102072005	1	8102	46	2017
778	8102092010	1	8102	13	2017
779	8102102007	1	8102	12	2017
780	8102102011	1	8102	15	2017
781	8102112008	1	8102	6	2017
1536	8103062006	1	8103	1	2017
1537	8103062901	1	8103	3	2017
2292	8104012005	1	8104	2	2017
2293	8104012023	1	8104	3	2017
2294	8104012029	1	8104	1	2017
2295	8104012037	1	8104	5	2017
2296	8104012043	1	8104	1	2017
2297	8104012044	1	8104	12	2017
2298	8104012052	1	8104	2	2017
2299	8104012054	1	8104	5	2017
2300	8104012901	1	8104	2	2017
2301	8104022001	1	8104	1	2017
2302	8104022036	1	8104	1	2017
2303	8104022040	1	8104	5	2017
2304	8104022901	1	8104	1	2017
2305	8104032004	1	8104	9	2017
2306	8104032012	1	8104	2	2017
2307	8104032017	1	8104	2	2017
2308	8104032019	1	8104	1	2017
2309	8104032028	1	8104	3	2017
2310	8104032045	1	8104	4	2017
2311	8104032050	1	8104	1	2017
2312	8104032053	1	8104	2	2017
2313	8104042008	1	8104	1	2017
2314	8104042012	1	8104	18	2017
2315	8104042014	1	8104	4	2017
2316	8104042027	1	8104	3	2017
2317	8104042030	1	8104	1	2017
2318	8104042042	1	8104	2	2017
2319	8104042047	1	8104	1	2017
2320	8104052003	1	8104	2	2017
2321	8104052010	1	8104	1	2017
2322	8104052020	1	8104	1	2017
2323	8104052025	1	8104	2	2017
2324	8104052027	1	8104	1	2017
2325	8104052031	1	8104	3	2017
2326	8104052032	1	8104	1	2017
2327	8104052039	1	8104	6	2017
2328	8104052043	1	8104	7	2017
2329	8104052054	1	8104	8	2017
2330	8104052059	1	8104	11	2017
2331	8104052901	1	8104	8	2017
2332	8104062001	1	8104	2	2017
2333	8104062002	1	8104	1	2017
2334	8104062003	1	8104	1	2017
2335	8104062013	1	8104	2	2017
2336	8104062024	1	8104	2	2017
2337	8104062036	1	8104	3	2017
2338	8104062049	1	8104	6	2017
2339	8104062051	1	8104	8	2017
2340	8104062901	1	8104	6	2017
3095	8105012014	1	8105	3	2017
3096	8105012028	1	8105	3	2017
3097	8105012034	1	8105	1	2017
3098	8105022024	1	8105	3	2017
3099	8105022034	1	8105	1	2017
3100	8105032001	1	8105	2	2017
3101	8105032003	1	8105	2	2017
3102	8105032009	1	8105	1	2017
3103	8105032034	1	8105	2	2017
3104	8105032039	1	8105	6	2017
3105	8105042003	1	8105	2	2017
3106	8105042005	1	8105	7	2017
3107	8105042007	1	8105	5	2017
3108	8105042023	1	8105	5	2017
3109	8105042047	1	8105	2	2017
3110	8105042050	1	8105	3	2017
3111	8105062017	1	8105	7	2017
3112	8105062037	1	8105	3	2017
3113	8105072004	1	8105	2	2017
3114	8105072048	1	8105	4	2017
3115	8105072053	1	8105	2	2017
3116	8105072901	1	8105	3	2017

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	8101252025	6	2017	08101
2	8101282901	3	2017	08101
3	8101292012	4	2017	08101
4	8101292020	3	2017	08101
5	8101292901	2	2017	08101
6	8101302001	8	2017	08101
7	8101302005	10	2017	08101
8	8101302008	3	2017	08101
9	8101302011	3	2017	08101
10	8101302019	1	2017	08101
11	8101302021	12	2017	08101
12	8101302024	1	2017	08101
13	8101302901	2	2017	08101
14	8101312004	6	2017	08101
15	8101312013	16	2017	08101
16	8101312014	30	2017	08101
17	8101312017	2	2017	08101
18	8101322025	2	2017	08101
773	8102052001	4	2017	08102
774	8102062004	2	2017	08102
775	8102062005	44	2017	08102
776	8102062009	52	2017	08102
777	8102072005	46	2017	08102
778	8102092010	13	2017	08102
779	8102102007	12	2017	08102
780	8102102011	15	2017	08102
781	8102112008	6	2017	08102
1536	8103062006	1	2017	08103
1537	8103062901	3	2017	08103
2292	8104012005	2	2017	08104
2293	8104012023	3	2017	08104
2294	8104012029	1	2017	08104
2295	8104012037	5	2017	08104
2296	8104012043	1	2017	08104
2297	8104012044	12	2017	08104
2298	8104012052	2	2017	08104
2299	8104012054	5	2017	08104
2300	8104012901	2	2017	08104
2301	8104022001	1	2017	08104
2302	8104022036	1	2017	08104
2303	8104022040	5	2017	08104
2304	8104022901	1	2017	08104
2305	8104032004	9	2017	08104
2306	8104032012	2	2017	08104
2307	8104032017	2	2017	08104
2308	8104032019	1	2017	08104
2309	8104032028	3	2017	08104
2310	8104032045	4	2017	08104
2311	8104032050	1	2017	08104
2312	8104032053	2	2017	08104
2313	8104042008	1	2017	08104
2314	8104042012	18	2017	08104
2315	8104042014	4	2017	08104
2316	8104042027	3	2017	08104
2317	8104042030	1	2017	08104
2318	8104042042	2	2017	08104
2319	8104042047	1	2017	08104
2320	8104052003	2	2017	08104
2321	8104052010	1	2017	08104
2322	8104052020	1	2017	08104
2323	8104052025	2	2017	08104
2324	8104052027	1	2017	08104
2325	8104052031	3	2017	08104
2326	8104052032	1	2017	08104
2327	8104052039	6	2017	08104
2328	8104052043	7	2017	08104
2329	8104052054	8	2017	08104
2330	8104052059	11	2017	08104
2331	8104052901	8	2017	08104
2332	8104062001	2	2017	08104
2333	8104062002	1	2017	08104
2334	8104062003	1	2017	08104
2335	8104062013	2	2017	08104
2336	8104062024	2	2017	08104
2337	8104062036	3	2017	08104
2338	8104062049	6	2017	08104
2339	8104062051	8	2017	08104
2340	8104062901	6	2017	08104
3095	8105012014	3	2017	08105
3096	8105012028	3	2017	08105
3097	8105012034	1	2017	08105
3098	8105022024	3	2017	08105
3099	8105022034	1	2017	08105
3100	8105032001	2	2017	08105
3101	8105032003	2	2017	08105
3102	8105032009	1	2017	08105
3103	8105032034	2	2017	08105
3104	8105032039	6	2017	08105
3105	8105042003	2	2017	08105
3106	8105042005	7	2017	08105
3107	8105042007	5	2017	08105
3108	8105042023	5	2017	08105
3109	8105042047	2	2017	08105
3110	8105042050	3	2017	08105
3111	8105062017	7	2017	08105
3112	8105062037	3	2017	08105
3113	8105072004	2	2017	08105
3114	8105072048	4	2017	08105
3115	8105072053	2	2017	08105
3116	8105072901	3	2017	08105

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_rural_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	289375.3	2017	1101	191468	55406102543	Rural
01401	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
01402	Camiña	262850.3	2017	1402	1250	328562901	Rural
01404	Huara	253968.5	2017	1404	2730	693334131	Rural
01405	Pica	290496.7	2017	1405	9296	2700457509	Rural
02103	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
02104	Taltal	345494.0	2017	2104	13317	4600943086	Rural
02201	Calama	310025.0	2017	2201	165731	51380756402	Rural
02203	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
02301	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
03101	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03103	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03202	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural
03301	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03302	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03303	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03304	Huasco	227560.7	2017	3304	10149	2309513927	Rural
04101	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04102	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04103	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04104	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04105	Paiguano	205942.1	2017	4105	4497	926121774	Rural
04106	Vicuña	176130.6	2017	4106	27771	4891322768	Rural
04201	Illapel	191976.8	2017	4201	30848	5922099530	Rural
04202	Canela	171370.3	2017	4202	9093	1558270441	Rural
04203	Los Vilos	173238.5	2017	4203	21382	3704185607	Rural
04204	Salamanca	223234.2	2017	4204	29347	6551254640	Rural
04301	Ovalle	241393.7	2017	4301	111272	26860360045	Rural
04302	Combarbalá	179139.6	2017	4302	13322	2386498044	Rural
04303	Monte Patria	201205.8	2017	4303	30751	6187280931	Rural
04304	Punitaqui	171931.7	2017	4304	10956	1883683880	Rural
04305	Río Hurtado	182027.2	2017	4305	4278	778712384	Rural
05101	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05102	Casablanca	268917.1	2017	5102	26867	7224996933	Rural
05105	Puchuncaví	279614.4	2017	5105	18546	5185728335	Rural
05107	Quintero	334628.2	2017	5107	31923	10682335196	Rural
05301	Los Andes	324402.1	2017	5301	66708	21640215030	Rural
05302	Calle Larga	242743.8	2017	5302	14832	3600375502	Rural
05303	Rinconada	326532.5	2017	5303	10207	3332917471	Rural
05304	San Esteban	223168.6	2017	5304	18855	4207844130	Rural
05401	La Ligua	181468.0	2017	5401	35390	6422154059	Rural
05402	Cabildo	231277.8	2017	5402	19388	4484014285	Rural
05404	Petorca	298208.9	2017	5404	9826	2930200178	Rural
05405	Zapallar	292882.3	2017	5405	7339	2149463129	Rural
05501	Quillota	220926.8	2017	5501	90517	19997628209	Rural
05502	Calera	226906.2	2017	5502	50554	11471016698	Rural
05503	Hijuelas	253739.9	2017	5503	17988	4564273201	Rural
05504	La Cruz	291124.1	2017	5504	22098	6433259569	Rural
05506	Nogales	264475.3	2017	5506	22120	5850194593	Rural
05601	San Antonio	266331.2	2017	5601	91350	24329353815	Rural

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
08101	8101292020	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101252025	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302005	10	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302008	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101292901	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302001	8	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101282901	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101292012	4	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101312017	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101312004	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302019	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302021	12	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302024	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101322025	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302901	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101312013	16	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101312014	30	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302011	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08102	8102052001	4	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102062004	2	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102062005	44	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102092010	13	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102112008	6	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102102011	15	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102072005	46	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102062009	52	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102102007	12	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08103	8103062006	1	2017	NA	NA	NA	NA	NA	NA	NA
08103	8103062901	3	2017	NA	NA	NA	NA	NA	NA	NA
08104	8104012005	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012037	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012043	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012023	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012029	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104022040	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104022901	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032004	9	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012052	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032017	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032019	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032028	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032012	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032050	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012054	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012901	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032045	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104022036	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042027	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042030	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042042	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042047	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052003	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052010	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052020	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052025	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052027	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032053	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042008	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042012	18	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042014	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052054	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052059	11	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052901	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062001	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062002	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062003	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062013	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062024	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012044	12	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062049	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062051	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062901	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104022001	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052039	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052043	7	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052031	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052032	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062036	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08105	8105022024	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105032009	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105022034	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105032001	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105032003	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105042005	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105032034	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105032039	6	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105012028	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105012034	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105062017	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105062037	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105072004	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105072048	4	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105072053	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105072901	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105082010	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105042003	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105082026	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105042007	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105042023	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105042047	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
08101	8101292020	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101252025	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302005	10	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302008	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101292901	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302001	8	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101282901	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101292012	4	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101312017	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101312004	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302019	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302021	12	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302024	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101322025	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302901	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101312013	16	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101312014	30	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08101	8101302011	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural
08102	8102052001	4	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102062004	2	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102062005	44	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102092010	13	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102112008	6	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102102011	15	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102072005	46	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102062009	52	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08102	8102102007	12	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural
08103	8103062006	1	2017	NA	NA	NA	NA	NA	NA	NA
08103	8103062901	3	2017	NA	NA	NA	NA	NA	NA	NA
08104	8104012005	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012037	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012043	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012023	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012029	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104022040	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104022901	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032004	9	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012052	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032017	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032019	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032028	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032012	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032050	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012054	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012901	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032045	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104022036	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042027	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042030	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042042	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042047	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052003	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052010	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052020	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052025	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052027	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104032053	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042008	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042012	18	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104042014	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052054	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052059	11	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052901	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062001	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062002	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062003	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062013	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062024	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104012044	12	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062049	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062051	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062901	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104022001	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052039	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052043	7	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052031	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104052032	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08104	8104062036	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural
08105	8105022024	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105032009	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105022034	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105032001	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105032003	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105042005	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105032034	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105032039	6	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105012028	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105012034	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105062017	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105062037	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105072004	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105072048	4	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105072053	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105072901	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105082010	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105042003	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105082026	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105042007	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105042023	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural
08105	8105042047	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
8101252025	08101	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	60	0.0002684	08101
8101282901	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	31	0.0001387	08101
8101292012	08101	4	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	86	0.0003847	08101
8101292020	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	61	0.0002728	08101
8101292901	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	21	0.0000939	08101
8101302001	08101	8	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	280	0.0012524	08101
8101302005	08101	10	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	688	0.0030773	08101
8101302008	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	98	0.0004383	08101
8101302011	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	469	0.0020977	08101
8101302019	08101	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	198	0.0008856	08101
8101302021	08101	12	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	401	0.0017936	08101
8101302024	08101	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	88	0.0003936	08101
8101302901	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	16	0.0000716	08101
8101312004	08101	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	63	0.0002818	08101
8101312013	08101	16	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	1045	0.0046741	08101
8101312014	08101	30	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	534	0.0023885	08101
8101312017	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	47	0.0002102	08101
8101322025	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	100	0.0004473	08101
8102052001	08102	4	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	62	0.0005333	08102
8102062004	08102	2	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	29	0.0002494	08102
8102062005	08102	44	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	350	0.0030104	08102
8102062009	08102	52	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	382	0.0032857	08102
8102072005	08102	46	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	624	0.0053672	08102
8102092010	08102	13	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	298	0.0025632	08102
8102102007	08102	12	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	744	0.0063993	08102
8102102011	08102	15	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	503	0.0043264	08102
8102112008	08102	6	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	104	0.0008945	08102
8103062006	08103	1	2017	NA	NA	NA	NA	NA	NA	NA	65	0.0007564	08103
8103062901	08103	3	2017	NA	NA	NA	NA	NA	NA	NA	10	0.0001164	08103
8104012005	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	27	0.0025414	08104
8104012023	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	163	0.0153426	08104
8104012029	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	45	0.0042357	08104
8104012037	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	162	0.0152485	08104
8104012043	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	68	0.0064006	08104
8104012044	08104	12	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	214	0.0201431	08104
8104012052	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	93	0.0087538	08104
8104012054	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	43	0.0040474	08104
8104012901	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	14	0.0013178	08104
8104022001	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	42	0.0039533	08104
8104022036	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	60	0.0056476	08104
8104022040	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	70	0.0065889	08104
8104022901	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	11	0.0010354	08104
8104032004	08104	9	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	175	0.0164721	08104
8104032012	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	26	0.0024473	08104
8104032017	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	19	0.0017884	08104
8104032019	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	21	0.0019767	08104
8104032028	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	174	0.0163780	08104
8104032045	08104	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	244	0.0229669	08104
8104032050	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	67	0.0063065	08104
8104032053	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	58	0.0054593	08104
8104042008	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	21	0.0019767	08104
8104042012	08104	18	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	745	0.0701242	08104
8104042014	08104	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	118	0.0111069	08104
8104042027	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	18	0.0016943	08104
8104042030	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	42	0.0039533	08104
8104042042	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	89	0.0083773	08104
8104042047	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	59	0.0055535	08104
8104052003	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	34	0.0032003	08104
8104052010	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	61	0.0057417	08104
8104052020	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	16	0.0015060	08104
8104052025	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	27	0.0025414	08104
8104052027	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	15	0.0014119	08104
8104052031	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	61	0.0057417	08104
8104052032	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	47	0.0044239	08104
8104052039	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	181	0.0170369	08104
8104052043	08104	7	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	291	0.0273908	08104
8104052054	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	124	0.0116717	08104
8104052059	08104	11	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	115	0.0108245	08104
8104052901	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	87	0.0081890	08104
8104062001	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	50	0.0047063	08104
8104062002	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	22	0.0020708	08104
8104062003	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	17	0.0016002	08104
8104062013	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	282	0.0265437	08104
8104062024	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	141	0.0132718	08104
8104062036	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	36	0.0033886	08104
8104062049	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	499	0.0469691	08104
8104062051	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	70	0.0065889	08104
8104062901	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	123	0.0115776	08104
8105012014	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	25	0.0010274	08105
8105012028	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	137	0.0056302	08105
8105012034	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	50	0.0020548	08105
8105022024	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	10	0.0004110	08105
8105022034	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	38	0.0015617	08105
8105032001	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	18	0.0007397	08105
8105032003	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	14	0.0005754	08105
8105032009	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	26	0.0010685	08105
8105032034	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	32	0.0013151	08105
8105032039	08105	6	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	94	0.0038631	08105
8105042003	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	23	0.0009452	08105
8105042005	08105	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	76	0.0031233	08105
8105042007	08105	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	70	0.0028768	08105
8105042023	08105	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	65	0.0026713	08105
8105042047	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	7	0.0002877	08105
8105042050	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	19	0.0007808	08105
8105062017	08105	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	96	0.0039453	08105
8105062037	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	38	0.0015617	08105
8105072004	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	42	0.0017261	08105
8105072048	08105	4	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	49	0.0020137	08105
8105072053	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	91	0.0037398	08105
8105072901	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	56	0.0023014	08105

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
8101252025	08101	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	60	0.0002684	08101	13210846
8101282901	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	31	0.0001387	08101	6825604
8101292012	08101	4	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	86	0.0003847	08101	18935547
8101292020	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	61	0.0002728	08101	13431027
8101292901	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	21	0.0000939	08101	4623796
8101302001	08101	8	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	280	0.0012524	08101	61650617
8101302005	08101	10	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	688	0.0030773	08101	151484373
8101302008	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	98	0.0004383	08101	21577716
8101302011	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	469	0.0020977	08101	103264783
8101302019	08101	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	198	0.0008856	08101	43595793
8101302021	08101	12	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	401	0.0017936	08101	88292491
8101302024	08101	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	88	0.0003936	08101	19375908
8101302901	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	16	0.0000716	08101	3522892
8101312004	08101	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	63	0.0002818	08101	13871389
8101312013	08101	16	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	1045	0.0046741	08101	230088909
8101312014	08101	30	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	534	0.0023885	08101	117576534
8101312017	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	47	0.0002102	08101	10348496
8101322025	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	100	0.0004473	08101	22018077
8102052001	08102	4	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	62	0.0005333	08102	16514777
8102062004	08102	2	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	29	0.0002494	08102	7724654
8102062005	08102	44	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	350	0.0030104	08102	93228582
8102062009	08102	52	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	382	0.0032857	08102	101752338
8102072005	08102	46	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	624	0.0053672	08102	166213243
8102092010	08102	13	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	298	0.0025632	08102	79377478
8102102007	08102	12	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	744	0.0063993	08102	198177329
8102102011	08102	15	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	503	0.0043264	08102	133982791
8102112008	08102	6	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	104	0.0008945	08102	27702207
8103062006	08103	1	2017	NA	NA	NA	NA	NA	NA	NA	65	0.0007564	08103	NA
8103062901	08103	3	2017	NA	NA	NA	NA	NA	NA	NA	10	0.0001164	08103	NA
8104012005	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	27	0.0025414	08104	4275227
8104012023	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	163	0.0153426	08104	25809703
8104012029	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	45	0.0042357	08104	7125378
8104012037	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	162	0.0152485	08104	25651361
8104012043	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	68	0.0064006	08104	10767238
8104012044	08104	12	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	214	0.0201431	08104	33885131
8104012052	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	93	0.0087538	08104	14725781
8104012054	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	43	0.0040474	08104	6808695
8104012901	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	14	0.0013178	08104	2216784
8104022001	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	42	0.0039533	08104	6650353
8104022036	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	60	0.0056476	08104	9500504
8104022040	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	70	0.0065889	08104	11083921
8104022901	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	11	0.0010354	08104	1741759
8104032004	08104	9	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	175	0.0164721	08104	27709804
8104032012	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	26	0.0024473	08104	4116885
8104032017	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	19	0.0017884	08104	3008493
8104032019	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	21	0.0019767	08104	3325176
8104032028	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	174	0.0163780	08104	27551462
8104032045	08104	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	244	0.0229669	08104	38635383
8104032050	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	67	0.0063065	08104	10608896
8104032053	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	58	0.0054593	08104	9183821
8104042008	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	21	0.0019767	08104	3325176
8104042012	08104	18	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	745	0.0701242	08104	117964593
8104042014	08104	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	118	0.0111069	08104	18684325
8104042027	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	18	0.0016943	08104	2850151
8104042030	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	42	0.0039533	08104	6650353
8104042042	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	89	0.0083773	08104	14092414
8104042047	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	59	0.0055535	08104	9342162
8104052003	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	34	0.0032003	08104	5383619
8104052010	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	61	0.0057417	08104	9658846
8104052020	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	16	0.0015060	08104	2533468
8104052025	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	27	0.0025414	08104	4275227
8104052027	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	15	0.0014119	08104	2375126
8104052031	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	61	0.0057417	08104	9658846
8104052032	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	47	0.0044239	08104	7442062
8104052039	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	181	0.0170369	08104	28659854
8104052043	08104	7	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	291	0.0273908	08104	46077445
8104052054	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	124	0.0116717	08104	19634375
8104052059	08104	11	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	115	0.0108245	08104	18209300
8104052901	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	87	0.0081890	08104	13775731
8104062001	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	50	0.0047063	08104	7917087
8104062002	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	22	0.0020708	08104	3483518
8104062003	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	17	0.0016002	08104	2691810
8104062013	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	282	0.0265437	08104	44652369
8104062024	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	141	0.0132718	08104	22326185
8104062036	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	36	0.0033886	08104	5700302
8104062049	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	499	0.0469691	08104	79012526
8104062051	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	70	0.0065889	08104	11083921
8104062901	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	123	0.0115776	08104	19476033
8105012014	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	25	0.0010274	08105	5242978
8105012028	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	137	0.0056302	08105	28731520
8105012034	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	50	0.0020548	08105	10485956
8105022024	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	10	0.0004110	08105	2097191
8105022034	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	38	0.0015617	08105	7969327
8105032001	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	18	0.0007397	08105	3774944
8105032003	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	14	0.0005754	08105	2936068
8105032009	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	26	0.0010685	08105	5452697
8105032034	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	32	0.0013151	08105	6711012
8105032039	08105	6	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	94	0.0038631	08105	19713598
8105042003	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	23	0.0009452	08105	4823540
8105042005	08105	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	76	0.0031233	08105	15938654
8105042007	08105	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	70	0.0028768	08105	14680339
8105042023	08105	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	65	0.0026713	08105	13631743
8105042047	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	7	0.0002877	08105	1468034
8105042050	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	19	0.0007808	08105	3984663
8105062017	08105	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	96	0.0039453	08105	20133036
8105062037	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	38	0.0015617	08105	7969327
8105072004	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	42	0.0017261	08105	8808203
8105072048	08105	4	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	49	0.0020137	08105	10276237
8105072053	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	91	0.0037398	08105	19084441
8105072901	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	56	0.0023014	08105	11744271

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -412526429  -21327794  -14153247    4434759  531550255 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 21602452    2105504   10.26   <2e-16 ***
## Freq.x       2815265     117698   23.92   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 51950000 on 744 degrees of freedom
##   (8 observations deleted due to missingness)
## Multiple R-squared:  0.4347, Adjusted R-squared:  0.434 
## F-statistic: 572.1 on 1 and 744 DF,  p-value: < 2.2e-16

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.433950454967059"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.433950454967059"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.443988432346371"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.548791391143826"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.584518160773268"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.448903258907332"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.563618666620665"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.521653695901717"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 1    cuadrático 0.433950454967059
## 2        cúbico 0.433950454967059
## 3   logarítmico 0.443988432346371
## 6      log-raíz 0.448903258907332
## 8       log-log 0.521653695901717
## 4 raíz cuadrada 0.548791391143826
## 7      raíz-log 0.563618666620665
## 5     raíz-raíz 0.584518160773268
##                                                                     sintaxis
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 5
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = sqrt(multi_pob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12639.7  -1544.2   -427.6   1160.5  11644.4 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1404.66     151.92   9.246   <2e-16 ***
## sqrt(Freq.x)  1776.46      54.85  32.390   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2398 on 744 degrees of freedom
##   (8 observations deleted due to missingness)
## Multiple R-squared:  0.5851, Adjusted R-squared:  0.5845 
## F-statistic:  1049 on 1 and 744 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    1404.663

bb <- linearMod$coefficients[2]
bb

## sqrt(Freq.x) 
##     1776.456

9 Modelo raíz-raíz (raíz-raíz)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.5845 ).

9.1 Diagrama de dispersión sobre raíz-raíz

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=sqrt(h_y_m_comuna_corr_01$Freq.x), y=sqrt(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo raíz-raíz

Observemos nuevamente el resultado sobre raíz-raíz.

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = sqrt(multi_pob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12639.7  -1544.2   -427.6   1160.5  11644.4 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1404.66     151.92   9.246   <2e-16 ***
## sqrt(Freq.x)  1776.46      54.85  32.390   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2398 on 744 degrees of freedom
##   (8 observations deleted due to missingness)
## Multiple R-squared:  0.5851, Adjusted R-squared:  0.5845 
## F-statistic:  1049 on 1 and 744 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = sqrt(Freq.x) , y = sqrt(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = {1404.663}^2 + 2 1404.663 1776.456 \sqrt{X}+ 1776.456^2 X \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- ((aa^2)+(2*(aa*bb)) * sqrt(h_y_m_comuna_corr_01$Freq.x)+((bb^2)*h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
8101252025	08101	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	60	0.0002684	08101	13210846	33132395
8101282901	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	31	0.0001387	08101	6825604	20084521
8101292012	08101	4	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	86	0.0003847	08101	18935547	24577557
8101292020	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	61	0.0002728	08101	13431027	20084521
8101292901	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	21	0.0000939	08101	4623796	15342511
8101302001	08101	8	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	280	0.0012524	08101	61650617	41335130
8101302005	08101	10	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	688	0.0030773	08101	151484373	49312854
8101302008	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	98	0.0004383	08101	21577716	20084521
8101302011	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	469	0.0020977	08101	103264783	20084521
8101302019	08101	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	198	0.0008856	08101	43595793	10119521
8101302021	08101	12	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	401	0.0017936	08101	88292491	57130744
8101302024	08101	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	88	0.0003936	08101	19375908	10119521
8101302901	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	16	0.0000716	08101	3522892	15342511
8101312004	08101	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	63	0.0002818	08101	13871389	33132395
8101312013	08101	16	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	1045	0.0046741	08101	230088909	72428410
8101312014	08101	30	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	534	0.0023885	08101	117576534	123981874
8101312017	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	47	0.0002102	08101	10348496	15342511
8101322025	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	100	0.0004473	08101	22018077	15342511
8102052001	08102	4	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	62	0.0005333	08102	16514777	24577557
8102062004	08102	2	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	29	0.0002494	08102	7724654	15342511
8102062005	08102	44	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	350	0.0030104	08102	93228582	173932334
8102062009	08102	52	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	382	0.0032857	08102	101752338	202062568
8102072005	08102	46	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	624	0.0053672	08102	166213243	180987935
8102092010	08102	13	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	298	0.0025632	08102	79377478	60992466
8102102007	08102	12	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	744	0.0063993	08102	198177329	57130744
8102102011	08102	15	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	503	0.0043264	08102	133982791	68638718
8102112008	08102	6	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	104	0.0008945	08102	27702207	33132395
8103062006	08103	1	2017	NA	NA	NA	NA	NA	NA	NA	65	0.0007564	08103	NA	10119521
8103062901	08103	3	2017	NA	NA	NA	NA	NA	NA	NA	10	0.0001164	08103	NA	20084521
8104012005	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	27	0.0025414	08104	4275227	15342511
8104012023	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	163	0.0153426	08104	25809703	20084521
8104012029	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	45	0.0042357	08104	7125378	10119521
8104012037	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	162	0.0152485	08104	25651361	28911486
8104012043	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	68	0.0064006	08104	10767238	10119521
8104012044	08104	12	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	214	0.0201431	08104	33885131	57130744
8104012052	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	93	0.0087538	08104	14725781	15342511
8104012054	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	43	0.0040474	08104	6808695	28911486
8104012901	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	14	0.0013178	08104	2216784	15342511
8104022001	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	42	0.0039533	08104	6650353	10119521
8104022036	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	60	0.0056476	08104	9500504	10119521
8104022040	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	70	0.0065889	08104	11083921	28911486
8104022901	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	11	0.0010354	08104	1741759	10119521
8104032004	08104	9	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	175	0.0164721	08104	27709804	45347187
8104032012	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	26	0.0024473	08104	4116885	15342511
8104032017	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	19	0.0017884	08104	3008493	15342511
8104032019	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	21	0.0019767	08104	3325176	10119521
8104032028	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	174	0.0163780	08104	27551462	20084521
8104032045	08104	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	244	0.0229669	08104	38635383	24577557
8104032050	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	67	0.0063065	08104	10608896	10119521
8104032053	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	58	0.0054593	08104	9183821	15342511
8104042008	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	21	0.0019767	08104	3325176	10119521
8104042012	08104	18	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	745	0.0701242	08104	117964593	79950937
8104042014	08104	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	118	0.0111069	08104	18684325	24577557
8104042027	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	18	0.0016943	08104	2850151	20084521
8104042030	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	42	0.0039533	08104	6650353	10119521
8104042042	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	89	0.0083773	08104	14092414	15342511
8104042047	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	59	0.0055535	08104	9342162	10119521
8104052003	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	34	0.0032003	08104	5383619	15342511
8104052010	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	61	0.0057417	08104	9658846	10119521
8104052020	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	16	0.0015060	08104	2533468	10119521
8104052025	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	27	0.0025414	08104	4275227	15342511
8104052027	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	15	0.0014119	08104	2375126	10119521
8104052031	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	61	0.0057417	08104	9658846	20084521
8104052032	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	47	0.0044239	08104	7442062	10119521
8104052039	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	181	0.0170369	08104	28659854	33132395
8104052043	08104	7	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	291	0.0273908	08104	46077445	37267663
8104052054	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	124	0.0116717	08104	19634375	41335130
8104052059	08104	11	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	115	0.0108245	08104	18209300	53238942
8104052901	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	87	0.0081890	08104	13775731	41335130
8104062001	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	50	0.0047063	08104	7917087	15342511
8104062002	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	22	0.0020708	08104	3483518	10119521
8104062003	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	17	0.0016002	08104	2691810	10119521
8104062013	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	282	0.0265437	08104	44652369	15342511
8104062024	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	141	0.0132718	08104	22326185	15342511
8104062036	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	36	0.0033886	08104	5700302	20084521
8104062049	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	499	0.0469691	08104	79012526	33132395
8104062051	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	70	0.0065889	08104	11083921	41335130
8104062901	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	123	0.0115776	08104	19476033	33132395
8105012014	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	25	0.0010274	08105	5242978	20084521
8105012028	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	137	0.0056302	08105	28731520	20084521
8105012034	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	50	0.0020548	08105	10485956	10119521
8105022024	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	10	0.0004110	08105	2097191	20084521
8105022034	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	38	0.0015617	08105	7969327	10119521
8105032001	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	18	0.0007397	08105	3774944	15342511
8105032003	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	14	0.0005754	08105	2936068	15342511
8105032009	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	26	0.0010685	08105	5452697	10119521
8105032034	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	32	0.0013151	08105	6711012	15342511
8105032039	08105	6	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	94	0.0038631	08105	19713598	33132395
8105042003	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	23	0.0009452	08105	4823540	15342511
8105042005	08105	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	76	0.0031233	08105	15938654	37267663
8105042007	08105	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	70	0.0028768	08105	14680339	28911486
8105042023	08105	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	65	0.0026713	08105	13631743	28911486
8105042047	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	7	0.0002877	08105	1468034	15342511
8105042050	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	19	0.0007808	08105	3984663	20084521
8105062017	08105	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	96	0.0039453	08105	20133036	37267663
8105062037	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	38	0.0015617	08105	7969327	20084521
8105072004	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	42	0.0017261	08105	8808203	15342511
8105072048	08105	4	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	49	0.0020137	08105	10276237	24577557
8105072053	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	91	0.0037398	08105	19084441	15342511
8105072901	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	56	0.0023014	08105	11744271	20084521

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
8101252025	08101	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	60	0.0002684	08101	13210846	33132395	552206.58
8101282901	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	31	0.0001387	08101	6825604	20084521	647887.77
8101292012	08101	4	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	86	0.0003847	08101	18935547	24577557	285785.55
8101292020	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	61	0.0002728	08101	13431027	20084521	329254.44
8101292901	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	21	0.0000939	08101	4623796	15342511	730595.77
8101302001	08101	8	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	280	0.0012524	08101	61650617	41335130	147625.47
8101302005	08101	10	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	688	0.0030773	08101	151484373	49312854	71675.66
8101302008	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	98	0.0004383	08101	21577716	20084521	204944.09
8101302011	08101	3	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	469	0.0020977	08101	103264783	20084521	42824.14
8101302019	08101	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	198	0.0008856	08101	43595793	10119521	51108.69
8101302021	08101	12	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	401	0.0017936	08101	88292491	57130744	142470.68
8101302024	08101	1	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	88	0.0003936	08101	19375908	10119521	114994.56
8101302901	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	16	0.0000716	08101	3522892	15342511	958906.95
8101312004	08101	6	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	63	0.0002818	08101	13871389	33132395	525911.03
8101312013	08101	16	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	1045	0.0046741	08101	230088909	72428410	69309.48
8101312014	08101	30	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	534	0.0023885	08101	117576534	123981874	232175.79
8101312017	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	47	0.0002102	08101	10348496	15342511	326436.41
8101322025	08101	2	2017	Concepción	220180.8	2017	8101	223574	49226696471	Rural	100	0.0004473	08101	22018077	15342511	153425.11
8102052001	08102	4	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	62	0.0005333	08102	16514777	24577557	396412.21
8102062004	08102	2	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	29	0.0002494	08102	7724654	15342511	529052.11
8102062005	08102	44	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	350	0.0030104	08102	93228582	173932334	496949.53
8102062009	08102	52	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	382	0.0032857	08102	101752338	202062568	528959.60
8102072005	08102	46	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	624	0.0053672	08102	166213243	180987935	290044.77
8102092010	08102	13	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	298	0.0025632	08102	79377478	60992466	204672.70
8102102007	08102	12	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	744	0.0063993	08102	198177329	57130744	76788.63
8102102011	08102	15	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	503	0.0043264	08102	133982791	68638718	136458.68
8102112008	08102	6	2017	Coronel	266367.4	2017	8102	116262	30968404026	Rural	104	0.0008945	08102	27702207	33132395	318580.72
8103062006	08103	1	2017	NA	NA	NA	NA	NA	NA	NA	65	0.0007564	08103	NA	10119521	NA
8103062901	08103	3	2017	NA	NA	NA	NA	NA	NA	NA	10	0.0001164	08103	NA	20084521	NA
8104012005	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	27	0.0025414	08104	4275227	15342511	568241.16
8104012023	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	163	0.0153426	08104	25809703	20084521	123217.92
8104012029	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	45	0.0042357	08104	7125378	10119521	224878.25
8104012037	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	162	0.0152485	08104	25651361	28911486	178465.96
8104012043	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	68	0.0064006	08104	10767238	10119521	148816.49
8104012044	08104	12	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	214	0.0201431	08104	33885131	57130744	266966.09
8104012052	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	93	0.0087538	08104	14725781	15342511	164973.24
8104012054	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	43	0.0040474	08104	6808695	28911486	672360.13
8104012901	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	14	0.0013178	08104	2216784	15342511	1095893.66
8104022001	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	42	0.0039533	08104	6650353	10119521	240940.98
8104022036	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	60	0.0056476	08104	9500504	10119521	168658.69
8104022040	08104	5	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	70	0.0065889	08104	11083921	28911486	413021.22
8104022901	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	11	0.0010354	08104	1741759	10119521	919956.48
8104032004	08104	9	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	175	0.0164721	08104	27709804	45347187	259126.78
8104032012	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	26	0.0024473	08104	4116885	15342511	590096.58
8104032017	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	19	0.0017884	08104	3008493	15342511	807500.59
8104032019	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	21	0.0019767	08104	3325176	10119521	481881.97
8104032028	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	174	0.0163780	08104	27551462	20084521	115428.28
8104032045	08104	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	244	0.0229669	08104	38635383	24577557	100727.69
8104032050	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	67	0.0063065	08104	10608896	10119521	151037.63
8104032053	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	58	0.0054593	08104	9183821	15342511	264526.05
8104042008	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	21	0.0019767	08104	3325176	10119521	481881.97
8104042012	08104	18	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	745	0.0701242	08104	117964593	79950937	107316.69
8104042014	08104	4	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	118	0.0111069	08104	18684325	24577557	208284.38
8104042027	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	18	0.0016943	08104	2850151	20084521	1115806.72
8104042030	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	42	0.0039533	08104	6650353	10119521	240940.98
8104042042	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	89	0.0083773	08104	14092414	15342511	172387.77
8104042047	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	59	0.0055535	08104	9342162	10119521	171517.31
8104052003	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	34	0.0032003	08104	5383619	15342511	451250.33
8104052010	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	61	0.0057417	08104	9658846	10119521	165893.79
8104052020	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	16	0.0015060	08104	2533468	10119521	632470.08
8104052025	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	27	0.0025414	08104	4275227	15342511	568241.16
8104052027	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	15	0.0014119	08104	2375126	10119521	674634.75
8104052031	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	61	0.0057417	08104	9658846	20084521	329254.44
8104052032	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	47	0.0044239	08104	7442062	10119521	215308.96
8104052039	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	181	0.0170369	08104	28659854	33132395	183051.90
8104052043	08104	7	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	291	0.0273908	08104	46077445	37267663	128067.57
8104052054	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	124	0.0116717	08104	19634375	41335130	333347.83
8104052059	08104	11	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	115	0.0108245	08104	18209300	53238942	462947.32
8104052901	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	87	0.0081890	08104	13775731	41335130	475116.44
8104062001	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	50	0.0047063	08104	7917087	15342511	306850.22
8104062002	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	22	0.0020708	08104	3483518	10119521	459978.24
8104062003	08104	1	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	17	0.0016002	08104	2691810	10119521	595265.96
8104062013	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	282	0.0265437	08104	44652369	15342511	54406.07
8104062024	08104	2	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	141	0.0132718	08104	22326185	15342511	108812.14
8104062036	08104	3	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	36	0.0033886	08104	5700302	20084521	557903.36
8104062049	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	499	0.0469691	08104	79012526	33132395	66397.58
8104062051	08104	8	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	70	0.0065889	08104	11083921	41335130	590501.86
8104062901	08104	6	2017	Florida	158341.7	2017	8104	10624	1682222596	Rural	123	0.0115776	08104	19476033	33132395	269369.06
8105012014	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	25	0.0010274	08105	5242978	20084521	803380.84
8105012028	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	137	0.0056302	08105	28731520	20084521	146602.34
8105012034	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	50	0.0020548	08105	10485956	10119521	202390.43
8105022024	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	10	0.0004110	08105	2097191	20084521	2008452.10
8105022034	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	38	0.0015617	08105	7969327	10119521	266303.19
8105032001	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	18	0.0007397	08105	3774944	15342511	852361.73
8105032003	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	14	0.0005754	08105	2936068	15342511	1095893.66
8105032009	08105	1	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	26	0.0010685	08105	5452697	10119521	389212.36
8105032034	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	32	0.0013151	08105	6711012	15342511	479453.47
8105032039	08105	6	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	94	0.0038631	08105	19713598	33132395	352472.28
8105042003	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	23	0.0009452	08105	4823540	15342511	667065.70
8105042005	08105	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	76	0.0031233	08105	15938654	37267663	490363.99
8105042007	08105	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	70	0.0028768	08105	14680339	28911486	413021.22
8105042023	08105	5	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	65	0.0026713	08105	13631743	28911486	444792.09
8105042047	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	7	0.0002877	08105	1468034	15342511	2191787.31
8105042050	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	19	0.0007808	08105	3984663	20084521	1057080.05
8105062017	08105	7	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	96	0.0039453	08105	20133036	37267663	388204.83
8105062037	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	38	0.0015617	08105	7969327	20084521	528540.03
8105072004	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	42	0.0017261	08105	8808203	15342511	365297.89
8105072048	08105	4	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	49	0.0020137	08105	10276237	24577557	501582.80
8105072053	08105	2	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	91	0.0037398	08105	19084441	15342511	168599.02
8105072901	08105	3	2017	Hualqui	209719.1	2017	8105	24333	5103095511	Rural	56	0.0023014	08105	11744271	20084521	358652.16

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_rural_nivel_nacional_17_r08.rds")

---

R-09

Urbano

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 1)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15201	1	1	1	1767	1	1	1	2	1	3	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	2	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	3	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	4	1	1	2	3	1	99	1	2	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	5	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	6	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	7	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	8	1	1	3	3	1	99	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	9	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	10	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	11	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	12	1	1	4	3	3	3	4	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	13	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	14	1	1	2	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	15	1	1	2	1	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	16	1	1	5	1	5	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	17	1	1	5	3	1	3	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	18	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	19	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	20	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	21	4	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	22	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	23	1	1	5	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	24	1	1	3	1	1	6	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	25	1	1	5	6	1	1	4	2	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	26	4	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	27	1	1	5	99	3	3	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	28	1	1	6	3	2	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	29	1	1	2	3	1	99	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	30	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	31	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	32	1	1	3	1	1	2	1	1	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	33	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	34	3	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	35	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	36	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	37	1	1	5	3	3	1	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	38	1	1	1	1	4	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	39	1	1	5	3	4	3	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	40	1	1	5	3	2	1	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	41	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	42	1	1	2	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	43	1	1	1	1	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	44	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	45	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	46	1	1	5	6	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	47	1	1	5	3	4	0	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	48	1	1	3	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	49	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	50	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	51	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	52	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	53	1	1	1	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	54	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	55	1	1	2	2	1	4	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	56	8	1	98	98	98	98	98	98	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	57	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	58	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	59	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	60	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	61	1	1	2	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	62	1	1	5	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	63	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	64	1	1	5	3	2	4	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	65	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	66	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	67	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	68	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	69	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	70	1	1	1	3	3	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	71	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	72	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	73	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	74	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	75	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	76	1	1	5	3	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	77	1	1	3	6	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	78	5	1	3	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	79	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	80	1	1	2	1	1	3	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	81	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	82	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	83	1	1	5	3	2	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	84	4	1	5	3	2	0	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	85	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	86	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	87	1	1	3	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	88	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	89	5	1	4	3	2	1	2	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	90	1	1	5	3	3	2	1	3	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	91	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	92	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	93	5	1	4	3	5	1	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	94	1	1	5	3	3	99	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	95	1	1	5	3	2	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	96	1	1	2	3	4	6	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	97	1	1	5	1	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	98	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	99	1	1	2	7	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	100	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 9:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 9)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	9101011001	35	2017	09101
2	9101011002	362	2017	09101
3	9101011003	176	2017	09101
4	9101011004	21	2017	09101
5	9101011005	257	2017	09101
6	9101021001	217	2017	09101
7	9101021002	209	2017	09101
8	9101021003	68	2017	09101
9	9101021004	37	2017	09101
10	9101031001	95	2017	09101
11	9101031002	93	2017	09101
12	9101031003	18	2017	09101
13	9101031004	22	2017	09101
14	9101031005	20	2017	09101
15	9101041001	88	2017	09101
16	9101041002	49	2017	09101
17	9101041003	107	2017	09101
18	9101051001	78	2017	09101
19	9101051002	13	2017	09101
20	9101051003	32	2017	09101
21	9101051004	40	2017	09101
22	9101051005	34	2017	09101
23	9101051006	27	2017	09101
24	9101051007	36	2017	09101
25	9101061001	213	2017	09101
26	9101061002	65	2017	09101
27	9101061003	33	2017	09101
28	9101061004	154	2017	09101
29	9101061005	47	2017	09101
30	9101061006	57	2017	09101
31	9101071001	52	2017	09101
32	9101071002	144	2017	09101
33	9101081001	32	2017	09101
34	9101081002	58	2017	09101
35	9101081003	56	2017	09101
36	9101081004	47	2017	09101
37	9101081005	14	2017	09101
38	9101081006	26	2017	09101
39	9101091001	1117	2017	09101
40	9101091002	81	2017	09101
41	9101091003	218	2017	09101
42	9101091004	193	2017	09101
43	9101091005	300	2017	09101
44	9101101001	142	2017	09101
45	9101101002	222	2017	09101
46	9101101003	56	2017	09101
47	9101101004	25	2017	09101
48	9101101005	75	2017	09101
49	9101101006	113	2017	09101
50	9101101007	54	2017	09101
51	9101101008	197	2017	09101
52	9101101009	49	2017	09101
53	9101111001	153	2017	09101
54	9101111002	60	2017	09101
55	9101111003	10	2017	09101
56	9101121001	106	2017	09101
57	9101121002	40	2017	09101
58	9101121003	83	2017	09101
59	9101141001	79	2017	09101
60	9101141002	22	2017	09101
61	9101141003	75	2017	09101
62	9101141004	33	2017	09101
63	9101161001	122	2017	09101
64	9101161002	42	2017	09101
65	9101161003	41	2017	09101
66	9101161004	56	2017	09101
67	9101171001	136	2017	09101
68	9101171002	372	2017	09101
69	9101171003	189	2017	09101
70	9101171004	72	2017	09101
71	9101181001	1071	2017	09101
72	9101181002	93	2017	09101
73	9101181003	144	2017	09101
74	9101181004	392	2017	09101
75	9101181005	177	2017	09101
76	9101181006	536	2017	09101
77	9101191001	33	2017	09101
78	9101191002	82	2017	09101
79	9101191003	210	2017	09101
80	9101191004	88	2017	09101
81	9101191005	42	2017	09101
82	9101191006	463	2017	09101
83	9101991999	32	2017	09101
341	9102011001	40	2017	09102
342	9102021001	83	2017	09102
343	9102021002	15	2017	09102
344	9102081001	26	2017	09102
345	9102991999	1	2017	09102
603	9103011001	17	2017	09103
604	9103021001	10	2017	09103
605	9103021002	17	2017	09103
606	9103101001	5	2017	09103
607	9103991999	2	2017	09103
865	9104011001	32	2017	09104
1123	9105011001	22	2017	09105
1124	9105011002	19	2017	09105
1125	9105021001	13	2017	09105
1126	9105061001	1	2017	09105
1127	9105091001	4	2017	09105
1128	9105991999	1	2017	09105

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_urbano_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
01107	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
01401	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
01405	Pica	330061.1	2017	1405	9296	3068247619	Urbano
02101	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02102	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02104	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02201	Calama	416281.1	2017	2201	165731	68990679686	Urbano
02203	San Pedro de Atacama	437934.7	2017	2203	10996	4815529626	Urbano
02301	Tocopilla	271720.8	2017	2301	25186	6843559467	Urbano
02302	María Elena	466266.9	2017	2302	6457	3010685220	Urbano
03101	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
03102	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
03103	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
03201	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
03202	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
03301	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
03303	Freirina	289049.9	2017	3303	7041	2035200054	Urbano
03304	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
04101	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04102	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04103	Andacollo	251267.7	2017	4103	11044	2775000288	Urbano
04104	La Higuera	214257.0	2017	4104	4241	908664019	Urbano
04106	Vicuña	245957.4	2017	4106	27771	6830481918	Urbano
04201	Illapel	270316.5	2017	4201	30848	8338722128	Urbano
04202	Canela	233397.3	2017	4202	9093	2122281844	Urbano
04203	Los Vilos	282415.6	2017	4203	21382	6038609501	Urbano
04204	Salamanca	262056.9	2017	4204	29347	7690585032	Urbano
04301	Ovalle	274771.4	2017	4301	111272	30574361012	Urbano
04302	Combarbalá	228990.4	2017	4302	13322	3050610572	Urbano
04303	Monte Patria	225369.1	2017	4303	30751	6930326684	Urbano
04304	Punitaqui	212496.1	2017	4304	10956	2328107498	Urbano
05101	Valparaíso	297929.0	2017	5101	296655	88382118059	Urbano
05102	Casablanca	341641.8	2017	5102	26867	9178890241	Urbano
05103	Concón	318496.3	2017	5103	42152	13425257057	Urbano
05105	Puchuncaví	296035.5	2017	5105	18546	5490274928	Urbano
05107	Quintero	308224.7	2017	5107	31923	9839456903	Urbano
05109	Viña del Mar	337006.1	2017	5109	334248	112643604611	Urbano
05301	Los Andes	339720.2	2017	5301	66708	22662055502	Urbano
05302	Calle Larga	246387.3	2017	5302	14832	3654416747	Urbano
05303	Rinconada	273904.7	2017	5303	10207	2795744821	Urbano
05304	San Esteban	219571.6	2017	5304	18855	4140022481	Urbano
05401	La Ligua	250134.4	2017	5401	35390	8852256241	Urbano
05402	Cabildo	262745.9	2017	5402	19388	5094117762	Urbano
05403	Papudo	294355.2	2017	5403	6356	1870921373	Urbano
05404	Petorca	237510.8	2017	5404	9826	2333781007	Urbano
05405	Zapallar	294389.2	2017	5405	7339	2160521991	Urbano
05501	Quillota	286029.5	2017	5501	90517	25890529852	Urbano
05502	Calera	277181.9	2017	5502	50554	14012652087	Urbano
05503	Hijuelas	254094.0	2017	5503	17988	4570642363	Urbano

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
09101	9101011001	35	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101011002	362	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101011003	176	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101011004	21	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101011005	257	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101021001	217	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101021002	209	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101021003	68	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101021004	37	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101031001	95	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101031002	93	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101031003	18	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101031004	22	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101031005	20	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101041001	88	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101041002	49	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101041003	107	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101051001	78	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101051002	13	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101051003	32	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101051004	40	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101051005	34	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101051006	27	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101051007	36	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101061001	213	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101061002	65	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101061003	33	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101061004	154	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101061005	47	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101061006	57	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101071001	52	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101071002	144	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101081001	32	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101081002	58	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101081003	56	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101081004	47	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101081005	14	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101081006	26	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101091001	1117	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101091002	81	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101091003	218	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101091004	193	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101091005	300	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101101001	142	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101101002	222	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101101003	56	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101101004	25	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101101005	75	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101101006	113	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101101007	54	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101101008	197	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101101009	49	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101111001	153	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101111002	60	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101111003	10	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101121001	106	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101121002	40	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101121003	83	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101141001	79	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101141002	22	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101141003	75	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101141004	33	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101161001	122	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101161002	42	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101161003	41	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101161004	56	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101171001	136	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101171002	372	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101171003	189	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101171004	72	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101181001	1071	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101181002	93	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101181003	144	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101181004	392	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101181005	177	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101181006	536	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101191001	33	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101191002	82	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101191003	210	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101191004	88	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101191005	42	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101191006	463	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101991999	32	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09102	9102011001	40	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano
09102	9102021001	83	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano
09102	9102021002	15	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano
09102	9102081001	26	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano
09102	9102991999	1	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano
09103	9103011001	17	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano
09103	9103021001	10	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano
09103	9103021002	17	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano
09103	9103101001	5	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano
09103	9103991999	2	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano
09104	9104011001	32	2017	Curarrehue	204180.7	2017	9104	7489	1529109215	Urbano
09105	9105011001	22	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano
09105	9105011002	19	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano
09105	9105021001	13	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano
09105	9105061001	1	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano
09105	9105091001	4	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano
09105	9105991999	1	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
09101	9101011001	35	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101011002	362	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101011003	176	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101011004	21	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101011005	257	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101021001	217	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101021002	209	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101021003	68	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101021004	37	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101031001	95	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101031002	93	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101031003	18	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101031004	22	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101031005	20	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101041001	88	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101041002	49	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101041003	107	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101051001	78	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101051002	13	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101051003	32	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101051004	40	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101051005	34	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101051006	27	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101051007	36	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101061001	213	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101061002	65	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101061003	33	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101061004	154	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101061005	47	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101061006	57	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101071001	52	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101071002	144	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101081001	32	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101081002	58	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101081003	56	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101081004	47	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101081005	14	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101081006	26	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101091001	1117	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101091002	81	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101091003	218	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101091004	193	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101091005	300	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101101001	142	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101101002	222	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101101003	56	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101101004	25	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101101005	75	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101101006	113	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101101007	54	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101101008	197	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101101009	49	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101111001	153	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101111002	60	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101111003	10	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101121001	106	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101121002	40	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101121003	83	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101141001	79	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101141002	22	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101141003	75	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101141004	33	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101161001	122	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101161002	42	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101161003	41	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101161004	56	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101171001	136	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101171002	372	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101171003	189	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101171004	72	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101181001	1071	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101181002	93	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101181003	144	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101181004	392	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101181005	177	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101181006	536	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101191001	33	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101191002	82	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101191003	210	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101191004	88	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101191005	42	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101191006	463	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09101	9101991999	32	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano
09102	9102011001	40	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano
09102	9102021001	83	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano
09102	9102021002	15	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano
09102	9102081001	26	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano
09102	9102991999	1	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano
09103	9103011001	17	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano
09103	9103021001	10	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano
09103	9103021002	17	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano
09103	9103101001	5	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano
09103	9103991999	2	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano
09104	9104011001	32	2017	Curarrehue	204180.7	2017	9104	7489	1529109215	Urbano
09105	9105011001	22	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano
09105	9105011002	19	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano
09105	9105021001	13	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano
09105	9105061001	1	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano
09105	9105091001	4	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano
09105	9105991999	1	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
9101011001	09101	35	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2220	0.0078608	09101
9101011002	09101	362	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3909	0.0138413	09101
9101011003	09101	176	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2067	0.0073190	09101
9101011004	09101	21	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	662	0.0023441	09101
9101011005	09101	257	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2592	0.0091780	09101
9101021001	09101	217	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3196	0.0113167	09101
9101021002	09101	209	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2969	0.0105129	09101
9101021003	09101	68	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1480	0.0052405	09101
9101021004	09101	37	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1826	0.0064657	09101
9101031001	09101	95	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2594	0.0091851	09101
9101031002	09101	93	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4200	0.0148717	09101
9101031003	09101	18	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2208	0.0078183	09101
9101031004	09101	22	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2390	0.0084627	09101
9101031005	09101	20	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2992	0.0105943	09101
9101041001	09101	88	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4994	0.0176832	09101
9101041002	09101	49	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4641	0.0164333	09101
9101041003	09101	107	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4375	0.0154914	09101
9101051001	09101	78	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3142	0.0111255	09101
9101051002	09101	13	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2998	0.0106156	09101
9101051003	09101	32	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3078	0.0108989	09101
9101051004	09101	40	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3587	0.0127012	09101
9101051005	09101	34	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2751	0.0097410	09101
9101051006	09101	27	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2499	0.0088487	09101
9101051007	09101	36	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2499	0.0088487	09101
9101061001	09101	213	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3146	0.0111396	09101
9101061002	09101	65	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2322	0.0082219	09101
9101061003	09101	33	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2821	0.0099888	09101
9101061004	09101	154	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5347	0.0189331	09101
9101061005	09101	47	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2768	0.0098012	09101
9101061006	09101	57	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2682	0.0094967	09101
9101071001	09101	52	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1795	0.0063559	09101
9101071002	09101	144	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3047	0.0107891	09101
9101081001	09101	32	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3402	0.0120461	09101
9101081002	09101	58	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3965	0.0140396	09101
9101081003	09101	56	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3304	0.0116991	09101
9101081004	09101	47	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2161	0.0076519	09101
9101081005	09101	14	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2222	0.0078679	09101
9101081006	09101	26	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2903	0.0102792	09101
9101091001	09101	1117	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5474	0.0193828	09101
9101091002	09101	81	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1649	0.0058389	09101
9101091003	09101	218	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3045	0.0107820	09101
9101091004	09101	193	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2668	0.0094471	09101
9101091005	09101	300	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1882	0.0066640	09101
9101101001	09101	142	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5434	0.0192412	09101
9101101002	09101	222	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1589	0.0056265	09101
9101101003	09101	56	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	326	0.0011543	09101
9101101004	09101	25	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3639	0.0128853	09101
9101101005	09101	75	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5033	0.0178213	09101
9101101006	09101	113	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5891	0.0208594	09101
9101101007	09101	54	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5050	0.0178815	09101
9101101008	09101	197	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	6739	0.0238620	09101
9101101009	09101	49	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4395	0.0155622	09101
9101111001	09101	153	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5176	0.0183276	09101
9101111002	09101	60	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4664	0.0165147	09101
9101111003	09101	10	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1407	0.0049820	09101
9101121001	09101	106	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2796	0.0099003	09101
9101121002	09101	40	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2711	0.0095993	09101
9101121003	09101	83	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3947	0.0139759	09101
9101141001	09101	79	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2408	0.0085265	09101
9101141002	09101	22	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3217	0.0113910	09101
9101141003	09101	75	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4707	0.0166670	09101
9101141004	09101	33	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2780	0.0098437	09101
9101161001	09101	122	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3387	0.0119930	09101
9101161002	09101	42	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1966	0.0069614	09101
9101161003	09101	41	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2025	0.0071703	09101
9101161004	09101	56	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2583	0.0091461	09101
9101171001	09101	136	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2682	0.0094967	09101
9101171002	09101	372	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4209	0.0149036	09101
9101171003	09101	189	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2211	0.0078289	09101
9101171004	09101	72	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2954	0.0104598	09101
9101181001	09101	1071	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4459	0.0157888	09101
9101181002	09101	93	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2620	0.0092771	09101
9101181003	09101	144	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2497	0.0088416	09101
9101181004	09101	392	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4641	0.0164333	09101
9101181005	09101	177	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1940	0.0068693	09101
9101181006	09101	536	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2902	0.0102757	09101
9101191001	09101	33	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3430	0.0121452	09101
9101191002	09101	82	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2483	0.0087920	09101
9101191003	09101	210	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5449	0.0192943	09101
9101191004	09101	88	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4595	0.0162704	09101
9101191005	09101	42	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4718	0.0167059	09101
9101191006	09101	463	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2165	0.0076660	09101
9101991999	09101	32	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	868	0.0030735	09101
9102011001	09102	40	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano	2178	0.0887784	09102
9102021001	09102	83	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano	5062	0.2063343	09102
9102021002	09102	15	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano	4085	0.1665104	09102
9102081001	09102	26	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano	1860	0.0758162	09102
9102991999	09102	1	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano	68	0.0027718	09102
9103011001	09103	17	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano	1592	0.0908365	09103
9103021001	09103	10	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano	2075	0.1183955	09103
9103021002	09103	17	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano	3499	0.1996462	09103
9103101001	09103	5	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano	1428	0.0814789	09103
9103991999	09103	2	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano	253	0.0144357	09103
9104011001	09104	32	2017	Curarrehue	204180.7	2017	9104	7489	1529109215	Urbano	2148	0.2868207	09104
9105011001	09105	22	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	2214	0.0899781	09105
9105011002	09105	19	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	3193	0.1297651	09105
9105021001	09105	13	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	2072	0.0842071	09105
9105061001	09105	1	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	36	0.0014631	09105
9105091001	09105	4	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	154	0.0062586	09105
9105991999	09105	1	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	217	0.0088190	09105

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
9101011001	09101	35	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2220	0.0078608	09101	626031418
9101011002	09101	362	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3909	0.0138413	09101	1102322889
9101011003	09101	176	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2067	0.0073190	09101	582886009
9101011004	09101	21	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	662	0.0023441	09101	186681441
9101011005	09101	257	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2592	0.0091780	09101	730933980
9101021001	09101	217	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3196	0.0113167	09101	901259645
9101021002	09101	209	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2969	0.0105129	09101	837246522
9101021003	09101	68	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1480	0.0052405	09101	417354279
9101021004	09101	37	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1826	0.0064657	09101	514924941
9101031001	09101	95	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2594	0.0091851	09101	731497972
9101031002	09101	93	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4200	0.0148717	09101	1184383764
9101031003	09101	18	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2208	0.0078183	09101	622647464
9101031004	09101	22	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2390	0.0084627	09101	673970761
9101031005	09101	20	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2992	0.0105943	09101	843732434
9101041001	09101	88	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4994	0.0176832	09101	1408288694
9101041002	09101	49	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4641	0.0164333	09101	1308744059
9101041003	09101	107	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4375	0.0154914	09101	1233733087
9101051001	09101	78	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3142	0.0111255	09101	886031854
9101051002	09101	13	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2998	0.0106156	09101	845424410
9101051003	09101	32	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3078	0.0108989	09101	867984101
9101051004	09101	40	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3587	0.0127012	09101	1011520133
9101051005	09101	34	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2751	0.0097410	09101	775771365
9101051006	09101	27	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2499	0.0088487	09101	704708339
9101051007	09101	36	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2499	0.0088487	09101	704708339
9101061001	09101	213	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3146	0.0111396	09101	887159838
9101061002	09101	65	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2322	0.0082219	09101	654795024
9101061003	09101	33	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2821	0.0099888	09101	795511095
9101061004	09101	154	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5347	0.0189331	09101	1507833330
9101061005	09101	47	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2768	0.0098012	09101	780565300
9101061006	09101	57	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2682	0.0094967	09101	756313632
9101071001	09101	52	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1795	0.0063559	09101	506183061
9101071002	09101	144	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3047	0.0107891	09101	859242221
9101081001	09101	32	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3402	0.0120461	09101	959350849
9101081002	09101	58	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3965	0.0140396	09101	1118114672
9101081003	09101	56	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3304	0.0116991	09101	931715227
9101081004	09101	47	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2161	0.0076519	09101	609393646
9101081005	09101	14	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2222	0.0078679	09101	626595410
9101081006	09101	26	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2903	0.0102792	09101	818634778
9101091001	09101	1117	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5474	0.0193828	09101	1543646839
9101091002	09101	81	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1649	0.0058389	09101	465011625
9101091003	09101	218	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3045	0.0107820	09101	858678229
9101091004	09101	193	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2668	0.0094471	09101	752365686
9101091005	09101	300	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1882	0.0066640	09101	530716725
9101101001	09101	142	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5434	0.0192412	09101	1532366993
9101101002	09101	222	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1589	0.0056265	09101	448091857
9101101003	09101	56	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	326	0.0011543	09101	91930740
9101101004	09101	25	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3639	0.0128853	09101	1026183932
9101101005	09101	75	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5033	0.0178213	09101	1419286544
9101101006	09101	113	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5891	0.0208594	09101	1661239227
9101101007	09101	54	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5050	0.0178815	09101	1424080478
9101101008	09101	197	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	6739	0.0238620	09101	1900371949
9101101009	09101	49	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4395	0.0155622	09101	1239373010
9101111001	09101	153	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5176	0.0183276	09101	1459611991
9101111002	09101	60	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4664	0.0165147	09101	1315229970
9101111003	09101	10	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1407	0.0049820	09101	396768561
9101121001	09101	106	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2796	0.0099003	09101	788461191
9101121002	09101	40	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2711	0.0095993	09101	764491520
9101121003	09101	83	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3947	0.0139759	09101	1113038742
9101141001	09101	79	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2408	0.0085265	09101	679046691
9101141002	09101	22	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3217	0.0113910	09101	907181564
9101141003	09101	75	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4707	0.0166670	09101	1327355804
9101141004	09101	33	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2780	0.0098437	09101	783949253
9101161001	09101	122	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3387	0.0119930	09101	955120907
9101161002	09101	42	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1966	0.0069614	09101	554404400
9101161003	09101	41	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2025	0.0071703	09101	571042172
9101161004	09101	56	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2583	0.0091461	09101	728396015
9101171001	09101	136	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2682	0.0094967	09101	756313632
9101171002	09101	372	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4209	0.0149036	09101	1186921729
9101171003	09101	189	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2211	0.0078289	09101	623493453
9101171004	09101	72	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2954	0.0104598	09101	833016580
9101181001	09101	1071	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4459	0.0157888	09101	1257420762
9101181002	09101	93	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2620	0.0092771	09101	738829872
9101181003	09101	144	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2497	0.0088416	09101	704144347
9101181004	09101	392	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4641	0.0164333	09101	1308744059
9101181005	09101	177	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1940	0.0068693	09101	547072500
9101181006	09101	536	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2902	0.0102757	09101	818352782
9101191001	09101	33	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3430	0.0121452	09101	967246740
9101191002	09101	82	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2483	0.0087920	09101	700196401
9101191003	09101	210	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5449	0.0192943	09101	1536596935
9101191004	09101	88	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4595	0.0162704	09101	1295772237
9101191005	09101	42	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4718	0.0167059	09101	1330457761
9101191006	09101	463	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2165	0.0076660	09101	610521631
9101991999	09101	32	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	868	0.0030735	09101	244772645
9102011001	09102	40	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano	2178	0.0887784	09102	480886204
9102021001	09102	83	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano	5062	0.2063343	09102	1117651958
9102021002	09102	15	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano	4085	0.1665104	09102	901937623
9102081001	09102	26	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano	1860	0.0758162	09102	410674169
9102991999	09102	1	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano	68	0.0027718	09102	15013894
9103011001	09103	17	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano	1592	0.0908365	09103	357647954
9103021001	09103	10	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano	2075	0.1183955	09103	466155468
9103021002	09103	17	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano	3499	0.1996462	09103	786061678
9103101001	09103	5	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano	1428	0.0814789	09103	320804823
9103991999	09103	2	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano	253	0.0144357	09103	56837269
9104011001	09104	32	2017	Curarrehue	204180.7	2017	9104	7489	1529109215	Urbano	2148	0.2868207	09104	438580130
9105011001	09105	22	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	2214	0.0899781	09105	659385583
9105011002	09105	19	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	3193	0.1297651	09105	950956715
9105021001	09105	13	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	2072	0.0842071	09105	617094367
9105061001	09105	1	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	36	0.0014631	09105	10721717
9105091001	09105	4	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	154	0.0062586	09105	45865122
9105991999	09105	1	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	217	0.0088190	09105	64628126

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -667578781 -236308008  -14189258  198131209 1138462265 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 591140158   26517005  22.293  < 2e-16 ***
## Freq.x        1245434     191166   6.515 3.86e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 358300000 on 255 degrees of freedom
## Multiple R-squared:  0.1427, Adjusted R-squared:  0.1393 
## F-statistic: 42.44 on 1 and 255 DF,  p-value: 3.856e-10

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                
## [1,] "cuadrático" "0.13933429927015"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                
## [1,] "cúbico" "0.13933429927015"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.393476081437449"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.281423408427874"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.292761146977689"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.250587758284861"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.505928001392123"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.569010565052427"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 1    cuadrático  0.13933429927015
## 2        cúbico  0.13933429927015
## 6      log-raíz 0.250587758284861
## 4 raíz cuadrada 0.281423408427874
## 5     raíz-raíz 0.292761146977689
## 3   logarítmico 0.393476081437449
## 7      raíz-log 0.505928001392123
## 8       log-log 0.569010565052427
##                                                                     sintaxis
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 8
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.7047 -0.4182  0.1252  0.4903  1.2081 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.73172    0.13263  133.69   <2e-16 ***
## log(Freq.x)  0.62985    0.03421   18.41   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6858 on 255 degrees of freedom
## Multiple R-squared:  0.5707, Adjusted R-squared:  0.569 
## F-statistic:   339 on 1 and 255 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    17.73172

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##   0.6298511

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.569 ).

9.1 Diagrama de dispersión sobre log-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.7047 -0.4182  0.1252  0.4903  1.2081 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.73172    0.13263  133.69   <2e-16 ***
## log(Freq.x)  0.62985    0.03421   18.41   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6858 on 255 degrees of freedom
## Multiple R-squared:  0.5707, Adjusted R-squared:  0.569 
## F-statistic:   339 on 1 and 255 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = e^{17.73172+0.6298511 \cdot ln{X}} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
9101011001	09101	35	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2220	0.0078608	09101	626031418	471323896
9101011002	09101	362	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3909	0.0138413	09101	1102322889	2053013942
9101011003	09101	176	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2067	0.0073190	09101	582886009	1303542270
9101011004	09101	21	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	662	0.0023441	09101	186681441	341655001
9101011005	09101	257	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2592	0.0091780	09101	730933980	1654571432
9101021001	09101	217	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3196	0.0113167	09101	901259645	1487333045
9101021002	09101	209	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2969	0.0105129	09101	837246522	1452557002
9101021003	09101	68	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1480	0.0052405	09101	417354279	716133935
9101021004	09101	37	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1826	0.0064657	09101	514924941	488112673
9101031001	09101	95	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2594	0.0091851	09101	731497972	884011442
9101031002	09101	93	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4200	0.0148717	09101	1184383764	872243312
9101031003	09101	18	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2208	0.0078183	09101	622647464	310042532
9101031004	09101	22	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2390	0.0084627	09101	673970761	351813830
9101031005	09101	20	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2992	0.0105943	09101	843732434	331315443
9101041001	09101	88	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4994	0.0176832	09101	1408288694	842405218
9101041002	09101	49	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4641	0.0164333	09101	1308744059	582583743
9101041003	09101	107	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4375	0.0154914	09101	1233733087	952787612
9101051001	09101	78	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3142	0.0111255	09101	886031854	780772243
9101051002	09101	13	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2998	0.0106156	09101	845424410	252583487
9101051003	09101	32	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3078	0.0108989	09101	867984101	445458113
9101051004	09101	40	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3587	0.0127012	09101	1011520133	512679261
9101051005	09101	34	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2751	0.0097410	09101	775771365	462796625
9101051006	09101	27	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2499	0.0088487	09101	704708339	400251110
9101051007	09101	36	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2499	0.0088487	09101	704708339	479761446
9101061001	09101	213	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3146	0.0111396	09101	887159838	1470005461
9101061002	09101	65	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2322	0.0082219	09101	654795024	696068486
9101061003	09101	33	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2821	0.0099888	09101	795511095	454175997
9101061004	09101	154	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5347	0.0189331	09101	1507833330	1198391797
9101061005	09101	47	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2768	0.0098012	09101	780565300	567491259
9101061006	09101	57	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2682	0.0094967	09101	756313632	640805469
9101071001	09101	52	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1795	0.0063559	09101	506183061	604801827
9101071002	09101	144	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3047	0.0107891	09101	859242221	1148771058
9101081001	09101	32	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3402	0.0120461	09101	959350849	445458113
9101081002	09101	58	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3965	0.0140396	09101	1118114672	647863573
9101081003	09101	56	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3304	0.0116991	09101	931715227	633701379
9101081004	09101	47	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2161	0.0076519	09101	609393646	567491259
9101081005	09101	14	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2222	0.0078679	09101	626595410	264652811
9101081006	09101	26	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2903	0.0102792	09101	818634778	390849017
9101091001	09101	1117	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5474	0.0193828	09101	1543646839	4174515993
9101091002	09101	81	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1649	0.0058389	09101	465011625	799554160
9101091003	09101	218	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3045	0.0107820	09101	858678229	1491646414
9101091004	09101	193	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2668	0.0094471	09101	752365686	1381488517
9101091005	09101	300	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1882	0.0066640	09101	530716725	1823912665
9101101001	09101	142	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5434	0.0192412	09101	1532366993	1138695690
9101101002	09101	222	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1589	0.0056265	09101	448091857	1508827168
9101101003	09101	56	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	326	0.0011543	09101	91930740	633701379
9101101004	09101	25	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3639	0.0128853	09101	1026183932	381312074
9101101005	09101	75	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5033	0.0178213	09101	1419286544	761720946
9101101006	09101	113	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5891	0.0208594	09101	1661239227	986098306
9101101007	09101	54	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5050	0.0178815	09101	1424080478	619350676
9101101008	09101	197	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	6739	0.0238620	09101	1900371949	1399453815
9101101009	09101	49	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4395	0.0155622	09101	1239373010	582583743
9101111001	09101	153	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5176	0.0183276	09101	1459611991	1193484536
9101111002	09101	60	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4664	0.0165147	09101	1315229970	661846111
9101111003	09101	10	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1407	0.0049820	09101	396768561	214110324
9101121001	09101	106	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2796	0.0099003	09101	788461191	947169324
9101121002	09101	40	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2711	0.0095993	09101	764491520	512679261
9101121003	09101	83	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3947	0.0139759	09101	1113038742	811932538
9101141001	09101	79	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2408	0.0085265	09101	679046691	787062117
9101141002	09101	22	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3217	0.0113910	09101	907181564	351813830
9101141003	09101	75	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4707	0.0166670	09101	1327355804	761720946
9101141004	09101	33	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2780	0.0098437	09101	783949253	454175997
9101161001	09101	122	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3387	0.0119930	09101	955120907	1034862216
9101161002	09101	42	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1966	0.0069614	09101	554404400	528678748
9101161003	09101	41	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2025	0.0071703	09101	571042172	520715120
9101161004	09101	56	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2583	0.0091461	09101	728396015	633701379
9101171001	09101	136	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2682	0.0094967	09101	756313632	1108149422
9101171002	09101	372	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4209	0.0149036	09101	1186921729	2088554388
9101171003	09101	189	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2211	0.0078289	09101	623493453	1363384856
9101171004	09101	72	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2954	0.0104598	09101	833016580	742385388
9101181001	09101	1071	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4459	0.0157888	09101	1257420762	4065394698
9101181002	09101	93	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2620	0.0092771	09101	738829872	872243312
9101181003	09101	144	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2497	0.0088416	09101	704144347	1148771058
9101181004	09101	392	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4641	0.0164333	09101	1308744059	2158592046
9101181005	09101	177	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1940	0.0068693	09101	547072500	1308202363
9101181006	09101	536	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2902	0.0102757	09101	818352782	2628778135
9101191001	09101	33	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3430	0.0121452	09101	967246740	454175997
9101191002	09101	82	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2483	0.0087920	09101	700196401	805757318
9101191003	09101	210	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5449	0.0192943	09101	1536596935	1456930620
9101191004	09101	88	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4595	0.0162704	09101	1295772237	842405218
9101191005	09101	42	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4718	0.0167059	09101	1330457761	528678748
9101191006	09101	463	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2165	0.0076660	09101	610521631	2397207397
9101991999	09101	32	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	868	0.0030735	09101	244772645	445458113
9102011001	09102	40	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano	2178	0.0887784	09102	480886204	512679261
9102021001	09102	83	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano	5062	0.2063343	09102	1117651958	811932538
9102021002	09102	15	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano	4085	0.1665104	09102	901937623	276406900
9102081001	09102	26	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano	1860	0.0758162	09102	410674169	390849017
9102991999	09102	1	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano	68	0.0027718	09102	15013894	50209572
9103011001	09103	17	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano	1592	0.0908365	09103	357647954	299079133
9103021001	09103	10	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano	2075	0.1183955	09103	466155468	214110324
9103021002	09103	17	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano	3499	0.1996462	09103	786061678	299079133
9103101001	09103	5	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano	1428	0.0814789	09103	320804823	138367323
9103991999	09103	2	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano	253	0.0144357	09103	56837269	77694556
9104011001	09104	32	2017	Curarrehue	204180.7	2017	9104	7489	1529109215	Urbano	2148	0.2868207	09104	438580130	445458113
9105011001	09105	22	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	2214	0.0899781	09105	659385583	351813830
9105011002	09105	19	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	3193	0.1297651	09105	950956715	320782648
9105021001	09105	13	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	2072	0.0842071	09105	617094367	252583487
9105061001	09105	1	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	36	0.0014631	09105	10721717	50209572
9105091001	09105	4	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	154	0.0062586	09105	45865122	120224966
9105991999	09105	1	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	217	0.0088190	09105	64628126	50209572

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
9101011001	09101	35	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2220	0.0078608	09101	626031418	471323896	212308.06
9101011002	09101	362	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3909	0.0138413	09101	1102322889	2053013942	525201.83
9101011003	09101	176	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2067	0.0073190	09101	582886009	1303542270	630644.54
9101011004	09101	21	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	662	0.0023441	09101	186681441	341655001	516095.17
9101011005	09101	257	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2592	0.0091780	09101	730933980	1654571432	638337.74
9101021001	09101	217	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3196	0.0113167	09101	901259645	1487333045	465373.29
9101021002	09101	209	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2969	0.0105129	09101	837246522	1452557002	489241.16
9101021003	09101	68	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1480	0.0052405	09101	417354279	716133935	483874.28
9101021004	09101	37	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1826	0.0064657	09101	514924941	488112673	267312.53
9101031001	09101	95	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2594	0.0091851	09101	731497972	884011442	340790.84
9101031002	09101	93	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4200	0.0148717	09101	1184383764	872243312	207676.98
9101031003	09101	18	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2208	0.0078183	09101	622647464	310042532	140417.81
9101031004	09101	22	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2390	0.0084627	09101	673970761	351813830	147202.44
9101031005	09101	20	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2992	0.0105943	09101	843732434	331315443	110733.77
9101041001	09101	88	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4994	0.0176832	09101	1408288694	842405218	168683.46
9101041002	09101	49	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4641	0.0164333	09101	1308744059	582583743	125529.79
9101041003	09101	107	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4375	0.0154914	09101	1233733087	952787612	217780.03
9101051001	09101	78	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3142	0.0111255	09101	886031854	780772243	248495.30
9101051002	09101	13	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2998	0.0106156	09101	845424410	252583487	84250.66
9101051003	09101	32	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3078	0.0108989	09101	867984101	445458113	144723.23
9101051004	09101	40	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3587	0.0127012	09101	1011520133	512679261	142927.03
9101051005	09101	34	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2751	0.0097410	09101	775771365	462796625	168228.51
9101051006	09101	27	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2499	0.0088487	09101	704708339	400251110	160164.51
9101051007	09101	36	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2499	0.0088487	09101	704708339	479761446	191981.37
9101061001	09101	213	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3146	0.0111396	09101	887159838	1470005461	467261.75
9101061002	09101	65	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2322	0.0082219	09101	654795024	696068486	299771.10
9101061003	09101	33	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2821	0.0099888	09101	795511095	454175997	160998.23
9101061004	09101	154	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5347	0.0189331	09101	1507833330	1198391797	224124.14
9101061005	09101	47	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2768	0.0098012	09101	780565300	567491259	205018.52
9101061006	09101	57	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2682	0.0094967	09101	756313632	640805469	238928.21
9101071001	09101	52	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1795	0.0063559	09101	506183061	604801827	336936.95
9101071002	09101	144	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3047	0.0107891	09101	859242221	1148771058	377017.09
9101081001	09101	32	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3402	0.0120461	09101	959350849	445458113	130940.07
9101081002	09101	58	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3965	0.0140396	09101	1118114672	647863573	163395.60
9101081003	09101	56	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3304	0.0116991	09101	931715227	633701379	191798.24
9101081004	09101	47	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2161	0.0076519	09101	609393646	567491259	262605.86
9101081005	09101	14	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2222	0.0078679	09101	626595410	264652811	119105.68
9101081006	09101	26	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2903	0.0102792	09101	818634778	390849017	134636.24
9101091001	09101	1117	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5474	0.0193828	09101	1543646839	4174515993	762607.96
9101091002	09101	81	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1649	0.0058389	09101	465011625	799554160	484872.14
9101091003	09101	218	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3045	0.0107820	09101	858678229	1491646414	489867.46
9101091004	09101	193	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2668	0.0094471	09101	752365686	1381488517	517799.29
9101091005	09101	300	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1882	0.0066640	09101	530716725	1823912665	969135.32
9101101001	09101	142	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5434	0.0192412	09101	1532366993	1138695690	209550.18
9101101002	09101	222	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1589	0.0056265	09101	448091857	1508827168	949545.10
9101101003	09101	56	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	326	0.0011543	09101	91930740	633701379	1943869.26
9101101004	09101	25	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3639	0.0128853	09101	1026183932	381312074	104784.85
9101101005	09101	75	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5033	0.0178213	09101	1419286544	761720946	151345.31
9101101006	09101	113	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5891	0.0208594	09101	1661239227	986098306	167390.65
9101101007	09101	54	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5050	0.0178815	09101	1424080478	619350676	122643.70
9101101008	09101	197	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	6739	0.0238620	09101	1900371949	1399453815	207664.91
9101101009	09101	49	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4395	0.0155622	09101	1239373010	582583743	132556.03
9101111001	09101	153	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5176	0.0183276	09101	1459611991	1193484536	230580.47
9101111002	09101	60	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4664	0.0165147	09101	1315229970	661846111	141905.26
9101111003	09101	10	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1407	0.0049820	09101	396768561	214110324	152175.07
9101121001	09101	106	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2796	0.0099003	09101	788461191	947169324	338758.70
9101121002	09101	40	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2711	0.0095993	09101	764491520	512679261	189110.76
9101121003	09101	83	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3947	0.0139759	09101	1113038742	811932538	205708.78
9101141001	09101	79	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2408	0.0085265	09101	679046691	787062117	326853.04
9101141002	09101	22	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3217	0.0113910	09101	907181564	351813830	109360.84
9101141003	09101	75	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4707	0.0166670	09101	1327355804	761720946	161827.27
9101141004	09101	33	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2780	0.0098437	09101	783949253	454175997	163372.66
9101161001	09101	122	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3387	0.0119930	09101	955120907	1034862216	305539.48
9101161002	09101	42	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1966	0.0069614	09101	554404400	528678748	268910.86
9101161003	09101	41	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2025	0.0071703	09101	571042172	520715120	257143.27
9101161004	09101	56	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2583	0.0091461	09101	728396015	633701379	245335.42
9101171001	09101	136	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2682	0.0094967	09101	756313632	1108149422	413180.25
9101171002	09101	372	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4209	0.0149036	09101	1186921729	2088554388	496211.54
9101171003	09101	189	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2211	0.0078289	09101	623493453	1363384856	616637.20
9101171004	09101	72	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2954	0.0104598	09101	833016580	742385388	251315.30
9101181001	09101	1071	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4459	0.0157888	09101	1257420762	4065394698	911727.90
9101181002	09101	93	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2620	0.0092771	09101	738829872	872243312	332917.29
9101181003	09101	144	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2497	0.0088416	09101	704144347	1148771058	460060.50
9101181004	09101	392	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4641	0.0164333	09101	1308744059	2158592046	465113.56
9101181005	09101	177	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	1940	0.0068693	09101	547072500	1308202363	674331.11
9101181006	09101	536	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2902	0.0102757	09101	818352782	2628778135	905850.49
9101191001	09101	33	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	3430	0.0121452	09101	967246740	454175997	132412.83
9101191002	09101	82	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2483	0.0087920	09101	700196401	805757318	324509.59
9101191003	09101	210	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	5449	0.0192943	09101	1536596935	1456930620	267375.78
9101191004	09101	88	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4595	0.0162704	09101	1295772237	842405218	183330.84
9101191005	09101	42	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	4718	0.0167059	09101	1330457761	528678748	112055.69
9101191006	09101	463	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	2165	0.0076660	09101	610521631	2397207397	1107255.15
9101991999	09101	32	2017	Temuco	281996.1	2017	9101	282415	79639938245	Urbano	868	0.0030735	09101	244772645	445458113	513200.59
9102011001	09102	40	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano	2178	0.0887784	09102	480886204	512679261	235389.93
9102021001	09102	83	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano	5062	0.2063343	09102	1117651958	811932538	160397.58
9102021002	09102	15	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano	4085	0.1665104	09102	901937623	276406900	67663.87
9102081001	09102	26	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano	1860	0.0758162	09102	410674169	390849017	210133.88
9102991999	09102	1	2017	Carahue	220792.6	2017	9102	24533	5416703968	Urbano	68	0.0027718	09102	15013894	50209572	738376.06
9103011001	09103	17	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano	1592	0.0908365	09103	357647954	299079133	187863.78
9103021001	09103	10	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano	2075	0.1183955	09103	466155468	214110324	103185.70
9103021002	09103	17	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano	3499	0.1996462	09103	786061678	299079133	85475.60
9103101001	09103	5	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano	1428	0.0814789	09103	320804823	138367323	96895.88
9103991999	09103	2	2017	Cunco	224653.2	2017	9103	17526	3937272643	Urbano	253	0.0144357	09103	56837269	77694556	307093.11
9104011001	09104	32	2017	Curarrehue	204180.7	2017	9104	7489	1529109215	Urbano	2148	0.2868207	09104	438580130	445458113	207382.73
9105011001	09105	22	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	2214	0.0899781	09105	659385583	351813830	158904.17
9105011002	09105	19	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	3193	0.1297651	09105	950956715	320782648	100464.34
9105021001	09105	13	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	2072	0.0842071	09105	617094367	252583487	121903.23
9105061001	09105	1	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	36	0.0014631	09105	10721717	50209572	1394710.34
9105091001	09105	4	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	154	0.0062586	09105	45865122	120224966	780681.60
9105991999	09105	1	2017	Freire	297825.5	2017	9105	24606	7328293433	Urbano	217	0.0088190	09105	64628126	50209572	231380.52

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_urbano_nivel_nacional_17_r09.rds")

---

Rural

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 2)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15202	1	2	6	13225	1	3	1	5	3	5	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	3	1	1	5	3	5	2	3	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	4	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	5	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	6	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	8	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	9	3	1	5	3	5	1	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	10	1	1	5	3	4	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	11	1	2	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	12	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	13	1	1	5	3	4	1	4	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	14	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	16	1	1	5	3	5	1	3	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	17	1	1	5	3	5	2	4	1	8	15	152	15202	15202012006
15	152	15202	1	2	6	13225	18	3	1	5	3	5	1	1	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	19	3	1	5	3	5	1	3	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	20	1	1	5	3	5	2	1	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	21	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	22	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	24	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	25	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	26	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	27	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	28	3	1	5	3	5	1	4	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	29	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	30	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	31	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	32	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	33	1	1	5	3	5	3	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	34	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	35	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	36	1	1	5	3	5	3	2	1	9	15	152	15202	15202012006
15	152	15202	1	2	6	13225	37	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	38	1	1	5	3	5	99	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	39	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	40	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	41	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	42	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	43	3	1	5	3	5	2	1	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	44	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	45	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	8	13910	1	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	3	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	4	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	5	3	1	5	3	5	2	3	1	3	15	152	15202	15202012008
15	152	15202	1	2	8	13910	6	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	7	1	1	5	99	5	2	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	8	3	1	5	3	5	3	3	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	9	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	10	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	11	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	12	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	13	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	14	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	16	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	18	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	20	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	21	1	1	5	3	5	1	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	22	3	1	5	3	5	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	23	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	24	3	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	25	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	26	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	27	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	28	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	29	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	30	1	1	5	1	4	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	12	8394	1	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	2	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	3	3	1	5	3	5	2	3	1	4	15	152	15202	15202012012
15	152	15202	1	2	12	8394	4	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	5	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	6	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	8	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	9	3	1	5	3	5	1	4	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	10	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	11	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	12	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	13	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	14	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	15	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	16	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	18	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	20	3	1	5	3	5	3	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	21	3	1	5	99	5	1	4	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	22	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	24	3	1	5	3	5	1	2	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	25	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 9:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 9)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Veamos los primeros 100 registros:

r3_100 <- d[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	unlist.c.	unlist.d.	Freq	anio
1	9101072007	1	9101	2	2017
2	9101072010	1	9101	2	2017
3	9101072042	1	9101	6	2017
4	9101072060	1	9101	10	2017
5	9101082007	1	9101	8	2017
6	9101102004	1	9101	114	2017
7	9101102006	1	9101	100	2017
8	9101102028	1	9101	26	2017
9	9101102029	1	9101	5	2017
10	9101102034	1	9101	1	2017
11	9101102057	1	9101	33	2017
12	9101112013	1	9101	1	2017
13	9101112019	1	9101	13	2017
14	9101112029	1	9101	2	2017
15	9101112044	1	9101	1	2017
16	9101112045	1	9101	4	2017
17	9101112047	1	9101	1	2017
18	9101112052	1	9101	4	2017
19	9101112054	1	9101	6	2017
20	9101112062	1	9101	15	2017
21	9101112067	1	9101	5	2017
22	9101112069	1	9101	2	2017
23	9101112070	1	9101	7	2017
24	9101112071	1	9101	1	2017
25	9101112073	1	9101	2	2017
26	9101122051	1	9101	28	2017
27	9101122060	1	9101	9	2017
28	9101122061	1	9101	3	2017
29	9101132033	1	9101	1	2017
30	9101132036	1	9101	1	2017
31	9101142012	1	9101	28	2017
32	9101142022	1	9101	1	2017
33	9101142024	1	9101	1	2017
34	9101142037	1	9101	1	2017
35	9101142040	1	9101	1	2017
36	9101142042	1	9101	10	2017
37	9101142054	1	9101	16	2017
38	9101142062	1	9101	12	2017
39	9101142064	1	9101	1	2017
40	9101142066	1	9101	1	2017
41	9101152002	1	9101	14	2017
42	9101152011	1	9101	6	2017
43	9101152017	1	9101	3	2017
44	9101152020	1	9101	1	2017
45	9101152023	1	9101	3	2017
46	9101152027	1	9101	3	2017
1101	9102022011	1	9102	4	2017
1102	9102022031	1	9102	1	2017
1103	9102022054	1	9102	1	2017
1104	9102032009	1	9102	5	2017
1105	9102032035	1	9102	3	2017
1106	9102032037	1	9102	1	2017
1107	9102032042	1	9102	3	2017
1108	9102032901	1	9102	3	2017
1109	9102042004	1	9102	5	2017
1110	9102042009	1	9102	1	2017
1111	9102042010	1	9102	2	2017
1112	9102042015	1	9102	2	2017
1113	9102042017	1	9102	1	2017
1114	9102042029	1	9102	2	2017
1115	9102042045	1	9102	1	2017
1116	9102052013	1	9102	2	2017
1117	9102052018	1	9102	2	2017
1118	9102052034	1	9102	2	2017
1119	9102052040	1	9102	8	2017
1120	9102052047	1	9102	2	2017
1121	9102052055	1	9102	2	2017
1122	9102062003	1	9102	1	2017
1123	9102062030	1	9102	1	2017
1124	9102062032	1	9102	5	2017
1125	9102062044	1	9102	1	2017
1126	9102072001	1	9102	4	2017
1127	9102072003	1	9102	11	2017
1128	9102082024	1	9102	4	2017
1129	9102082027	1	9102	2	2017
1130	9102082036	1	9102	1	2017
1131	9102092024	1	9102	1	2017
1132	9102092036	1	9102	2	2017
1133	9102092053	1	9102	3	2017
1134	9102102012	1	9102	2	2017
1135	9102112019	1	9102	8	2017
1136	9102112022	1	9102	2	2017
1137	9102112023	1	9102	8	2017
1138	9102112033	1	9102	2	2017
1139	9102122018	1	9102	3	2017
1140	9102122046	1	9102	1	2017
2195	9103012020	1	9103	1	2017
2196	9103012045	1	9103	2	2017
2197	9103022001	1	9103	1	2017
2198	9103022042	1	9103	1	2017
2199	9103032901	1	9103	4	2017
2200	9103042019	1	9103	2	2017
2201	9103042027	1	9103	11	2017
2202	9103052009	1	9103	3	2017
2203	9103052032	1	9103	15	2017
2204	9103052043	1	9103	1	2017
2205	9103062018	1	9103	1	2017
2206	9103062019	1	9103	2	2017
2207	9103062022	1	9103	26	2017
2208	9103062040	1	9103	6	2017

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	9101072007	2	2017	09101
2	9101072010	2	2017	09101
3	9101072042	6	2017	09101
4	9101072060	10	2017	09101
5	9101082007	8	2017	09101
6	9101102004	114	2017	09101
7	9101102006	100	2017	09101
8	9101102028	26	2017	09101
9	9101102029	5	2017	09101
10	9101102034	1	2017	09101
11	9101102057	33	2017	09101
12	9101112013	1	2017	09101
13	9101112019	13	2017	09101
14	9101112029	2	2017	09101
15	9101112044	1	2017	09101
16	9101112045	4	2017	09101
17	9101112047	1	2017	09101
18	9101112052	4	2017	09101
19	9101112054	6	2017	09101
20	9101112062	15	2017	09101
21	9101112067	5	2017	09101
22	9101112069	2	2017	09101
23	9101112070	7	2017	09101
24	9101112071	1	2017	09101
25	9101112073	2	2017	09101
26	9101122051	28	2017	09101
27	9101122060	9	2017	09101
28	9101122061	3	2017	09101
29	9101132033	1	2017	09101
30	9101132036	1	2017	09101
31	9101142012	28	2017	09101
32	9101142022	1	2017	09101
33	9101142024	1	2017	09101
34	9101142037	1	2017	09101
35	9101142040	1	2017	09101
36	9101142042	10	2017	09101
37	9101142054	16	2017	09101
38	9101142062	12	2017	09101
39	9101142064	1	2017	09101
40	9101142066	1	2017	09101
41	9101152002	14	2017	09101
42	9101152011	6	2017	09101
43	9101152017	3	2017	09101
44	9101152020	1	2017	09101
45	9101152023	3	2017	09101
46	9101152027	3	2017	09101
1101	9102022011	4	2017	09102
1102	9102022031	1	2017	09102
1103	9102022054	1	2017	09102
1104	9102032009	5	2017	09102
1105	9102032035	3	2017	09102
1106	9102032037	1	2017	09102
1107	9102032042	3	2017	09102
1108	9102032901	3	2017	09102
1109	9102042004	5	2017	09102
1110	9102042009	1	2017	09102
1111	9102042010	2	2017	09102
1112	9102042015	2	2017	09102
1113	9102042017	1	2017	09102
1114	9102042029	2	2017	09102
1115	9102042045	1	2017	09102
1116	9102052013	2	2017	09102
1117	9102052018	2	2017	09102
1118	9102052034	2	2017	09102
1119	9102052040	8	2017	09102
1120	9102052047	2	2017	09102
1121	9102052055	2	2017	09102
1122	9102062003	1	2017	09102
1123	9102062030	1	2017	09102
1124	9102062032	5	2017	09102
1125	9102062044	1	2017	09102
1126	9102072001	4	2017	09102
1127	9102072003	11	2017	09102
1128	9102082024	4	2017	09102
1129	9102082027	2	2017	09102
1130	9102082036	1	2017	09102
1131	9102092024	1	2017	09102
1132	9102092036	2	2017	09102
1133	9102092053	3	2017	09102
1134	9102102012	2	2017	09102
1135	9102112019	8	2017	09102
1136	9102112022	2	2017	09102
1137	9102112023	8	2017	09102
1138	9102112033	2	2017	09102
1139	9102122018	3	2017	09102
1140	9102122046	1	2017	09102
2195	9103012020	1	2017	09103
2196	9103012045	2	2017	09103
2197	9103022001	1	2017	09103
2198	9103022042	1	2017	09103
2199	9103032901	4	2017	09103
2200	9103042019	2	2017	09103
2201	9103042027	11	2017	09103
2202	9103052009	3	2017	09103
2203	9103052032	15	2017	09103
2204	9103052043	1	2017	09103
2205	9103062018	1	2017	09103
2206	9103062019	2	2017	09103
2207	9103062022	26	2017	09103
2208	9103062040	6	2017	09103

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_rural_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	289375.3	2017	1101	191468	55406102543	Rural
01401	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
01402	Camiña	262850.3	2017	1402	1250	328562901	Rural
01404	Huara	253968.5	2017	1404	2730	693334131	Rural
01405	Pica	290496.7	2017	1405	9296	2700457509	Rural
02103	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
02104	Taltal	345494.0	2017	2104	13317	4600943086	Rural
02201	Calama	310025.0	2017	2201	165731	51380756402	Rural
02203	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
02301	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
03101	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03103	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03202	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural
03301	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03302	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03303	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03304	Huasco	227560.7	2017	3304	10149	2309513927	Rural
04101	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04102	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04103	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04104	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04105	Paiguano	205942.1	2017	4105	4497	926121774	Rural
04106	Vicuña	176130.6	2017	4106	27771	4891322768	Rural
04201	Illapel	191976.8	2017	4201	30848	5922099530	Rural
04202	Canela	171370.3	2017	4202	9093	1558270441	Rural
04203	Los Vilos	173238.5	2017	4203	21382	3704185607	Rural
04204	Salamanca	223234.2	2017	4204	29347	6551254640	Rural
04301	Ovalle	241393.7	2017	4301	111272	26860360045	Rural
04302	Combarbalá	179139.6	2017	4302	13322	2386498044	Rural
04303	Monte Patria	201205.8	2017	4303	30751	6187280931	Rural
04304	Punitaqui	171931.7	2017	4304	10956	1883683880	Rural
04305	Río Hurtado	182027.2	2017	4305	4278	778712384	Rural
05101	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05102	Casablanca	268917.1	2017	5102	26867	7224996933	Rural
05105	Puchuncaví	279614.4	2017	5105	18546	5185728335	Rural
05107	Quintero	334628.2	2017	5107	31923	10682335196	Rural
05301	Los Andes	324402.1	2017	5301	66708	21640215030	Rural
05302	Calle Larga	242743.8	2017	5302	14832	3600375502	Rural
05303	Rinconada	326532.5	2017	5303	10207	3332917471	Rural
05304	San Esteban	223168.6	2017	5304	18855	4207844130	Rural
05401	La Ligua	181468.0	2017	5401	35390	6422154059	Rural
05402	Cabildo	231277.8	2017	5402	19388	4484014285	Rural
05404	Petorca	298208.9	2017	5404	9826	2930200178	Rural
05405	Zapallar	292882.3	2017	5405	7339	2149463129	Rural
05501	Quillota	220926.8	2017	5501	90517	19997628209	Rural
05502	Calera	226906.2	2017	5502	50554	11471016698	Rural
05503	Hijuelas	253739.9	2017	5503	17988	4564273201	Rural
05504	La Cruz	291124.1	2017	5504	22098	6433259569	Rural
05506	Nogales	264475.3	2017	5506	22120	5850194593	Rural
05601	San Antonio	266331.2	2017	5601	91350	24329353815	Rural

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
09101	9101072007	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101072010	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101072042	6	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101072060	10	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101082007	8	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101102004	114	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101102006	100	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101102028	26	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101102029	5	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101102034	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101102057	33	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112013	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112019	13	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112029	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112044	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112045	4	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112047	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112052	4	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112054	6	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112062	15	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112067	5	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112069	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112070	7	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112071	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112073	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101122051	28	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101122060	9	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101122061	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101132033	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101132036	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101142012	28	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101142022	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101142024	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101142037	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101142040	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101142042	10	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101142054	16	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101142062	12	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101142064	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101142066	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101152002	14	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101152011	6	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101152017	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101152020	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101152023	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101152027	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09102	9102022011	4	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102022031	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102022054	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102032009	5	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102032035	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102032037	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102032042	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102032901	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102042004	5	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102042009	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102042010	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102042015	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102042017	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102042029	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102042045	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102052013	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102052018	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102052034	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102052040	8	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102052047	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102052055	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102062003	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102062030	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102062032	5	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102062044	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102072001	4	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102072003	11	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102082024	4	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102082027	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102082036	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102092024	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102092036	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102092053	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102102012	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102112019	8	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102112022	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102112023	8	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102112033	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102122018	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102122046	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09103	9103012020	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103012045	2	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103022001	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103022042	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103032901	4	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103042019	2	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103042027	11	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103052009	3	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103052032	15	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103052043	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103062018	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103062019	2	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103062022	26	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103062040	6	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
09101	9101072007	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101072010	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101072042	6	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101072060	10	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101082007	8	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101102004	114	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101102006	100	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101102028	26	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101102029	5	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101102034	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101102057	33	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112013	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112019	13	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112029	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112044	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112045	4	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112047	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112052	4	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112054	6	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112062	15	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112067	5	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112069	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112070	7	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112071	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101112073	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101122051	28	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101122060	9	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101122061	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101132033	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101132036	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101142012	28	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101142022	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101142024	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101142037	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101142040	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101142042	10	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101142054	16	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101142062	12	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101142064	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101142066	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101152002	14	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101152011	6	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101152017	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101152020	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101152023	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09101	9101152027	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural
09102	9102022011	4	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102022031	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102022054	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102032009	5	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102032035	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102032037	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102032042	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102032901	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102042004	5	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102042009	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102042010	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102042015	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102042017	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102042029	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102042045	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102052013	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102052018	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102052034	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102052040	8	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102052047	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102052055	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102062003	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102062030	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102062032	5	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102062044	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102072001	4	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102072003	11	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102082024	4	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102082027	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102082036	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102092024	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102092036	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102092053	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102102012	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102112019	8	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102112022	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102112023	8	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102112033	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102122018	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09102	9102122046	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural
09103	9103012020	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103012045	2	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103022001	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103022042	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103032901	4	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103042019	2	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103042027	11	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103052009	3	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103052032	15	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103052043	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103062018	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103062019	2	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103062022	26	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural
09103	9103062040	6	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
9101072007	09101	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	60	0.0002125	09101
9101072010	09101	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	43	0.0001523	09101
9101072042	09101	6	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	59	0.0002089	09101
9101072060	09101	10	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	61	0.0002160	09101
9101082007	09101	8	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	79	0.0002797	09101
9101102004	09101	114	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	3065	0.0108528	09101
9101102006	09101	100	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	726	0.0025707	09101
9101102028	09101	26	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	843	0.0029850	09101
9101102029	09101	5	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	496	0.0017563	09101
9101102034	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	172	0.0006090	09101
9101102057	09101	33	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	1762	0.0062390	09101
9101112013	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	214	0.0007578	09101
9101112019	09101	13	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	142	0.0005028	09101
9101112029	09101	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	181	0.0006409	09101
9101112044	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	46	0.0001629	09101
9101112045	09101	4	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	292	0.0010339	09101
9101112047	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	124	0.0004391	09101
9101112052	09101	4	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	100	0.0003541	09101
9101112054	09101	6	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	443	0.0015686	09101
9101112062	09101	15	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	167	0.0005913	09101
9101112067	09101	5	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	514	0.0018200	09101
9101112069	09101	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	129	0.0004568	09101
9101112070	09101	7	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	325	0.0011508	09101
9101112071	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	230	0.0008144	09101
9101112073	09101	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	132	0.0004674	09101
9101122051	09101	28	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	357	0.0012641	09101
9101122060	09101	9	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	80	0.0002833	09101
9101122061	09101	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	21	0.0000744	09101
9101132033	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	36	0.0001275	09101
9101132036	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	35	0.0001239	09101
9101142012	09101	28	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	136	0.0004816	09101
9101142022	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	90	0.0003187	09101
9101142024	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	57	0.0002018	09101
9101142037	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	48	0.0001700	09101
9101142040	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	218	0.0007719	09101
9101142042	09101	10	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	426	0.0015084	09101
9101142054	09101	16	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	1860	0.0065861	09101
9101142062	09101	12	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	113	0.0004001	09101
9101142064	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	111	0.0003930	09101
9101142066	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	65	0.0002302	09101
9101152002	09101	14	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	933	0.0033036	09101
9101152011	09101	6	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	290	0.0010269	09101
9101152017	09101	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	317	0.0011225	09101
9101152020	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	101	0.0003576	09101
9101152023	09101	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	111	0.0003930	09101
9101152027	09101	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	148	0.0005241	09101
9102022011	09102	4	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	327	0.0133290	09102
9102022031	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	163	0.0066441	09102
9102022054	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	190	0.0077447	09102
9102032009	09102	5	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	496	0.0202177	09102
9102032035	09102	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	255	0.0103942	09102
9102032037	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	104	0.0042392	09102
9102032042	09102	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	190	0.0077447	09102
9102032901	09102	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	52	0.0021196	09102
9102042004	09102	5	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	221	0.0090083	09102
9102042009	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	128	0.0052175	09102
9102042010	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	302	0.0123099	09102
9102042015	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	430	0.0175274	09102
9102042017	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	148	0.0060327	09102
9102042029	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	215	0.0087637	09102
9102042045	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	205	0.0083561	09102
9102052013	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	156	0.0063588	09102
9102052018	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	338	0.0137774	09102
9102052034	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	273	0.0111279	09102
9102052040	09102	8	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	730	0.0297558	09102
9102052047	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	327	0.0133290	09102
9102052055	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	216	0.0088045	09102
9102062003	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	47	0.0019158	09102
9102062030	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	70	0.0028533	09102
9102062032	09102	5	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	409	0.0166714	09102
9102062044	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	222	0.0090490	09102
9102072001	09102	4	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	233	0.0094974	09102
9102072003	09102	11	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	542	0.0220927	09102
9102082024	09102	4	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	307	0.0125138	09102
9102082027	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	218	0.0088860	09102
9102082036	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	201	0.0081930	09102
9102092024	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	117	0.0047691	09102
9102092036	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	123	0.0050137	09102
9102092053	09102	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	225	0.0091713	09102
9102102012	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	30	0.0012228	09102
9102112019	09102	8	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	136	0.0055436	09102
9102112022	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	57	0.0023234	09102
9102112023	09102	8	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	177	0.0072148	09102
9102112033	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	186	0.0075816	09102
9102122018	09102	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	282	0.0114947	09102
9102122046	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	64	0.0026087	09102
9103012020	09103	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	174	0.0099281	09103
9103012045	09103	2	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	151	0.0086158	09103
9103022001	09103	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	183	0.0104416	09103
9103022042	09103	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	105	0.0059911	09103
9103032901	09103	4	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	212	0.0120963	09103
9103042019	09103	2	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	185	0.0105557	09103
9103042027	09103	11	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	145	0.0082734	09103
9103052009	09103	3	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	65	0.0037088	09103
9103052032	09103	15	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	74	0.0042223	09103
9103052043	09103	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	56	0.0031953	09103
9103062018	09103	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	162	0.0092434	09103
9103062019	09103	2	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	139	0.0079311	09103
9103062022	09103	26	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	610	0.0348054	09103
9103062040	09103	6	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	83	0.0047358	09103

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
9101072007	09101	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	60	0.0002125	09101	12104785
9101072010	09101	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	43	0.0001523	09101	8675096
9101072042	09101	6	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	59	0.0002089	09101	11903039
9101072060	09101	10	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	61	0.0002160	09101	12306532
9101082007	09101	8	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	79	0.0002797	09101	15937967
9101102004	09101	114	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	3065	0.0108528	09101	618352772
9101102006	09101	100	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	726	0.0025707	09101	146467900
9101102028	09101	26	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	843	0.0029850	09101	170072231
9101102029	09101	5	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	496	0.0017563	09101	100066223
9101102034	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	172	0.0006090	09101	34700384
9101102057	09101	33	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	1762	0.0062390	09101	355477189
9101112013	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	214	0.0007578	09101	43173733
9101112019	09101	13	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	142	0.0005028	09101	28647991
9101112029	09101	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	181	0.0006409	09101	36516102
9101112044	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	46	0.0001629	09101	9280335
9101112045	09101	4	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	292	0.0010339	09101	58909954
9101112047	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	124	0.0004391	09101	25016556
9101112052	09101	4	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	100	0.0003541	09101	20174642
9101112054	09101	6	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	443	0.0015686	09101	89373663
9101112062	09101	15	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	167	0.0005913	09101	33691652
9101112067	09101	5	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	514	0.0018200	09101	103697659
9101112069	09101	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	129	0.0004568	09101	26025288
9101112070	09101	7	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	325	0.0011508	09101	65567586
9101112071	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	230	0.0008144	09101	46401676
9101112073	09101	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	132	0.0004674	09101	26630527
9101122051	09101	28	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	357	0.0012641	09101	72023471
9101122060	09101	9	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	80	0.0002833	09101	16139713
9101122061	09101	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	21	0.0000744	09101	4236675
9101132033	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	36	0.0001275	09101	7262871
9101132036	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	35	0.0001239	09101	7061125
9101142012	09101	28	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	136	0.0004816	09101	27437513
9101142022	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	90	0.0003187	09101	18157178
9101142024	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	57	0.0002018	09101	11499546
9101142037	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	48	0.0001700	09101	9683828
9101142040	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	218	0.0007719	09101	43980719
9101142042	09101	10	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	426	0.0015084	09101	85943974
9101142054	09101	16	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	1860	0.0065861	09101	375248338
9101142062	09101	12	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	113	0.0004001	09101	22797345
9101142064	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	111	0.0003930	09101	22393852
9101142066	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	65	0.0002302	09101	13113517
9101152002	09101	14	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	933	0.0033036	09101	188229408
9101152011	09101	6	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	290	0.0010269	09101	58506461
9101152017	09101	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	317	0.0011225	09101	63953615
9101152020	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	101	0.0003576	09101	20376388
9101152023	09101	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	111	0.0003930	09101	22393852
9101152027	09101	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	148	0.0005241	09101	29858470
9102022011	09102	4	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	327	0.0133290	09102	49430829
9102022031	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	163	0.0066441	09102	24639832
9102022054	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	190	0.0077447	09102	28721277
9102032009	09102	5	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	496	0.0202177	09102	74977648
9102032035	09102	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	255	0.0103942	09102	38546976
9102032037	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	104	0.0042392	09102	15721120
9102032042	09102	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	190	0.0077447	09102	28721277
9102032901	09102	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	52	0.0021196	09102	7860560
9102042004	09102	5	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	221	0.0090083	09102	33407380
9102042009	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	128	0.0052175	09102	19349070
9102042010	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	302	0.0123099	09102	45651713
9102042015	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	430	0.0175274	09102	65000784
9102042017	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	148	0.0060327	09102	22372363
9102042029	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	215	0.0087637	09102	32500392
9102042045	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	205	0.0083561	09102	30988746
9102052013	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	156	0.0063588	09102	23581680
9102052018	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	338	0.0137774	09102	51093639
9102052034	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	273	0.0111279	09102	41267939
9102052040	09102	8	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	730	0.0297558	09102	110350168
9102052047	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	327	0.0133290	09102	49430829
9102052055	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	216	0.0088045	09102	32651556
9102062003	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	47	0.0019158	09102	7104737
9102062030	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	70	0.0028533	09102	10581523
9102062032	09102	5	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	409	0.0166714	09102	61826327
9102062044	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	222	0.0090490	09102	33558544
9102072001	09102	4	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	233	0.0094974	09102	35221355
9102072003	09102	11	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	542	0.0220927	09102	81931220
9102082024	09102	4	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	307	0.0125138	09102	46407536
9102082027	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	218	0.0088860	09102	32953886
9102082036	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	201	0.0081930	09102	30384087
9102092024	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	117	0.0047691	09102	17686260
9102092036	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	123	0.0050137	09102	18593247
9102092053	09102	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	225	0.0091713	09102	34012038
9102102012	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	30	0.0012228	09102	4534938
9102112019	09102	8	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	136	0.0055436	09102	20558387
9102112022	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	57	0.0023234	09102	8616383
9102112023	09102	8	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	177	0.0072148	09102	26756137
9102112033	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	186	0.0075816	09102	28116618
9102122018	09102	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	282	0.0114947	09102	42628421
9102122046	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	64	0.0026087	09102	9674535
9103012020	09103	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	174	0.0099281	09103	28247888
9103012045	09103	2	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	151	0.0086158	09103	24513972
9103022001	09103	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	183	0.0104416	09103	29708986
9103022042	09103	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	105	0.0059911	09103	17046139
9103032901	09103	4	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	212	0.0120963	09103	34416967
9103042019	09103	2	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	185	0.0105557	09103	30033674
9103042027	09103	11	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	145	0.0082734	09103	23539907
9103052009	09103	3	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	65	0.0037088	09103	10552372
9103052032	09103	15	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	74	0.0042223	09103	12013470
9103052043	09103	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	56	0.0031953	09103	9091274
9103062018	09103	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	162	0.0092434	09103	26299758
9103062019	09103	2	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	139	0.0079311	09103	22565842
9103062022	09103	26	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	610	0.0348054	09103	99029953
9103062040	09103	6	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	83	0.0047358	09103	13474567

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -229523763  -21308572   -9048366    9804009  336803477 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 22721921    1416852   16.04   <2e-16 ***
## Freq.x       3532697     141875   24.90   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 40070000 on 1052 degrees of freedom
## Multiple R-squared:  0.3708, Adjusted R-squared:  0.3702 
## F-statistic:   620 on 1 and 1052 DF,  p-value: < 2.2e-16

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.370219842613184"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.370219842613184"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.318705559196486"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                
## [1,] "raíz cuadrada" "0.39385326223372"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.370949021675871"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.268510002030447"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                
## [1,] "raíz-log" "0.34657870260844"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.287486064851976"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 6      log-raíz 0.268510002030447
## 8       log-log 0.287486064851976
## 3   logarítmico 0.318705559196486
## 7      raíz-log  0.34657870260844
## 1    cuadrático 0.370219842613184
## 2        cúbico 0.370219842613184
## 5     raíz-raíz 0.370949021675871
## 4 raíz cuadrada  0.39385326223372
##                                                                     sintaxis
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 4
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = multi_pob ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -132383700  -18444071   -6623014   11083313  337565205 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -11678875    2317602  -5.039  5.5e-07 ***
## sqrt(Freq.x)  27391992    1046439  26.176  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 39310000 on 1052 degrees of freedom
## Multiple R-squared:  0.3944, Adjusted R-squared:  0.3939 
## F-statistic: 685.2 on 1 and 1052 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##   -11678875

bb <- linearMod$coefficients[2]
bb

## sqrt(Freq.x) 
##     27391992

9 Modelo raíz cuadrada (raíz cuadrada)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.3939 ).

9.1 Diagrama de dispersión sobre raíz cuadrada

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=(h_y_m_comuna_corr_01$Freq.x), y=sqrt(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo raíz cuadrada

Observemos nuevamente el resultado sobre raíz cuadrada.

linearMod <- lm(( multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = (multi_pob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -132383700  -18444071   -6623014   11083313  337565205 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -11678875    2317602  -5.039  5.5e-07 ***
## sqrt(Freq.x)  27391992    1046439  26.176  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 39310000 on 1052 degrees of freedom
## Multiple R-squared:  0.3944, Adjusted R-squared:  0.3939 
## F-statistic: 685.2 on 1 and 1052 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = (Freq.x) , y = sqrt(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = 11678875 + 27391992\cdot \sqrt {X} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- aa+bb * sqrt(h_y_m_comuna_corr_01$Freq.x)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
9101072007	09101	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	60	0.0002125	09101	12104785	27059252
9101072010	09101	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	43	0.0001523	09101	8675096	27059252
9101072042	09101	6	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	59	0.0002089	09101	11903039	55417529
9101072060	09101	10	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	61	0.0002160	09101	12306532	74942210
9101082007	09101	8	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	79	0.0002797	09101	15937967	65797378
9101102004	09101	114	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	3065	0.0108528	09101	618352772	280787567
9101102006	09101	100	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	726	0.0025707	09101	146467900	262241045
9101102028	09101	26	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	843	0.0029850	09101	170072231	127993427
9101102029	09101	5	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	496	0.0017563	09101	100066223	49571481
9101102034	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	172	0.0006090	09101	34700384	15713117
9101102057	09101	33	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	1762	0.0062390	09101	355477189	145676139
9101112013	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	214	0.0007578	09101	43173733	15713117
9101112019	09101	13	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	142	0.0005028	09101	28647991	87084357
9101112029	09101	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	181	0.0006409	09101	36516102	27059252
9101112044	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	46	0.0001629	09101	9280335	15713117
9101112045	09101	4	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	292	0.0010339	09101	58909954	43105109
9101112047	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	124	0.0004391	09101	25016556	15713117
9101112052	09101	4	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	100	0.0003541	09101	20174642	43105109
9101112054	09101	6	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	443	0.0015686	09101	89373663	55417529
9101112062	09101	15	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	167	0.0005913	09101	33691652	94409854
9101112067	09101	5	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	514	0.0018200	09101	103697659	49571481
9101112069	09101	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	129	0.0004568	09101	26025288	27059252
9101112070	09101	7	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	325	0.0011508	09101	65567586	60793524
9101112071	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	230	0.0008144	09101	46401676	15713117
9101112073	09101	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	132	0.0004674	09101	26630527	27059252
9101122051	09101	28	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	357	0.0012641	09101	72023471	133265922
9101122060	09101	9	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	80	0.0002833	09101	16139713	70497101
9101122061	09101	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	21	0.0000744	09101	4236675	35765447
9101132033	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	36	0.0001275	09101	7262871	15713117
9101132036	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	35	0.0001239	09101	7061125	15713117
9101142012	09101	28	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	136	0.0004816	09101	27437513	133265922
9101142022	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	90	0.0003187	09101	18157178	15713117
9101142024	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	57	0.0002018	09101	11499546	15713117
9101142037	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	48	0.0001700	09101	9683828	15713117
9101142040	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	218	0.0007719	09101	43980719	15713117
9101142042	09101	10	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	426	0.0015084	09101	85943974	74942210
9101142054	09101	16	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	1860	0.0065861	09101	375248338	97889093
9101142062	09101	12	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	113	0.0004001	09101	22797345	83209769
9101142064	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	111	0.0003930	09101	22393852	15713117
9101142066	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	65	0.0002302	09101	13113517	15713117
9101152002	09101	14	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	933	0.0033036	09101	188229408	90812574
9101152011	09101	6	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	290	0.0010269	09101	58506461	55417529
9101152017	09101	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	317	0.0011225	09101	63953615	35765447
9101152020	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	101	0.0003576	09101	20376388	15713117
9101152023	09101	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	111	0.0003930	09101	22393852	35765447
9101152027	09101	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	148	0.0005241	09101	29858470	35765447
9102022011	09102	4	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	327	0.0133290	09102	49430829	43105109
9102022031	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	163	0.0066441	09102	24639832	15713117
9102022054	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	190	0.0077447	09102	28721277	15713117
9102032009	09102	5	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	496	0.0202177	09102	74977648	49571481
9102032035	09102	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	255	0.0103942	09102	38546976	35765447
9102032037	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	104	0.0042392	09102	15721120	15713117
9102032042	09102	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	190	0.0077447	09102	28721277	35765447
9102032901	09102	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	52	0.0021196	09102	7860560	35765447
9102042004	09102	5	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	221	0.0090083	09102	33407380	49571481
9102042009	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	128	0.0052175	09102	19349070	15713117
9102042010	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	302	0.0123099	09102	45651713	27059252
9102042015	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	430	0.0175274	09102	65000784	27059252
9102042017	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	148	0.0060327	09102	22372363	15713117
9102042029	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	215	0.0087637	09102	32500392	27059252
9102042045	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	205	0.0083561	09102	30988746	15713117
9102052013	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	156	0.0063588	09102	23581680	27059252
9102052018	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	338	0.0137774	09102	51093639	27059252
9102052034	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	273	0.0111279	09102	41267939	27059252
9102052040	09102	8	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	730	0.0297558	09102	110350168	65797378
9102052047	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	327	0.0133290	09102	49430829	27059252
9102052055	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	216	0.0088045	09102	32651556	27059252
9102062003	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	47	0.0019158	09102	7104737	15713117
9102062030	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	70	0.0028533	09102	10581523	15713117
9102062032	09102	5	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	409	0.0166714	09102	61826327	49571481
9102062044	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	222	0.0090490	09102	33558544	15713117
9102072001	09102	4	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	233	0.0094974	09102	35221355	43105109
9102072003	09102	11	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	542	0.0220927	09102	81931220	79170085
9102082024	09102	4	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	307	0.0125138	09102	46407536	43105109
9102082027	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	218	0.0088860	09102	32953886	27059252
9102082036	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	201	0.0081930	09102	30384087	15713117
9102092024	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	117	0.0047691	09102	17686260	15713117
9102092036	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	123	0.0050137	09102	18593247	27059252
9102092053	09102	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	225	0.0091713	09102	34012038	35765447
9102102012	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	30	0.0012228	09102	4534938	27059252
9102112019	09102	8	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	136	0.0055436	09102	20558387	65797378
9102112022	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	57	0.0023234	09102	8616383	27059252
9102112023	09102	8	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	177	0.0072148	09102	26756137	65797378
9102112033	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	186	0.0075816	09102	28116618	27059252
9102122018	09102	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	282	0.0114947	09102	42628421	35765447
9102122046	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	64	0.0026087	09102	9674535	15713117
9103012020	09103	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	174	0.0099281	09103	28247888	15713117
9103012045	09103	2	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	151	0.0086158	09103	24513972	27059252
9103022001	09103	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	183	0.0104416	09103	29708986	15713117
9103022042	09103	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	105	0.0059911	09103	17046139	15713117
9103032901	09103	4	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	212	0.0120963	09103	34416967	43105109
9103042019	09103	2	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	185	0.0105557	09103	30033674	27059252
9103042027	09103	11	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	145	0.0082734	09103	23539907	79170085
9103052009	09103	3	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	65	0.0037088	09103	10552372	35765447
9103052032	09103	15	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	74	0.0042223	09103	12013470	94409854
9103052043	09103	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	56	0.0031953	09103	9091274	15713117
9103062018	09103	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	162	0.0092434	09103	26299758	15713117
9103062019	09103	2	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	139	0.0079311	09103	22565842	27059252
9103062022	09103	26	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	610	0.0348054	09103	99029953	127993427
9103062040	09103	6	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	83	0.0047358	09103	13474567	55417529

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
9101072007	09101	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	60	0.0002125	09101	12104785	27059252	450987.53
9101072010	09101	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	43	0.0001523	09101	8675096	27059252	629284.93
9101072042	09101	6	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	59	0.0002089	09101	11903039	55417529	939280.15
9101072060	09101	10	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	61	0.0002160	09101	12306532	74942210	1228560.81
9101082007	09101	8	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	79	0.0002797	09101	15937967	65797378	832878.21
9101102004	09101	114	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	3065	0.0108528	09101	618352772	280787567	91610.95
9101102006	09101	100	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	726	0.0025707	09101	146467900	262241045	361213.56
9101102028	09101	26	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	843	0.0029850	09101	170072231	127993427	151830.87
9101102029	09101	5	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	496	0.0017563	09101	100066223	49571481	99942.50
9101102034	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	172	0.0006090	09101	34700384	15713117	91355.33
9101102057	09101	33	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	1762	0.0062390	09101	355477189	145676139	82676.58
9101112013	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	214	0.0007578	09101	43173733	15713117	73425.78
9101112019	09101	13	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	142	0.0005028	09101	28647991	87084357	613270.12
9101112029	09101	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	181	0.0006409	09101	36516102	27059252	149498.63
9101112044	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	46	0.0001629	09101	9280335	15713117	341589.51
9101112045	09101	4	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	292	0.0010339	09101	58909954	43105109	147620.24
9101112047	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	124	0.0004391	09101	25016556	15713117	126718.69
9101112052	09101	4	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	100	0.0003541	09101	20174642	43105109	431051.09
9101112054	09101	6	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	443	0.0015686	09101	89373663	55417529	125096.00
9101112062	09101	15	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	167	0.0005913	09101	33691652	94409854	565328.47
9101112067	09101	5	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	514	0.0018200	09101	103697659	49571481	96442.57
9101112069	09101	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	129	0.0004568	09101	26025288	27059252	209761.64
9101112070	09101	7	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	325	0.0011508	09101	65567586	60793524	187057.00
9101112071	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	230	0.0008144	09101	46401676	15713117	68317.90
9101112073	09101	2	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	132	0.0004674	09101	26630527	27059252	204994.33
9101122051	09101	28	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	357	0.0012641	09101	72023471	133265922	373293.90
9101122060	09101	9	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	80	0.0002833	09101	16139713	70497101	881213.77
9101122061	09101	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	21	0.0000744	09101	4236675	35765447	1703116.53
9101132033	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	36	0.0001275	09101	7262871	15713117	436475.48
9101132036	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	35	0.0001239	09101	7061125	15713117	448946.21
9101142012	09101	28	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	136	0.0004816	09101	27437513	133265922	979896.49
9101142022	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	90	0.0003187	09101	18157178	15713117	174590.19
9101142024	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	57	0.0002018	09101	11499546	15713117	275668.73
9101142037	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	48	0.0001700	09101	9683828	15713117	327356.61
9101142040	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	218	0.0007719	09101	43980719	15713117	72078.52
9101142042	09101	10	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	426	0.0015084	09101	85943974	74942210	175920.68
9101142054	09101	16	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	1860	0.0065861	09101	375248338	97889093	52628.54
9101142062	09101	12	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	113	0.0004001	09101	22797345	83209769	736369.64
9101142064	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	111	0.0003930	09101	22393852	15713117	141559.62
9101142066	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	65	0.0002302	09101	13113517	15713117	241740.27
9101152002	09101	14	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	933	0.0033036	09101	188229408	90812574	97333.95
9101152011	09101	6	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	290	0.0010269	09101	58506461	55417529	191094.93
9101152017	09101	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	317	0.0011225	09101	63953615	35765447	112824.75
9101152020	09101	1	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	101	0.0003576	09101	20376388	15713117	155575.42
9101152023	09101	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	111	0.0003930	09101	22393852	35765447	322211.24
9101152027	09101	3	2017	Temuco	201746.4	2017	9101	282415	56976214691	Rural	148	0.0005241	09101	29858470	35765447	241658.43
9102022011	09102	4	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	327	0.0133290	09102	49430829	43105109	131819.91
9102022031	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	163	0.0066441	09102	24639832	15713117	96399.49
9102022054	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	190	0.0077447	09102	28721277	15713117	82700.62
9102032009	09102	5	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	496	0.0202177	09102	74977648	49571481	99942.50
9102032035	09102	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	255	0.0103942	09102	38546976	35765447	140256.66
9102032037	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	104	0.0042392	09102	15721120	15713117	151087.67
9102032042	09102	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	190	0.0077447	09102	28721277	35765447	188239.20
9102032901	09102	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	52	0.0021196	09102	7860560	35765447	687797.06
9102042004	09102	5	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	221	0.0090083	09102	33407380	49571481	224305.35
9102042009	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	128	0.0052175	09102	19349070	15713117	122758.73
9102042010	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	302	0.0123099	09102	45651713	27059252	89600.17
9102042015	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	430	0.0175274	09102	65000784	27059252	62928.49
9102042017	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	148	0.0060327	09102	22372363	15713117	106169.71
9102042029	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	215	0.0087637	09102	32500392	27059252	125856.99
9102042045	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	205	0.0083561	09102	30988746	15713117	76649.35
9102052013	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	156	0.0063588	09102	23581680	27059252	173456.74
9102052018	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	338	0.0137774	09102	51093639	27059252	80056.96
9102052034	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	273	0.0111279	09102	41267939	27059252	99118.14
9102052040	09102	8	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	730	0.0297558	09102	110350168	65797378	90133.40
9102052047	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	327	0.0133290	09102	49430829	27059252	82750.01
9102052055	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	216	0.0088045	09102	32651556	27059252	125274.31
9102062003	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	47	0.0019158	09102	7104737	15713117	334321.65
9102062030	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	70	0.0028533	09102	10581523	15713117	224473.11
9102062032	09102	5	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	409	0.0166714	09102	61826327	49571481	121201.67
9102062044	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	222	0.0090490	09102	33558544	15713117	70779.81
9102072001	09102	4	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	233	0.0094974	09102	35221355	43105109	185000.47
9102072003	09102	11	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	542	0.0220927	09102	81931220	79170085	146070.27
9102082024	09102	4	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	307	0.0125138	09102	46407536	43105109	140407.52
9102082027	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	218	0.0088860	09102	32953886	27059252	124125.01
9102082036	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	201	0.0081930	09102	30384087	15713117	78174.71
9102092024	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	117	0.0047691	09102	17686260	15713117	134300.15
9102092036	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	123	0.0050137	09102	18593247	27059252	219993.92
9102092053	09102	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	225	0.0091713	09102	34012038	35765447	158957.54
9102102012	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	30	0.0012228	09102	4534938	27059252	901975.06
9102112019	09102	8	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	136	0.0055436	09102	20558387	65797378	483804.25
9102112022	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	57	0.0023234	09102	8616383	27059252	474723.72
9102112023	09102	8	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	177	0.0072148	09102	26756137	65797378	371736.60
9102112033	09102	2	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	186	0.0075816	09102	28116618	27059252	145479.85
9102122018	09102	3	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	282	0.0114947	09102	42628421	35765447	126827.83
9102122046	09102	1	2017	Carahue	151164.6	2017	9102	24533	3708521457	Rural	64	0.0026087	09102	9674535	15713117	245517.46
9103012020	09103	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	174	0.0099281	09103	28247888	15713117	90305.27
9103012045	09103	2	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	151	0.0086158	09103	24513972	27059252	179200.34
9103022001	09103	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	183	0.0104416	09103	29708986	15713117	85864.03
9103022042	09103	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	105	0.0059911	09103	17046139	15713117	149648.74
9103032901	09103	4	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	212	0.0120963	09103	34416967	43105109	203325.99
9103042019	09103	2	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	185	0.0105557	09103	30033674	27059252	146266.23
9103042027	09103	11	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	145	0.0082734	09103	23539907	79170085	546000.59
9103052009	09103	3	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	65	0.0037088	09103	10552372	35765447	550237.65
9103052032	09103	15	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	74	0.0042223	09103	12013470	94409854	1275808.84
9103052043	09103	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	56	0.0031953	09103	9091274	15713117	280591.38
9103062018	09103	1	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	162	0.0092434	09103	26299758	15713117	96994.55
9103062019	09103	2	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	139	0.0079311	09103	22565842	27059252	194670.88
9103062022	09103	26	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	610	0.0348054	09103	99029953	127993427	209825.29
9103062040	09103	6	2017	Cunco	162344.2	2017	9103	17526	2845244196	Rural	83	0.0047358	09103	13474567	55417529	667681.07

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_rural_nivel_nacional_17_r09.rds")

---

R-10

Urbano

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 1)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15201	1	1	1	1767	1	1	1	2	1	3	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	2	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	3	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	4	1	1	2	3	1	99	1	2	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	5	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	6	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	7	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	8	1	1	3	3	1	99	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	9	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	10	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	11	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	12	1	1	4	3	3	3	4	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	13	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	14	1	1	2	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	15	1	1	2	1	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	16	1	1	5	1	5	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	17	1	1	5	3	1	3	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	18	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	19	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	20	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	21	4	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	22	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	23	1	1	5	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	24	1	1	3	1	1	6	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	25	1	1	5	6	1	1	4	2	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	26	4	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	27	1	1	5	99	3	3	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	28	1	1	6	3	2	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	29	1	1	2	3	1	99	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	30	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	31	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	32	1	1	3	1	1	2	1	1	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	33	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	34	3	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	35	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	36	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	37	1	1	5	3	3	1	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	38	1	1	1	1	4	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	39	1	1	5	3	4	3	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	40	1	1	5	3	2	1	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	41	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	42	1	1	2	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	43	1	1	1	1	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	44	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	45	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	46	1	1	5	6	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	47	1	1	5	3	4	0	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	48	1	1	3	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	49	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	50	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	51	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	52	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	53	1	1	1	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	54	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	55	1	1	2	2	1	4	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	56	8	1	98	98	98	98	98	98	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	57	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	58	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	59	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	60	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	61	1	1	2	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	62	1	1	5	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	63	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	64	1	1	5	3	2	4	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	65	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	66	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	67	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	68	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	69	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	70	1	1	1	3	3	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	71	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	72	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	73	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	74	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	75	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	76	1	1	5	3	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	77	1	1	3	6	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	78	5	1	3	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	79	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	80	1	1	2	1	1	3	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	81	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	82	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	83	1	1	5	3	2	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	84	4	1	5	3	2	0	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	85	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	86	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	87	1	1	3	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	88	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	89	5	1	4	3	2	1	2	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	90	1	1	5	3	3	2	1	3	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	91	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	92	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	93	5	1	4	3	5	1	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	94	1	1	5	3	3	99	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	95	1	1	5	3	2	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	96	1	1	2	3	4	6	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	97	1	1	5	1	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	98	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	99	1	1	2	7	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	100	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 10:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 10)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	10101011001	60	2017	10101
2	10101011002	177	2017	10101
3	10101021001	82	2017	10101
4	10101021002	77	2017	10101
5	10101021003	70	2017	10101
6	10101021004	99	2017	10101
7	10101021005	171	2017	10101
8	10101031001	133	2017	10101
9	10101031002	115	2017	10101
10	10101031003	94	2017	10101
11	10101031004	88	2017	10101
12	10101031005	146	2017	10101
13	10101031006	94	2017	10101
14	10101031007	39	2017	10101
15	10101031008	54	2017	10101
16	10101031009	166	2017	10101
17	10101031010	92	2017	10101
18	10101031011	49	2017	10101
19	10101031012	94	2017	10101
20	10101031013	73	2017	10101
21	10101031014	109	2017	10101
22	10101031015	31	2017	10101
23	10101031016	248	2017	10101
24	10101031017	60	2017	10101
25	10101041001	70	2017	10101
26	10101041002	55	2017	10101
27	10101041003	774	2017	10101
28	10101051001	246	2017	10101
29	10101051002	33	2017	10101
30	10101051003	65	2017	10101
31	10101051004	307	2017	10101
32	10101061001	1239	2017	10101
33	10101061002	329	2017	10101
34	10101061003	160	2017	10101
35	10101061004	110	2017	10101
36	10101061005	312	2017	10101
37	10101061006	401	2017	10101
38	10101061007	12	2017	10101
39	10101061008	388	2017	10101
40	10101061009	1	2017	10101
41	10101061010	6	2017	10101
42	10101071001	20	2017	10101
43	10101071002	54	2017	10101
44	10101071003	112	2017	10101
45	10101071004	75	2017	10101
46	10101071005	61	2017	10101
47	10101071006	60	2017	10101
48	10101071007	29	2017	10101
49	10101071008	77	2017	10101
50	10101071009	49	2017	10101
51	10101071010	48	2017	10101
52	10101071011	43	2017	10101
53	10101071012	47	2017	10101
54	10101071014	26	2017	10101
55	10101131001	88	2017	10101
56	10101151001	65	2017	10101
57	10101151002	136	2017	10101
58	10101151003	25	2017	10101
59	10101151004	7	2017	10101
60	10101151005	3	2017	10101
61	10101161001	10	2017	10101
62	10101161002	49	2017	10101
63	10101161003	77	2017	10101
64	10101161004	27	2017	10101
65	10101161005	1	2017	10101
66	10101161006	12	2017	10101
67	10101171001	25	2017	10101
68	10101171002	125	2017	10101
69	10101171003	531	2017	10101
70	10101171004	521	2017	10101
71	10101171005	153	2017	10101
72	10101171006	85	2017	10101
73	10101181001	54	2017	10101
74	10101181002	44	2017	10101
75	10101181003	33	2017	10101
76	10101181004	24	2017	10101
77	10101991999	27	2017	10101
296	10102051001	36	2017	10102
297	10102051002	59	2017	10102
298	10102141001	64	2017	10102
299	10102141002	95	2017	10102
300	10102991999	1	2017	10102
519	10104011001	54	2017	10104
520	10104011002	83	2017	10104
739	10105011001	57	2017	10105
740	10105011002	38	2017	10105
741	10105011003	198	2017	10105
742	10105011004	97	2017	10105
743	10105991999	9	2017	10105
962	10106011001	91	2017	10106
963	10106011002	39	2017	10106
964	10106991999	4	2017	10106
1183	10107011001	49	2017	10107
1184	10107011002	20	2017	10107
1185	10107011003	113	2017	10107
1186	10107021001	35	2017	10107
1187	10107021002	95	2017	10107
1188	10107991999	5	2017	10107
1407	10108011001	45	2017	10108
1408	10108071001	52	2017	10108

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_urbano_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
01107	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
01401	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
01405	Pica	330061.1	2017	1405	9296	3068247619	Urbano
02101	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02102	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02104	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02201	Calama	416281.1	2017	2201	165731	68990679686	Urbano
02203	San Pedro de Atacama	437934.7	2017	2203	10996	4815529626	Urbano
02301	Tocopilla	271720.8	2017	2301	25186	6843559467	Urbano
02302	María Elena	466266.9	2017	2302	6457	3010685220	Urbano
03101	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
03102	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
03103	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
03201	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
03202	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
03301	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
03303	Freirina	289049.9	2017	3303	7041	2035200054	Urbano
03304	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
04101	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04102	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04103	Andacollo	251267.7	2017	4103	11044	2775000288	Urbano
04104	La Higuera	214257.0	2017	4104	4241	908664019	Urbano
04106	Vicuña	245957.4	2017	4106	27771	6830481918	Urbano
04201	Illapel	270316.5	2017	4201	30848	8338722128	Urbano
04202	Canela	233397.3	2017	4202	9093	2122281844	Urbano
04203	Los Vilos	282415.6	2017	4203	21382	6038609501	Urbano
04204	Salamanca	262056.9	2017	4204	29347	7690585032	Urbano
04301	Ovalle	274771.4	2017	4301	111272	30574361012	Urbano
04302	Combarbalá	228990.4	2017	4302	13322	3050610572	Urbano
04303	Monte Patria	225369.1	2017	4303	30751	6930326684	Urbano
04304	Punitaqui	212496.1	2017	4304	10956	2328107498	Urbano
05101	Valparaíso	297929.0	2017	5101	296655	88382118059	Urbano
05102	Casablanca	341641.8	2017	5102	26867	9178890241	Urbano
05103	Concón	318496.3	2017	5103	42152	13425257057	Urbano
05105	Puchuncaví	296035.5	2017	5105	18546	5490274928	Urbano
05107	Quintero	308224.7	2017	5107	31923	9839456903	Urbano
05109	Viña del Mar	337006.1	2017	5109	334248	112643604611	Urbano
05301	Los Andes	339720.2	2017	5301	66708	22662055502	Urbano
05302	Calle Larga	246387.3	2017	5302	14832	3654416747	Urbano
05303	Rinconada	273904.7	2017	5303	10207	2795744821	Urbano
05304	San Esteban	219571.6	2017	5304	18855	4140022481	Urbano
05401	La Ligua	250134.4	2017	5401	35390	8852256241	Urbano
05402	Cabildo	262745.9	2017	5402	19388	5094117762	Urbano
05403	Papudo	294355.2	2017	5403	6356	1870921373	Urbano
05404	Petorca	237510.8	2017	5404	9826	2333781007	Urbano
05405	Zapallar	294389.2	2017	5405	7339	2160521991	Urbano
05501	Quillota	286029.5	2017	5501	90517	25890529852	Urbano
05502	Calera	277181.9	2017	5502	50554	14012652087	Urbano
05503	Hijuelas	254094.0	2017	5503	17988	4570642363	Urbano

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
10101	10101021004	99	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031003	94	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031004	88	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031005	146	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031006	94	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101011001	60	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101011002	177	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101021001	82	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101021002	77	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101021003	70	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031012	94	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101021005	171	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031001	133	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031002	115	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031016	248	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031017	60	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101041001	70	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101041002	55	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101041003	774	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101051001	246	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101051002	33	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101051003	65	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101051004	307	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101061001	1239	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101061002	329	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101061003	160	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101061004	110	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101061005	312	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101061006	401	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101061007	12	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101061008	388	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101061009	1	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101061010	6	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071001	20	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071002	54	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071003	112	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071004	75	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071005	61	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071006	60	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071007	29	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071008	77	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071009	49	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071010	48	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071011	43	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071012	47	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031007	39	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031008	54	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031009	166	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031010	92	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031011	49	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101151004	7	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031013	73	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031014	109	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031015	31	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101161003	77	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101161004	27	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101161005	1	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101161006	12	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101171001	25	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101171002	125	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101171003	531	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101171004	521	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101171005	153	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101171006	85	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101181001	54	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101181002	44	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101181003	33	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101181004	24	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101991999	27	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101131001	88	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101151001	65	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101151002	136	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071014	26	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101151005	3	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101161001	10	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101151003	25	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101161002	49	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10102	10102051001	36	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano
10102	10102141002	95	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano
10102	10102051002	59	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano
10102	10102141001	64	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano
10102	10102991999	1	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano
10104	10104011001	54	2017	Fresia	214742.9	2017	10104	12261	2632962373	Urbano
10104	10104011002	83	2017	Fresia	214742.9	2017	10104	12261	2632962373	Urbano
10105	10105011002	38	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano
10105	10105011001	57	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano
10105	10105011003	198	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano
10105	10105011004	97	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano
10105	10105991999	9	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano
10106	10106011002	39	2017	Los Muermos	225441.2	2017	10106	17068	3847831214	Urbano
10106	10106991999	4	2017	Los Muermos	225441.2	2017	10106	17068	3847831214	Urbano
10106	10106011001	91	2017	Los Muermos	225441.2	2017	10106	17068	3847831214	Urbano
10107	10107991999	5	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano
10107	10107011001	49	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano
10107	10107021002	95	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano
10107	10107011002	20	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano
10107	10107011003	113	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano
10107	10107021001	35	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano
10108	10108991999	1	2017	Maullín	261660.2	2017	10108	14216	3719761516	Urbano
10108	10108011001	45	2017	Maullín	261660.2	2017	10108	14216	3719761516	Urbano

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
10101	10101021004	99	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031003	94	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031004	88	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031005	146	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031006	94	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101011001	60	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101011002	177	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101021001	82	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101021002	77	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101021003	70	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031012	94	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101021005	171	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031001	133	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031002	115	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031016	248	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031017	60	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101041001	70	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101041002	55	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101041003	774	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101051001	246	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101051002	33	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101051003	65	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101051004	307	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101061001	1239	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101061002	329	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101061003	160	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101061004	110	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101061005	312	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101061006	401	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101061007	12	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101061008	388	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101061009	1	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101061010	6	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071001	20	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071002	54	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071003	112	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071004	75	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071005	61	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071006	60	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071007	29	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071008	77	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071009	49	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071010	48	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071011	43	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071012	47	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031007	39	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031008	54	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031009	166	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031010	92	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031011	49	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101151004	7	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031013	73	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031014	109	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101031015	31	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101161003	77	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101161004	27	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101161005	1	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101161006	12	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101171001	25	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101171002	125	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101171003	531	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101171004	521	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101171005	153	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101171006	85	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101181001	54	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101181002	44	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101181003	33	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101181004	24	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101991999	27	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101131001	88	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101151001	65	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101151002	136	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101071014	26	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101151005	3	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101161001	10	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101151003	25	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10101	10101161002	49	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano
10102	10102051001	36	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano
10102	10102141002	95	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano
10102	10102051002	59	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano
10102	10102141001	64	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano
10102	10102991999	1	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano
10104	10104011001	54	2017	Fresia	214742.9	2017	10104	12261	2632962373	Urbano
10104	10104011002	83	2017	Fresia	214742.9	2017	10104	12261	2632962373	Urbano
10105	10105011002	38	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano
10105	10105011001	57	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano
10105	10105011003	198	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano
10105	10105011004	97	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano
10105	10105991999	9	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano
10106	10106011002	39	2017	Los Muermos	225441.2	2017	10106	17068	3847831214	Urbano
10106	10106991999	4	2017	Los Muermos	225441.2	2017	10106	17068	3847831214	Urbano
10106	10106011001	91	2017	Los Muermos	225441.2	2017	10106	17068	3847831214	Urbano
10107	10107991999	5	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano
10107	10107011001	49	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano
10107	10107021002	95	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano
10107	10107011002	20	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano
10107	10107011003	113	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano
10107	10107021001	35	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano
10108	10108991999	1	2017	Maullín	261660.2	2017	10108	14216	3719761516	Urbano
10108	10108011001	45	2017	Maullín	261660.2	2017	10108	14216	3719761516	Urbano

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
10101011001	10101	60	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	584	0.0023749	10101
10101011002	10101	177	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2941	0.0119600	10101
10101021001	10101	82	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3953	0.0160755	10101
10101021002	10101	77	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1107	0.0045018	10101
10101021003	10101	70	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2294	0.0093289	10101
10101021004	10101	99	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3391	0.0137900	10101
10101021005	10101	171	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2564	0.0104269	10101
10101031001	10101	133	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4530	0.0184220	10101
10101031002	10101	115	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4740	0.0192760	10101
10101031003	10101	94	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4107	0.0167018	10101
10101031004	10101	88	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2856	0.0116144	10101
10101031005	10101	146	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	5690	0.0231393	10101
10101031006	10101	94	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2460	0.0100040	10101
10101031007	10101	39	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2292	0.0093208	10101
10101031008	10101	54	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3585	0.0145790	10101
10101031009	10101	166	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4436	0.0180397	10101
10101031010	10101	92	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3566	0.0145017	10101
10101031011	10101	49	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2757	0.0112118	10101
10101031012	10101	94	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1849	0.0075193	10101
10101031013	10101	73	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3945	0.0160430	10101
10101031014	10101	109	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2265	0.0092110	10101
10101031015	10101	31	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1930	0.0078487	10101
10101031016	10101	248	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3071	0.0124887	10101
10101031017	10101	60	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3885	0.0157990	10101
10101041001	10101	70	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4342	0.0176574	10101
10101041002	10101	55	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2169	0.0088206	10101
10101041003	10101	774	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	5202	0.0211548	10101
10101051001	10101	246	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2463	0.0100162	10101
10101051002	10101	33	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1913	0.0077795	10101
10101051003	10101	65	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3272	0.0133061	10101
10101051004	10101	307	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3633	0.0147742	10101
10101061001	10101	1239	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	6787	0.0276004	10101
10101061002	10101	329	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2729	0.0110979	10101
10101061003	10101	160	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3668	0.0149165	10101
10101061004	10101	110	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2995	0.0121796	10101
10101061005	10101	312	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2571	0.0104554	10101
10101061006	10101	401	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4130	0.0167953	10101
10101061007	10101	12	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	817	0.0033225	10101
10101061008	10101	388	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2109	0.0085766	10101
10101061009	10101	1	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	168	0.0006832	10101
10101061010	10101	6	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1543	0.0062749	10101
10101071001	10101	20	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2352	0.0095648	10101
10101071002	10101	54	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3919	0.0159372	10101
10101071003	10101	112	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4978	0.0202438	10101
10101071004	10101	75	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3443	0.0140015	10101
10101071005	10101	61	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2751	0.0111874	10101
10101071006	10101	60	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4214	0.0171369	10101
10101071007	10101	29	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2345	0.0095363	10101
10101071008	10101	77	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	5480	0.0222853	10101
10101071009	10101	49	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3549	0.0144326	10101
10101071010	10101	48	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3521	0.0143187	10101
10101071011	10101	43	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3094	0.0125822	10101
10101071012	10101	47	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2621	0.0106587	10101
10101071014	10101	26	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	875	0.0035583	10101
10101131001	10101	88	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	604	0.0024563	10101
10101151001	10101	65	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3973	0.0161568	10101
10101151002	10101	136	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4655	0.0189303	10101
10101151003	10101	25	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	592	0.0024075	10101
10101151004	10101	7	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	325	0.0013217	10101
10101151005	10101	3	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	384	0.0015616	10101
10101161001	10101	10	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	739	0.0030053	10101
10101161002	10101	49	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	6507	0.0264618	10101
10101161003	10101	77	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2841	0.0115534	10101
10101161004	10101	27	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1224	0.0049776	10101
10101161005	10101	1	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	188	0.0007645	10101
10101161006	10101	12	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	435	0.0017690	10101
10101171001	10101	25	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1747	0.0071045	10101
10101171002	10101	125	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2902	0.0118014	10101
10101171003	10101	531	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2873	0.0116835	10101
10101171004	10101	521	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4707	0.0191418	10101
10101171005	10101	153	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3782	0.0153801	10101
10101171006	10101	85	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3515	0.0142943	10101
10101181001	10101	54	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3155	0.0128303	10101
10101181002	10101	44	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2282	0.0092801	10101
10101181003	10101	33	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1312	0.0053355	10101
10101181004	10101	24	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1466	0.0059617	10101
10101991999	10101	27	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1400	0.0056933	10101
10102051001	10102	36	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano	3082	0.0906871	10102
10102051002	10102	59	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano	3879	0.1141386	10102
10102141001	10102	64	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano	3356	0.0987494	10102
10102141002	10102	95	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano	5586	0.1643666	10102
10102991999	10102	1	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano	93	0.0027365	10102
10104011001	10104	54	2017	Fresia	214742.9	2017	10104	12261	2632962373	Urbano	2769	0.2258380	10104
10104011002	10104	83	2017	Fresia	214742.9	2017	10104	12261	2632962373	Urbano	4559	0.3718294	10104
10105011001	10105	57	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano	3426	0.1859127	10105
10105011002	10105	38	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano	3126	0.1696332	10105
10105011003	10105	198	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano	3037	0.1648036	10105
10105011004	10105	97	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano	3287	0.1783699	10105
10105991999	10105	9	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano	76	0.0041242	10105
10106011001	10106	91	2017	Los Muermos	225441.2	2017	10106	17068	3847831214	Urbano	5180	0.3034919	10106
10106011002	10106	39	2017	Los Muermos	225441.2	2017	10106	17068	3847831214	Urbano	2748	0.1610030	10106
10106991999	10106	4	2017	Los Muermos	225441.2	2017	10106	17068	3847831214	Urbano	178	0.0104289	10106
10107011001	10107	49	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	4286	0.2436473	10107
10107011002	10107	20	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	1159	0.0658860	10107
10107011003	10107	113	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	3146	0.1788415	10107
10107021001	10107	35	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	2292	0.1302939	10107
10107021002	10107	95	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	3221	0.1831050	10107
10107991999	10107	5	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	118	0.0067080	10107
10108011001	10108	45	2017	Maullín	261660.2	2017	10108	14216	3719761516	Urbano	3797	0.2670934	10108
10108071001	10108	52	2017	Maullín	261660.2	2017	10108	14216	3719761516	Urbano	2824	0.1986494	10108

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
10101011001	10101	60	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	584	0.0023749	10101	174098232
10101011002	10101	177	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2941	0.0119600	10101	876751541
10101021001	10101	82	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3953	0.0160755	10101	1178442312
10101021002	10101	77	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1107	0.0045018	10101	330011546
10101021003	10101	70	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2294	0.0093289	10101	683872164
10101021004	10101	99	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3391	0.0137900	10101	1010902575
10101021005	10101	171	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2564	0.0104269	10101	764362785
10101031001	10101	133	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4530	0.0184220	10101	1350453750
10101031002	10101	115	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4740	0.0192760	10101	1413057566
10101031003	10101	94	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4107	0.0167018	10101	1224351777
10101031004	10101	88	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2856	0.0116144	10101	851411901
10101031005	10101	146	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	5690	0.0231393	10101	1696265306
10101031006	10101	94	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2460	0.0100040	10101	733358990
10101031007	10101	39	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2292	0.0093208	10101	683275937
10101031008	10101	54	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3585	0.0145790	10101	1068736577
10101031009	10101	166	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4436	0.0180397	10101	1322431089
10101031010	10101	92	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3566	0.0145017	10101	1063072422
10101031011	10101	49	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2757	0.0112118	10101	821898673
10101031012	10101	94	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1849	0.0075193	10101	551211696
10101031013	10101	73	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3945	0.0160430	10101	1176057405
10101031014	10101	109	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2265	0.0092110	10101	675226875
10101031015	10101	31	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1930	0.0078487	10101	575358882
10101031016	10101	248	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3071	0.0124887	10101	915506284
10101031017	10101	60	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3885	0.0157990	10101	1158170600
10101041001	10101	70	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4342	0.0176574	10101	1294408429
10101041002	10101	55	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2169	0.0088206	10101	646607988
10101041003	10101	774	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	5202	0.0211548	10101	1550785962
10101051001	10101	246	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2463	0.0100162	10101	734253330
10101051002	10101	33	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1913	0.0077795	10101	570290954
10101051003	10101	65	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3272	0.0133061	10101	975427079
10101051004	10101	307	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3633	0.0147742	10101	1083046021
10101061001	10101	1239	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	6787	0.0276004	10101	2023295718
10101061002	10101	329	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2729	0.0110979	10101	813551497
10101061003	10101	160	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3668	0.0149165	10101	1093479990
10101061004	10101	110	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2995	0.0121796	10101	892849665
10101061005	10101	312	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2571	0.0104554	10101	766449579
10101061006	10101	401	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4130	0.0167953	10101	1231208386
10101061007	10101	12	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	817	0.0033225	10101	243558656
10101061008	10101	388	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2109	0.0085766	10101	628721183
10101061009	10101	1	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	168	0.0006832	10101	50083053
10101061010	10101	6	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1543	0.0062749	10101	459988993
10101071001	10101	20	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2352	0.0095648	10101	701162742
10101071002	10101	54	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3919	0.0159372	10101	1168306456
10101071003	10101	112	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4978	0.0202438	10101	1484008558
10101071004	10101	75	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3443	0.0140015	10101	1026404473
10101071005	10101	61	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2751	0.0111874	10101	820109993
10101071006	10101	60	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4214	0.0171369	10101	1256249912
10101071007	10101	29	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2345	0.0095363	10101	699075948
10101071008	10101	77	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	5480	0.0222853	10101	1633661490
10101071009	10101	49	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3549	0.0144326	10101	1058004494
10101071010	10101	48	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3521	0.0143187	10101	1049657319
10101071011	10101	43	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3094	0.0125822	10101	922362892
10101071012	10101	47	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2621	0.0106587	10101	781355249
10101071014	10101	26	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	875	0.0035583	10101	260849234
10101131001	10101	88	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	604	0.0024563	10101	180060500
10101151001	10101	65	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3973	0.0161568	10101	1184404580
10101151002	10101	136	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4655	0.0189303	10101	1387717926
10101151003	10101	25	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	592	0.0024075	10101	176483139
10101151004	10101	7	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	325	0.0013217	10101	96886858
10101151005	10101	3	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	384	0.0015616	10101	114475550
10101161001	10101	10	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	739	0.0030053	10101	220305810
10101161002	10101	49	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	6507	0.0264618	10101	1939823963
10101161003	10101	77	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2841	0.0115534	10101	846940199
10101161004	10101	27	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1224	0.0049776	10101	364890815
10101161005	10101	1	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	188	0.0007645	10101	56045321
10101161006	10101	12	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	435	0.0017690	10101	129679334
10101171001	10101	25	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1747	0.0071045	10101	520804128
10101171002	10101	125	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2902	0.0118014	10101	865125118
10101171003	10101	531	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2873	0.0116835	10101	856479829
10101171004	10101	521	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4707	0.0191418	10101	1403219824
10101171005	10101	153	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3782	0.0153801	10101	1127464919
10101171006	10101	85	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3515	0.0142943	10101	1047868638
10101181001	10101	54	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3155	0.0128303	10101	940547810
10101181002	10101	44	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2282	0.0092801	10101	680294803
10101181003	10101	33	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1312	0.0053355	10101	391124795
10101181004	10101	24	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1466	0.0059617	10101	437034260
10101991999	10101	27	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1400	0.0056933	10101	417358775
10102051001	10102	36	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano	3082	0.0906871	10102	821793583
10102051002	10102	59	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano	3879	0.1141386	10102	1034308017
10102141001	10102	64	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano	3356	0.0987494	10102	894853753
10102141002	10102	95	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano	5586	0.1643666	10102	1489467539
10102991999	10102	1	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano	93	0.0027365	10102	24797795
10104011001	10104	54	2017	Fresia	214742.9	2017	10104	12261	2632962373	Urbano	2769	0.2258380	10104	594623017
10104011002	10104	83	2017	Fresia	214742.9	2017	10104	12261	2632962373	Urbano	4559	0.3718294	10104	979012761
10105011001	10105	57	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano	3426	0.1859127	10105	914336264
10105011002	10105	38	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano	3126	0.1696332	10105	834271792
10105011003	10105	198	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano	3037	0.1648036	10105	810519332
10105011004	10105	97	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano	3287	0.1783699	10105	877239725
10105991999	10105	9	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano	76	0.0041242	10105	20282999
10106011001	10106	91	2017	Los Muermos	225441.2	2017	10106	17068	3847831214	Urbano	5180	0.3034919	10106	1167785663
10106011002	10106	39	2017	Los Muermos	225441.2	2017	10106	17068	3847831214	Urbano	2748	0.1610030	10106	619512548
10106991999	10106	4	2017	Los Muermos	225441.2	2017	10106	17068	3847831214	Urbano	178	0.0104289	10106	40128542
10107011001	10107	49	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	4286	0.2436473	10107	1017092114
10107011002	10107	20	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	1159	0.0658860	10107	275037275
10107011003	10107	113	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	3146	0.1788415	10107	746563647
10107021001	10107	35	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	2292	0.1302939	10107	543904602
10107021002	10107	95	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	3221	0.1831050	10107	764361572
10107991999	10107	5	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	118	0.0067080	10107	28002069
10108011001	10108	45	2017	Maullín	261660.2	2017	10108	14216	3719761516	Urbano	3797	0.2670934	10108	993523809
10108071001	10108	52	2017	Maullín	261660.2	2017	10108	14216	3719761516	Urbano	2824	0.1986494	10108	738928427

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -687628840 -243648553     964103  265793190 1200856128 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 687894834   31814825  21.622  < 2e-16 ***
## Freq.x        1042306     179154   5.818 2.23e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 381500000 on 208 degrees of freedom
##   (8 observations deleted due to missingness)
## Multiple R-squared:   0.14,  Adjusted R-squared:  0.1358 
## F-statistic: 33.85 on 1 and 208 DF,  p-value: 2.226e-08

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.135822249559619"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.135822249559619"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.400198349578994"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.247818588701607"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                
## [1,] "raíz-raíz" "0.26818474899705"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.239771124407456"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.528147354700177"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.595490396176806"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 1    cuadrático 0.135822249559619
## 2        cúbico 0.135822249559619
## 6      log-raíz 0.239771124407456
## 4 raíz cuadrada 0.247818588701607
## 5     raíz-raíz  0.26818474899705
## 3   logarítmico 0.400198349578994
## 7      raíz-log 0.528147354700177
## 8       log-log 0.595490396176806
##                                                                     sintaxis
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 8
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5516 -0.4054  0.1458  0.4733  1.2693 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.63582    0.15289  115.35   <2e-16 ***
## log(Freq.x)  0.64717    0.03684   17.57   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6867 on 208 degrees of freedom
##   (8 observations deleted due to missingness)
## Multiple R-squared:  0.5974, Adjusted R-squared:  0.5955 
## F-statistic: 308.7 on 1 and 208 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    17.63582

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##   0.6471664

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.5955 ).

9.1 Diagrama de dispersión sobre log-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5516 -0.4054  0.1458  0.4733  1.2693 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.63582    0.15289  115.35   <2e-16 ***
## log(Freq.x)  0.64717    0.03684   17.57   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6867 on 208 degrees of freedom
##   (8 observations deleted due to missingness)
## Multiple R-squared:  0.5974, Adjusted R-squared:  0.5955 
## F-statistic: 308.7 on 1 and 208 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = e^{17.63582+0.6471664 \cdot ln{X}} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
10101011001	10101	60	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	584	0.0023749	10101	174098232	645502658
10101011002	10101	177	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2941	0.0119600	10101	876751541	1300023118
10101021001	10101	82	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3953	0.0160755	10101	1178442312	790122297
10101021002	10101	77	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1107	0.0045018	10101	330011546	758598024
10101021003	10101	70	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2294	0.0093289	10101	683872164	713220390
10101021004	10101	99	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3391	0.0137900	10101	1010902575	892578341
10101021005	10101	171	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2564	0.0104269	10101	764362785	1271330197
10101031001	10101	133	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4530	0.0184220	10101	1350453750	1080497398
10101031002	10101	115	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4740	0.0192760	10101	1413057566	983450952
10101031003	10101	94	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4107	0.0167018	10101	1224351777	863138222
10101031004	10101	88	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2856	0.0116144	10101	851411901	827069773
10101031005	10101	146	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	5690	0.0231393	10101	1696265306	1147716835
10101031006	10101	94	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2460	0.0100040	10101	733358990	863138222
10101031007	10101	39	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2292	0.0093208	10101	683275937	488451940
10101031008	10101	54	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3585	0.0145790	10101	1068736577	602955586
10101031009	10101	166	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4436	0.0180397	10101	1322431089	1247147060
10101031010	10101	92	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3566	0.0145017	10101	1063072422	851208197
10101031011	10101	49	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2757	0.0112118	10101	821898673	566208521
10101031012	10101	94	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1849	0.0075193	10101	551211696	863138222
10101031013	10101	73	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3945	0.0160430	10101	1176057405	732855316
10101031014	10101	109	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2265	0.0092110	10101	675226875	949931465
10101031015	10101	31	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1930	0.0078487	10101	575358882	421014894
10101031016	10101	248	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3071	0.0124887	10101	915506284	1617137652
10101031017	10101	60	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3885	0.0157990	10101	1158170600	645502658
10101041001	10101	70	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4342	0.0176574	10101	1294408429	713220390
10101041002	10101	55	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2169	0.0088206	10101	646607988	610158333
10101041003	10101	774	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	5202	0.0211548	10101	1550785962	3377803810
10101051001	10101	246	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2463	0.0100162	10101	734253330	1608685623
10101051002	10101	33	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1913	0.0077795	10101	570290954	438398926
10101051003	10101	65	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3272	0.0133061	10101	975427079	679821513
10101051004	10101	307	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3633	0.0147742	10101	1083046021	1856652843
10101061001	10101	1239	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	6787	0.0276004	10101	2023295718	4580049408
10101061002	10101	329	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2729	0.0110979	10101	813551497	1941703559
10101061003	10101	160	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3668	0.0149165	10101	1093479990	1217785229
10101061004	10101	110	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2995	0.0121796	10101	892849665	955562409
10101061005	10101	312	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2571	0.0104554	10101	766449579	1876166461
10101061006	10101	401	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4130	0.0167953	10101	1231208386	2207018100
10101061007	10101	12	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	817	0.0033225	10101	243558656	227797056
10101061008	10101	388	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2109	0.0085766	10101	628721183	2160445077
10101061009	10101	1	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	168	0.0006832	10101	50083053	45618208
10101061010	10101	6	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1543	0.0062749	10101	459988993	145455986
10101071001	10101	20	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2352	0.0095648	10101	701162742	317045193
10101071002	10101	54	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3919	0.0159372	10101	1168306456	602955586
10101071003	10101	112	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4978	0.0202438	10101	1484008558	966770416
10101071004	10101	75	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3443	0.0140015	10101	1026404473	745787230
10101071005	10101	61	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2751	0.0111874	10101	820109993	652444800
10101071006	10101	60	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4214	0.0171369	10101	1256249912	645502658
10101071007	10101	29	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2345	0.0095363	10101	699075948	403230271
10101071008	10101	77	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	5480	0.0222853	10101	1633661490	758598024
10101071009	10101	49	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3549	0.0144326	10101	1058004494	566208521
10101071010	10101	48	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3521	0.0143187	10101	1049657319	558703159
10101071011	10101	43	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3094	0.0125822	10101	922362892	520312412
10101071012	10101	47	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2621	0.0106587	10101	781355249	551142420
10101071014	10101	26	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	875	0.0035583	10101	260849234	375717554
10101131001	10101	88	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	604	0.0024563	10101	180060500	827069773
10101151001	10101	65	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3973	0.0161568	10101	1184404580	679821513
10101151002	10101	136	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4655	0.0189303	10101	1387717926	1096208082
10101151003	10101	25	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	592	0.0024075	10101	176483139	366300980
10101151004	10101	7	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	325	0.0013217	10101	96886858	160715333
10101151005	10101	3	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	384	0.0015616	10101	114475550	92878478
10101161001	10101	10	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	739	0.0030053	10101	220305810	202443885
10101161002	10101	49	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	6507	0.0264618	10101	1939823963	566208521
10101161003	10101	77	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2841	0.0115534	10101	846940199	758598024
10101161004	10101	27	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1224	0.0049776	10101	364890815	385007166
10101161005	10101	1	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	188	0.0007645	10101	56045321	45618208
10101161006	10101	12	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	435	0.0017690	10101	129679334	227797056
10101171001	10101	25	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1747	0.0071045	10101	520804128	366300980
10101171002	10101	125	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2902	0.0118014	10101	865125118	1037977663
10101171003	10101	531	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2873	0.0116835	10101	856479829	2646841516
10101171004	10101	521	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4707	0.0191418	10101	1403219824	2614474531
10101171005	10101	153	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3782	0.0153801	10101	1127464919	1183033916
10101171006	10101	85	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3515	0.0142943	10101	1047868638	808711088
10101181001	10101	54	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3155	0.0128303	10101	940547810	602955586
10101181002	10101	44	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2282	0.0092801	10101	680294803	528111517
10101181003	10101	33	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1312	0.0053355	10101	391124795	438398926
10101181004	10101	24	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1466	0.0059617	10101	437034260	356750522
10101991999	10101	27	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1400	0.0056933	10101	417358775	385007166
10102051001	10102	36	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano	3082	0.0906871	10102	821793583	463793834
10102051002	10102	59	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano	3879	0.1141386	10102	1034308017	638519570
10102141001	10102	64	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano	3356	0.0987494	10102	894853753	673034434
10102141002	10102	95	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano	5586	0.1643666	10102	1489467539	869069613
10102991999	10102	1	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano	93	0.0027365	10102	24797795	45618208
10104011001	10104	54	2017	Fresia	214742.9	2017	10104	12261	2632962373	Urbano	2769	0.2258380	10104	594623017	602955586
10104011002	10104	83	2017	Fresia	214742.9	2017	10104	12261	2632962373	Urbano	4559	0.3718294	10104	979012761	796344816
10105011001	10105	57	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano	3426	0.1859127	10105	914336264	624426751
10105011002	10105	38	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano	3126	0.1696332	10105	834271792	480309468
10105011003	10105	198	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano	3037	0.1648036	10105	810519332	1397857350
10105011004	10105	97	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano	3287	0.1783699	10105	877239725	880866737
10105991999	10105	9	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano	76	0.0041242	10105	20282999	189100184
10106011001	10106	91	2017	Los Muermos	225441.2	2017	10106	17068	3847831214	Urbano	5180	0.3034919	10106	1167785663	845208904
10106011002	10106	39	2017	Los Muermos	225441.2	2017	10106	17068	3847831214	Urbano	2748	0.1610030	10106	619512548	488451940
10106991999	10106	4	2017	Los Muermos	225441.2	2017	10106	17068	3847831214	Urbano	178	0.0104289	10106	40128542	111884840
10107011001	10107	49	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	4286	0.2436473	10107	1017092114	566208521
10107011002	10107	20	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	1159	0.0658860	10107	275037275	317045193
10107011003	10107	113	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	3146	0.1788415	10107	746563647	972347915
10107021001	10107	35	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	2292	0.1302939	10107	543904602	455414905
10107021002	10107	95	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	3221	0.1831050	10107	764361572	869069613
10107991999	10107	5	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	118	0.0067080	10107	28002069	129267144
10108011001	10108	45	2017	Maullín	261660.2	2017	10108	14216	3719761516	Urbano	3797	0.2670934	10108	993523809	535848326
10108071001	10108	52	2017	Maullín	261660.2	2017	10108	14216	3719761516	Urbano	2824	0.1986494	10108	738928427	588407224

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
10101011001	10101	60	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	584	0.0023749	10101	174098232	645502658	1105312.77
10101011002	10101	177	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2941	0.0119600	10101	876751541	1300023118	442034.38
10101021001	10101	82	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3953	0.0160755	10101	1178442312	790122297	199879.15
10101021002	10101	77	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1107	0.0045018	10101	330011546	758598024	685273.73
10101021003	10101	70	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2294	0.0093289	10101	683872164	713220390	310906.88
10101021004	10101	99	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3391	0.0137900	10101	1010902575	892578341	263219.80
10101021005	10101	171	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2564	0.0104269	10101	764362785	1271330197	495838.61
10101031001	10101	133	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4530	0.0184220	10101	1350453750	1080497398	238520.40
10101031002	10101	115	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4740	0.0192760	10101	1413057566	983450952	207479.10
10101031003	10101	94	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4107	0.0167018	10101	1224351777	863138222	210162.70
10101031004	10101	88	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2856	0.0116144	10101	851411901	827069773	289590.26
10101031005	10101	146	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	5690	0.0231393	10101	1696265306	1147716835	201707.70
10101031006	10101	94	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2460	0.0100040	10101	733358990	863138222	350869.20
10101031007	10101	39	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2292	0.0093208	10101	683275937	488451940	213111.67
10101031008	10101	54	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3585	0.0145790	10101	1068736577	602955586	168188.45
10101031009	10101	166	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4436	0.0180397	10101	1322431089	1247147060	281142.26
10101031010	10101	92	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3566	0.0145017	10101	1063072422	851208197	238701.12
10101031011	10101	49	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2757	0.0112118	10101	821898673	566208521	205371.24
10101031012	10101	94	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1849	0.0075193	10101	551211696	863138222	466813.53
10101031013	10101	73	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3945	0.0160430	10101	1176057405	732855316	185768.14
10101031014	10101	109	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2265	0.0092110	10101	675226875	949931465	419395.79
10101031015	10101	31	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1930	0.0078487	10101	575358882	421014894	218142.43
10101031016	10101	248	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3071	0.0124887	10101	915506284	1617137652	526583.41
10101031017	10101	60	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3885	0.0157990	10101	1158170600	645502658	166152.55
10101041001	10101	70	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4342	0.0176574	10101	1294408429	713220390	164260.80
10101041002	10101	55	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2169	0.0088206	10101	646607988	610158333	281308.59
10101041003	10101	774	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	5202	0.0211548	10101	1550785962	3377803810	649327.91
10101051001	10101	246	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2463	0.0100162	10101	734253330	1608685623	653140.73
10101051002	10101	33	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1913	0.0077795	10101	570290954	438398926	229168.28
10101051003	10101	65	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3272	0.0133061	10101	975427079	679821513	207769.41
10101051004	10101	307	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3633	0.0147742	10101	1083046021	1856652843	511052.26
10101061001	10101	1239	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	6787	0.0276004	10101	2023295718	4580049408	674826.79
10101061002	10101	329	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2729	0.0110979	10101	813551497	1941703559	711507.35
10101061003	10101	160	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3668	0.0149165	10101	1093479990	1217785229	332002.52
10101061004	10101	110	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2995	0.0121796	10101	892849665	955562409	319052.56
10101061005	10101	312	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2571	0.0104554	10101	766449579	1876166461	729741.91
10101061006	10101	401	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4130	0.0167953	10101	1231208386	2207018100	534386.95
10101061007	10101	12	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	817	0.0033225	10101	243558656	227797056	278821.37
10101061008	10101	388	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2109	0.0085766	10101	628721183	2160445077	1024393.11
10101061009	10101	1	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	168	0.0006832	10101	50083053	45618208	271536.96
10101061010	10101	6	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1543	0.0062749	10101	459988993	145455986	94268.30
10101071001	10101	20	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2352	0.0095648	10101	701162742	317045193	134798.13
10101071002	10101	54	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3919	0.0159372	10101	1168306456	602955586	153854.45
10101071003	10101	112	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4978	0.0202438	10101	1484008558	966770416	194208.60
10101071004	10101	75	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3443	0.0140015	10101	1026404473	745787230	216609.71
10101071005	10101	61	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2751	0.0111874	10101	820109993	652444800	237166.41
10101071006	10101	60	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4214	0.0171369	10101	1256249912	645502658	153180.51
10101071007	10101	29	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2345	0.0095363	10101	699075948	403230271	171953.21
10101071008	10101	77	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	5480	0.0222853	10101	1633661490	758598024	138430.30
10101071009	10101	49	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3549	0.0144326	10101	1058004494	566208521	159540.30
10101071010	10101	48	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3521	0.0143187	10101	1049657319	558703159	158677.41
10101071011	10101	43	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3094	0.0125822	10101	922362892	520312412	168168.20
10101071012	10101	47	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2621	0.0106587	10101	781355249	551142420	210279.44
10101071014	10101	26	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	875	0.0035583	10101	260849234	375717554	429391.49
10101131001	10101	88	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	604	0.0024563	10101	180060500	827069773	1369320.82
10101151001	10101	65	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3973	0.0161568	10101	1184404580	679821513	171110.37
10101151002	10101	136	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4655	0.0189303	10101	1387717926	1096208082	235490.46
10101151003	10101	25	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	592	0.0024075	10101	176483139	366300980	618751.66
10101151004	10101	7	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	325	0.0013217	10101	96886858	160715333	494508.72
10101151005	10101	3	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	384	0.0015616	10101	114475550	92878478	241871.04
10101161001	10101	10	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	739	0.0030053	10101	220305810	202443885	273943.01
10101161002	10101	49	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	6507	0.0264618	10101	1939823963	566208521	87015.29
10101161003	10101	77	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2841	0.0115534	10101	846940199	758598024	267017.96
10101161004	10101	27	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1224	0.0049776	10101	364890815	385007166	314548.34
10101161005	10101	1	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	188	0.0007645	10101	56045321	45618208	242650.04
10101161006	10101	12	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	435	0.0017690	10101	129679334	227797056	523671.39
10101171001	10101	25	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1747	0.0071045	10101	520804128	366300980	209674.29
10101171002	10101	125	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2902	0.0118014	10101	865125118	1037977663	357676.66
10101171003	10101	531	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2873	0.0116835	10101	856479829	2646841516	921281.42
10101171004	10101	521	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	4707	0.0191418	10101	1403219824	2614474531	555443.92
10101171005	10101	153	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3782	0.0153801	10101	1127464919	1183033916	312806.43
10101171006	10101	85	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3515	0.0142943	10101	1047868638	808711088	230074.28
10101181001	10101	54	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	3155	0.0128303	10101	940547810	602955586	191111.12
10101181002	10101	44	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	2282	0.0092801	10101	680294803	528111517	231424.85
10101181003	10101	33	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1312	0.0053355	10101	391124795	438398926	334145.52
10101181004	10101	24	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1466	0.0059617	10101	437034260	356750522	243349.61
10101991999	10101	27	2017	Puerto Montt	298113.4	2017	10101	245902	73306683889	Urbano	1400	0.0056933	10101	417358775	385007166	275005.12
10102051001	10102	36	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano	3082	0.0906871	10102	821793583	463793834	150484.70
10102051002	10102	59	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano	3879	0.1141386	10102	1034308017	638519570	164609.32
10102141001	10102	64	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano	3356	0.0987494	10102	894853753	673034434	200546.61
10102141002	10102	95	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano	5586	0.1643666	10102	1489467539	869069613	155579.95
10102991999	10102	1	2017	Calbuco	266643.0	2017	10102	33985	9061860782	Urbano	93	0.0027365	10102	24797795	45618208	490518.37
10104011001	10104	54	2017	Fresia	214742.9	2017	10104	12261	2632962373	Urbano	2769	0.2258380	10104	594623017	602955586	217752.11
10104011002	10104	83	2017	Fresia	214742.9	2017	10104	12261	2632962373	Urbano	4559	0.3718294	10104	979012761	796344816	174675.33
10105011001	10105	57	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano	3426	0.1859127	10105	914336264	624426751	182261.16
10105011002	10105	38	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano	3126	0.1696332	10105	834271792	480309468	153649.86
10105011003	10105	198	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano	3037	0.1648036	10105	810519332	1397857350	460275.72
10105011004	10105	97	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano	3287	0.1783699	10105	877239725	880866737	267985.01
10105991999	10105	9	2017	Frutillar	266881.6	2017	10105	18428	4918093598	Urbano	76	0.0041242	10105	20282999	189100184	2488160.31
10106011001	10106	91	2017	Los Muermos	225441.2	2017	10106	17068	3847831214	Urbano	5180	0.3034919	10106	1167785663	845208904	163167.74
10106011002	10106	39	2017	Los Muermos	225441.2	2017	10106	17068	3847831214	Urbano	2748	0.1610030	10106	619512548	488451940	177748.16
10106991999	10106	4	2017	Los Muermos	225441.2	2017	10106	17068	3847831214	Urbano	178	0.0104289	10106	40128542	111884840	628566.52
10107011001	10107	49	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	4286	0.2436473	10107	1017092114	566208521	132106.51
10107011002	10107	20	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	1159	0.0658860	10107	275037275	317045193	273550.64
10107011003	10107	113	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	3146	0.1788415	10107	746563647	972347915	309074.35
10107021001	10107	35	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	2292	0.1302939	10107	543904602	455414905	198697.60
10107021002	10107	95	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	3221	0.1831050	10107	764361572	869069613	269813.60
10107991999	10107	5	2017	Llanquihue	237305.7	2017	10107	17591	4174444092	Urbano	118	0.0067080	10107	28002069	129267144	1095484.27
10108011001	10108	45	2017	Maullín	261660.2	2017	10108	14216	3719761516	Urbano	3797	0.2670934	10108	993523809	535848326	141124.13
10108071001	10108	52	2017	Maullín	261660.2	2017	10108	14216	3719761516	Urbano	2824	0.1986494	10108	738928427	588407224	208359.50

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_urbano_nivel_nacional_17_r10.rds")

---

Rural

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 2)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15202	1	2	6	13225	1	3	1	5	3	5	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	3	1	1	5	3	5	2	3	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	4	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	5	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	6	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	8	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	9	3	1	5	3	5	1	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	10	1	1	5	3	4	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	11	1	2	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	12	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	13	1	1	5	3	4	1	4	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	14	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	16	1	1	5	3	5	1	3	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	17	1	1	5	3	5	2	4	1	8	15	152	15202	15202012006
15	152	15202	1	2	6	13225	18	3	1	5	3	5	1	1	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	19	3	1	5	3	5	1	3	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	20	1	1	5	3	5	2	1	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	21	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	22	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	24	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	25	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	26	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	27	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	28	3	1	5	3	5	1	4	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	29	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	30	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	31	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	32	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	33	1	1	5	3	5	3	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	34	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	35	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	36	1	1	5	3	5	3	2	1	9	15	152	15202	15202012006
15	152	15202	1	2	6	13225	37	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	38	1	1	5	3	5	99	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	39	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	40	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	41	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	42	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	43	3	1	5	3	5	2	1	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	44	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	45	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	8	13910	1	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	3	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	4	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	5	3	1	5	3	5	2	3	1	3	15	152	15202	15202012008
15	152	15202	1	2	8	13910	6	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	7	1	1	5	99	5	2	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	8	3	1	5	3	5	3	3	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	9	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	10	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	11	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	12	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	13	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	14	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	16	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	18	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	20	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	21	1	1	5	3	5	1	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	22	3	1	5	3	5	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	23	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	24	3	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	25	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	26	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	27	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	28	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	29	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	30	1	1	5	1	4	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	12	8394	1	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	2	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	3	3	1	5	3	5	2	3	1	4	15	152	15202	15202012012
15	152	15202	1	2	12	8394	4	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	5	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	6	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	8	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	9	3	1	5	3	5	1	4	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	10	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	11	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	12	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	13	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	14	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	15	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	16	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	18	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	20	3	1	5	3	5	3	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	21	3	1	5	99	5	1	4	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	22	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	24	3	1	5	3	5	1	2	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	25	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 10:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 10)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Veamos los primeros 100 registros:

r3_100 <- d[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	unlist.c.	unlist.d.	Freq	anio
1	10101032002	1	10101	2	2017
2	10101032011	1	10101	20	2017
3	10101032019	1	10101	32	2017
4	10101062003	1	10101	10	2017
5	10101062008	1	10101	72	2017
6	10101062013	1	10101	61	2017
7	10101062029	1	10101	1	2017
8	10101062039	1	10101	4	2017
9	10101072014	1	10101	36	2017
10	10101072021	1	10101	4	2017
11	10101072028	1	10101	4	2017
12	10101072029	1	10101	36	2017
13	10101072036	1	10101	1	2017
14	10101072045	1	10101	7	2017
15	10101082016	1	10101	13	2017
16	10101082017	1	10101	5	2017
17	10101082018	1	10101	13	2017
18	10101082030	1	10101	3	2017
19	10101082034	1	10101	5	2017
20	10101082042	1	10101	12	2017
21	10101082045	1	10101	6	2017
22	10101092004	1	10101	6	2017
23	10101092008	1	10101	83	2017
24	10101092037	1	10101	11	2017
25	10101092040	1	10101	33	2017
26	10101092041	1	10101	44	2017
27	10101092044	1	10101	21	2017
28	10101102005	1	10101	1	2017
29	10101102007	1	10101	12	2017
30	10101102035	1	10101	22	2017
31	10101102037	1	10101	3	2017
32	10101102051	1	10101	1	2017
33	10101112025	1	10101	13	2017
34	10101122024	1	10101	6	2017
35	10101132022	1	10101	15	2017
36	10101132023	1	10101	12	2017
37	10101132027	1	10101	1	2017
38	10101132049	1	10101	77	2017
39	10101142009	1	10101	2	2017
40	10101142015	1	10101	4	2017
41	10101142027	1	10101	4	2017
42	10101142038	1	10101	3	2017
43	10101142046	1	10101	9	2017
44	10101142047	1	10101	11	2017
45	10101142049	1	10101	61	2017
46	10101152002	1	10101	9	2017
47	10101152006	1	10101	6	2017
48	10101152020	1	10101	22	2017
49	10101152031	1	10101	15	2017
50	10101152033	1	10101	36	2017
51	10101152049	1	10101	9	2017
52	10101152901	1	10101	1	2017
53	10101162006	1	10101	6	2017
54	10101162010	1	10101	5	2017
55	10101162020	1	10101	23	2017
56	10101162031	1	10101	46	2017
57	10101162032	1	10101	2	2017
58	10101162033	1	10101	12	2017
59	10101172029	1	10101	91	2017
1002	10102012004	1	10102	2	2017
1003	10102012008	1	10102	13	2017
1004	10102012033	1	10102	21	2017
1005	10102022002	1	10102	2	2017
1006	10102022003	1	10102	2	2017
1007	10102022010	1	10102	3	2017
1008	10102022013	1	10102	8	2017
1009	10102032002	1	10102	2	2017
1010	10102032011	1	10102	2	2017
1011	10102032016	1	10102	1	2017
1012	10102032018	1	10102	5	2017
1013	10102032034	1	10102	10	2017
1014	10102032901	1	10102	4	2017
1015	10102042014	1	10102	4	2017
1016	10102042019	1	10102	14	2017
1017	10102042028	1	10102	2	2017
1018	10102042029	1	10102	2	2017
1019	10102042035	1	10102	2	2017
1020	10102042043	1	10102	2	2017
1021	10102042901	1	10102	7	2017
1022	10102052012	1	10102	9	2017
1023	10102052019	1	10102	8	2017
1024	10102052042	1	10102	10	2017
1025	10102052043	1	10102	2	2017
1026	10102062015	1	10102	12	2017
1027	10102062019	1	10102	4	2017
1028	10102062027	1	10102	1	2017
1029	10102062038	1	10102	5	2017
1030	10102062039	1	10102	6	2017
1031	10102072022	1	10102	5	2017
1032	10102082022	1	10102	10	2017
1033	10102092022	1	10102	3	2017
1034	10102102023	1	10102	14	2017
1035	10102112023	1	10102	14	2017
1036	10102122023	1	10102	4	2017
1037	10102132023	1	10102	7	2017
1038	10102142901	1	10102	2	2017
1039	10102152006	1	10102	14	2017
1040	10102162030	1	10102	7	2017
1041	10102162031	1	10102	1	2017
1042	10102162036	1	10102	3	2017

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	10101032002	2	2017	10101
2	10101032011	20	2017	10101
3	10101032019	32	2017	10101
4	10101062003	10	2017	10101
5	10101062008	72	2017	10101
6	10101062013	61	2017	10101
7	10101062029	1	2017	10101
8	10101062039	4	2017	10101
9	10101072014	36	2017	10101
10	10101072021	4	2017	10101
11	10101072028	4	2017	10101
12	10101072029	36	2017	10101
13	10101072036	1	2017	10101
14	10101072045	7	2017	10101
15	10101082016	13	2017	10101
16	10101082017	5	2017	10101
17	10101082018	13	2017	10101
18	10101082030	3	2017	10101
19	10101082034	5	2017	10101
20	10101082042	12	2017	10101
21	10101082045	6	2017	10101
22	10101092004	6	2017	10101
23	10101092008	83	2017	10101
24	10101092037	11	2017	10101
25	10101092040	33	2017	10101
26	10101092041	44	2017	10101
27	10101092044	21	2017	10101
28	10101102005	1	2017	10101
29	10101102007	12	2017	10101
30	10101102035	22	2017	10101
31	10101102037	3	2017	10101
32	10101102051	1	2017	10101
33	10101112025	13	2017	10101
34	10101122024	6	2017	10101
35	10101132022	15	2017	10101
36	10101132023	12	2017	10101
37	10101132027	1	2017	10101
38	10101132049	77	2017	10101
39	10101142009	2	2017	10101
40	10101142015	4	2017	10101
41	10101142027	4	2017	10101
42	10101142038	3	2017	10101
43	10101142046	9	2017	10101
44	10101142047	11	2017	10101
45	10101142049	61	2017	10101
46	10101152002	9	2017	10101
47	10101152006	6	2017	10101
48	10101152020	22	2017	10101
49	10101152031	15	2017	10101
50	10101152033	36	2017	10101
51	10101152049	9	2017	10101
52	10101152901	1	2017	10101
53	10101162006	6	2017	10101
54	10101162010	5	2017	10101
55	10101162020	23	2017	10101
56	10101162031	46	2017	10101
57	10101162032	2	2017	10101
58	10101162033	12	2017	10101
59	10101172029	91	2017	10101
1002	10102012004	2	2017	10102
1003	10102012008	13	2017	10102
1004	10102012033	21	2017	10102
1005	10102022002	2	2017	10102
1006	10102022003	2	2017	10102
1007	10102022010	3	2017	10102
1008	10102022013	8	2017	10102
1009	10102032002	2	2017	10102
1010	10102032011	2	2017	10102
1011	10102032016	1	2017	10102
1012	10102032018	5	2017	10102
1013	10102032034	10	2017	10102
1014	10102032901	4	2017	10102
1015	10102042014	4	2017	10102
1016	10102042019	14	2017	10102
1017	10102042028	2	2017	10102
1018	10102042029	2	2017	10102
1019	10102042035	2	2017	10102
1020	10102042043	2	2017	10102
1021	10102042901	7	2017	10102
1022	10102052012	9	2017	10102
1023	10102052019	8	2017	10102
1024	10102052042	10	2017	10102
1025	10102052043	2	2017	10102
1026	10102062015	12	2017	10102
1027	10102062019	4	2017	10102
1028	10102062027	1	2017	10102
1029	10102062038	5	2017	10102
1030	10102062039	6	2017	10102
1031	10102072022	5	2017	10102
1032	10102082022	10	2017	10102
1033	10102092022	3	2017	10102
1034	10102102023	14	2017	10102
1035	10102112023	14	2017	10102
1036	10102122023	4	2017	10102
1037	10102132023	7	2017	10102
1038	10102142901	2	2017	10102
1039	10102152006	14	2017	10102
1040	10102162030	7	2017	10102
1041	10102162031	1	2017	10102
1042	10102162036	3	2017	10102

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_rural_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	289375.3	2017	1101	191468	55406102543	Rural
01401	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
01402	Camiña	262850.3	2017	1402	1250	328562901	Rural
01404	Huara	253968.5	2017	1404	2730	693334131	Rural
01405	Pica	290496.7	2017	1405	9296	2700457509	Rural
02103	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
02104	Taltal	345494.0	2017	2104	13317	4600943086	Rural
02201	Calama	310025.0	2017	2201	165731	51380756402	Rural
02203	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
02301	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
03101	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03103	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03202	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural
03301	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03302	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03303	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03304	Huasco	227560.7	2017	3304	10149	2309513927	Rural
04101	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04102	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04103	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04104	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04105	Paiguano	205942.1	2017	4105	4497	926121774	Rural
04106	Vicuña	176130.6	2017	4106	27771	4891322768	Rural
04201	Illapel	191976.8	2017	4201	30848	5922099530	Rural
04202	Canela	171370.3	2017	4202	9093	1558270441	Rural
04203	Los Vilos	173238.5	2017	4203	21382	3704185607	Rural
04204	Salamanca	223234.2	2017	4204	29347	6551254640	Rural
04301	Ovalle	241393.7	2017	4301	111272	26860360045	Rural
04302	Combarbalá	179139.6	2017	4302	13322	2386498044	Rural
04303	Monte Patria	201205.8	2017	4303	30751	6187280931	Rural
04304	Punitaqui	171931.7	2017	4304	10956	1883683880	Rural
04305	Río Hurtado	182027.2	2017	4305	4278	778712384	Rural
05101	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05102	Casablanca	268917.1	2017	5102	26867	7224996933	Rural
05105	Puchuncaví	279614.4	2017	5105	18546	5185728335	Rural
05107	Quintero	334628.2	2017	5107	31923	10682335196	Rural
05301	Los Andes	324402.1	2017	5301	66708	21640215030	Rural
05302	Calle Larga	242743.8	2017	5302	14832	3600375502	Rural
05303	Rinconada	326532.5	2017	5303	10207	3332917471	Rural
05304	San Esteban	223168.6	2017	5304	18855	4207844130	Rural
05401	La Ligua	181468.0	2017	5401	35390	6422154059	Rural
05402	Cabildo	231277.8	2017	5402	19388	4484014285	Rural
05404	Petorca	298208.9	2017	5404	9826	2930200178	Rural
05405	Zapallar	292882.3	2017	5405	7339	2149463129	Rural
05501	Quillota	220926.8	2017	5501	90517	19997628209	Rural
05502	Calera	226906.2	2017	5502	50554	11471016698	Rural
05503	Hijuelas	253739.9	2017	5503	17988	4564273201	Rural
05504	La Cruz	291124.1	2017	5504	22098	6433259569	Rural
05506	Nogales	264475.3	2017	5506	22120	5850194593	Rural
05601	San Antonio	266331.2	2017	5601	91350	24329353815	Rural

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
10101	10101062003	10	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101082017	5	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101082018	13	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101032019	32	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101082042	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101082045	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101082030	3	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101082034	5	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101092037	11	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101092040	33	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101092004	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101092008	83	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101032011	20	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101142038	3	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101142046	9	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101142047	11	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101062008	72	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101062013	61	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101062029	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101062039	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101072014	36	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101072021	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101072028	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101072029	36	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101072036	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101072045	7	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101082016	13	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101162020	23	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101162031	46	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101162032	2	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101162033	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101172029	91	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101122024	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101132022	15	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101132023	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101132027	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101132049	77	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101092041	44	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101092044	21	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101102005	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101102007	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101102035	22	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101102037	3	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101102051	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101112025	13	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101152002	9	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101152006	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101152020	22	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101152031	15	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101152033	36	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101142009	2	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101142015	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101142027	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101032002	2	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101162010	5	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101152049	9	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101152901	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101142049	61	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101162006	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10102	10102052042	10	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102052043	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102062015	12	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102162040	3	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102162041	3	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102172024	1	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102182021	5	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102192026	1	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102142901	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102152006	14	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102162030	7	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102162031	1	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102162036	3	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102012004	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102012008	13	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102012033	21	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102022002	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102022003	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102022010	3	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102022013	8	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102032002	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102032011	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102032016	1	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102032018	5	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102032034	10	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102032901	4	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102042014	4	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102042019	14	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102042028	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102042029	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102042035	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102042043	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102042901	7	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102052012	9	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102052019	8	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102132023	7	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102062038	5	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102062039	6	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102062019	4	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102062027	1	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102092022	3	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
10101	10101062003	10	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101082017	5	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101082018	13	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101032019	32	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101082042	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101082045	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101082030	3	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101082034	5	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101092037	11	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101092040	33	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101092004	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101092008	83	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101032011	20	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101142038	3	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101142046	9	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101142047	11	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101062008	72	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101062013	61	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101062029	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101062039	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101072014	36	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101072021	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101072028	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101072029	36	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101072036	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101072045	7	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101082016	13	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101162020	23	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101162031	46	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101162032	2	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101162033	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101172029	91	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101122024	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101132022	15	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101132023	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101132027	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101132049	77	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101092041	44	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101092044	21	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101102005	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101102007	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101102035	22	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101102037	3	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101102051	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101112025	13	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101152002	9	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101152006	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101152020	22	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101152031	15	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101152033	36	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101142009	2	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101142015	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101142027	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101032002	2	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101162010	5	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101152049	9	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101152901	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101142049	61	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10101	10101162006	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural
10102	10102052042	10	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102052043	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102062015	12	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102162040	3	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102162041	3	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102172024	1	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102182021	5	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102192026	1	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102142901	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102152006	14	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102162030	7	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102162031	1	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102162036	3	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102012004	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102012008	13	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102012033	21	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102022002	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102022003	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102022010	3	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102022013	8	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102032002	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102032011	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102032016	1	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102032018	5	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102032034	10	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102032901	4	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102042014	4	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102042019	14	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102042028	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102042029	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102042035	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102042043	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102042901	7	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102052012	9	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102052019	8	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102132023	7	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102062038	5	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102062039	6	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102062019	4	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102062027	1	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural
10102	10102092022	3	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
10101032002	10101	2	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	129	0.0005246	10101
10101032011	10101	20	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	426	0.0017324	10101
10101032019	10101	32	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	829	0.0033713	10101
10101062003	10101	10	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	158	0.0006425	10101
10101062008	10101	72	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	581	0.0023627	10101
10101062013	10101	61	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	571	0.0023221	10101
10101062029	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	47	0.0001911	10101
10101062039	10101	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	67	0.0002725	10101
10101072014	10101	36	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	997	0.0040545	10101
10101072021	10101	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	44	0.0001789	10101
10101072028	10101	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	145	0.0005897	10101
10101072029	10101	36	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	1051	0.0042741	10101
10101072036	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	118	0.0004799	10101
10101072045	10101	7	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	113	0.0004595	10101
10101082016	10101	13	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	121	0.0004921	10101
10101082017	10101	5	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	38	0.0001545	10101
10101082018	10101	13	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	623	0.0025335	10101
10101082030	10101	3	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	176	0.0007157	10101
10101082034	10101	5	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	66	0.0002684	10101
10101082042	10101	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	253	0.0010289	10101
10101082045	10101	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	123	0.0005002	10101
10101092004	10101	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	97	0.0003945	10101
10101092008	10101	83	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	752	0.0030581	10101
10101092037	10101	11	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	276	0.0011224	10101
10101092040	10101	33	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	509	0.0020699	10101
10101092041	10101	44	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	1683	0.0068442	10101
10101092044	10101	21	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	530	0.0021553	10101
10101102005	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	147	0.0005978	10101
10101102007	10101	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	824	0.0033509	10101
10101102035	10101	22	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	940	0.0038227	10101
10101102037	10101	3	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	164	0.0006669	10101
10101102051	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	57	0.0002318	10101
10101112025	10101	13	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	1078	0.0043839	10101
10101122024	10101	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	952	0.0038715	10101
10101132022	10101	15	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	703	0.0028589	10101
10101132023	10101	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	603	0.0024522	10101
10101132027	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	105	0.0004270	10101
10101132049	10101	77	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	1883	0.0076575	10101
10101142009	10101	2	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	59	0.0002399	10101
10101142015	10101	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	124	0.0005043	10101
10101142027	10101	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	192	0.0007808	10101
10101142038	10101	3	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	53	0.0002155	10101
10101142046	10101	9	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	317	0.0012891	10101
10101142047	10101	11	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	263	0.0010695	10101
10101142049	10101	61	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	973	0.0039569	10101
10101152002	10101	9	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	554	0.0022529	10101
10101152006	10101	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	214	0.0008703	10101
10101152020	10101	22	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	617	0.0025091	10101
10101152031	10101	15	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	642	0.0026108	10101
10101152033	10101	36	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	700	0.0028467	10101
10101152049	10101	9	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	96	0.0003904	10101
10101152901	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	46	0.0001871	10101
10101162006	10101	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	233	0.0009475	10101
10101162010	10101	5	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	351	0.0014274	10101
10101162020	10101	23	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	205	0.0008337	10101
10101162031	10101	46	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	762	0.0030988	10101
10101162032	10101	2	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	24	0.0000976	10101
10101162033	10101	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	168	0.0006832	10101
10101172029	10101	91	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	854	0.0034729	10101
10102012004	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	283	0.0083272	10102
10102012008	10102	13	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	697	0.0205090	10102
10102012033	10102	21	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	1490	0.0438429	10102
10102022002	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	308	0.0090628	10102
10102022003	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	184	0.0054142	10102
10102022010	10102	3	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	294	0.0086509	10102
10102022013	10102	8	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	450	0.0132411	10102
10102032002	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	54	0.0015889	10102
10102032011	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	163	0.0047962	10102
10102032016	10102	1	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	95	0.0027954	10102
10102032018	10102	5	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	470	0.0138296	10102
10102032034	10102	10	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	695	0.0204502	10102
10102032901	10102	4	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	89	0.0026188	10102
10102042014	10102	4	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	87	0.0025600	10102
10102042019	10102	14	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	336	0.0098867	10102
10102042028	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	171	0.0050316	10102
10102042029	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	183	0.0053847	10102
10102042035	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	271	0.0079741	10102
10102042043	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	86	0.0025305	10102
10102042901	10102	7	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	127	0.0037369	10102
10102052012	10102	9	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	302	0.0088863	10102
10102052019	10102	8	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	332	0.0097690	10102
10102052042	10102	10	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	410	0.0120641	10102
10102052043	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	273	0.0080330	10102
10102062015	10102	12	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	1129	0.0332205	10102
10102062019	10102	4	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	362	0.0106518	10102
10102062027	10102	1	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	25	0.0007356	10102
10102062038	10102	5	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	357	0.0105046	10102
10102062039	10102	6	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	643	0.0189201	10102
10102072022	10102	5	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	148	0.0043549	10102
10102082022	10102	10	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	565	0.0166250	10102
10102092022	10102	3	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	308	0.0090628	10102
10102102023	10102	14	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	630	0.0185376	10102
10102112023	10102	14	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	650	0.0191261	10102
10102122023	10102	4	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	511	0.0150360	10102
10102132023	10102	7	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	775	0.0228042	10102
10102142901	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	55	0.0016184	10102
10102152006	10102	14	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	586	0.0172429	10102
10102162030	10102	7	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	341	0.0100338	10102
10102162031	10102	1	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	361	0.0106223	10102
10102162036	10102	3	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	260	0.0076504	10102

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
10101032002	10101	2	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	129	0.0005246	10101	23890269
10101032011	10101	20	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	426	0.0017324	10101	78893445
10101032019	10101	32	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	829	0.0033713	10101	153527386
10101062003	10101	10	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	158	0.0006425	10101	29260949
10101062008	10101	72	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	581	0.0023627	10101	107598807
10101062013	10101	61	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	571	0.0023221	10101	105746848
10101062029	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	47	0.0001911	10101	8704206
10101062039	10101	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	67	0.0002725	10101	12408124
10101072014	10101	36	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	997	0.0040545	10101	184640294
10101072021	10101	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	44	0.0001789	10101	8148619
10101072028	10101	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	145	0.0005897	10101	26853403
10101072029	10101	36	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	1051	0.0042741	10101	194640871
10101072036	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	118	0.0004799	10101	21853114
10101072045	10101	7	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	113	0.0004595	10101	20927135
10101082016	10101	13	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	121	0.0004921	10101	22408702
10101082017	10101	5	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	38	0.0001545	10101	7037443
10101082018	10101	13	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	623	0.0025335	10101	115377034
10101082030	10101	3	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	176	0.0007157	10101	32594475
10101082034	10101	5	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	66	0.0002684	10101	12222928
10101082042	10101	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	253	0.0010289	10101	46854558
10101082045	10101	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	123	0.0005002	10101	22779093
10101092004	10101	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	97	0.0003945	10101	17964000
10101092008	10101	83	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	752	0.0030581	10101	139267303
10101092037	10101	11	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	276	0.0011224	10101	51114063
10101092040	10101	33	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	509	0.0020699	10101	94264704
10101092041	10101	44	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	1683	0.0068442	10101	311684668
10101092044	10101	21	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	530	0.0021553	10101	98153817
10101102005	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	147	0.0005978	10101	27223795
10101102007	10101	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	824	0.0033509	10101	152601406
10101102035	10101	22	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	940	0.0038227	10101	174084128
10101102037	10101	3	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	164	0.0006669	10101	30372125
10101102051	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	57	0.0002318	10101	10556165
10101112025	10101	13	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	1078	0.0043839	10101	199641160
10101122024	10101	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	952	0.0038715	10101	176306479
10101132022	10101	15	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	703	0.0028589	10101	130192705
10101132023	10101	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	603	0.0024522	10101	111673116
10101132027	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	105	0.0004270	10101	19445568
10101132049	10101	77	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	1883	0.0076575	10101	348723845
10101142009	10101	2	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	59	0.0002399	10101	10926557
10101142015	10101	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	124	0.0005043	10101	22964289
10101142027	10101	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	192	0.0007808	10101	35557609
10101142038	10101	3	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	53	0.0002155	10101	9815382
10101142046	10101	9	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	317	0.0012891	10101	58707094
10101142047	10101	11	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	263	0.0010695	10101	48706517
10101142049	10101	61	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	973	0.0039569	10101	180195593
10101152002	10101	9	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	554	0.0022529	10101	102598518
10101152006	10101	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	214	0.0008703	10101	39631919
10101152020	10101	22	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	617	0.0025091	10101	114265859
10101152031	10101	15	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	642	0.0026108	10101	118895756
10101152033	10101	36	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	700	0.0028467	10101	129637117
10101152049	10101	9	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	96	0.0003904	10101	17778805
10101152901	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	46	0.0001871	10101	8519011
10101162006	10101	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	233	0.0009475	10101	43150640
10101162010	10101	5	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	351	0.0014274	10101	65003754
10101162020	10101	23	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	205	0.0008337	10101	37965156
10101162031	10101	46	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	762	0.0030988	10101	141119262
10101162032	10101	2	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	24	0.0000976	10101	4444701
10101162033	10101	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	168	0.0006832	10101	31112908
10101172029	10101	91	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	854	0.0034729	10101	158157283
10102012004	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	283	0.0083272	10102	52720692
10102012008	10102	13	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	697	0.0205090	10102	129845662
10102012033	10102	21	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	1490	0.0438429	10102	277575376
10102022002	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	308	0.0090628	10102	57377997
10102022003	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	184	0.0054142	10102	34277765
10102022010	10102	3	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	294	0.0086509	10102	54769906
10102022013	10102	8	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	450	0.0132411	10102	83831489
10102032002	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	54	0.0015889	10102	10059779
10102032011	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	163	0.0047962	10102	30365628
10102032016	10102	1	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	95	0.0027954	10102	17697759
10102032018	10102	5	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	470	0.0138296	10102	87557333
10102032034	10102	10	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	695	0.0204502	10102	129473078
10102032901	10102	4	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	89	0.0026188	10102	16580006
10102042014	10102	4	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	87	0.0025600	10102	16207421
10102042019	10102	14	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	336	0.0098867	10102	62594179
10102042028	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	171	0.0050316	10102	31855966
10102042029	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	183	0.0053847	10102	34091472
10102042035	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	271	0.0079741	10102	50485186
10102042043	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	86	0.0025305	10102	16021129
10102042901	10102	7	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	127	0.0037369	10102	23659109
10102052012	10102	9	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	302	0.0088863	10102	56260244
10102052019	10102	8	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	332	0.0097690	10102	61849010
10102052042	10102	10	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	410	0.0120641	10102	76379801
10102052043	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	273	0.0080330	10102	50857770
10102062015	10102	12	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	1129	0.0332205	10102	210323892
10102062019	10102	4	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	362	0.0106518	10102	67437776
10102062027	10102	1	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	25	0.0007356	10102	4657305
10102062038	10102	5	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	357	0.0105046	10102	66506315
10102062039	10102	6	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	643	0.0189201	10102	119785884
10102072022	10102	5	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	148	0.0043549	10102	27571245
10102082022	10102	10	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	565	0.0166250	10102	105255092
10102092022	10102	3	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	308	0.0090628	10102	57377997
10102102023	10102	14	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	630	0.0185376	10102	117364085
10102112023	10102	14	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	650	0.0191261	10102	121089929
10102122023	10102	4	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	511	0.0150360	10102	95195314
10102132023	10102	7	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	775	0.0228042	10102	144376454
10102142901	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	55	0.0016184	10102	10246071
10102152006	10102	14	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	586	0.0172429	10102	109167228
10102162030	10102	7	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	341	0.0100338	10102	63525640
10102162031	10102	1	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	361	0.0106223	10102	67251484
10102162036	10102	3	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	260	0.0076504	10102	48435972

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -168301372  -20678339  -10169601    9677443  216878576 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 28281598    1315791   21.49   <2e-16 ***
## Freq.x       1883179      66169   28.46   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 35760000 on 872 degrees of freedom
##   (68 observations deleted due to missingness)
## Multiple R-squared:  0.4816, Adjusted R-squared:  0.481 
## F-statistic:   810 on 1 and 872 DF,  p-value: < 2.2e-16

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.480965454264592"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.480965454264592"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.438596242673399"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.559664141417381"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.505604293381093"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.366007145590761"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.485783802615996"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.424255940605594"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 6      log-raíz 0.366007145590761
## 8       log-log 0.424255940605594
## 3   logarítmico 0.438596242673399
## 1    cuadrático 0.480965454264592
## 2        cúbico 0.480965454264592
## 7      raíz-log 0.485783802615996
## 5     raíz-raíz 0.505604293381093
## 4 raíz cuadrada 0.559664141417381
##                                                                     sintaxis
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 4
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = multi_pob ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -122875576  -16947500   -4273799   10101109  187535066 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -8811358    1913283  -4.605 4.73e-06 ***
## sqrt(Freq.x) 22786943     683773  33.325  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32930000 on 872 degrees of freedom
##   (68 observations deleted due to missingness)
## Multiple R-squared:  0.5602, Adjusted R-squared:  0.5597 
## F-statistic:  1111 on 1 and 872 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    -8811358

bb <- linearMod$coefficients[2]
bb

## sqrt(Freq.x) 
##     22786943

9 Modelo raíz cuadrada (raíz cuadrada)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.5597 ).

9.1 Diagrama de dispersión sobre raíz cuadrada

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=(h_y_m_comuna_corr_01$Freq.x), y=sqrt(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo raíz cuadrada

Observemos nuevamente el resultado sobre raíz cuadrada.

linearMod <- lm(( multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = (multi_pob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -122875576  -16947500   -4273799   10101109  187535066 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -8811358    1913283  -4.605 4.73e-06 ***
## sqrt(Freq.x) 22786943     683773  33.325  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32930000 on 872 degrees of freedom
##   (68 observations deleted due to missingness)
## Multiple R-squared:  0.5602, Adjusted R-squared:  0.5597 
## F-statistic:  1111 on 1 and 872 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = (Freq.x) , y = sqrt(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = 8811358 + 22786943\cdot \sqrt {X} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- aa+bb * sqrt(h_y_m_comuna_corr_01$Freq.x)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
10101032002	10101	2	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	129	0.0005246	10101	23890269	23414246
10101032011	10101	20	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	426	0.0017324	10101	78893445	93094951
10101032019	10101	32	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	829	0.0033713	10101	153527386	120091060
10101062003	10101	10	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	158	0.0006425	10101	29260949	63247284
10101062008	10101	72	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	581	0.0023627	10101	107598807	184542269
10101062013	10101	61	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	571	0.0023221	10101	105746848	169160359
10101062029	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	47	0.0001911	10101	8704206	13975585
10101062039	10101	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	67	0.0002725	10101	12408124	36762529
10101072014	10101	36	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	997	0.0040545	10101	184640294	127910303
10101072021	10101	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	44	0.0001789	10101	8148619	36762529
10101072028	10101	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	145	0.0005897	10101	26853403	36762529
10101072029	10101	36	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	1051	0.0042741	10101	194640871	127910303
10101072036	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	118	0.0004799	10101	21853114	13975585
10101072045	10101	7	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	113	0.0004595	10101	20927135	51477227
10101082016	10101	13	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	121	0.0004921	10101	22408702	73348135
10101082017	10101	5	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	38	0.0001545	10101	7037443	42141797
10101082018	10101	13	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	623	0.0025335	10101	115377034	73348135
10101082030	10101	3	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	176	0.0007157	10101	32594475	30656786
10101082034	10101	5	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	66	0.0002684	10101	12222928	42141797
10101082042	10101	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	253	0.0010289	10101	46854558	70124930
10101082045	10101	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	123	0.0005002	10101	22779093	47005026
10101092004	10101	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	97	0.0003945	10101	17964000	47005026
10101092008	10101	83	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	752	0.0030581	10101	139267303	198787577
10101092037	10101	11	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	276	0.0011224	10101	51114063	66764383
10101092040	10101	33	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	509	0.0020699	10101	94264704	122089666
10101092041	10101	44	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	1683	0.0068442	10101	311684668	142340125
10101092044	10101	21	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	530	0.0021553	10101	98153817	95611535
10101102005	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	147	0.0005978	10101	27223795	13975585
10101102007	10101	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	824	0.0033509	10101	152601406	70124930
10101102035	10101	22	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	940	0.0038227	10101	174084128	98068881
10101102037	10101	3	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	164	0.0006669	10101	30372125	30656786
10101102051	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	57	0.0002318	10101	10556165	13975585
10101112025	10101	13	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	1078	0.0043839	10101	199641160	73348135
10101122024	10101	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	952	0.0038715	10101	176306479	47005026
10101132022	10101	15	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	703	0.0028589	10101	130192705	79442094
10101132023	10101	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	603	0.0024522	10101	111673116	70124930
10101132027	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	105	0.0004270	10101	19445568	13975585
10101132049	10101	77	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	1883	0.0076575	10101	348723845	191143259
10101142009	10101	2	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	59	0.0002399	10101	10926557	23414246
10101142015	10101	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	124	0.0005043	10101	22964289	36762529
10101142027	10101	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	192	0.0007808	10101	35557609	36762529
10101142038	10101	3	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	53	0.0002155	10101	9815382	30656786
10101142046	10101	9	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	317	0.0012891	10101	58707094	59549472
10101142047	10101	11	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	263	0.0010695	10101	48706517	66764383
10101142049	10101	61	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	973	0.0039569	10101	180195593	169160359
10101152002	10101	9	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	554	0.0022529	10101	102598518	59549472
10101152006	10101	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	214	0.0008703	10101	39631919	47005026
10101152020	10101	22	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	617	0.0025091	10101	114265859	98068881
10101152031	10101	15	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	642	0.0026108	10101	118895756	79442094
10101152033	10101	36	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	700	0.0028467	10101	129637117	127910303
10101152049	10101	9	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	96	0.0003904	10101	17778805	59549472
10101152901	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	46	0.0001871	10101	8519011	13975585
10101162006	10101	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	233	0.0009475	10101	43150640	47005026
10101162010	10101	5	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	351	0.0014274	10101	65003754	42141797
10101162020	10101	23	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	205	0.0008337	10101	37965156	100470984
10101162031	10101	46	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	762	0.0030988	10101	141119262	145737212
10101162032	10101	2	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	24	0.0000976	10101	4444701	23414246
10101162033	10101	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	168	0.0006832	10101	31112908	70124930
10101172029	10101	91	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	854	0.0034729	10101	158157283	208562228
10102012004	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	283	0.0083272	10102	52720692	23414246
10102012008	10102	13	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	697	0.0205090	10102	129845662	73348135
10102012033	10102	21	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	1490	0.0438429	10102	277575376	95611535
10102022002	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	308	0.0090628	10102	57377997	23414246
10102022003	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	184	0.0054142	10102	34277765	23414246
10102022010	10102	3	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	294	0.0086509	10102	54769906	30656786
10102022013	10102	8	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	450	0.0132411	10102	83831489	55639851
10102032002	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	54	0.0015889	10102	10059779	23414246
10102032011	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	163	0.0047962	10102	30365628	23414246
10102032016	10102	1	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	95	0.0027954	10102	17697759	13975585
10102032018	10102	5	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	470	0.0138296	10102	87557333	42141797
10102032034	10102	10	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	695	0.0204502	10102	129473078	63247284
10102032901	10102	4	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	89	0.0026188	10102	16580006	36762529
10102042014	10102	4	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	87	0.0025600	10102	16207421	36762529
10102042019	10102	14	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	336	0.0098867	10102	62594179	76449577
10102042028	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	171	0.0050316	10102	31855966	23414246
10102042029	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	183	0.0053847	10102	34091472	23414246
10102042035	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	271	0.0079741	10102	50485186	23414246
10102042043	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	86	0.0025305	10102	16021129	23414246
10102042901	10102	7	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	127	0.0037369	10102	23659109	51477227
10102052012	10102	9	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	302	0.0088863	10102	56260244	59549472
10102052019	10102	8	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	332	0.0097690	10102	61849010	55639851
10102052042	10102	10	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	410	0.0120641	10102	76379801	63247284
10102052043	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	273	0.0080330	10102	50857770	23414246
10102062015	10102	12	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	1129	0.0332205	10102	210323892	70124930
10102062019	10102	4	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	362	0.0106518	10102	67437776	36762529
10102062027	10102	1	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	25	0.0007356	10102	4657305	13975585
10102062038	10102	5	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	357	0.0105046	10102	66506315	42141797
10102062039	10102	6	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	643	0.0189201	10102	119785884	47005026
10102072022	10102	5	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	148	0.0043549	10102	27571245	42141797
10102082022	10102	10	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	565	0.0166250	10102	105255092	63247284
10102092022	10102	3	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	308	0.0090628	10102	57377997	30656786
10102102023	10102	14	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	630	0.0185376	10102	117364085	76449577
10102112023	10102	14	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	650	0.0191261	10102	121089929	76449577
10102122023	10102	4	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	511	0.0150360	10102	95195314	36762529
10102132023	10102	7	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	775	0.0228042	10102	144376454	51477227
10102142901	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	55	0.0016184	10102	10246071	23414246
10102152006	10102	14	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	586	0.0172429	10102	109167228	76449577
10102162030	10102	7	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	341	0.0100338	10102	63525640	51477227
10102162031	10102	1	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	361	0.0106223	10102	67251484	13975585
10102162036	10102	3	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	260	0.0076504	10102	48435972	30656786

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
10101032002	10101	2	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	129	0.0005246	10101	23890269	23414246	181505.79
10101032011	10101	20	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	426	0.0017324	10101	78893445	93094951	218532.75
10101032019	10101	32	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	829	0.0033713	10101	153527386	120091060	144862.56
10101062003	10101	10	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	158	0.0006425	10101	29260949	63247284	400299.27
10101062008	10101	72	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	581	0.0023627	10101	107598807	184542269	317628.69
10101062013	10101	61	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	571	0.0023221	10101	105746848	169160359	296252.82
10101062029	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	47	0.0001911	10101	8704206	13975585	297352.88
10101062039	10101	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	67	0.0002725	10101	12408124	36762529	548694.46
10101072014	10101	36	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	997	0.0040545	10101	184640294	127910303	128295.19
10101072021	10101	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	44	0.0001789	10101	8148619	36762529	835512.02
10101072028	10101	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	145	0.0005897	10101	26853403	36762529	253534.68
10101072029	10101	36	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	1051	0.0042741	10101	194640871	127910303	121703.43
10101072036	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	118	0.0004799	10101	21853114	13975585	118437.16
10101072045	10101	7	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	113	0.0004595	10101	20927135	51477227	455550.69
10101082016	10101	13	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	121	0.0004921	10101	22408702	73348135	606182.93
10101082017	10101	5	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	38	0.0001545	10101	7037443	42141797	1108994.64
10101082018	10101	13	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	623	0.0025335	10101	115377034	73348135	117733.76
10101082030	10101	3	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	176	0.0007157	10101	32594475	30656786	174186.28
10101082034	10101	5	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	66	0.0002684	10101	12222928	42141797	638512.07
10101082042	10101	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	253	0.0010289	10101	46854558	70124930	277173.63
10101082045	10101	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	123	0.0005002	10101	22779093	47005026	382154.68
10101092004	10101	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	97	0.0003945	10101	17964000	47005026	484587.90
10101092008	10101	83	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	752	0.0030581	10101	139267303	198787577	264345.18
10101092037	10101	11	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	276	0.0011224	10101	51114063	66764383	241899.94
10101092040	10101	33	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	509	0.0020699	10101	94264704	122089666	239861.82
10101092041	10101	44	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	1683	0.0068442	10101	311684668	142340125	84575.24
10101092044	10101	21	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	530	0.0021553	10101	98153817	95611535	180399.12
10101102005	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	147	0.0005978	10101	27223795	13975585	95072.01
10101102007	10101	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	824	0.0033509	10101	152601406	70124930	85103.07
10101102035	10101	22	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	940	0.0038227	10101	174084128	98068881	104328.60
10101102037	10101	3	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	164	0.0006669	10101	30372125	30656786	186931.62
10101102051	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	57	0.0002318	10101	10556165	13975585	245185.71
10101112025	10101	13	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	1078	0.0043839	10101	199641160	73348135	68040.94
10101122024	10101	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	952	0.0038715	10101	176306479	47005026	49375.03
10101132022	10101	15	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	703	0.0028589	10101	130192705	79442094	113004.40
10101132023	10101	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	603	0.0024522	10101	111673116	70124930	116293.42
10101132027	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	105	0.0004270	10101	19445568	13975585	133100.81
10101132049	10101	77	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	1883	0.0076575	10101	348723845	191143259	101509.96
10101142009	10101	2	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	59	0.0002399	10101	10926557	23414246	396851.63
10101142015	10101	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	124	0.0005043	10101	22964289	36762529	296472.01
10101142027	10101	4	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	192	0.0007808	10101	35557609	36762529	191471.50
10101142038	10101	3	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	53	0.0002155	10101	9815382	30656786	578429.92
10101142046	10101	9	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	317	0.0012891	10101	58707094	59549472	187853.22
10101142047	10101	11	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	263	0.0010695	10101	48706517	66764383	253856.97
10101142049	10101	61	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	973	0.0039569	10101	180195593	169160359	173854.43
10101152002	10101	9	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	554	0.0022529	10101	102598518	59549472	107490.02
10101152006	10101	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	214	0.0008703	10101	39631919	47005026	219649.66
10101152020	10101	22	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	617	0.0025091	10101	114265859	98068881	158944.70
10101152031	10101	15	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	642	0.0026108	10101	118895756	79442094	123741.58
10101152033	10101	36	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	700	0.0028467	10101	129637117	127910303	182729.00
10101152049	10101	9	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	96	0.0003904	10101	17778805	59549472	620307.00
10101152901	10101	1	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	46	0.0001871	10101	8519011	13975585	303817.07
10101162006	10101	6	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	233	0.0009475	10101	43150640	47005026	201738.31
10101162010	10101	5	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	351	0.0014274	10101	65003754	42141797	120062.10
10101162020	10101	23	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	205	0.0008337	10101	37965156	100470984	490102.36
10101162031	10101	46	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	762	0.0030988	10101	141119262	145737212	191256.18
10101162032	10101	2	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	24	0.0000976	10101	4444701	23414246	975593.60
10101162033	10101	12	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	168	0.0006832	10101	31112908	70124930	417410.29
10101172029	10101	91	2017	Puerto Montt	185195.9	2017	10101	245902	45540037617	Rural	854	0.0034729	10101	158157283	208562228	244218.07
10102012004	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	283	0.0083272	10102	52720692	23414246	82735.85
10102012008	10102	13	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	697	0.0205090	10102	129845662	73348135	105234.05
10102012033	10102	21	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	1490	0.0438429	10102	277575376	95611535	64168.82
10102022002	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	308	0.0090628	10102	57377997	23414246	76020.28
10102022003	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	184	0.0054142	10102	34277765	23414246	127251.34
10102022010	10102	3	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	294	0.0086509	10102	54769906	30656786	104274.78
10102022013	10102	8	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	450	0.0132411	10102	83831489	55639851	123644.11
10102032002	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	54	0.0015889	10102	10059779	23414246	433597.16
10102032011	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	163	0.0047962	10102	30365628	23414246	143645.68
10102032016	10102	1	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	95	0.0027954	10102	17697759	13975585	147111.43
10102032018	10102	5	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	470	0.0138296	10102	87557333	42141797	89663.40
10102032034	10102	10	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	695	0.0204502	10102	129473078	63247284	91003.29
10102032901	10102	4	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	89	0.0026188	10102	16580006	36762529	413062.12
10102042014	10102	4	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	87	0.0025600	10102	16207421	36762529	422557.80
10102042019	10102	14	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	336	0.0098867	10102	62594179	76449577	227528.50
10102042028	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	171	0.0050316	10102	31855966	23414246	136925.42
10102042029	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	183	0.0053847	10102	34091472	23414246	127946.70
10102042035	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	271	0.0079741	10102	50485186	23414246	86399.43
10102042043	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	86	0.0025305	10102	16021129	23414246	272258.68
10102042901	10102	7	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	127	0.0037369	10102	23659109	51477227	405332.50
10102052012	10102	9	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	302	0.0088863	10102	56260244	59549472	197183.68
10102052019	10102	8	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	332	0.0097690	10102	61849010	55639851	167589.91
10102052042	10102	10	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	410	0.0120641	10102	76379801	63247284	154261.67
10102052043	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	273	0.0080330	10102	50857770	23414246	85766.47
10102062015	10102	12	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	1129	0.0332205	10102	210323892	70124930	62112.43
10102062019	10102	4	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	362	0.0106518	10102	67437776	36762529	101553.95
10102062027	10102	1	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	25	0.0007356	10102	4657305	13975585	559023.42
10102062038	10102	5	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	357	0.0105046	10102	66506315	42141797	118044.25
10102062039	10102	6	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	643	0.0189201	10102	119785884	47005026	73102.68
10102072022	10102	5	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	148	0.0043549	10102	27571245	42141797	284741.87
10102082022	10102	10	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	565	0.0166250	10102	105255092	63247284	111942.10
10102092022	10102	3	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	308	0.0090628	10102	57377997	30656786	99535.02
10102102023	10102	14	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	630	0.0185376	10102	117364085	76449577	121348.54
10102112023	10102	14	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	650	0.0191261	10102	121089929	76449577	117614.73
10102122023	10102	4	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	511	0.0150360	10102	95195314	36762529	71942.33
10102132023	10102	7	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	775	0.0228042	10102	144376454	51477227	66422.23
10102142901	10102	2	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	55	0.0016184	10102	10246071	23414246	425713.57
10102152006	10102	14	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	586	0.0172429	10102	109167228	76449577	130460.03
10102162030	10102	7	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	341	0.0100338	10102	63525640	51477227	150959.61
10102162031	10102	1	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	361	0.0106223	10102	67251484	13975585	38713.53
10102162036	10102	3	2017	Calbuco	186292.2	2017	10102	33985	6331140370	Rural	260	0.0076504	10102	48435972	30656786	117910.71

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_rural_nivel_nacional_17_r10.rds")

---

R-11

Urbano

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 1)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15201	1	1	1	1767	1	1	1	2	1	3	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	2	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	3	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	4	1	1	2	3	1	99	1	2	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	5	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	6	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	7	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	8	1	1	3	3	1	99	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	9	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	10	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	11	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	12	1	1	4	3	3	3	4	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	13	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	14	1	1	2	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	15	1	1	2	1	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	16	1	1	5	1	5	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	17	1	1	5	3	1	3	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	18	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	19	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	20	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	21	4	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	22	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	23	1	1	5	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	24	1	1	3	1	1	6	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	25	1	1	5	6	1	1	4	2	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	26	4	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	27	1	1	5	99	3	3	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	28	1	1	6	3	2	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	29	1	1	2	3	1	99	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	30	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	31	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	32	1	1	3	1	1	2	1	1	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	33	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	34	3	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	35	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	36	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	37	1	1	5	3	3	1	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	38	1	1	1	1	4	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	39	1	1	5	3	4	3	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	40	1	1	5	3	2	1	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	41	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	42	1	1	2	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	43	1	1	1	1	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	44	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	45	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	46	1	1	5	6	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	47	1	1	5	3	4	0	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	48	1	1	3	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	49	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	50	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	51	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	52	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	53	1	1	1	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	54	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	55	1	1	2	2	1	4	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	56	8	1	98	98	98	98	98	98	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	57	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	58	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	59	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	60	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	61	1	1	2	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	62	1	1	5	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	63	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	64	1	1	5	3	2	4	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	65	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	66	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	67	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	68	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	69	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	70	1	1	1	3	3	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	71	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	72	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	73	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	74	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	75	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	76	1	1	5	3	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	77	1	1	3	6	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	78	5	1	3	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	79	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	80	1	1	2	1	1	3	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	81	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	82	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	83	1	1	5	3	2	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	84	4	1	5	3	2	0	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	85	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	86	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	87	1	1	3	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	88	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	89	5	1	4	3	2	1	2	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	90	1	1	5	3	3	2	1	3	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	91	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	92	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	93	5	1	4	3	5	1	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	94	1	1	5	3	3	99	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	95	1	1	5	3	2	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	96	1	1	2	3	4	6	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	97	1	1	5	1	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	98	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	99	1	1	2	7	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	100	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 11:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 11)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	11101011001	30	2017	11101
2	11101011002	80	2017	11101
3	11101011003	20	2017	11101
4	11101011004	22	2017	11101
5	11101011005	32	2017	11101
6	11101011006	93	2017	11101
7	11101011007	75	2017	11101
8	11101121001	26	2017	11101
9	11101121002	41	2017	11101
10	11101121003	161	2017	11101
11	11101121004	81	2017	11101
12	11101121005	115	2017	11101
13	11101121006	110	2017	11101
14	11101121007	36	2017	11101
15	11101121008	31	2017	11101
16	11101131001	67	2017	11101
17	11101131002	67	2017	11101
18	11101131003	93	2017	11101
19	11101131004	72	2017	11101
20	11101131005	42	2017	11101
21	11101131006	3	2017	11101
22	11101131007	41	2017	11101
23	11101991999	20	2017	11101
62	11201011001	47	2017	11201
63	11201011002	62	2017	11201
64	11201011003	83	2017	11201
65	11201021001	8	2017	11201
66	11201041001	12	2017	11201
67	11201041002	68	2017	11201
68	11201041003	36	2017	11201
69	11201071001	9	2017	11201
108	11202011001	51	2017	11202
109	11202021001	15	2017	11202
110	11202991999	1	2017	11202
149	11203011001	13	2017	11203
188	11301011001	87	2017	11301
189	11301991999	1	2017	11301
228	11401011001	42	2017	11401
NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA
NA.2	NA	NA	NA	NA
NA.3	NA	NA	NA	NA
NA.4	NA	NA	NA	NA
NA.5	NA	NA	NA	NA
NA.6	NA	NA	NA	NA
NA.7	NA	NA	NA	NA
NA.8	NA	NA	NA	NA
NA.9	NA	NA	NA	NA
NA.10	NA	NA	NA	NA
NA.11	NA	NA	NA	NA
NA.12	NA	NA	NA	NA
NA.13	NA	NA	NA	NA
NA.14	NA	NA	NA	NA
NA.15	NA	NA	NA	NA
NA.16	NA	NA	NA	NA
NA.17	NA	NA	NA	NA
NA.18	NA	NA	NA	NA
NA.19	NA	NA	NA	NA
NA.20	NA	NA	NA	NA
NA.21	NA	NA	NA	NA
NA.22	NA	NA	NA	NA
NA.23	NA	NA	NA	NA
NA.24	NA	NA	NA	NA
NA.25	NA	NA	NA	NA
NA.26	NA	NA	NA	NA
NA.27	NA	NA	NA	NA
NA.28	NA	NA	NA	NA
NA.29	NA	NA	NA	NA
NA.30	NA	NA	NA	NA
NA.31	NA	NA	NA	NA
NA.32	NA	NA	NA	NA
NA.33	NA	NA	NA	NA
NA.34	NA	NA	NA	NA
NA.35	NA	NA	NA	NA
NA.36	NA	NA	NA	NA
NA.37	NA	NA	NA	NA
NA.38	NA	NA	NA	NA
NA.39	NA	NA	NA	NA
NA.40	NA	NA	NA	NA
NA.41	NA	NA	NA	NA
NA.42	NA	NA	NA	NA
NA.43	NA	NA	NA	NA
NA.44	NA	NA	NA	NA
NA.45	NA	NA	NA	NA
NA.46	NA	NA	NA	NA
NA.47	NA	NA	NA	NA
NA.48	NA	NA	NA	NA
NA.49	NA	NA	NA	NA
NA.50	NA	NA	NA	NA
NA.51	NA	NA	NA	NA
NA.52	NA	NA	NA	NA
NA.53	NA	NA	NA	NA
NA.54	NA	NA	NA	NA
NA.55	NA	NA	NA	NA
NA.56	NA	NA	NA	NA
NA.57	NA	NA	NA	NA
NA.58	NA	NA	NA	NA
NA.59	NA	NA	NA	NA
NA.60	NA	NA	NA	NA
NA.61	NA	NA	NA	NA

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_urbano_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
01107	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
01401	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
01405	Pica	330061.1	2017	1405	9296	3068247619	Urbano
02101	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02102	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02104	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02201	Calama	416281.1	2017	2201	165731	68990679686	Urbano
02203	San Pedro de Atacama	437934.7	2017	2203	10996	4815529626	Urbano
02301	Tocopilla	271720.8	2017	2301	25186	6843559467	Urbano
02302	María Elena	466266.9	2017	2302	6457	3010685220	Urbano
03101	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
03102	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
03103	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
03201	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
03202	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
03301	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
03303	Freirina	289049.9	2017	3303	7041	2035200054	Urbano
03304	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
04101	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04102	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04103	Andacollo	251267.7	2017	4103	11044	2775000288	Urbano
04104	La Higuera	214257.0	2017	4104	4241	908664019	Urbano
04106	Vicuña	245957.4	2017	4106	27771	6830481918	Urbano
04201	Illapel	270316.5	2017	4201	30848	8338722128	Urbano
04202	Canela	233397.3	2017	4202	9093	2122281844	Urbano
04203	Los Vilos	282415.6	2017	4203	21382	6038609501	Urbano
04204	Salamanca	262056.9	2017	4204	29347	7690585032	Urbano
04301	Ovalle	274771.4	2017	4301	111272	30574361012	Urbano
04302	Combarbalá	228990.4	2017	4302	13322	3050610572	Urbano
04303	Monte Patria	225369.1	2017	4303	30751	6930326684	Urbano
04304	Punitaqui	212496.1	2017	4304	10956	2328107498	Urbano
05101	Valparaíso	297929.0	2017	5101	296655	88382118059	Urbano
05102	Casablanca	341641.8	2017	5102	26867	9178890241	Urbano
05103	Concón	318496.3	2017	5103	42152	13425257057	Urbano
05105	Puchuncaví	296035.5	2017	5105	18546	5490274928	Urbano
05107	Quintero	308224.7	2017	5107	31923	9839456903	Urbano
05109	Viña del Mar	337006.1	2017	5109	334248	112643604611	Urbano
05301	Los Andes	339720.2	2017	5301	66708	22662055502	Urbano
05302	Calle Larga	246387.3	2017	5302	14832	3654416747	Urbano
05303	Rinconada	273904.7	2017	5303	10207	2795744821	Urbano
05304	San Esteban	219571.6	2017	5304	18855	4140022481	Urbano
05401	La Ligua	250134.4	2017	5401	35390	8852256241	Urbano
05402	Cabildo	262745.9	2017	5402	19388	5094117762	Urbano
05403	Papudo	294355.2	2017	5403	6356	1870921373	Urbano
05404	Petorca	237510.8	2017	5404	9826	2333781007	Urbano
05405	Zapallar	294389.2	2017	5405	7339	2160521991	Urbano
05501	Quillota	286029.5	2017	5501	90517	25890529852	Urbano
05502	Calera	277181.9	2017	5502	50554	14012652087	Urbano
05503	Hijuelas	254094.0	2017	5503	17988	4570642363	Urbano

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
1	11101	11101011002	80	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
2	11101	11101011003	20	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
3	11101	11101011004	22	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
4	11101	11101011001	30	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
5	11101	11101011006	93	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
6	11101	11101011007	75	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
7	11101	11101121001	26	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
8	11101	11101011005	32	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
9	11101	11101121003	161	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
10	11101	11101121004	81	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
11	11101	11101121005	115	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
12	11101	11101121006	110	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
13	11101	11101121007	36	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
14	11101	11101121008	31	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
15	11101	11101131001	67	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
16	11101	11101131002	67	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
17	11101	11101131003	93	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
18	11101	11101131004	72	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
19	11101	11101131005	42	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
20	11101	11101131006	3	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
21	11101	11101121002	41	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
22	11101	11101991999	20	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
23	11101	11101131007	41	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
24	11201	11201011001	47	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano
25	11201	11201011002	62	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano
26	11201	11201011003	83	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano
27	11201	11201041001	12	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano
28	11201	11201041002	68	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano
29	11201	11201041003	36	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano
30	11201	11201021001	8	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano
31	11201	11201071001	9	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano
32	11202	11202011001	51	2017	Cisnes	234313.5	2017	11202	6517	1527021007	Urbano
33	11202	11202021001	15	2017	Cisnes	234313.5	2017	11202	6517	1527021007	Urbano
34	11202	11202991999	1	2017	Cisnes	234313.5	2017	11202	6517	1527021007	Urbano
35	11203	11203011001	13	2017	NA	NA	NA	NA	NA	NA	NA
36	11301	11301011001	87	2017	Cochrane	323131.5	2017	11301	3490	1127728935	Urbano
37	11301	11301991999	1	2017	Cochrane	323131.5	2017	11301	3490	1127728935	Urbano
38	11401	11401011001	42	2017	Chile Chico	325312.1	2017	11401	4865	1582643433	Urbano
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.41	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.42	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.43	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.44	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.45	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.46	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.47	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.48	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.49	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.50	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.51	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.52	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.53	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.54	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.55	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.56	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.57	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.58	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.59	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.60	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.61	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
1	11101	11101011002	80	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
2	11101	11101011003	20	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
3	11101	11101011004	22	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
4	11101	11101011001	30	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
5	11101	11101011006	93	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
6	11101	11101011007	75	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
7	11101	11101121001	26	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
8	11101	11101011005	32	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
9	11101	11101121003	161	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
10	11101	11101121004	81	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
11	11101	11101121005	115	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
12	11101	11101121006	110	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
13	11101	11101121007	36	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
14	11101	11101121008	31	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
15	11101	11101131001	67	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
16	11101	11101131002	67	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
17	11101	11101131003	93	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
18	11101	11101131004	72	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
19	11101	11101131005	42	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
20	11101	11101131006	3	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
21	11101	11101121002	41	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
22	11101	11101991999	20	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
23	11101	11101131007	41	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano
24	11201	11201011001	47	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano
25	11201	11201011002	62	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano
26	11201	11201011003	83	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano
27	11201	11201041001	12	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano
28	11201	11201041002	68	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano
29	11201	11201041003	36	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano
30	11201	11201021001	8	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano
31	11201	11201071001	9	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano
32	11202	11202011001	51	2017	Cisnes	234313.5	2017	11202	6517	1527021007	Urbano
33	11202	11202021001	15	2017	Cisnes	234313.5	2017	11202	6517	1527021007	Urbano
34	11202	11202991999	1	2017	Cisnes	234313.5	2017	11202	6517	1527021007	Urbano
35	11203	11203011001	13	2017	NA	NA	NA	NA	NA	NA	NA
36	11301	11301011001	87	2017	Cochrane	323131.5	2017	11301	3490	1127728935	Urbano
37	11301	11301991999	1	2017	Cochrane	323131.5	2017	11301	3490	1127728935	Urbano
38	11401	11401011001	42	2017	Chile Chico	325312.1	2017	11401	4865	1582643433	Urbano
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.41	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.42	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.43	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.44	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.45	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.46	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.47	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.48	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.49	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.50	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.51	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.52	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.53	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.54	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.55	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.56	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.57	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.58	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.59	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.60	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.61	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
11101011001	11101	30	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	324	0.0056038	11101
11101011002	11101	80	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	1672	0.0289183	11101
11101011003	11101	20	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	499	0.0086305	11101
11101011004	11101	22	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	667	0.0115362	11101
11101011005	11101	32	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	977	0.0168979	11101
11101011006	11101	93	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	1595	0.0275866	11101
11101011007	11101	75	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	2251	0.0389325	11101
11101121001	11101	26	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	903	0.0156180	11101
11101121002	11101	41	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3281	0.0567470	11101
11101121003	11101	161	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	2413	0.0417344	11101
11101121004	11101	81	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3717	0.0642879	11101
11101121005	11101	115	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	2462	0.0425819	11101
11101121006	11101	110	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	2381	0.0411809	11101
11101121007	11101	36	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3498	0.0605002	11101
11101121008	11101	31	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3334	0.0576637	11101
11101131001	11101	67	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	1906	0.0329655	11101
11101131002	11101	67	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3237	0.0559860	11101
11101131003	11101	93	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3439	0.0594797	11101
11101131004	11101	72	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3238	0.0560033	11101
11101131005	11101	42	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	2580	0.0446228	11101
11101131006	11101	3	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	1524	0.0263586	11101
11101131007	11101	41	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3756	0.0649625	11101
11101991999	11101	20	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	301	0.0052060	11101
11201011001	11201	47	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	2035	0.0849368	11201
11201011002	11201	62	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	3187	0.1330189	11201
11201011003	11201	83	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	3870	0.1615259	11201
11201021001	11201	8	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	1561	0.0651530	11201
11201041001	11201	12	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	2623	0.1094787	11201
11201041002	11201	68	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	2692	0.1123586	11201
11201041003	11201	36	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	3034	0.1266330	11201
11201071001	11201	9	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	1239	0.0517133	11201
11202011001	11202	51	2017	Cisnes	234313.5	2017	11202	6517	1527021007	Urbano	2558	0.3925119	11202
11202021001	11202	15	2017	Cisnes	234313.5	2017	11202	6517	1527021007	Urbano	1431	0.2195796	11202
11202991999	11202	1	2017	Cisnes	234313.5	2017	11202	6517	1527021007	Urbano	125	0.0191806	11202
11203011001	11203	13	2017	NA	NA	NA	NA	NA	NA	NA	1329	0.7211069	11203
11301011001	11301	87	2017	Cochrane	323131.5	2017	11301	3490	1127728935	Urbano	2789	0.7991404	11301
11301991999	11301	1	2017	Cochrane	323131.5	2017	11301	3490	1127728935	Urbano	52	0.0148997	11301
11401011001	11401	42	2017	Chile Chico	325312.1	2017	11401	4865	1582643433	Urbano	3129	0.6431655	11401

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
11101011001	11101	30	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	324	0.0056038	11101	99357358
11101011002	11101	80	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	1672	0.0289183	11101	512733033
11101011003	11101	20	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	499	0.0086305	11101	153022598
11101011004	11101	22	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	667	0.0115362	11101	204541228
11101011005	11101	32	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	977	0.0168979	11101	299605367
11101011006	11101	93	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	1595	0.0275866	11101	489120328
11101011007	11101	75	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	2251	0.0389325	11101	690288312
11101121001	11101	26	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	903	0.0156180	11101	276912637
11101121002	11101	41	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3281	0.0567470	11101	1006146580
11101121003	11101	161	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	2413	0.0417344	11101	739966991
11101121004	11101	81	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3717	0.0642879	11101	1139849691
11101121005	11101	115	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	2462	0.0425819	11101	754993258
11101121006	11101	110	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	2381	0.0411809	11101	730153918
11101121007	11101	36	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3498	0.0605002	11101	1072691477
11101121008	11101	31	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3334	0.0576637	11101	1022399481
11101131001	11101	67	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	1906	0.0329655	11101	584491125
11101131002	11101	67	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3237	0.0559860	11101	992653605
11101131003	11101	93	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3439	0.0594797	11101	1054598625
11101131004	11101	72	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3238	0.0560033	11101	992960264
11101131005	11101	42	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	2580	0.0446228	11101	791178962
11101131006	11101	3	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	1524	0.0263586	11101	467347573
11101131007	11101	41	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3756	0.0649625	11101	1151809373
11101991999	11101	20	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	301	0.0052060	11101	92304212
11201011001	11201	47	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	2035	0.0849368	11201	603996503
11201011002	11201	62	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	3187	0.1330189	11201	945914916
11201011003	11201	83	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	3870	0.1615259	11201	1148632170
11201021001	11201	8	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	1561	0.0651530	11201	463311322
11201041001	11201	12	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	2623	0.1094787	11201	778517360
11201041002	11201	68	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	2692	0.1123586	11201	798996848
11201041003	11201	36	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	3034	0.1266330	11201	900503877
11201071001	11201	9	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	1239	0.0517133	11201	367740377
11202011001	11202	51	2017	Cisnes	234313.5	2017	11202	6517	1527021007	Urbano	2558	0.3925119	11202	599373905
11202021001	11202	15	2017	Cisnes	234313.5	2017	11202	6517	1527021007	Urbano	1431	0.2195796	11202	335302603
11202991999	11202	1	2017	Cisnes	234313.5	2017	11202	6517	1527021007	Urbano	125	0.0191806	11202	29289186
11203011001	11203	13	2017	NA	NA	NA	NA	NA	NA	NA	1329	0.7211069	11203	NA
11301011001	11301	87	2017	Cochrane	323131.5	2017	11301	3490	1127728935	Urbano	2789	0.7991404	11301	901213754
11301991999	11301	1	2017	Cochrane	323131.5	2017	11301	3490	1127728935	Urbano	52	0.0148997	11301	16802838
11401011001	11401	42	2017	Chile Chico	325312.1	2017	11401	4865	1582643433	Urbano	3129	0.6431655	11401	1017901604

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -458143670 -267243934  -32964848  262209521  542892842 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 417276928   87982637   4.743 3.48e-05 ***
## Freq.x        4674137    1410905   3.313  0.00215 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 310300000 on 35 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.2387, Adjusted R-squared:  0.217 
## F-statistic: 10.98 on 1 and 35 DF,  p-value: 0.002153

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.216967049325077"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.216967049325077"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.354350960990584"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.311819387319976"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.365087102900086"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.385478281220576"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.443915319897771"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                
## [1,] "log-log" "0.53699864988939"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 1    cuadrático 0.216967049325077
## 2        cúbico 0.216967049325077
## 4 raíz cuadrada 0.311819387319976
## 3   logarítmico 0.354350960990584
## 5     raíz-raíz 0.365087102900086
## 6      log-raíz 0.385478281220576
## 7      raíz-log 0.443915319897771
## 8       log-log  0.53699864988939
##                                                                     sintaxis
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 8
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.52852 -0.46503  0.04573  0.53106  1.45597 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.82140    0.35257  50.547  < 2e-16 ***
## log(Freq.x)  0.62371    0.09539   6.539 1.52e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6817 on 35 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.5499, Adjusted R-squared:  0.537 
## F-statistic: 42.75 on 1 and 35 DF,  p-value: 1.517e-07

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##     17.8214

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##   0.6237069

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.537 ).

9.1 Diagrama de dispersión sobre log-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.52852 -0.46503  0.04573  0.53106  1.45597 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.82140    0.35257  50.547  < 2e-16 ***
## log(Freq.x)  0.62371    0.09539   6.539 1.52e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6817 on 35 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.5499, Adjusted R-squared:  0.537 
## F-statistic: 42.75 on 1 and 35 DF,  p-value: 1.517e-07

ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = e^{17.8214+0.6237069 \cdot ln{X}} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
1	11101011001	11101	30	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	324	0.0056038	11101	99357358	458169183
2	11101011002	11101	80	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	1672	0.0289183	11101	512733033	844705835
3	11101011003	11101	20	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	499	0.0086305	11101	153022598	355792303
4	11101011004	11101	22	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	667	0.0115362	11101	204541228	377583885
5	11101011005	11101	32	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	977	0.0168979	11101	299605367	476988141
6	11101011006	11101	93	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	1595	0.0275866	11101	489120328	927879400
7	11101011007	11101	75	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	2251	0.0389325	11101	690288312	811379045
8	11101121001	11101	26	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	903	0.0156180	11101	276912637	419048026
9	11101121002	11101	41	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3281	0.0567470	11101	1006146580	556723521
10	11101121003	11101	161	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	2413	0.0417344	11101	739966991	1306615036
11	11101121004	11101	81	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3717	0.0642879	11101	1139849691	851276046
12	11101121005	11101	115	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	2462	0.0425819	11101	754993258	1059269807
13	11101121006	11101	110	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	2381	0.0411809	11101	730153918	1030305067
14	11101121007	11101	36	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3498	0.0605002	11101	1072691477	513347866
15	11101121008	11101	31	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3334	0.0576637	11101	1022399481	467635781
16	11101131001	11101	67	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	1906	0.0329655	11101	584491125	756259069
17	11101131002	11101	67	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3237	0.0559860	11101	992653605	756259069
18	11101131003	11101	93	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3439	0.0594797	11101	1054598625	927879400
19	11101131004	11101	72	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3238	0.0560033	11101	992960264	790981332
20	11101131005	11101	42	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	2580	0.0446228	11101	791178962	565154165
21	11101131006	11101	3	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	1524	0.0263586	11101	467347573	108973027
22	11101131007	11101	41	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3756	0.0649625	11101	1151809373	556723521
23	11101991999	11101	20	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	301	0.0052060	11101	92304212	355792303
24	11201011001	11201	47	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	2035	0.0849368	11201	603996503	606225387
25	11201011002	11201	62	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	3187	0.1330189	11201	945914916	720546822
26	11201011003	11201	83	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	3870	0.1615259	11201	1148632170	864325618
27	11201021001	11201	8	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	1561	0.0651530	11201	463311322	200908650
28	11201041001	11201	12	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	2623	0.1094787	11201	778517360	258718784
29	11201041002	11201	68	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	2692	0.1123586	11201	798996848	763279493
30	11201041003	11201	36	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	3034	0.1266330	11201	900503877	513347866
31	11201071001	11201	9	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	1239	0.0517133	11201	367740377	216223461
32	11202011001	11202	51	2017	Cisnes	234313.5	2017	11202	6517	1527021007	Urbano	2558	0.3925119	11202	599373905	637908586
33	11202021001	11202	15	2017	Cisnes	234313.5	2017	11202	6517	1527021007	Urbano	1431	0.2195796	11202	335302603	297352368
34	11202991999	11202	1	2017	Cisnes	234313.5	2017	11202	6517	1527021007	Urbano	125	0.0191806	11202	29289186	54920593
35	11203011001	11203	13	2017	NA	NA	NA	NA	NA	NA	NA	1329	0.7211069	11203	NA	271962689
36	11301011001	11301	87	2017	Cochrane	323131.5	2017	11301	3490	1127728935	Urbano	2789	0.7991404	11301	901213754	890075152
37	11301991999	11301	1	2017	Cochrane	323131.5	2017	11301	3490	1127728935	Urbano	52	0.0148997	11301	16802838	54920593
38	11401011001	11401	42	2017	Chile Chico	325312.1	2017	11401	4865	1582643433	Urbano	3129	0.6431655	11401	1017901604	565154165
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.41	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.42	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.43	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.44	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.45	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.46	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.47	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.48	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.49	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.50	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.51	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.52	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.53	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.54	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.55	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.56	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.57	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.58	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.59	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.60	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.61	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
1	11101011001	11101	30	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	324	0.0056038	11101	99357358	458169183	1414102.42
2	11101011002	11101	80	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	1672	0.0289183	11101	512733033	844705835	505206.84
3	11101011003	11101	20	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	499	0.0086305	11101	153022598	355792303	713010.63
4	11101011004	11101	22	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	667	0.0115362	11101	204541228	377583885	566092.78
5	11101011005	11101	32	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	977	0.0168979	11101	299605367	476988141	488217.14
6	11101011006	11101	93	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	1595	0.0275866	11101	489120328	927879400	581742.57
7	11101011007	11101	75	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	2251	0.0389325	11101	690288312	811379045	360452.71
8	11101121001	11101	26	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	903	0.0156180	11101	276912637	419048026	464062.04
9	11101121002	11101	41	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3281	0.0567470	11101	1006146580	556723521	169681.05
10	11101121003	11101	161	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	2413	0.0417344	11101	739966991	1306615036	541489.86
11	11101121004	11101	81	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3717	0.0642879	11101	1139849691	851276046	229022.34
12	11101121005	11101	115	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	2462	0.0425819	11101	754993258	1059269807	430247.69
13	11101121006	11101	110	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	2381	0.0411809	11101	730153918	1030305067	432719.47
14	11101121007	11101	36	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3498	0.0605002	11101	1072691477	513347866	146754.68
15	11101121008	11101	31	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3334	0.0576637	11101	1022399481	467635781	140262.68
16	11101131001	11101	67	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	1906	0.0329655	11101	584491125	756259069	396778.11
17	11101131002	11101	67	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3237	0.0559860	11101	992653605	756259069	233629.62
18	11101131003	11101	93	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3439	0.0594797	11101	1054598625	927879400	269810.82
19	11101131004	11101	72	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3238	0.0560033	11101	992960264	790981332	244280.83
20	11101131005	11101	42	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	2580	0.0446228	11101	791178962	565154165	219052.00
21	11101131006	11101	3	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	1524	0.0263586	11101	467347573	108973027	71504.61
22	11101131007	11101	41	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	3756	0.0649625	11101	1151809373	556723521	148222.45
23	11101991999	11101	20	2017	Coyhaique	306658.5	2017	11101	57818	17730381878	Urbano	301	0.0052060	11101	92304212	355792303	1182034.23
24	11201011001	11201	47	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	2035	0.0849368	11201	603996503	606225387	297899.45
25	11201011002	11201	62	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	3187	0.1330189	11201	945914916	720546822	226089.37
26	11201011003	11201	83	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	3870	0.1615259	11201	1148632170	864325618	223339.95
27	11201021001	11201	8	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	1561	0.0651530	11201	463311322	200908650	128705.09
28	11201041001	11201	12	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	2623	0.1094787	11201	778517360	258718784	98634.69
29	11201041002	11201	68	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	2692	0.1123586	11201	798996848	763279493	283536.22
30	11201041003	11201	36	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	3034	0.1266330	11201	900503877	513347866	169198.37
31	11201071001	11201	9	2017	Aysén	296804.2	2017	11201	23959	7111131309	Urbano	1239	0.0517133	11201	367740377	216223461	174514.50
32	11202011001	11202	51	2017	Cisnes	234313.5	2017	11202	6517	1527021007	Urbano	2558	0.3925119	11202	599373905	637908586	249377.87
33	11202021001	11202	15	2017	Cisnes	234313.5	2017	11202	6517	1527021007	Urbano	1431	0.2195796	11202	335302603	297352368	207793.41
34	11202991999	11202	1	2017	Cisnes	234313.5	2017	11202	6517	1527021007	Urbano	125	0.0191806	11202	29289186	54920593	439364.74
35	11203011001	11203	13	2017	NA	NA	NA	NA	NA	NA	NA	1329	0.7211069	11203	NA	271962689	NA
36	11301011001	11301	87	2017	Cochrane	323131.5	2017	11301	3490	1127728935	Urbano	2789	0.7991404	11301	901213754	890075152	319137.74
37	11301991999	11301	1	2017	Cochrane	323131.5	2017	11301	3490	1127728935	Urbano	52	0.0148997	11301	16802838	54920593	1056165.25
38	11401011001	11401	42	2017	Chile Chico	325312.1	2017	11401	4865	1582643433	Urbano	3129	0.6431655	11401	1017901604	565154165	180618.14
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.41	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.42	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.43	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.44	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.45	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.46	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.47	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.48	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.49	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.50	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.51	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.52	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.53	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.54	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.55	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.56	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.57	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.58	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.59	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.60	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.61	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_urbano_nivel_nacional_17_r11.rds")

---

Rural

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 2)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15202	1	2	6	13225	1	3	1	5	3	5	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	3	1	1	5	3	5	2	3	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	4	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	5	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	6	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	8	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	9	3	1	5	3	5	1	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	10	1	1	5	3	4	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	11	1	2	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	12	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	13	1	1	5	3	4	1	4	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	14	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	16	1	1	5	3	5	1	3	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	17	1	1	5	3	5	2	4	1	8	15	152	15202	15202012006
15	152	15202	1	2	6	13225	18	3	1	5	3	5	1	1	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	19	3	1	5	3	5	1	3	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	20	1	1	5	3	5	2	1	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	21	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	22	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	24	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	25	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	26	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	27	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	28	3	1	5	3	5	1	4	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	29	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	30	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	31	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	32	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	33	1	1	5	3	5	3	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	34	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	35	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	36	1	1	5	3	5	3	2	1	9	15	152	15202	15202012006
15	152	15202	1	2	6	13225	37	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	38	1	1	5	3	5	99	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	39	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	40	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	41	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	42	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	43	3	1	5	3	5	2	1	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	44	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	45	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	8	13910	1	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	3	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	4	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	5	3	1	5	3	5	2	3	1	3	15	152	15202	15202012008
15	152	15202	1	2	8	13910	6	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	7	1	1	5	99	5	2	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	8	3	1	5	3	5	3	3	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	9	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	10	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	11	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	12	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	13	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	14	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	16	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	18	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	20	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	21	1	1	5	3	5	1	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	22	3	1	5	3	5	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	23	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	24	3	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	25	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	26	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	27	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	28	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	29	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	30	1	1	5	1	4	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	12	8394	1	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	2	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	3	3	1	5	3	5	2	3	1	4	15	152	15202	15202012012
15	152	15202	1	2	12	8394	4	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	5	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	6	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	8	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	9	3	1	5	3	5	1	4	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	10	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	11	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	12	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	13	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	14	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	15	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	16	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	18	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	20	3	1	5	3	5	3	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	21	3	1	5	99	5	1	4	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	22	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	24	3	1	5	3	5	1	2	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	25	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 11:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 11)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Veamos los primeros 100 registros:

r3_100 <- d[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	unlist.c.	unlist.d.	Freq	anio
1	11101022003	1	11101	2	2017
2	11101022010	1	11101	2	2017
3	11101022031	1	11101	1	2017
4	11101022038	1	11101	3	2017
5	11101032005	1	11101	1	2017
6	11101032032	1	11101	5	2017
7	11101032034	1	11101	1	2017
8	11101032901	1	11101	2	2017
9	11101042022	1	11101	3	2017
10	11101042024	1	11101	3	2017
11	11101042027	1	11101	6	2017
12	11101052004	1	11101	6	2017
13	11101052008	1	11101	5	2017
14	11101052014	1	11101	6	2017
15	11101052901	1	11101	3	2017
16	11101062023	1	11101	1	2017
17	11101062025	1	11101	2	2017
18	11101072014	1	11101	4	2017
19	11101072018	1	11101	57	2017
20	11101072019	1	11101	7	2017
21	11101072020	1	11101	4	2017
22	11101072035	1	11101	3	2017
23	11101072036	1	11101	8	2017
24	11101072037	1	11101	7	2017
25	11101082021	1	11101	1	2017
26	11101092007	1	11101	1	2017
27	11101102011	1	11101	10	2017
28	11101102020	1	11101	20	2017
29	11101102033	1	11101	69	2017
30	11101112001	1	11101	3	2017
31	11101112009	1	11101	23	2017
32	11101112011	1	11101	78	2017
33	11101112012	1	11101	5	2017
34	11101112013	1	11101	61	2017
35	11101122011	1	11101	36	2017
36	11101132011	1	11101	1	2017
133	11102012004	1	11102	1	2017
134	11102042003	1	11102	4	2017
135	11102052002	1	11102	3	2017
232	11201012009	1	11201	3	2017
233	11201012013	1	11201	2	2017
234	11201012055	1	11201	1	2017
235	11201012067	1	11201	1	2017
236	11201012901	1	11201	2	2017
237	11201022011	1	11201	2	2017
238	11201022057	1	11201	1	2017
239	11201032068	1	11201	4	2017
240	11201042006	1	11201	13	2017
241	11201042007	1	11201	3	2017
242	11201052901	1	11201	2	2017
243	11201062044	1	11201	35	2017
244	11201072901	1	11201	3	2017
341	11202022016	1	11202	10	2017
342	11202022020	1	11202	14	2017
343	11202032022	1	11202	1	2017
344	11202052021	1	11202	2	2017
345	11202052023	1	11202	16	2017
346	11202052901	1	11202	1	2017
443	11203012004	1	11203	3	2017
444	11203012005	1	11203	1	2017
445	11203012901	1	11203	3	2017
542	11301012002	1	11301	1	2017
543	11301012003	1	11301	1	2017
544	11301012004	1	11301	2	2017
545	11301012901	1	11301	1	2017
546	11301022009	1	11301	1	2017
547	11301022016	1	11301	2	2017
548	11301032005	1	11301	1	2017
549	11301032901	1	11301	1	2017
550	11301052010	1	11301	1	2017
551	11301062011	1	11301	1	2017
648	11302042003	1	11302	2	2017
649	11302042005	1	11302	9	2017
746	11303012010	1	11303	1	2017
747	11303012013	1	11303	7	2017
748	11303012901	1	11303	1	2017
845	11401012001	1	11401	2	2017
846	11401012002	1	11401	8	2017
847	11401022008	1	11401	4	2017
848	11401022010	1	11401	7	2017
849	11401032005	1	11401	1	2017
850	11401032006	1	11401	1	2017
851	11401032009	1	11401	1	2017
852	11401042011	1	11401	1	2017
949	11402012004	1	11402	1	2017
950	11402012007	1	11402	11	2017
951	11402012019	1	11402	12	2017
952	11402022901	1	11402	1	2017
953	11402032014	1	11402	1	2017
954	11402032023	1	11402	2	2017
955	11402042001	1	11402	3	2017
956	11402042901	1	11402	1	2017
957	11402062020	1	11402	9	2017
958	11402062024	1	11402	1	2017
959	11402072003	1	11402	25	2017
960	11402992999	1	11402	2	2017
NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	11101022003	2	2017	11101
2	11101022010	2	2017	11101
3	11101022031	1	2017	11101
4	11101022038	3	2017	11101
5	11101032005	1	2017	11101
6	11101032032	5	2017	11101
7	11101032034	1	2017	11101
8	11101032901	2	2017	11101
9	11101042022	3	2017	11101
10	11101042024	3	2017	11101
11	11101042027	6	2017	11101
12	11101052004	6	2017	11101
13	11101052008	5	2017	11101
14	11101052014	6	2017	11101
15	11101052901	3	2017	11101
16	11101062023	1	2017	11101
17	11101062025	2	2017	11101
18	11101072014	4	2017	11101
19	11101072018	57	2017	11101
20	11101072019	7	2017	11101
21	11101072020	4	2017	11101
22	11101072035	3	2017	11101
23	11101072036	8	2017	11101
24	11101072037	7	2017	11101
25	11101082021	1	2017	11101
26	11101092007	1	2017	11101
27	11101102011	10	2017	11101
28	11101102020	20	2017	11101
29	11101102033	69	2017	11101
30	11101112001	3	2017	11101
31	11101112009	23	2017	11101
32	11101112011	78	2017	11101
33	11101112012	5	2017	11101
34	11101112013	61	2017	11101
35	11101122011	36	2017	11101
36	11101132011	1	2017	11101
133	11102012004	1	2017	11102
134	11102042003	4	2017	11102
135	11102052002	3	2017	11102
232	11201012009	3	2017	11201
233	11201012013	2	2017	11201
234	11201012055	1	2017	11201
235	11201012067	1	2017	11201
236	11201012901	2	2017	11201
237	11201022011	2	2017	11201
238	11201022057	1	2017	11201
239	11201032068	4	2017	11201
240	11201042006	13	2017	11201
241	11201042007	3	2017	11201
242	11201052901	2	2017	11201
243	11201062044	35	2017	11201
244	11201072901	3	2017	11201
341	11202022016	10	2017	11202
342	11202022020	14	2017	11202
343	11202032022	1	2017	11202
344	11202052021	2	2017	11202
345	11202052023	16	2017	11202
346	11202052901	1	2017	11202
443	11203012004	3	2017	11203
444	11203012005	1	2017	11203
445	11203012901	3	2017	11203
542	11301012002	1	2017	11301
543	11301012003	1	2017	11301
544	11301012004	2	2017	11301
545	11301012901	1	2017	11301
546	11301022009	1	2017	11301
547	11301022016	2	2017	11301
548	11301032005	1	2017	11301
549	11301032901	1	2017	11301
550	11301052010	1	2017	11301
551	11301062011	1	2017	11301
648	11302042003	2	2017	11302
649	11302042005	9	2017	11302
746	11303012010	1	2017	11303
747	11303012013	7	2017	11303
748	11303012901	1	2017	11303
845	11401012001	2	2017	11401
846	11401012002	8	2017	11401
847	11401022008	4	2017	11401
848	11401022010	7	2017	11401
849	11401032005	1	2017	11401
850	11401032006	1	2017	11401
851	11401032009	1	2017	11401
852	11401042011	1	2017	11401
949	11402012004	1	2017	11402
950	11402012007	11	2017	11402
951	11402012019	12	2017	11402
952	11402022901	1	2017	11402
953	11402032014	1	2017	11402
954	11402032023	2	2017	11402
955	11402042001	3	2017	11402
956	11402042901	1	2017	11402
957	11402062020	9	2017	11402
958	11402062024	1	2017	11402
959	11402072003	25	2017	11402
960	11402992999	2	2017	11402
NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA
NA.2	NA	NA	NA	NA
NA.3	NA	NA	NA	NA

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_rural_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	289375.3	2017	1101	191468	55406102543	Rural
01401	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
01402	Camiña	262850.3	2017	1402	1250	328562901	Rural
01404	Huara	253968.5	2017	1404	2730	693334131	Rural
01405	Pica	290496.7	2017	1405	9296	2700457509	Rural
02103	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
02104	Taltal	345494.0	2017	2104	13317	4600943086	Rural
02201	Calama	310025.0	2017	2201	165731	51380756402	Rural
02203	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
02301	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
03101	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03103	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03202	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural
03301	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03302	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03303	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03304	Huasco	227560.7	2017	3304	10149	2309513927	Rural
04101	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04102	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04103	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04104	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04105	Paiguano	205942.1	2017	4105	4497	926121774	Rural
04106	Vicuña	176130.6	2017	4106	27771	4891322768	Rural
04201	Illapel	191976.8	2017	4201	30848	5922099530	Rural
04202	Canela	171370.3	2017	4202	9093	1558270441	Rural
04203	Los Vilos	173238.5	2017	4203	21382	3704185607	Rural
04204	Salamanca	223234.2	2017	4204	29347	6551254640	Rural
04301	Ovalle	241393.7	2017	4301	111272	26860360045	Rural
04302	Combarbalá	179139.6	2017	4302	13322	2386498044	Rural
04303	Monte Patria	201205.8	2017	4303	30751	6187280931	Rural
04304	Punitaqui	171931.7	2017	4304	10956	1883683880	Rural
04305	Río Hurtado	182027.2	2017	4305	4278	778712384	Rural
05101	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05102	Casablanca	268917.1	2017	5102	26867	7224996933	Rural
05105	Puchuncaví	279614.4	2017	5105	18546	5185728335	Rural
05107	Quintero	334628.2	2017	5107	31923	10682335196	Rural
05301	Los Andes	324402.1	2017	5301	66708	21640215030	Rural
05302	Calle Larga	242743.8	2017	5302	14832	3600375502	Rural
05303	Rinconada	326532.5	2017	5303	10207	3332917471	Rural
05304	San Esteban	223168.6	2017	5304	18855	4207844130	Rural
05401	La Ligua	181468.0	2017	5401	35390	6422154059	Rural
05402	Cabildo	231277.8	2017	5402	19388	4484014285	Rural
05404	Petorca	298208.9	2017	5404	9826	2930200178	Rural
05405	Zapallar	292882.3	2017	5405	7339	2149463129	Rural
05501	Quillota	220926.8	2017	5501	90517	19997628209	Rural
05502	Calera	226906.2	2017	5502	50554	11471016698	Rural
05503	Hijuelas	253739.9	2017	5503	17988	4564273201	Rural
05504	La Cruz	291124.1	2017	5504	22098	6433259569	Rural
05506	Nogales	264475.3	2017	5506	22120	5850194593	Rural
05601	San Antonio	266331.2	2017	5601	91350	24329353815	Rural

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
1	11101	11101022010	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
2	11101	11101022031	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
3	11101	11101022003	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
4	11101	11101032032	5	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
5	11101	11101052901	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
6	11101	11101032005	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
7	11101	11101052014	6	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
8	11101	11101072018	57	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
9	11101	11101022038	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
10	11101	11101062025	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
11	11101	11101072014	4	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
12	11101	11101042024	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
13	11101	11101062023	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
14	11101	11101052004	6	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
15	11101	11101052008	5	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
16	11101	11101102011	10	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
17	11101	11101102020	20	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
18	11101	11101102033	69	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
19	11101	11101112001	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
20	11101	11101112009	23	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
21	11101	11101112011	78	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
22	11101	11101032034	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
23	11101	11101032901	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
24	11101	11101042022	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
25	11101	11101072036	8	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
26	11101	11101042027	6	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
27	11101	11101082021	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
28	11101	11101092007	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
29	11101	11101072035	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
30	11101	11101132011	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
31	11101	11101072019	7	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
32	11101	11101072020	4	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
33	11101	11101112013	61	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
34	11101	11101122011	36	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
35	11101	11101072037	7	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
36	11101	11101112012	5	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
37	11102	11102052002	3	2017	NA	NA	NA	NA	NA	NA	NA
38	11102	11102012004	1	2017	NA	NA	NA	NA	NA	NA	NA
39	11102	11102042003	4	2017	NA	NA	NA	NA	NA	NA	NA
40	11201	11201012009	3	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
41	11201	11201012013	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
42	11201	11201012055	1	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
43	11201	11201012067	1	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
44	11201	11201012901	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
45	11201	11201022011	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
46	11201	11201042007	3	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
47	11201	11201062044	35	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
48	11201	11201072901	3	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
49	11201	11201032068	4	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
50	11201	11201042006	13	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
51	11201	11201052901	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
52	11201	11201022057	1	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
53	11202	11202022016	10	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural
54	11202	11202022020	14	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural
55	11202	11202032022	1	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural
56	11202	11202052021	2	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural
57	11202	11202052023	16	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural
58	11202	11202052901	1	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural
59	11203	11203012901	3	2017	NA	NA	NA	NA	NA	NA	NA
60	11203	11203012004	3	2017	NA	NA	NA	NA	NA	NA	NA
61	11203	11203012005	1	2017	NA	NA	NA	NA	NA	NA	NA
62	11301	11301022016	2	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural
63	11301	11301012004	2	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural
64	11301	11301012901	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural
65	11301	11301022009	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural
66	11301	11301062011	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural
67	11301	11301032005	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural
68	11301	11301032901	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural
69	11301	11301052010	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural
70	11301	11301012002	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural
71	11301	11301012003	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural
72	11302	11302042005	9	2017	NA	NA	NA	NA	NA	NA	NA
73	11302	11302042003	2	2017	NA	NA	NA	NA	NA	NA	NA
74	11303	11303012013	7	2017	NA	NA	NA	NA	NA	NA	NA
75	11303	11303012010	1	2017	NA	NA	NA	NA	NA	NA	NA
76	11303	11303012901	1	2017	NA	NA	NA	NA	NA	NA	NA
77	11401	11401012001	2	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural
78	11401	11401012002	8	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural
79	11401	11401022008	4	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural
80	11401	11401022010	7	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural
81	11401	11401032005	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural
82	11401	11401032006	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural
83	11401	11401032009	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural
84	11401	11401042011	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural
85	11402	11402012004	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
86	11402	11402012007	11	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
87	11402	11402012019	12	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
88	11402	11402022901	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
89	11402	11402032014	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
90	11402	11402032023	2	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
91	11402	11402042001	3	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
92	11402	11402042901	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
93	11402	11402062020	9	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
94	11402	11402062024	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
95	11402	11402072003	25	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
96	11402	11402992999	2	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
1	11101	11101022010	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
2	11101	11101022031	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
3	11101	11101022003	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
4	11101	11101032032	5	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
5	11101	11101052901	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
6	11101	11101032005	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
7	11101	11101052014	6	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
8	11101	11101072018	57	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
9	11101	11101022038	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
10	11101	11101062025	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
11	11101	11101072014	4	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
12	11101	11101042024	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
13	11101	11101062023	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
14	11101	11101052004	6	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
15	11101	11101052008	5	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
16	11101	11101102011	10	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
17	11101	11101102020	20	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
18	11101	11101102033	69	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
19	11101	11101112001	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
20	11101	11101112009	23	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
21	11101	11101112011	78	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
22	11101	11101032034	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
23	11101	11101032901	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
24	11101	11101042022	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
25	11101	11101072036	8	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
26	11101	11101042027	6	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
27	11101	11101082021	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
28	11101	11101092007	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
29	11101	11101072035	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
30	11101	11101132011	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
31	11101	11101072019	7	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
32	11101	11101072020	4	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
33	11101	11101112013	61	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
34	11101	11101122011	36	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
35	11101	11101072037	7	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
36	11101	11101112012	5	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural
37	11102	11102052002	3	2017	NA	NA	NA	NA	NA	NA	NA
38	11102	11102012004	1	2017	NA	NA	NA	NA	NA	NA	NA
39	11102	11102042003	4	2017	NA	NA	NA	NA	NA	NA	NA
40	11201	11201012009	3	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
41	11201	11201012013	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
42	11201	11201012055	1	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
43	11201	11201012067	1	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
44	11201	11201012901	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
45	11201	11201022011	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
46	11201	11201042007	3	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
47	11201	11201062044	35	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
48	11201	11201072901	3	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
49	11201	11201032068	4	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
50	11201	11201042006	13	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
51	11201	11201052901	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
52	11201	11201022057	1	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural
53	11202	11202022016	10	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural
54	11202	11202022020	14	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural
55	11202	11202032022	1	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural
56	11202	11202052021	2	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural
57	11202	11202052023	16	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural
58	11202	11202052901	1	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural
59	11203	11203012901	3	2017	NA	NA	NA	NA	NA	NA	NA
60	11203	11203012004	3	2017	NA	NA	NA	NA	NA	NA	NA
61	11203	11203012005	1	2017	NA	NA	NA	NA	NA	NA	NA
62	11301	11301022016	2	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural
63	11301	11301012004	2	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural
64	11301	11301012901	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural
65	11301	11301022009	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural
66	11301	11301062011	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural
67	11301	11301032005	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural
68	11301	11301032901	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural
69	11301	11301052010	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural
70	11301	11301012002	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural
71	11301	11301012003	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural
72	11302	11302042005	9	2017	NA	NA	NA	NA	NA	NA	NA
73	11302	11302042003	2	2017	NA	NA	NA	NA	NA	NA	NA
74	11303	11303012013	7	2017	NA	NA	NA	NA	NA	NA	NA
75	11303	11303012010	1	2017	NA	NA	NA	NA	NA	NA	NA
76	11303	11303012901	1	2017	NA	NA	NA	NA	NA	NA	NA
77	11401	11401012001	2	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural
78	11401	11401012002	8	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural
79	11401	11401022008	4	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural
80	11401	11401022010	7	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural
81	11401	11401032005	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural
82	11401	11401032006	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural
83	11401	11401032009	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural
84	11401	11401042011	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural
85	11402	11402012004	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
86	11402	11402012007	11	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
87	11402	11402012019	12	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
88	11402	11402022901	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
89	11402	11402032014	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
90	11402	11402032023	2	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
91	11402	11402042001	3	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
92	11402	11402042901	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
93	11402	11402062020	9	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
94	11402	11402062024	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
95	11402	11402072003	25	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
96	11402	11402992999	2	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
11101022003	11101	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	11	0.0001903	11101
11101022010	11101	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	53	0.0009167	11101
11101022031	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	55	0.0009513	11101
11101022038	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	339	0.0058632	11101
11101032005	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	53	0.0009167	11101
11101032032	11101	5	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	257	0.0044450	11101
11101032034	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	31	0.0005362	11101
11101032901	11101	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	43	0.0007437	11101
11101042022	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	27	0.0004670	11101
11101042024	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	47	0.0008129	11101
11101042027	11101	6	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	77	0.0013318	11101
11101052004	11101	6	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	474	0.0081981	11101
11101052008	11101	5	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	76	0.0013145	11101
11101052014	11101	6	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	250	0.0043239	11101
11101052901	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	50	0.0008648	11101
11101062023	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	54	0.0009340	11101
11101062025	11101	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	24	0.0004151	11101
11101072014	11101	4	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	107	0.0018506	11101
11101072018	11101	57	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	806	0.0139403	11101
11101072019	11101	7	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	102	0.0017642	11101
11101072020	11101	4	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	190	0.0032862	11101
11101072035	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	109	0.0018852	11101
11101072036	11101	8	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	539	0.0093224	11101
11101072037	11101	7	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	265	0.0045833	11101
11101082021	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	8	0.0001384	11101
11101092007	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	29	0.0005016	11101
11101102011	11101	10	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	143	0.0024733	11101
11101102020	11101	20	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	250	0.0043239	11101
11101102033	11101	69	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	763	0.0131966	11101
11101112001	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	186	0.0032170	11101
11101112009	11101	23	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	198	0.0034245	11101
11101112011	11101	78	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	579	0.0100142	11101
11101112012	11101	5	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	54	0.0009340	11101
11101112013	11101	61	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	615	0.0106368	11101
11101122011	11101	36	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	206	0.0035629	11101
11101132011	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	44	0.0007610	11101
11102012004	11102	1	2017	NA	NA	NA	NA	NA	NA	NA	308	0.3615023	11102
11102042003	11102	4	2017	NA	NA	NA	NA	NA	NA	NA	244	0.2863850	11102
11102052002	11102	3	2017	NA	NA	NA	NA	NA	NA	NA	203	0.2382629	11102
11201012009	11201	3	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	52	0.0021704	11201
11201012013	11201	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	35	0.0014608	11201
11201012055	11201	1	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	148	0.0061772	11201
11201012067	11201	1	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	98	0.0040903	11201
11201012901	11201	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	172	0.0071789	11201
11201022011	11201	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	32	0.0013356	11201
11201022057	11201	1	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	58	0.0024208	11201
11201032068	11201	4	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	149	0.0062190	11201
11201042006	11201	13	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	244	0.0101841	11201
11201042007	11201	3	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	54	0.0022539	11201
11201052901	11201	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	14	0.0005843	11201
11201062044	11201	35	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	541	0.0225802	11201
11201072901	11201	3	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	208	0.0086815	11201
11202022016	11202	10	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	280	0.0429646	11202
11202022020	11202	14	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	1037	0.1591223	11202
11202032022	11202	1	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	6	0.0009207	11202
11202052021	11202	2	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	239	0.0366733	11202
11202052023	11202	16	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	170	0.0260856	11202
11202052901	11202	1	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	40	0.0061378	11202
11203012004	11203	3	2017	NA	NA	NA	NA	NA	NA	NA	32	0.0173630	11203
11203012005	11203	1	2017	NA	NA	NA	NA	NA	NA	NA	31	0.0168204	11203
11203012901	11203	3	2017	NA	NA	NA	NA	NA	NA	NA	75	0.0406945	11203
11301012002	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	26	0.0074499	11301
11301012003	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	78	0.0223496	11301
11301012004	11301	2	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	76	0.0217765	11301
11301012901	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	26	0.0074499	11301
11301022009	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	14	0.0040115	11301
11301022016	11301	2	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	78	0.0223496	11301
11301032005	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	30	0.0085960	11301
11301032901	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	26	0.0074499	11301
11301052010	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	28	0.0080229	11301
11301062011	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	23	0.0065903	11301
11302042003	11302	2	2017	NA	NA	NA	NA	NA	NA	NA	59	0.0944000	11302
11302042005	11302	9	2017	NA	NA	NA	NA	NA	NA	NA	523	0.8368000	11302
11303012010	11303	1	2017	NA	NA	NA	NA	NA	NA	NA	26	0.0497132	11303
11303012013	11303	7	2017	NA	NA	NA	NA	NA	NA	NA	445	0.8508604	11303
11303012901	11303	1	2017	NA	NA	NA	NA	NA	NA	NA	18	0.0344168	11303
11401012001	11401	2	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	60	0.0123330	11401
11401012002	11401	8	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	572	0.1175745	11401
11401022008	11401	4	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	172	0.0353546	11401
11401022010	11401	7	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	571	0.1173690	11401
11401032005	11401	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	10	0.0020555	11401
11401032006	11401	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	7	0.0014388	11401
11401032009	11401	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	118	0.0242549	11401
11401042011	11401	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	53	0.0108941	11401
11402012004	11402	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	64	0.0240060	11402
11402012007	11402	11	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	509	0.1909227	11402
11402012019	11402	12	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	865	0.3244561	11402
11402022901	11402	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	27	0.0101275	11402
11402032014	11402	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	14	0.0052513	11402
11402032023	11402	2	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	22	0.0082521	11402
11402042001	11402	3	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	18	0.0067517	11402
11402042901	11402	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	15	0.0056264	11402
11402062020	11402	9	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	522	0.1957989	11402
11402062024	11402	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	18	0.0067517	11402
11402072003	11402	25	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	287	0.1076519	11402
11402992999	11402	2	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	31	0.0116279	11402

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
11101022003	11101	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	11	0.0001903	11101	3073287
11101022010	11101	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	53	0.0009167	11101	14807656
11101022031	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	55	0.0009513	11101	15366435
11101022038	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	339	0.0058632	11101	94713119
11101032005	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	53	0.0009167	11101	14807656
11101032032	11101	5	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	257	0.0044450	11101	71803161
11101032034	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	31	0.0005362	11101	8661082
11101032901	11101	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	43	0.0007437	11101	12013758
11101042022	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	27	0.0004670	11101	7543523
11101042024	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	47	0.0008129	11101	13131317
11101042027	11101	6	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	77	0.0013318	11101	21513009
11101052004	11101	6	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	474	0.0081981	11101	132430733
11101052008	11101	5	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	76	0.0013145	11101	21233620
11101052014	11101	6	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	250	0.0043239	11101	69847433
11101052901	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	50	0.0008648	11101	13969487
11101062023	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	54	0.0009340	11101	15087046
11101062025	11101	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	24	0.0004151	11101	6705354
11101072014	11101	4	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	107	0.0018506	11101	29894701
11101072018	11101	57	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	806	0.0139403	11101	225188124
11101072019	11101	7	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	102	0.0017642	11101	28497753
11101072020	11101	4	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	190	0.0032862	11101	53084049
11101072035	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	109	0.0018852	11101	30453481
11101072036	11101	8	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	539	0.0093224	11101	150591065
11101072037	11101	7	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	265	0.0045833	11101	74038279
11101082021	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	8	0.0001384	11101	2235118
11101092007	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	29	0.0005016	11101	8102302
11101102011	11101	10	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	143	0.0024733	11101	39952732
11101102020	11101	20	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	250	0.0043239	11101	69847433
11101102033	11101	69	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	763	0.0131966	11101	213174365
11101112001	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	186	0.0032170	11101	51966490
11101112009	11101	23	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	198	0.0034245	11101	55319167
11101112011	11101	78	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	579	0.0100142	11101	161766655
11101112012	11101	5	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	54	0.0009340	11101	15087046
11101112013	11101	61	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	615	0.0106368	11101	171824685
11101122011	11101	36	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	206	0.0035629	11101	57554285
11101132011	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	44	0.0007610	11101	12293148
11102012004	11102	1	2017	NA	NA	NA	NA	NA	NA	NA	308	0.3615023	11102	NA
11102042003	11102	4	2017	NA	NA	NA	NA	NA	NA	NA	244	0.2863850	11102	NA
11102052002	11102	3	2017	NA	NA	NA	NA	NA	NA	NA	203	0.2382629	11102	NA
11201012009	11201	3	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	52	0.0021704	11201	16423910
11201012013	11201	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	35	0.0014608	11201	11054555
11201012055	11201	1	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	148	0.0061772	11201	46744976
11201012067	11201	1	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	98	0.0040903	11201	30952754
11201012901	11201	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	172	0.0071789	11201	54325242
11201022011	11201	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	32	0.0013356	11201	10107022
11201022057	11201	1	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	58	0.0024208	11201	18318977
11201032068	11201	4	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	149	0.0062190	11201	47060820
11201042006	11201	13	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	244	0.0101841	11201	77066042
11201042007	11201	3	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	54	0.0022539	11201	17055599
11201052901	11201	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	14	0.0005843	11201	4421822
11201062044	11201	35	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	541	0.0225802	11201	170871838
11201072901	11201	3	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	208	0.0086815	11201	65695642
11202022016	11202	10	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	280	0.0429646	11202	84027293
11202022020	11202	14	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	1037	0.1591223	11202	311201083
11202032022	11202	1	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	6	0.0009207	11202	1800585
11202052021	11202	2	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	239	0.0366733	11202	71723297
11202052023	11202	16	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	170	0.0260856	11202	51016571
11202052901	11202	1	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	40	0.0061378	11202	12003899
11203012004	11203	3	2017	NA	NA	NA	NA	NA	NA	NA	32	0.0173630	11203	NA
11203012005	11203	1	2017	NA	NA	NA	NA	NA	NA	NA	31	0.0168204	11203	NA
11203012901	11203	3	2017	NA	NA	NA	NA	NA	NA	NA	75	0.0406945	11203	NA
11301012002	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	26	0.0074499	11301	7805354
11301012003	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	78	0.0223496	11301	23416061
11301012004	11301	2	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	76	0.0217765	11301	22815650
11301012901	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	26	0.0074499	11301	7805354
11301022009	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	14	0.0040115	11301	4202883
11301022016	11301	2	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	78	0.0223496	11301	23416061
11301032005	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	30	0.0085960	11301	9006177
11301032901	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	26	0.0074499	11301	7805354
11301052010	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	28	0.0080229	11301	8405766
11301062011	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	23	0.0065903	11301	6904736
11302042003	11302	2	2017	NA	NA	NA	NA	NA	NA	NA	59	0.0944000	11302	NA
11302042005	11302	9	2017	NA	NA	NA	NA	NA	NA	NA	523	0.8368000	11302	NA
11303012010	11303	1	2017	NA	NA	NA	NA	NA	NA	NA	26	0.0497132	11303	NA
11303012013	11303	7	2017	NA	NA	NA	NA	NA	NA	NA	445	0.8508604	11303	NA
11303012901	11303	1	2017	NA	NA	NA	NA	NA	NA	NA	18	0.0344168	11303	NA
11401012001	11401	2	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	60	0.0123330	11401	15672862
11401012002	11401	8	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	572	0.1175745	11401	149414621
11401022008	11401	4	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	172	0.0353546	11401	44928872
11401022010	11401	7	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	571	0.1173690	11401	149153407
11401032005	11401	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	10	0.0020555	11401	2612144
11401032006	11401	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	7	0.0014388	11401	1828501
11401032009	11401	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	118	0.0242549	11401	30823296
11401042011	11401	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	53	0.0108941	11401	13844362
11402012004	11402	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	64	0.0240060	11402	11449477
11402012007	11402	11	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	509	0.1909227	11402	91059123
11402012019	11402	12	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	865	0.3244561	11402	154746839
11402022901	11402	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	27	0.0101275	11402	4830248
11402032014	11402	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	14	0.0052513	11402	2504573
11402032023	11402	2	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	22	0.0082521	11402	3935758
11402042001	11402	3	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	18	0.0067517	11402	3220165
11402042901	11402	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	15	0.0056264	11402	2683471
11402062020	11402	9	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	522	0.1957989	11402	93384798
11402062024	11402	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	18	0.0067517	11402	3220165
11402072003	11402	25	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	287	0.1076519	11402	51343749
11402992999	11402	2	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	31	0.0116279	11402	5545840

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -77067373 -21870291 -16695410  15554547 246961549 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 26046988    5490274   4.744 8.61e-06 ***
## Freq.x       2728039     328424   8.306 1.59e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 44600000 on 83 degrees of freedom
##   (11 observations deleted due to missingness)
## Multiple R-squared:  0.4539, Adjusted R-squared:  0.4474 
## F-statistic:    69 on 1 and 83 DF,  p-value: 1.593e-12

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.447357937098467"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.447357937098467"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.563859011155116"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.546400849569341"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.556418853189472"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.451591318274115"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.633565488847547"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.576517790610194"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 1    cuadrático 0.447357937098467
## 2        cúbico 0.447357937098467
## 6      log-raíz 0.451591318274115
## 4 raíz cuadrada 0.546400849569341
## 5     raíz-raíz 0.556418853189472
## 3   logarítmico 0.563859011155116
## 8       log-log 0.576517790610194
## 7      raíz-log 0.633565488847547
##                                                                     sintaxis
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 7
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = sqrt(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4244.3 -1536.7   -95.1  1031.1  8166.8 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   2888.9      344.8    8.38 1.14e-12 ***
## log(Freq.x)   2495.3      206.3   12.09  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2272 on 83 degrees of freedom
##   (11 observations deleted due to missingness)
## Multiple R-squared:  0.6379, Adjusted R-squared:  0.6336 
## F-statistic: 146.2 on 1 and 83 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    2888.927

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##    2495.272

9 Modelo raíz-log (raíz-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.6336 ).

9.1 Diagrama de dispersión sobre raíz-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=sqrt(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo raíz-log

Observemos nuevamente el resultado sobre raíz-log.

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = sqrt(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4244.3 -1536.7   -95.1  1031.1  8166.8 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   2888.9      344.8    8.38 1.14e-12 ***
## log(Freq.x)   2495.3      206.3   12.09  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2272 on 83 degrees of freedom
##   (11 observations deleted due to missingness)
## Multiple R-squared:  0.6379, Adjusted R-squared:  0.6336 
## F-statistic: 146.2 on 1 and 83 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = sqrt(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = {2888.927}^2 + 2 2888.927 2495.272 \ln{X}+ 2495.272^2 ln^2X \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- (aa^2)+2*(aa*bb)*log(h_y_m_comuna_corr_01$Freq.x) + bb^2*(log(h_y_m_comuna_corr_01$Freq.x))^2

# h_y_m_comuna_corr_01$est_ing <- (aa^2)+2*(aa*bb)*log(h_y_m_comuna_corr_01$Freq.x)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
1	11101022003	11101	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	11	0.0001903	11101	3073287	21330707
2	11101022010	11101	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	53	0.0009167	11101	14807656	21330707
3	11101022031	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	55	0.0009513	11101	15366435	8345900
4	11101022038	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	339	0.0058632	11101	94713119	31699868
5	11101032005	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	53	0.0009167	11101	14807656	8345900
6	11101032032	11101	5	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	257	0.0044450	11101	71803161	47677817
7	11101032034	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	31	0.0005362	11101	8661082	8345900
8	11101032901	11101	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	43	0.0007437	11101	12013758	21330707
9	11101042022	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	27	0.0004670	11101	7543523	31699868
10	11101042024	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	47	0.0008129	11101	13131317	31699868
11	11101042027	11101	6	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	77	0.0013318	11101	21513009	54167456
12	11101052004	11101	6	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	474	0.0081981	11101	132430733	54167456
13	11101052008	11101	5	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	76	0.0013145	11101	21233620	47677817
14	11101052014	11101	6	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	250	0.0043239	11101	69847433	54167456
15	11101052901	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	50	0.0008648	11101	13969487	31699868
16	11101062023	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	54	0.0009340	11101	15087046	8345900
17	11101062025	11101	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	24	0.0004151	11101	6705354	21330707
18	11101072014	11101	4	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	107	0.0018506	11101	29894701	40298483
19	11101072018	11101	57	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	806	0.0139403	11101	225188124	168413945
20	11101072019	11101	7	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	102	0.0017642	11101	28497753	59977315
21	11101072020	11101	4	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	190	0.0032862	11101	53084049	40298483
22	11101072035	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	109	0.0018852	11101	30453481	31699868
23	11101072036	11101	8	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	539	0.0093224	11101	150591065	65249228
24	11101072037	11101	7	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	265	0.0045833	11101	74038279	59977315
25	11101082021	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	8	0.0001384	11101	2235118	8345900
26	11101092007	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	29	0.0005016	11101	8102302	8345900
27	11101102011	11101	10	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	143	0.0024733	11101	39952732	74554647
28	11101102020	11101	20	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	250	0.0043239	11101	69847433	107414445
29	11101102033	11101	69	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	763	0.0131966	11101	213174365	181014815
30	11101112001	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	186	0.0032170	11101	51966490	31699868
31	11101112009	11101	23	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	198	0.0034245	11101	55319167	114764900
32	11101112011	11101	78	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	579	0.0100142	11101	161766655	189340373
33	11101112012	11101	5	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	54	0.0009340	11101	15087046	47677817
34	11101112013	11101	61	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	615	0.0106368	11101	171824685	172835081
35	11101122011	11101	36	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	206	0.0035629	11101	57554285	139967395
36	11101132011	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	44	0.0007610	11101	12293148	8345900
37	11102012004	11102	1	2017	NA	NA	NA	NA	NA	NA	NA	308	0.3615023	11102	NA	8345900
38	11102042003	11102	4	2017	NA	NA	NA	NA	NA	NA	NA	244	0.2863850	11102	NA	40298483
39	11102052002	11102	3	2017	NA	NA	NA	NA	NA	NA	NA	203	0.2382629	11102	NA	31699868
40	11201012009	11201	3	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	52	0.0021704	11201	16423910	31699868
41	11201012013	11201	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	35	0.0014608	11201	11054555	21330707
42	11201012055	11201	1	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	148	0.0061772	11201	46744976	8345900
43	11201012067	11201	1	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	98	0.0040903	11201	30952754	8345900
44	11201012901	11201	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	172	0.0071789	11201	54325242	21330707
45	11201022011	11201	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	32	0.0013356	11201	10107022	21330707
46	11201022057	11201	1	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	58	0.0024208	11201	18318977	8345900
47	11201032068	11201	4	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	149	0.0062190	11201	47060820	40298483
48	11201042006	11201	13	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	244	0.0101841	11201	77066042	86288744
49	11201042007	11201	3	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	54	0.0022539	11201	17055599	31699868
50	11201052901	11201	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	14	0.0005843	11201	4421822	21330707
51	11201062044	11201	35	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	541	0.0225802	11201	170871838	138309070
52	11201072901	11201	3	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	208	0.0086815	11201	65695642	31699868
53	11202022016	11202	10	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	280	0.0429646	11202	84027293	74554647
54	11202022020	11202	14	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	1037	0.1591223	11202	311201083	89758439
55	11202032022	11202	1	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	6	0.0009207	11202	1800585	8345900
56	11202052021	11202	2	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	239	0.0366733	11202	71723297	21330707
57	11202052023	11202	16	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	170	0.0260856	11202	51016571	96182941
58	11202052901	11202	1	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	40	0.0061378	11202	12003899	8345900
59	11203012004	11203	3	2017	NA	NA	NA	NA	NA	NA	NA	32	0.0173630	11203	NA	31699868
60	11203012005	11203	1	2017	NA	NA	NA	NA	NA	NA	NA	31	0.0168204	11203	NA	8345900
61	11203012901	11203	3	2017	NA	NA	NA	NA	NA	NA	NA	75	0.0406945	11203	NA	31699868
62	11301012002	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	26	0.0074499	11301	7805354	8345900
63	11301012003	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	78	0.0223496	11301	23416061	8345900
64	11301012004	11301	2	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	76	0.0217765	11301	22815650	21330707
65	11301012901	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	26	0.0074499	11301	7805354	8345900
66	11301022009	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	14	0.0040115	11301	4202883	8345900
67	11301022016	11301	2	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	78	0.0223496	11301	23416061	21330707
68	11301032005	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	30	0.0085960	11301	9006177	8345900
69	11301032901	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	26	0.0074499	11301	7805354	8345900
70	11301052010	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	28	0.0080229	11301	8405766	8345900
71	11301062011	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	23	0.0065903	11301	6904736	8345900
72	11302042003	11302	2	2017	NA	NA	NA	NA	NA	NA	NA	59	0.0944000	11302	NA	21330707
73	11302042005	11302	9	2017	NA	NA	NA	NA	NA	NA	NA	523	0.8368000	11302	NA	70083689
74	11303012010	11303	1	2017	NA	NA	NA	NA	NA	NA	NA	26	0.0497132	11303	NA	8345900
75	11303012013	11303	7	2017	NA	NA	NA	NA	NA	NA	NA	445	0.8508604	11303	NA	59977315
76	11303012901	11303	1	2017	NA	NA	NA	NA	NA	NA	NA	18	0.0344168	11303	NA	8345900
77	11401012001	11401	2	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	60	0.0123330	11401	15672862	21330707
78	11401012002	11401	8	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	572	0.1175745	11401	149414621	65249228
79	11401022008	11401	4	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	172	0.0353546	11401	44928872	40298483
80	11401022010	11401	7	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	571	0.1173690	11401	149153407	59977315
81	11401032005	11401	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	10	0.0020555	11401	2612144	8345900
82	11401032006	11401	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	7	0.0014388	11401	1828501	8345900
83	11401032009	11401	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	118	0.0242549	11401	30823296	8345900
84	11401042011	11401	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	53	0.0108941	11401	13844362	8345900
85	11402012004	11402	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	64	0.0240060	11402	11449477	8345900
86	11402012007	11402	11	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	509	0.1909227	11402	91059123	78718206
87	11402012019	11402	12	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	865	0.3244561	11402	154746839	82618013
88	11402022901	11402	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	27	0.0101275	11402	4830248	8345900
89	11402032014	11402	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	14	0.0052513	11402	2504573	8345900
90	11402032023	11402	2	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	22	0.0082521	11402	3935758	21330707
91	11402042001	11402	3	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	18	0.0067517	11402	3220165	31699868
92	11402042901	11402	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	15	0.0056264	11402	2683471	8345900
93	11402062020	11402	9	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	522	0.1957989	11402	93384798	70083689
94	11402062024	11402	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	18	0.0067517	11402	3220165	8345900
95	11402072003	11402	25	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	287	0.1076519	11402	51343749	119266011
96	11402992999	11402	2	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	31	0.0116279	11402	5545840	21330707
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
1	11101022003	11101	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	11	0.0001903	11101	3073287	21330707	1939155.20
2	11101022010	11101	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	53	0.0009167	11101	14807656	21330707	402466.17
3	11101022031	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	55	0.0009513	11101	15366435	8345900	151743.63
4	11101022038	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	339	0.0058632	11101	94713119	31699868	93509.94
5	11101032005	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	53	0.0009167	11101	14807656	8345900	157469.81
6	11101032032	11101	5	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	257	0.0044450	11101	71803161	47677817	185516.80
7	11101032034	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	31	0.0005362	11101	8661082	8345900	269222.57
8	11101032901	11101	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	43	0.0007437	11101	12013758	21330707	496062.96
9	11101042022	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	27	0.0004670	11101	7543523	31699868	1174069.19
10	11101042024	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	47	0.0008129	11101	13131317	31699868	674465.28
11	11101042027	11101	6	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	77	0.0013318	11101	21513009	54167456	703473.46
12	11101052004	11101	6	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	474	0.0081981	11101	132430733	54167456	114277.33
13	11101052008	11101	5	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	76	0.0013145	11101	21233620	47677817	627339.69
14	11101052014	11101	6	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	250	0.0043239	11101	69847433	54167456	216669.83
15	11101052901	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	50	0.0008648	11101	13969487	31699868	633997.36
16	11101062023	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	54	0.0009340	11101	15087046	8345900	154553.70
17	11101062025	11101	2	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	24	0.0004151	11101	6705354	21330707	888779.47
18	11101072014	11101	4	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	107	0.0018506	11101	29894701	40298483	376621.34
19	11101072018	11101	57	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	806	0.0139403	11101	225188124	168413945	208950.30
20	11101072019	11101	7	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	102	0.0017642	11101	28497753	59977315	588012.89
21	11101072020	11101	4	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	190	0.0032862	11101	53084049	40298483	212097.28
22	11101072035	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	109	0.0018852	11101	30453481	31699868	290824.48
23	11101072036	11101	8	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	539	0.0093224	11101	150591065	65249228	121056.08
24	11101072037	11101	7	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	265	0.0045833	11101	74038279	59977315	226329.49
25	11101082021	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	8	0.0001384	11101	2235118	8345900	1043237.46
26	11101092007	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	29	0.0005016	11101	8102302	8345900	287789.64
27	11101102011	11101	10	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	143	0.0024733	11101	39952732	74554647	521361.17
28	11101102020	11101	20	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	250	0.0043239	11101	69847433	107414445	429657.78
29	11101102033	11101	69	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	763	0.0131966	11101	213174365	181014815	237240.91
30	11101112001	11101	3	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	186	0.0032170	11101	51966490	31699868	170429.40
31	11101112009	11101	23	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	198	0.0034245	11101	55319167	114764900	579620.71
32	11101112011	11101	78	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	579	0.0100142	11101	161766655	189340373	327012.73
33	11101112012	11101	5	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	54	0.0009340	11101	15087046	47677817	882922.53
34	11101112013	11101	61	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	615	0.0106368	11101	171824685	172835081	281032.65
35	11101122011	11101	36	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	206	0.0035629	11101	57554285	139967395	679453.37
36	11101132011	11101	1	2017	Coyhaique	279389.7	2017	11101	57818	16153755495	Rural	44	0.0007610	11101	12293148	8345900	189679.54
37	11102012004	11102	1	2017	NA	NA	NA	NA	NA	NA	NA	308	0.3615023	11102	NA	8345900	NA
38	11102042003	11102	4	2017	NA	NA	NA	NA	NA	NA	NA	244	0.2863850	11102	NA	40298483	NA
39	11102052002	11102	3	2017	NA	NA	NA	NA	NA	NA	NA	203	0.2382629	11102	NA	31699868	NA
40	11201012009	11201	3	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	52	0.0021704	11201	16423910	31699868	609612.85
41	11201012013	11201	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	35	0.0014608	11201	11054555	21330707	609448.78
42	11201012055	11201	1	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	148	0.0061772	11201	46744976	8345900	56391.21
43	11201012067	11201	1	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	98	0.0040903	11201	30952754	8345900	85162.24
44	11201012901	11201	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	172	0.0071789	11201	54325242	21330707	124015.74
45	11201022011	11201	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	32	0.0013356	11201	10107022	21330707	666584.60
46	11201022057	11201	1	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	58	0.0024208	11201	18318977	8345900	143894.82
47	11201032068	11201	4	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	149	0.0062190	11201	47060820	40298483	270459.62
48	11201042006	11201	13	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	244	0.0101841	11201	77066042	86288744	353642.39
49	11201042007	11201	3	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	54	0.0022539	11201	17055599	31699868	587034.60
50	11201052901	11201	2	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	14	0.0005843	11201	4421822	21330707	1523621.94
51	11201062044	11201	35	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	541	0.0225802	11201	170871838	138309070	255654.47
52	11201072901	11201	3	2017	Aysén	315844.4	2017	11201	23959	7567316757	Rural	208	0.0086815	11201	65695642	31699868	152403.21
53	11202022016	11202	10	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	280	0.0429646	11202	84027293	74554647	266266.60
54	11202022020	11202	14	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	1037	0.1591223	11202	311201083	89758439	86555.87
55	11202032022	11202	1	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	6	0.0009207	11202	1800585	8345900	1390983.28
56	11202052021	11202	2	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	239	0.0366733	11202	71723297	21330707	89249.82
57	11202052023	11202	16	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	170	0.0260856	11202	51016571	96182941	565782.00
58	11202052901	11202	1	2017	Cisnes	300097.5	2017	11202	6517	1955735252	Rural	40	0.0061378	11202	12003899	8345900	208647.49
59	11203012004	11203	3	2017	NA	NA	NA	NA	NA	NA	NA	32	0.0173630	11203	NA	31699868	NA
60	11203012005	11203	1	2017	NA	NA	NA	NA	NA	NA	NA	31	0.0168204	11203	NA	8345900	NA
61	11203012901	11203	3	2017	NA	NA	NA	NA	NA	NA	NA	75	0.0406945	11203	NA	31699868	NA
62	11301012002	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	26	0.0074499	11301	7805354	8345900	320996.14
63	11301012003	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	78	0.0223496	11301	23416061	8345900	106998.71
64	11301012004	11301	2	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	76	0.0217765	11301	22815650	21330707	280667.20
65	11301012901	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	26	0.0074499	11301	7805354	8345900	320996.14
66	11301022009	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	14	0.0040115	11301	4202883	8345900	596135.69
67	11301022016	11301	2	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	78	0.0223496	11301	23416061	21330707	273470.60
68	11301032005	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	30	0.0085960	11301	9006177	8345900	278196.66
69	11301032901	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	26	0.0074499	11301	7805354	8345900	320996.14
70	11301052010	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	28	0.0080229	11301	8405766	8345900	298067.85
71	11301062011	11301	1	2017	Cochrane	300205.9	2017	11301	3490	1047718643	Rural	23	0.0065903	11301	6904736	8345900	362865.20
72	11302042003	11302	2	2017	NA	NA	NA	NA	NA	NA	NA	59	0.0944000	11302	NA	21330707	NA
73	11302042005	11302	9	2017	NA	NA	NA	NA	NA	NA	NA	523	0.8368000	11302	NA	70083689	NA
74	11303012010	11303	1	2017	NA	NA	NA	NA	NA	NA	NA	26	0.0497132	11303	NA	8345900	NA
75	11303012013	11303	7	2017	NA	NA	NA	NA	NA	NA	NA	445	0.8508604	11303	NA	59977315	NA
76	11303012901	11303	1	2017	NA	NA	NA	NA	NA	NA	NA	18	0.0344168	11303	NA	8345900	NA
77	11401012001	11401	2	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	60	0.0123330	11401	15672862	21330707	355511.79
78	11401012002	11401	8	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	572	0.1175745	11401	149414621	65249228	114072.08
79	11401022008	11401	4	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	172	0.0353546	11401	44928872	40298483	234293.51
80	11401022010	11401	7	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	571	0.1173690	11401	149153407	59977315	105039.08
81	11401032005	11401	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	10	0.0020555	11401	2612144	8345900	834589.97
82	11401032006	11401	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	7	0.0014388	11401	1828501	8345900	1192271.38
83	11401032009	11401	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	118	0.0242549	11401	30823296	8345900	70727.96
84	11401042011	11401	1	2017	Chile Chico	261214.4	2017	11401	4865	1270807924	Rural	53	0.0108941	11401	13844362	8345900	157469.81
85	11402012004	11402	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	64	0.0240060	11402	11449477	8345900	130404.68
86	11402012007	11402	11	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	509	0.1909227	11402	91059123	78718206	154652.66
87	11402012019	11402	12	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	865	0.3244561	11402	154746839	82618013	95512.15
88	11402022901	11402	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	27	0.0101275	11402	4830248	8345900	309107.40
89	11402032014	11402	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	14	0.0052513	11402	2504573	8345900	596135.69
90	11402032023	11402	2	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	22	0.0082521	11402	3935758	21330707	969577.60
91	11402042001	11402	3	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	18	0.0067517	11402	3220165	31699868	1761103.79
92	11402042901	11402	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	15	0.0056264	11402	2683471	8345900	556393.31
93	11402062020	11402	9	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	522	0.1957989	11402	93384798	70083689	134259.94
94	11402062024	11402	1	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	18	0.0067517	11402	3220165	8345900	463661.09
95	11402072003	11402	25	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	287	0.1076519	11402	51343749	119266011	415561.01
96	11402992999	11402	2	2017	Río Ibáñez	178898.1	2017	11402	2666	476942281	Rural	31	0.0116279	11402	5545840	21330707	688087.33
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_rural_nivel_nacional_17_r11.rds")

---

R-12

Urbano

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 1)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15201	1	1	1	1767	1	1	1	2	1	3	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	2	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	3	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	4	1	1	2	3	1	99	1	2	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	5	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	6	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	7	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	8	1	1	3	3	1	99	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	9	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	10	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	11	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	12	1	1	4	3	3	3	4	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	13	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	14	1	1	2	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	15	1	1	2	1	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	16	1	1	5	1	5	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	17	1	1	5	3	1	3	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	18	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	19	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	20	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	21	4	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	22	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	23	1	1	5	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	24	1	1	3	1	1	6	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	25	1	1	5	6	1	1	4	2	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	26	4	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	27	1	1	5	99	3	3	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	28	1	1	6	3	2	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	29	1	1	2	3	1	99	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	30	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	31	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	32	1	1	3	1	1	2	1	1	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	33	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	34	3	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	35	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	36	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	37	1	1	5	3	3	1	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	38	1	1	1	1	4	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	39	1	1	5	3	4	3	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	40	1	1	5	3	2	1	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	41	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	42	1	1	2	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	43	1	1	1	1	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	44	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	45	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	46	1	1	5	6	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	47	1	1	5	3	4	0	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	48	1	1	3	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	49	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	50	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	51	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	52	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	53	1	1	1	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	54	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	55	1	1	2	2	1	4	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	56	8	1	98	98	98	98	98	98	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	57	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	58	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	59	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	60	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	61	1	1	2	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	62	1	1	5	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	63	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	64	1	1	5	3	2	4	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	65	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	66	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	67	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	68	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	69	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	70	1	1	1	3	3	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	71	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	72	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	73	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	74	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	75	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	76	1	1	5	3	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	77	1	1	3	6	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	78	5	1	3	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	79	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	80	1	1	2	1	1	3	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	81	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	82	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	83	1	1	5	3	2	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	84	4	1	5	3	2	0	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	85	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	86	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	87	1	1	3	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	88	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	89	5	1	4	3	2	1	2	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	90	1	1	5	3	3	2	1	3	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	91	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	92	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	93	5	1	4	3	5	1	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	94	1	1	5	3	3	99	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	95	1	1	5	3	2	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	96	1	1	2	3	4	6	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	97	1	1	5	1	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	98	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	99	1	1	2	7	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	100	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 12:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 12)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	12101011001	108	2017	12101
2	12101011002	563	2017	12101
3	12101011003	125	2017	12101
4	12101011004	162	2017	12101
5	12101021001	137	2017	12101
6	12101021002	148	2017	12101
7	12101031001	303	2017	12101
8	12101031002	171	2017	12101
9	12101031003	98	2017	12101
10	12101031004	161	2017	12101
11	12101031005	180	2017	12101
12	12101041001	163	2017	12101
13	12101041002	151	2017	12101
14	12101041003	104	2017	12101
15	12101041004	159	2017	12101
16	12101041005	82	2017	12101
17	12101041006	75	2017	12101
18	12101041007	86	2017	12101
19	12101051001	85	2017	12101
20	12101051002	30	2017	12101
21	12101061001	75	2017	12101
22	12101061002	92	2017	12101
23	12101061003	63	2017	12101
24	12101071001	56	2017	12101
25	12101071002	193	2017	12101
26	12101071003	113	2017	12101
27	12101071004	96	2017	12101
28	12101071005	120	2017	12101
29	12101071006	411	2017	12101
30	12101071007	213	2017	12101
31	12101081001	83	2017	12101
32	12101081002	83	2017	12101
33	12101081003	62	2017	12101
34	12101081004	157	2017	12101
35	12101081005	97	2017	12101
36	12101081006	137	2017	12101
37	12101091001	19	2017	12101
38	12101101001	2	2017	12101
39	12101101002	73	2017	12101
40	12101101003	169	2017	12101
41	12101101004	149	2017	12101
42	12101101005	85	2017	12101
43	12101991999	3	2017	12101
100	12201011001	140	2017	12201
157	12301011001	61	2017	12301
158	12301011002	87	2017	12301
215	12401011001	68	2017	12401
216	12401011002	154	2017	12401
217	12401011003	63	2017	12401
218	12401011004	103	2017	12401
219	12401011005	65	2017	12401
220	12401011006	89	2017	12401
221	12401011007	80	2017	12401
222	12401011008	196	2017	12401
223	12401051001	54	2017	12401
224	12401991999	8	2017	12401
NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA
NA.2	NA	NA	NA	NA
NA.3	NA	NA	NA	NA
NA.4	NA	NA	NA	NA
NA.5	NA	NA	NA	NA
NA.6	NA	NA	NA	NA
NA.7	NA	NA	NA	NA
NA.8	NA	NA	NA	NA
NA.9	NA	NA	NA	NA
NA.10	NA	NA	NA	NA
NA.11	NA	NA	NA	NA
NA.12	NA	NA	NA	NA
NA.13	NA	NA	NA	NA
NA.14	NA	NA	NA	NA
NA.15	NA	NA	NA	NA
NA.16	NA	NA	NA	NA
NA.17	NA	NA	NA	NA
NA.18	NA	NA	NA	NA
NA.19	NA	NA	NA	NA
NA.20	NA	NA	NA	NA
NA.21	NA	NA	NA	NA
NA.22	NA	NA	NA	NA
NA.23	NA	NA	NA	NA
NA.24	NA	NA	NA	NA
NA.25	NA	NA	NA	NA
NA.26	NA	NA	NA	NA
NA.27	NA	NA	NA	NA
NA.28	NA	NA	NA	NA
NA.29	NA	NA	NA	NA
NA.30	NA	NA	NA	NA
NA.31	NA	NA	NA	NA
NA.32	NA	NA	NA	NA
NA.33	NA	NA	NA	NA
NA.34	NA	NA	NA	NA
NA.35	NA	NA	NA	NA
NA.36	NA	NA	NA	NA
NA.37	NA	NA	NA	NA
NA.38	NA	NA	NA	NA
NA.39	NA	NA	NA	NA
NA.40	NA	NA	NA	NA
NA.41	NA	NA	NA	NA
NA.42	NA	NA	NA	NA
NA.43	NA	NA	NA	NA

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_urbano_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
01107	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
01401	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
01405	Pica	330061.1	2017	1405	9296	3068247619	Urbano
02101	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02102	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02104	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02201	Calama	416281.1	2017	2201	165731	68990679686	Urbano
02203	San Pedro de Atacama	437934.7	2017	2203	10996	4815529626	Urbano
02301	Tocopilla	271720.8	2017	2301	25186	6843559467	Urbano
02302	María Elena	466266.9	2017	2302	6457	3010685220	Urbano
03101	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
03102	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
03103	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
03201	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
03202	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
03301	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
03303	Freirina	289049.9	2017	3303	7041	2035200054	Urbano
03304	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
04101	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04102	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04103	Andacollo	251267.7	2017	4103	11044	2775000288	Urbano
04104	La Higuera	214257.0	2017	4104	4241	908664019	Urbano
04106	Vicuña	245957.4	2017	4106	27771	6830481918	Urbano
04201	Illapel	270316.5	2017	4201	30848	8338722128	Urbano
04202	Canela	233397.3	2017	4202	9093	2122281844	Urbano
04203	Los Vilos	282415.6	2017	4203	21382	6038609501	Urbano
04204	Salamanca	262056.9	2017	4204	29347	7690585032	Urbano
04301	Ovalle	274771.4	2017	4301	111272	30574361012	Urbano
04302	Combarbalá	228990.4	2017	4302	13322	3050610572	Urbano
04303	Monte Patria	225369.1	2017	4303	30751	6930326684	Urbano
04304	Punitaqui	212496.1	2017	4304	10956	2328107498	Urbano
05101	Valparaíso	297929.0	2017	5101	296655	88382118059	Urbano
05102	Casablanca	341641.8	2017	5102	26867	9178890241	Urbano
05103	Concón	318496.3	2017	5103	42152	13425257057	Urbano
05105	Puchuncaví	296035.5	2017	5105	18546	5490274928	Urbano
05107	Quintero	308224.7	2017	5107	31923	9839456903	Urbano
05109	Viña del Mar	337006.1	2017	5109	334248	112643604611	Urbano
05301	Los Andes	339720.2	2017	5301	66708	22662055502	Urbano
05302	Calle Larga	246387.3	2017	5302	14832	3654416747	Urbano
05303	Rinconada	273904.7	2017	5303	10207	2795744821	Urbano
05304	San Esteban	219571.6	2017	5304	18855	4140022481	Urbano
05401	La Ligua	250134.4	2017	5401	35390	8852256241	Urbano
05402	Cabildo	262745.9	2017	5402	19388	5094117762	Urbano
05403	Papudo	294355.2	2017	5403	6356	1870921373	Urbano
05404	Petorca	237510.8	2017	5404	9826	2333781007	Urbano
05405	Zapallar	294389.2	2017	5405	7339	2160521991	Urbano
05501	Quillota	286029.5	2017	5501	90517	25890529852	Urbano
05502	Calera	277181.9	2017	5502	50554	14012652087	Urbano
05503	Hijuelas	254094.0	2017	5503	17988	4570642363	Urbano

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
1	12101	12101011002	563	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
2	12101	12101011003	125	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
3	12101	12101011004	162	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
4	12101	12101011001	108	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
5	12101	12101021002	148	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
6	12101	12101031001	303	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
7	12101	12101031002	171	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
8	12101	12101031003	98	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
9	12101	12101031004	161	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
10	12101	12101031005	180	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
11	12101	12101041001	163	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
12	12101	12101041002	151	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
13	12101	12101041003	104	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
14	12101	12101041004	159	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
15	12101	12101041005	82	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
16	12101	12101041006	75	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
17	12101	12101021001	137	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
18	12101	12101051001	85	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
19	12101	12101051002	30	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
20	12101	12101061001	75	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
21	12101	12101061002	92	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
22	12101	12101061003	63	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
23	12101	12101071001	56	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
24	12101	12101071002	193	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
25	12101	12101071003	113	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
26	12101	12101071004	96	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
27	12101	12101071005	120	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
28	12101	12101071006	411	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
29	12101	12101071007	213	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
30	12101	12101041007	86	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
31	12101	12101081002	83	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
32	12101	12101081003	62	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
33	12101	12101081004	157	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
34	12101	12101081005	97	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
35	12101	12101081006	137	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
36	12101	12101091001	19	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
37	12101	12101101001	2	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
38	12101	12101101002	73	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
39	12101	12101101003	169	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
40	12101	12101101004	149	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
41	12101	12101101005	85	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
42	12101	12101991999	3	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
43	12101	12101081001	83	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
44	12201	12201011001	140	2017	NA	NA	NA	NA	NA	NA	NA
45	12301	12301011001	61	2017	Porvenir	398616.7	2017	12301	6801	2710992403	Urbano
46	12301	12301011002	87	2017	Porvenir	398616.7	2017	12301	6801	2710992403	Urbano
47	12401	12401011001	68	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano
48	12401	12401011002	154	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano
49	12401	12401011003	63	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano
50	12401	12401011004	103	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano
51	12401	12401011005	65	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano
52	12401	12401011006	89	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano
53	12401	12401011007	80	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano
54	12401	12401011008	196	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano
55	12401	12401051001	54	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano
56	12401	12401991999	8	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.41	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.42	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.43	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
1	12101	12101011002	563	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
2	12101	12101011003	125	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
3	12101	12101011004	162	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
4	12101	12101011001	108	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
5	12101	12101021002	148	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
6	12101	12101031001	303	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
7	12101	12101031002	171	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
8	12101	12101031003	98	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
9	12101	12101031004	161	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
10	12101	12101031005	180	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
11	12101	12101041001	163	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
12	12101	12101041002	151	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
13	12101	12101041003	104	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
14	12101	12101041004	159	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
15	12101	12101041005	82	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
16	12101	12101041006	75	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
17	12101	12101021001	137	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
18	12101	12101051001	85	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
19	12101	12101051002	30	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
20	12101	12101061001	75	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
21	12101	12101061002	92	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
22	12101	12101061003	63	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
23	12101	12101071001	56	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
24	12101	12101071002	193	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
25	12101	12101071003	113	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
26	12101	12101071004	96	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
27	12101	12101071005	120	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
28	12101	12101071006	411	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
29	12101	12101071007	213	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
30	12101	12101041007	86	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
31	12101	12101081002	83	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
32	12101	12101081003	62	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
33	12101	12101081004	157	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
34	12101	12101081005	97	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
35	12101	12101081006	137	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
36	12101	12101091001	19	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
37	12101	12101101001	2	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
38	12101	12101101002	73	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
39	12101	12101101003	169	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
40	12101	12101101004	149	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
41	12101	12101101005	85	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
42	12101	12101991999	3	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
43	12101	12101081001	83	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano
44	12201	12201011001	140	2017	NA	NA	NA	NA	NA	NA	NA
45	12301	12301011001	61	2017	Porvenir	398616.7	2017	12301	6801	2710992403	Urbano
46	12301	12301011002	87	2017	Porvenir	398616.7	2017	12301	6801	2710992403	Urbano
47	12401	12401011001	68	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano
48	12401	12401011002	154	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano
49	12401	12401011003	63	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano
50	12401	12401011004	103	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano
51	12401	12401011005	65	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano
52	12401	12401011006	89	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano
53	12401	12401011007	80	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano
54	12401	12401011008	196	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano
55	12401	12401051001	54	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano
56	12401	12401991999	8	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.41	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.42	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.43	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
12101011001	12101	108	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3531	0.0268329	12101
12101011002	12101	563	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3026	0.0229953	12101
12101011003	12101	125	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2985	0.0226837	12101
12101011004	12101	162	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	1595	0.0121208	12101
12101021001	12101	137	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2549	0.0193705	12101
12101021002	12101	148	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3389	0.0257538	12101
12101031001	12101	303	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2497	0.0189753	12101
12101031002	12101	171	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3363	0.0255563	12101
12101031003	12101	98	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3844	0.0292115	12101
12101031004	12101	161	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3035	0.0230637	12101
12101031005	12101	180	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2132	0.0162016	12101
12101041001	12101	163	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2931	0.0222734	12101
12101041002	12101	151	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3427	0.0260426	12101
12101041003	12101	104	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3021	0.0229573	12101
12101041004	12101	159	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	4652	0.0353517	12101
12101041005	12101	82	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3038	0.0230865	12101
12101041006	12101	75	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2472	0.0187853	12101
12101041007	12101	86	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3294	0.0250319	12101
12101051001	12101	85	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2180	0.0165664	12101
12101051002	12101	30	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	1013	0.0076980	12101
12101061001	12101	75	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2372	0.0180254	12101
12101061002	12101	92	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2905	0.0220758	12101
12101061003	12101	63	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2197	0.0166955	12101
12101071001	12101	56	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	1765	0.0134127	12101
12101071002	12101	193	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3727	0.0283224	12101
12101071003	12101	113	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2859	0.0217262	12101
12101071004	12101	96	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2382	0.0181014	12101
12101071005	12101	120	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3151	0.0239452	12101
12101071006	12101	411	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3159	0.0240060	12101
12101071007	12101	213	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3543	0.0269241	12101
12101081001	12101	83	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2522	0.0191653	12101
12101081002	12101	83	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3136	0.0238312	12101
12101081003	12101	62	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2280	0.0173263	12101
12101081004	12101	157	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	6468	0.0491519	12101
12101081005	12101	97	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3165	0.0240516	12101
12101081006	12101	137	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	5200	0.0395161	12101
12101091001	12101	19	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	692	0.0052587	12101
12101101001	12101	2	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	205	0.0015578	12101
12101101002	12101	73	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	1386	0.0105326	12101
12101101003	12101	169	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3937	0.0299182	12101
12101101004	12101	149	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	5280	0.0401240	12101
12101101005	12101	85	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3098	0.0235425	12101
12101991999	12101	3	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2529	0.0192185	12101
12201011001	12201	140	2017	NA	NA	NA	NA	NA	NA	NA	1868	0.9054775	12201
12301011001	12301	61	2017	Porvenir	398616.7	2017	12301	6801	2710992403	Urbano	2543	0.3739156	12301
12301011002	12301	87	2017	Porvenir	398616.7	2017	12301	6801	2710992403	Urbano	3449	0.5071313	12301
12401011001	12401	68	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	1266	0.0589468	12401
12401011002	12401	154	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	1994	0.0928435	12401
12401011003	12401	63	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	1327	0.0617870	12401
12401011004	12401	103	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	3053	0.1421521	12401
12401011005	12401	65	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	2710	0.1261815	12401
12401011006	12401	89	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	2158	0.1004796	12401
12401011007	12401	80	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	1489	0.0693300	12401
12401011008	12401	196	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	3985	0.1855473	12401
12401051001	12401	54	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	1041	0.0484705	12401
12401991999	12401	8	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	157	0.0073101	12401

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
12101011001	12101	108	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3531	0.0268329	12101	1298708303
12101011002	12101	563	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3026	0.0229953	12101	1112968373
12101011003	12101	125	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2985	0.0226837	12101	1097888498
12101011004	12101	162	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	1595	0.0121208	12101	586643938
12101021001	12101	137	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2549	0.0193705	12101	937526895
12101021002	12101	148	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3389	0.0257538	12101	1246480442
12101031001	12101	303	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2497	0.0189753	12101	918401199
12101031002	12101	171	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3363	0.0255563	12101	1236917594
12101031003	12101	98	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3844	0.0292115	12101	1413830280
12101031004	12101	161	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3035	0.0230637	12101	1116278590
12101031005	12101	180	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2132	0.0162016	12101	784153527
12101041001	12101	163	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2931	0.0222734	12101	1078027198
12101041002	12101	151	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3427	0.0260426	12101	1260456912
12101041003	12101	104	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3021	0.0229573	12101	1111129364
12101041004	12101	159	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	4652	0.0353517	12101	1711014168
12101041005	12101	82	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3038	0.0230865	12101	1117381995
12101041006	12101	75	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2472	0.0187853	12101	909206153
12101041007	12101	86	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3294	0.0250319	12101	1211539267
12101051001	12101	85	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2180	0.0165664	12101	801808015
12101051002	12101	30	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	1013	0.0076980	12101	372583266
12101061001	12101	75	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2372	0.0180254	12101	872425969
12101061002	12101	92	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2905	0.0220758	12101	1068464350
12101061003	12101	63	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2197	0.0166955	12101	808060646
12101071001	12101	56	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	1765	0.0134127	12101	649170251
12101071002	12101	193	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3727	0.0283224	12101	1370797464
12101071003	12101	113	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2859	0.0217262	12101	1051545466
12101071004	12101	96	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2382	0.0181014	12101	876103987
12101071005	12101	120	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3151	0.0239452	12101	1158943603
12101071006	12101	411	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3159	0.0240060	12101	1161886018
12101071007	12101	213	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3543	0.0269241	12101	1303121925
12101081001	12101	83	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2522	0.0191653	12101	927596245
12101081002	12101	83	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3136	0.0238312	12101	1153426576
12101081003	12101	62	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2280	0.0173263	12101	838588199
12101081004	12101	157	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	6468	0.0491519	12101	2378942312
12101081005	12101	97	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3165	0.0240516	12101	1164092829
12101081006	12101	137	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	5200	0.0395161	12101	1912569577
12101091001	12101	19	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	692	0.0052587	12101	254518874
12101101001	12101	2	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	205	0.0015578	12101	75399378
12101101002	12101	73	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	1386	0.0105326	12101	509773353
12101101003	12101	169	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3937	0.0299182	12101	1448035851
12101101004	12101	149	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	5280	0.0401240	12101	1941993724
12101101005	12101	85	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3098	0.0235425	12101	1139450106
12101991999	12101	3	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2529	0.0192185	12101	930170858
12201011001	12201	140	2017	NA	NA	NA	NA	NA	NA	NA	1868	0.9054775	12201	NA
12301011001	12301	61	2017	Porvenir	398616.7	2017	12301	6801	2710992403	Urbano	2543	0.3739156	12301	1013682353
12301011002	12301	87	2017	Porvenir	398616.7	2017	12301	6801	2710992403	Urbano	3449	0.5071313	12301	1374829113
12401011001	12401	68	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	1266	0.0589468	12401	402480145
12401011002	12401	154	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	1994	0.0928435	12401	633922125
12401011003	12401	63	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	1327	0.0617870	12401	421872948
12401011004	12401	103	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	3053	0.1421521	12401	970593905
12401011005	12401	65	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	2710	0.1261815	12401	861549126
12401011006	12401	89	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	2158	0.1004796	12401	686060153
12401011007	12401	80	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	1489	0.0693300	12401	473375147
12401011008	12401	196	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	3985	0.1855473	12401	1266890505
12401051001	12401	54	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	1041	0.0484705	12401	330949314
12401991999	12401	8	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	157	0.0073101	12401	49912625

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -743460884 -335758448    6912752  205082509 1318454710 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 779031813   92616689   8.411 2.48e-11 ***
## Freq.x        1792712     609279   2.942  0.00482 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 414100000 on 53 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1404, Adjusted R-squared:  0.1242 
## F-statistic: 8.657 on 1 and 53 DF,  p-value: 0.004823

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.124193204750285"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.124193204750285"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.285306624642732"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq               
## [1,] "raíz cuadrada" "0.2434721229194"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.303608751294634"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.328480447164534"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.388419455789757"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.475139717190282"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 1    cuadrático 0.124193204750285
## 2        cúbico 0.124193204750285
## 4 raíz cuadrada   0.2434721229194
## 3   logarítmico 0.285306624642732
## 5     raíz-raíz 0.303608751294634
## 6      log-raíz 0.328480447164534
## 7      raíz-log 0.388419455789757
## 8       log-log 0.475139717190282
##                                                                     sintaxis
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 8
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.66001 -0.35666  0.09315  0.24289  1.74461 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 18.36916    0.31821  57.727  < 2e-16 ***
## log(Freq.x)  0.48890    0.06922   7.063 3.57e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4928 on 53 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.4849, Adjusted R-squared:  0.4751 
## F-statistic: 49.88 on 1 and 53 DF,  p-value: 3.572e-09

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    18.36916

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##   0.4888988

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.4751 ).

9.1 Diagrama de dispersión sobre log-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.66001 -0.35666  0.09315  0.24289  1.74461 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 18.36916    0.31821  57.727  < 2e-16 ***
## log(Freq.x)  0.48890    0.06922   7.063 3.57e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4928 on 53 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.4849, Adjusted R-squared:  0.4751 
## F-statistic: 49.88 on 1 and 53 DF,  p-value: 3.572e-09

ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = e^{18.36916+0.4888988 \cdot ln{X}} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
1	12101011001	12101	108	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3531	0.0268329	12101	1298708303	937053807
2	12101011002	12101	563	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3026	0.0229953	12101	1112968373	2100614397
3	12101011003	12101	125	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2985	0.0226837	12101	1097888498	1006474749
4	12101011004	12101	162	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	1595	0.0121208	12101	586643938	1142497693
5	12101021001	12101	137	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2549	0.0193705	12101	937526895	1052606906
6	12101021002	12101	148	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3389	0.0257538	12101	1246480442	1093111445
7	12101031001	12101	303	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2497	0.0189753	12101	918401199	1551673745
8	12101031002	12101	171	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3363	0.0255563	12101	1236917594	1173100476
9	12101031003	12101	98	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3844	0.0292115	12101	1413830280	893581430
10	12101031004	12101	161	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3035	0.0230637	12101	1116278590	1139044300
11	12101031005	12101	180	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2132	0.0162016	12101	784153527	1202890551
12	12101041001	12101	163	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2931	0.0222734	12101	1078027198	1145940207
13	12101041002	12101	151	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3427	0.0260426	12101	1260456912	1103888751
14	12101041003	12101	104	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3021	0.0229573	12101	1111129364	919922571
15	12101041004	12101	159	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	4652	0.0353517	12101	1711014168	1132104470
16	12101041005	12101	82	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3038	0.0230865	12101	1117381995	819006584
17	12101041006	12101	75	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2472	0.0187853	12101	909206153	784045559
18	12101041007	12101	86	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3294	0.0250319	12101	1211539267	838301162
19	12101051001	12101	85	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2180	0.0165664	12101	801808015	833521285
20	12101051002	12101	30	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	1013	0.0076980	12101	372583266	500943693
21	12101061001	12101	75	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2372	0.0180254	12101	872425969	784045559
22	12101061002	12101	92	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2905	0.0220758	12101	1068464350	866402324
23	12101061003	12101	63	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2197	0.0166955	12101	808060646	719981827
24	12101071001	12101	56	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	1765	0.0134127	12101	649170251	679693518
25	12101071002	12101	193	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3727	0.0283224	12101	1370797464	1244607223
26	12101071003	12101	113	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2859	0.0217262	12101	1051545466	958018029
27	12101071004	12101	96	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2382	0.0181014	12101	876103987	884618715
28	12101071005	12101	120	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3151	0.0239452	12101	1158943603	986586823
29	12101071006	12101	411	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3159	0.0240060	12101	1161886018	1801068441
30	12101071007	12101	213	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3543	0.0269241	12101	1303121925	1306074905
31	12101081001	12101	83	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2522	0.0191653	12101	927596245	823874524
32	12101081002	12101	83	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3136	0.0238312	12101	1153426576	823874524
33	12101081003	12101	62	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2280	0.0173263	12101	838588199	714371706
34	12101081004	12101	157	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	6468	0.0491519	12101	2378942312	1125119878
35	12101081005	12101	97	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3165	0.0240516	12101	1164092829	889111879
36	12101081006	12101	137	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	5200	0.0395161	12101	1912569577	1052606906
37	12101091001	12101	19	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	692	0.0052587	12101	254518874	400688805
38	12101101001	12101	2	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	205	0.0015578	12101	75399378	133290540
39	12101101002	12101	73	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	1386	0.0105326	12101	509773353	773753111
40	12101101003	12101	169	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3937	0.0299182	12101	1448035851	1166372385
41	12101101004	12101	149	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	5280	0.0401240	12101	1941993724	1096716183
42	12101101005	12101	85	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3098	0.0235425	12101	1139450106	833521285
43	12101991999	12101	3	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2529	0.0192185	12101	930170858	162513756
44	12201011001	12201	140	2017	NA	NA	NA	NA	NA	NA	NA	1868	0.9054775	12201	NA	1063813543
45	12301011001	12301	61	2017	Porvenir	398616.7	2017	12301	6801	2710992403	Urbano	2543	0.3739156	12301	1013682353	708715144
46	12301011002	12301	87	2017	Porvenir	398616.7	2017	12301	6801	2710992403	Urbano	3449	0.5071313	12301	1374829113	843052716
47	12401011001	12401	68	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	1266	0.0589468	12401	402480145	747373177
48	12401011002	12401	154	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	1994	0.0928435	12401	633922125	1114557166
49	12401011003	12401	63	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	1327	0.0617870	12401	421872948	719981827
50	12401011004	12401	103	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	3053	0.1421521	12401	970593905	915587383
51	12401011005	12401	65	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	2710	0.1261815	12401	861549126	731067140
52	12401011006	12401	89	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	2158	0.1004796	12401	686060153	852472802
53	12401011007	12401	80	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	1489	0.0693300	12401	473375147	809178823
54	12401011008	12401	196	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	3985	0.1855473	12401	1266890505	1254028290
55	12401051001	12401	54	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	1041	0.0484705	12401	330949314	667715304
56	12401991999	12401	8	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	157	0.0073101	12401	49912625	262509923
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.41	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.42	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.43	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
1	12101011001	12101	108	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3531	0.0268329	12101	1298708303	937053807	265379.16
2	12101011002	12101	563	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3026	0.0229953	12101	1112968373	2100614397	694188.50
3	12101011003	12101	125	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2985	0.0226837	12101	1097888498	1006474749	337177.47
4	12101011004	12101	162	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	1595	0.0121208	12101	586643938	1142497693	716299.49
5	12101021001	12101	137	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2549	0.0193705	12101	937526895	1052606906	412948.96
6	12101021002	12101	148	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3389	0.0257538	12101	1246480442	1093111445	322546.90
7	12101031001	12101	303	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2497	0.0189753	12101	918401199	1551673745	621415.20
8	12101031002	12101	171	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3363	0.0255563	12101	1236917594	1173100476	348825.60
9	12101031003	12101	98	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3844	0.0292115	12101	1413830280	893581430	232461.35
10	12101031004	12101	161	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3035	0.0230637	12101	1116278590	1139044300	375302.90
11	12101031005	12101	180	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2132	0.0162016	12101	784153527	1202890551	564207.58
12	12101041001	12101	163	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2931	0.0222734	12101	1078027198	1145940207	390972.44
13	12101041002	12101	151	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3427	0.0260426	12101	1260456912	1103888751	322115.19
14	12101041003	12101	104	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3021	0.0229573	12101	1111129364	919922571	304509.29
15	12101041004	12101	159	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	4652	0.0353517	12101	1711014168	1132104470	243358.66
16	12101041005	12101	82	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3038	0.0230865	12101	1117381995	819006584	269587.42
17	12101041006	12101	75	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2472	0.0187853	12101	909206153	784045559	317170.53
18	12101041007	12101	86	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3294	0.0250319	12101	1211539267	838301162	254493.37
19	12101051001	12101	85	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2180	0.0165664	12101	801808015	833521285	382349.21
20	12101051002	12101	30	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	1013	0.0076980	12101	372583266	500943693	494515.00
21	12101061001	12101	75	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2372	0.0180254	12101	872425969	784045559	330541.97
22	12101061002	12101	92	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2905	0.0220758	12101	1068464350	866402324	298245.21
23	12101061003	12101	63	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2197	0.0166955	12101	808060646	719981827	327711.35
24	12101071001	12101	56	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	1765	0.0134127	12101	649170251	679693518	385095.48
25	12101071002	12101	193	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3727	0.0283224	12101	1370797464	1244607223	333943.45
26	12101071003	12101	113	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2859	0.0217262	12101	1051545466	958018029	335088.50
27	12101071004	12101	96	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2382	0.0181014	12101	876103987	884618715	371376.45
28	12101071005	12101	120	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3151	0.0239452	12101	1158943603	986586823	313102.77
29	12101071006	12101	411	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3159	0.0240060	12101	1161886018	1801068441	570138.79
30	12101071007	12101	213	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3543	0.0269241	12101	1303121925	1306074905	368635.31
31	12101081001	12101	83	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2522	0.0191653	12101	927596245	823874524	326675.07
32	12101081002	12101	83	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3136	0.0238312	12101	1153426576	823874524	262715.09
33	12101081003	12101	62	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2280	0.0173263	12101	838588199	714371706	313320.92
34	12101081004	12101	157	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	6468	0.0491519	12101	2378942312	1125119878	173951.74
35	12101081005	12101	97	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3165	0.0240516	12101	1164092829	889111879	280920.03
36	12101081006	12101	137	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	5200	0.0395161	12101	1912569577	1052606906	202424.40
37	12101091001	12101	19	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	692	0.0052587	12101	254518874	400688805	579030.06
38	12101101001	12101	2	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	205	0.0015578	12101	75399378	133290540	650197.75
39	12101101002	12101	73	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	1386	0.0105326	12101	509773353	773753111	558263.43
40	12101101003	12101	169	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3937	0.0299182	12101	1448035851	1166372385	296259.18
41	12101101004	12101	149	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	5280	0.0401240	12101	1941993724	1096716183	207711.40
42	12101101005	12101	85	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	3098	0.0235425	12101	1139450106	833521285	269051.42
43	12101991999	12101	3	2017	Punta Arenas	367801.8	2017	12101	131592	48399779957	Urbano	2529	0.0192185	12101	930170858	162513756	64260.09
44	12201011001	12201	140	2017	NA	NA	NA	NA	NA	NA	NA	1868	0.9054775	12201	NA	1063813543	NA
45	12301011001	12301	61	2017	Porvenir	398616.7	2017	12301	6801	2710992403	Urbano	2543	0.3739156	12301	1013682353	708715144	278692.55
46	12301011002	12301	87	2017	Porvenir	398616.7	2017	12301	6801	2710992403	Urbano	3449	0.5071313	12301	1374829113	843052716	244433.96
47	12401011001	12401	68	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	1266	0.0589468	12401	402480145	747373177	590342.16
48	12401011002	12401	154	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	1994	0.0928435	12401	633922125	1114557166	558955.45
49	12401011003	12401	63	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	1327	0.0617870	12401	421872948	719981827	542563.55
50	12401011004	12401	103	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	3053	0.1421521	12401	970593905	915587383	299897.60
51	12401011005	12401	65	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	2710	0.1261815	12401	861549126	731067140	269766.47
52	12401011006	12401	89	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	2158	0.1004796	12401	686060153	852472802	395029.10
53	12401011007	12401	80	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	1489	0.0693300	12401	473375147	809178823	543437.76
54	12401011008	12401	196	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	3985	0.1855473	12401	1266890505	1254028290	314687.15
55	12401051001	12401	54	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	1041	0.0484705	12401	330949314	667715304	641417.20
56	12401991999	12401	8	2017	Natales	317914.8	2017	12401	21477	6827856304	Urbano	157	0.0073101	12401	49912625	262509923	1672037.72
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.41	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.42	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.43	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_urbano_nivel_nacional_17_r12.rds")

---

Rural

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 2)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15202	1	2	6	13225	1	3	1	5	3	5	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	3	1	1	5	3	5	2	3	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	4	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	5	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	6	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	8	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	9	3	1	5	3	5	1	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	10	1	1	5	3	4	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	11	1	2	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	12	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	13	1	1	5	3	4	1	4	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	14	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	16	1	1	5	3	5	1	3	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	17	1	1	5	3	5	2	4	1	8	15	152	15202	15202012006
15	152	15202	1	2	6	13225	18	3	1	5	3	5	1	1	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	19	3	1	5	3	5	1	3	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	20	1	1	5	3	5	2	1	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	21	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	22	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	24	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	25	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	26	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	27	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	28	3	1	5	3	5	1	4	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	29	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	30	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	31	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	32	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	33	1	1	5	3	5	3	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	34	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	35	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	36	1	1	5	3	5	3	2	1	9	15	152	15202	15202012006
15	152	15202	1	2	6	13225	37	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	38	1	1	5	3	5	99	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	39	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	40	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	41	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	42	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	43	3	1	5	3	5	2	1	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	44	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	45	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	8	13910	1	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	3	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	4	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	5	3	1	5	3	5	2	3	1	3	15	152	15202	15202012008
15	152	15202	1	2	8	13910	6	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	7	1	1	5	99	5	2	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	8	3	1	5	3	5	3	3	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	9	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	10	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	11	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	12	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	13	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	14	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	16	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	18	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	20	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	21	1	1	5	3	5	1	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	22	3	1	5	3	5	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	23	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	24	3	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	25	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	26	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	27	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	28	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	29	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	30	1	1	5	1	4	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	12	8394	1	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	2	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	3	3	1	5	3	5	2	3	1	4	15	152	15202	15202012012
15	152	15202	1	2	12	8394	4	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	5	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	6	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	8	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	9	3	1	5	3	5	1	4	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	10	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	11	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	12	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	13	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	14	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	15	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	16	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	18	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	20	3	1	5	3	5	3	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	21	3	1	5	99	5	1	4	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	22	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	24	3	1	5	3	5	1	2	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	25	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 12:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 12)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Veamos los primeros 100 registros:

r3_100 <- d[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	unlist.c.	unlist.d.	Freq	anio
1	12101092005	1	12101	66	2017
2	12101092006	1	12101	2	2017
3	12101092009	1	12101	4	2017
4	12101092016	1	12101	7	2017
5	12101092019	1	12101	24	2017
6	12101092020	1	12101	41	2017
7	12101092032	1	12101	54	2017
8	12101092901	1	12101	1	2017
9	12101102002	1	12101	1	2017
10	12101102015	1	12101	23	2017
11	12101102021	1	12101	24	2017
12	12101102029	1	12101	6	2017
13	12101102030	1	12101	7	2017
14	12101102033	1	12101	1	2017
15	12101102901	1	12101	1	2017
16	12101122001	1	12101	4	2017
17	12101142011	1	12101	3	2017
68	12102012012	1	12102	1	2017
69	12102012014	1	12102	3	2017
70	12102012015	1	12102	1	2017
71	12102012017	1	12102	4	2017
72	12102012020	1	12102	2	2017
73	12102012901	1	12102	5	2017
124	12103012014	1	12103	1	2017
125	12103012019	1	12103	1	2017
126	12103022009	1	12103	1	2017
177	12104012008	1	12104	1	2017
178	12104012010	1	12104	2	2017
179	12104022901	1	12104	1	2017
230	12201012901	1	12201	2	2017
281	12301012005	1	12301	2	2017
282	12301012007	1	12301	5	2017
283	12301012008	1	12301	3	2017
284	12301022001	1	12301	1	2017
285	12301032003	1	12301	1	2017
286	12301032011	1	12301	1	2017
337	12302012005	1	12302	15	2017
338	12302012901	1	12302	1	2017
389	12303012005	1	12303	1	2017
390	12303012009	1	12303	1	2017
391	12303012010	1	12303	1	2017
392	12303012012	1	12303	1	2017
393	12303012901	1	12303	3	2017
444	12401012010	1	12401	3	2017
445	12401012017	1	12401	1	2017
446	12401012022	1	12401	1	2017
447	12401022013	1	12401	1	2017
448	12401042021	1	12401	1	2017
449	12401052022	1	12401	37	2017
500	12402012004	1	12402	1	2017
NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA
NA.41	NA	NA	NA	NA	NA
NA.42	NA	NA	NA	NA	NA
NA.43	NA	NA	NA	NA	NA
NA.44	NA	NA	NA	NA	NA
NA.45	NA	NA	NA	NA	NA
NA.46	NA	NA	NA	NA	NA
NA.47	NA	NA	NA	NA	NA
NA.48	NA	NA	NA	NA	NA
NA.49	NA	NA	NA	NA	NA

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	12101092005	66	2017	12101
2	12101092006	2	2017	12101
3	12101092009	4	2017	12101
4	12101092016	7	2017	12101
5	12101092019	24	2017	12101
6	12101092020	41	2017	12101
7	12101092032	54	2017	12101
8	12101092901	1	2017	12101
9	12101102002	1	2017	12101
10	12101102015	23	2017	12101
11	12101102021	24	2017	12101
12	12101102029	6	2017	12101
13	12101102030	7	2017	12101
14	12101102033	1	2017	12101
15	12101102901	1	2017	12101
16	12101122001	4	2017	12101
17	12101142011	3	2017	12101
68	12102012012	1	2017	12102
69	12102012014	3	2017	12102
70	12102012015	1	2017	12102
71	12102012017	4	2017	12102
72	12102012020	2	2017	12102
73	12102012901	5	2017	12102
124	12103012014	1	2017	12103
125	12103012019	1	2017	12103
126	12103022009	1	2017	12103
177	12104012008	1	2017	12104
178	12104012010	2	2017	12104
179	12104022901	1	2017	12104
230	12201012901	2	2017	12201
281	12301012005	2	2017	12301
282	12301012007	5	2017	12301
283	12301012008	3	2017	12301
284	12301022001	1	2017	12301
285	12301032003	1	2017	12301
286	12301032011	1	2017	12301
337	12302012005	15	2017	12302
338	12302012901	1	2017	12302
389	12303012005	1	2017	12303
390	12303012009	1	2017	12303
391	12303012010	1	2017	12303
392	12303012012	1	2017	12303
393	12303012901	3	2017	12303
444	12401012010	3	2017	12401
445	12401012017	1	2017	12401
446	12401012022	1	2017	12401
447	12401022013	1	2017	12401
448	12401042021	1	2017	12401
449	12401052022	37	2017	12401
500	12402012004	1	2017	12402
NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA
NA.2	NA	NA	NA	NA
NA.3	NA	NA	NA	NA
NA.4	NA	NA	NA	NA
NA.5	NA	NA	NA	NA
NA.6	NA	NA	NA	NA
NA.7	NA	NA	NA	NA
NA.8	NA	NA	NA	NA
NA.9	NA	NA	NA	NA
NA.10	NA	NA	NA	NA
NA.11	NA	NA	NA	NA
NA.12	NA	NA	NA	NA
NA.13	NA	NA	NA	NA
NA.14	NA	NA	NA	NA
NA.15	NA	NA	NA	NA
NA.16	NA	NA	NA	NA
NA.17	NA	NA	NA	NA
NA.18	NA	NA	NA	NA
NA.19	NA	NA	NA	NA
NA.20	NA	NA	NA	NA
NA.21	NA	NA	NA	NA
NA.22	NA	NA	NA	NA
NA.23	NA	NA	NA	NA
NA.24	NA	NA	NA	NA
NA.25	NA	NA	NA	NA
NA.26	NA	NA	NA	NA
NA.27	NA	NA	NA	NA
NA.28	NA	NA	NA	NA
NA.29	NA	NA	NA	NA
NA.30	NA	NA	NA	NA
NA.31	NA	NA	NA	NA
NA.32	NA	NA	NA	NA
NA.33	NA	NA	NA	NA
NA.34	NA	NA	NA	NA
NA.35	NA	NA	NA	NA
NA.36	NA	NA	NA	NA
NA.37	NA	NA	NA	NA
NA.38	NA	NA	NA	NA
NA.39	NA	NA	NA	NA
NA.40	NA	NA	NA	NA
NA.41	NA	NA	NA	NA
NA.42	NA	NA	NA	NA
NA.43	NA	NA	NA	NA
NA.44	NA	NA	NA	NA
NA.45	NA	NA	NA	NA
NA.46	NA	NA	NA	NA
NA.47	NA	NA	NA	NA
NA.48	NA	NA	NA	NA
NA.49	NA	NA	NA	NA

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_rural_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	289375.3	2017	1101	191468	55406102543	Rural
01401	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
01402	Camiña	262850.3	2017	1402	1250	328562901	Rural
01404	Huara	253968.5	2017	1404	2730	693334131	Rural
01405	Pica	290496.7	2017	1405	9296	2700457509	Rural
02103	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
02104	Taltal	345494.0	2017	2104	13317	4600943086	Rural
02201	Calama	310025.0	2017	2201	165731	51380756402	Rural
02203	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
02301	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
03101	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03103	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03202	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural
03301	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03302	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03303	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03304	Huasco	227560.7	2017	3304	10149	2309513927	Rural
04101	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04102	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04103	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04104	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04105	Paiguano	205942.1	2017	4105	4497	926121774	Rural
04106	Vicuña	176130.6	2017	4106	27771	4891322768	Rural
04201	Illapel	191976.8	2017	4201	30848	5922099530	Rural
04202	Canela	171370.3	2017	4202	9093	1558270441	Rural
04203	Los Vilos	173238.5	2017	4203	21382	3704185607	Rural
04204	Salamanca	223234.2	2017	4204	29347	6551254640	Rural
04301	Ovalle	241393.7	2017	4301	111272	26860360045	Rural
04302	Combarbalá	179139.6	2017	4302	13322	2386498044	Rural
04303	Monte Patria	201205.8	2017	4303	30751	6187280931	Rural
04304	Punitaqui	171931.7	2017	4304	10956	1883683880	Rural
04305	Río Hurtado	182027.2	2017	4305	4278	778712384	Rural
05101	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05102	Casablanca	268917.1	2017	5102	26867	7224996933	Rural
05105	Puchuncaví	279614.4	2017	5105	18546	5185728335	Rural
05107	Quintero	334628.2	2017	5107	31923	10682335196	Rural
05301	Los Andes	324402.1	2017	5301	66708	21640215030	Rural
05302	Calle Larga	242743.8	2017	5302	14832	3600375502	Rural
05303	Rinconada	326532.5	2017	5303	10207	3332917471	Rural
05304	San Esteban	223168.6	2017	5304	18855	4207844130	Rural
05401	La Ligua	181468.0	2017	5401	35390	6422154059	Rural
05402	Cabildo	231277.8	2017	5402	19388	4484014285	Rural
05404	Petorca	298208.9	2017	5404	9826	2930200178	Rural
05405	Zapallar	292882.3	2017	5405	7339	2149463129	Rural
05501	Quillota	220926.8	2017	5501	90517	19997628209	Rural
05502	Calera	226906.2	2017	5502	50554	11471016698	Rural
05503	Hijuelas	253739.9	2017	5503	17988	4564273201	Rural
05504	La Cruz	291124.1	2017	5504	22098	6433259569	Rural
05506	Nogales	264475.3	2017	5506	22120	5850194593	Rural
05601	San Antonio	266331.2	2017	5601	91350	24329353815	Rural

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
1	12101	12101092020	41	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
2	12101	12101092019	24	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
3	12101	12101102015	23	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
4	12101	12101092032	54	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
5	12101	12101102002	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
6	12101	12101092005	66	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
7	12101	12101092006	2	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
8	12101	12101092901	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
9	12101	12101092016	7	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
10	12101	12101102030	7	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
11	12101	12101102033	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
12	12101	12101092009	4	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
13	12101	12101102029	6	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
14	12101	12101142011	3	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
15	12101	12101102901	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
16	12101	12101122001	4	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
17	12101	12101102021	24	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
18	12102	12102012012	1	2017	NA	NA	NA	NA	NA	NA	NA
19	12102	12102012014	3	2017	NA	NA	NA	NA	NA	NA	NA
20	12102	12102012015	1	2017	NA	NA	NA	NA	NA	NA	NA
21	12102	12102012017	4	2017	NA	NA	NA	NA	NA	NA	NA
22	12102	12102012020	2	2017	NA	NA	NA	NA	NA	NA	NA
23	12102	12102012901	5	2017	NA	NA	NA	NA	NA	NA	NA
24	12103	12103012014	1	2017	NA	NA	NA	NA	NA	NA	NA
25	12103	12103012019	1	2017	NA	NA	NA	NA	NA	NA	NA
26	12103	12103022009	1	2017	NA	NA	NA	NA	NA	NA	NA
27	12104	12104012008	1	2017	NA	NA	NA	NA	NA	NA	NA
28	12104	12104012010	2	2017	NA	NA	NA	NA	NA	NA	NA
29	12104	12104022901	1	2017	NA	NA	NA	NA	NA	NA	NA
30	12201	12201012901	2	2017	NA	NA	NA	NA	NA	NA	NA
31	12301	12301012007	5	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural
32	12301	12301012005	2	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural
33	12301	12301032011	1	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural
34	12301	12301012008	3	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural
35	12301	12301032003	1	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural
36	12301	12301022001	1	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural
37	12302	12302012005	15	2017	NA	NA	NA	NA	NA	NA	NA
38	12302	12302012901	1	2017	NA	NA	NA	NA	NA	NA	NA
39	12303	12303012009	1	2017	NA	NA	NA	NA	NA	NA	NA
40	12303	12303012010	1	2017	NA	NA	NA	NA	NA	NA	NA
41	12303	12303012012	1	2017	NA	NA	NA	NA	NA	NA	NA
42	12303	12303012901	3	2017	NA	NA	NA	NA	NA	NA	NA
43	12303	12303012005	1	2017	NA	NA	NA	NA	NA	NA	NA
44	12401	12401012010	3	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural
45	12401	12401012017	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural
46	12401	12401022013	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural
47	12401	12401042021	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural
48	12401	12401052022	37	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural
49	12401	12401012022	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural
50	12402	12402012004	1	2017	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.41	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.42	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.43	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.44	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.45	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.46	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.47	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.48	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.49	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
1	12101	12101092020	41	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
2	12101	12101092019	24	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
3	12101	12101102015	23	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
4	12101	12101092032	54	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
5	12101	12101102002	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
6	12101	12101092005	66	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
7	12101	12101092006	2	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
8	12101	12101092901	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
9	12101	12101092016	7	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
10	12101	12101102030	7	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
11	12101	12101102033	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
12	12101	12101092009	4	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
13	12101	12101102029	6	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
14	12101	12101142011	3	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
15	12101	12101102901	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
16	12101	12101122001	4	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
17	12101	12101102021	24	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural
18	12102	12102012012	1	2017	NA	NA	NA	NA	NA	NA	NA
19	12102	12102012014	3	2017	NA	NA	NA	NA	NA	NA	NA
20	12102	12102012015	1	2017	NA	NA	NA	NA	NA	NA	NA
21	12102	12102012017	4	2017	NA	NA	NA	NA	NA	NA	NA
22	12102	12102012020	2	2017	NA	NA	NA	NA	NA	NA	NA
23	12102	12102012901	5	2017	NA	NA	NA	NA	NA	NA	NA
24	12103	12103012014	1	2017	NA	NA	NA	NA	NA	NA	NA
25	12103	12103012019	1	2017	NA	NA	NA	NA	NA	NA	NA
26	12103	12103022009	1	2017	NA	NA	NA	NA	NA	NA	NA
27	12104	12104012008	1	2017	NA	NA	NA	NA	NA	NA	NA
28	12104	12104012010	2	2017	NA	NA	NA	NA	NA	NA	NA
29	12104	12104022901	1	2017	NA	NA	NA	NA	NA	NA	NA
30	12201	12201012901	2	2017	NA	NA	NA	NA	NA	NA	NA
31	12301	12301012007	5	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural
32	12301	12301012005	2	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural
33	12301	12301032011	1	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural
34	12301	12301012008	3	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural
35	12301	12301032003	1	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural
36	12301	12301022001	1	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural
37	12302	12302012005	15	2017	NA	NA	NA	NA	NA	NA	NA
38	12302	12302012901	1	2017	NA	NA	NA	NA	NA	NA	NA
39	12303	12303012009	1	2017	NA	NA	NA	NA	NA	NA	NA
40	12303	12303012010	1	2017	NA	NA	NA	NA	NA	NA	NA
41	12303	12303012012	1	2017	NA	NA	NA	NA	NA	NA	NA
42	12303	12303012901	3	2017	NA	NA	NA	NA	NA	NA	NA
43	12303	12303012005	1	2017	NA	NA	NA	NA	NA	NA	NA
44	12401	12401012010	3	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural
45	12401	12401012017	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural
46	12401	12401022013	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural
47	12401	12401042021	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural
48	12401	12401052022	37	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural
49	12401	12401012022	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural
50	12402	12402012004	1	2017	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.41	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.42	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.43	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.44	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.45	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.46	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.47	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.48	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.49	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
12101092005	12101	66	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	671	0.0050991	12101
12101092006	12101	2	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	79	0.0006003	12101
12101092009	12101	4	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	95	0.0007219	12101
12101092016	12101	7	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	79	0.0006003	12101
12101092019	12101	24	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	1186	0.0090127	12101
12101092020	12101	41	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	491	0.0037312	12101
12101092032	12101	54	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	1064	0.0080856	12101
12101092901	12101	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	19	0.0001444	12101
12101102002	12101	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	180	0.0013679	12101
12101102015	12101	23	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	252	0.0019150	12101
12101102021	12101	24	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	567	0.0043088	12101
12101102029	12101	6	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	76	0.0005775	12101
12101102030	12101	7	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	85	0.0006459	12101
12101102033	12101	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	116	0.0008815	12101
12101102901	12101	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	59	0.0004484	12101
12101122001	12101	4	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	184	0.0013983	12101
12101142011	12101	3	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	217	0.0016490	12101
12102012012	12102	1	2017	NA	NA	NA	NA	NA	NA	NA	13	0.0474453	12102
12102012014	12102	3	2017	NA	NA	NA	NA	NA	NA	NA	19	0.0693431	12102
12102012015	12102	1	2017	NA	NA	NA	NA	NA	NA	NA	9	0.0328467	12102
12102012017	12102	4	2017	NA	NA	NA	NA	NA	NA	NA	35	0.1277372	12102
12102012020	12102	2	2017	NA	NA	NA	NA	NA	NA	NA	115	0.4197080	12102
12102012901	12102	5	2017	NA	NA	NA	NA	NA	NA	NA	70	0.2554745	12102
12103012014	12103	1	2017	NA	NA	NA	NA	NA	NA	NA	51	0.0826580	12103
12103012019	12103	1	2017	NA	NA	NA	NA	NA	NA	NA	122	0.1977310	12103
12103022009	12103	1	2017	NA	NA	NA	NA	NA	NA	NA	272	0.4408428	12103
12104012008	12104	1	2017	NA	NA	NA	NA	NA	NA	NA	30	0.0375469	12104
12104012010	12104	2	2017	NA	NA	NA	NA	NA	NA	NA	313	0.3917397	12104
12104022901	12104	1	2017	NA	NA	NA	NA	NA	NA	NA	52	0.0650814	12104
12201012901	12201	2	2017	NA	NA	NA	NA	NA	NA	NA	44	0.0213282	12201
12301012005	12301	2	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	14	0.0020585	12301
12301012007	12301	5	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	112	0.0164682	12301
12301012008	12301	3	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	74	0.0108808	12301
12301022001	12301	1	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	23	0.0033819	12301
12301032003	12301	1	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	62	0.0091163	12301
12301032011	12301	1	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	285	0.0419056	12301
12302012005	12302	15	2017	NA	NA	NA	NA	NA	NA	NA	795	0.6865285	12302
12302012901	12302	1	2017	NA	NA	NA	NA	NA	NA	NA	109	0.0941278	12302
12303012005	12303	1	2017	NA	NA	NA	NA	NA	NA	NA	70	0.1728395	12303
12303012009	12303	1	2017	NA	NA	NA	NA	NA	NA	NA	22	0.0543210	12303
12303012010	12303	1	2017	NA	NA	NA	NA	NA	NA	NA	100	0.2469136	12303
12303012012	12303	1	2017	NA	NA	NA	NA	NA	NA	NA	19	0.0469136	12303
12303012901	12303	3	2017	NA	NA	NA	NA	NA	NA	NA	29	0.0716049	12303
12401012010	12401	3	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	102	0.0047493	12401
12401012017	12401	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	39	0.0018159	12401
12401012022	12401	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	185	0.0086139	12401
12401022013	12401	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	10	0.0004656	12401
12401042021	12401	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	100	0.0046561	12401
12401052022	12401	37	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	963	0.0448387	12401
12402012004	12402	1	2017	NA	NA	NA	NA	NA	NA	NA	138	0.1141439	12402

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
12101092005	12101	66	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	671	0.0050991	12101	208498998
12101092006	12101	2	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	79	0.0006003	12101	24547572
12101092009	12101	4	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	95	0.0007219	12101	29519232
12101092016	12101	7	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	79	0.0006003	12101	24547572
12101092019	12101	24	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	1186	0.0090127	12101	368524309
12101092020	12101	41	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	491	0.0037312	12101	152567821
12101092032	12101	54	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	1064	0.0080856	12101	330615400
12101092901	12101	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	19	0.0001444	12101	5903846
12101102002	12101	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	180	0.0013679	12101	55931177
12101102015	12101	23	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	252	0.0019150	12101	78303647
12101102021	12101	24	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	567	0.0043088	12101	176183207
12101102029	12101	6	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	76	0.0005775	12101	23615386
12101102030	12101	7	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	85	0.0006459	12101	26411945
12101102033	12101	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	116	0.0008815	12101	36044536
12101102901	12101	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	59	0.0004484	12101	18332997
12101122001	12101	4	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	184	0.0013983	12101	57174092
12101142011	12101	3	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	217	0.0016490	12101	67428141
12102012012	12102	1	2017	NA	NA	NA	NA	NA	NA	NA	13	0.0474453	12102	NA
12102012014	12102	3	2017	NA	NA	NA	NA	NA	NA	NA	19	0.0693431	12102	NA
12102012015	12102	1	2017	NA	NA	NA	NA	NA	NA	NA	9	0.0328467	12102	NA
12102012017	12102	4	2017	NA	NA	NA	NA	NA	NA	NA	35	0.1277372	12102	NA
12102012020	12102	2	2017	NA	NA	NA	NA	NA	NA	NA	115	0.4197080	12102	NA
12102012901	12102	5	2017	NA	NA	NA	NA	NA	NA	NA	70	0.2554745	12102	NA
12103012014	12103	1	2017	NA	NA	NA	NA	NA	NA	NA	51	0.0826580	12103	NA
12103012019	12103	1	2017	NA	NA	NA	NA	NA	NA	NA	122	0.1977310	12103	NA
12103022009	12103	1	2017	NA	NA	NA	NA	NA	NA	NA	272	0.4408428	12103	NA
12104012008	12104	1	2017	NA	NA	NA	NA	NA	NA	NA	30	0.0375469	12104	NA
12104012010	12104	2	2017	NA	NA	NA	NA	NA	NA	NA	313	0.3917397	12104	NA
12104022901	12104	1	2017	NA	NA	NA	NA	NA	NA	NA	52	0.0650814	12104	NA
12201012901	12201	2	2017	NA	NA	NA	NA	NA	NA	NA	44	0.0213282	12201	NA
12301012005	12301	2	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	14	0.0020585	12301	6368619
12301012007	12301	5	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	112	0.0164682	12301	50948948
12301012008	12301	3	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	74	0.0108808	12301	33662698
12301022001	12301	1	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	23	0.0033819	12301	10462730
12301032003	12301	1	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	62	0.0091163	12301	28203882
12301032011	12301	1	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	285	0.0419056	12301	129646877
12302012005	12302	15	2017	NA	NA	NA	NA	NA	NA	NA	795	0.6865285	12302	NA
12302012901	12302	1	2017	NA	NA	NA	NA	NA	NA	NA	109	0.0941278	12302	NA
12303012005	12303	1	2017	NA	NA	NA	NA	NA	NA	NA	70	0.1728395	12303	NA
12303012009	12303	1	2017	NA	NA	NA	NA	NA	NA	NA	22	0.0543210	12303	NA
12303012010	12303	1	2017	NA	NA	NA	NA	NA	NA	NA	100	0.2469136	12303	NA
12303012012	12303	1	2017	NA	NA	NA	NA	NA	NA	NA	19	0.0469136	12303	NA
12303012901	12303	3	2017	NA	NA	NA	NA	NA	NA	NA	29	0.0716049	12303	NA
12401012010	12401	3	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	102	0.0047493	12401	33845252
12401012017	12401	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	39	0.0018159	12401	12940831
12401012022	12401	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	185	0.0086139	12401	61385995
12401022013	12401	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	10	0.0004656	12401	3318162
12401042021	12401	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	100	0.0046561	12401	33181619
12401052022	12401	37	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	963	0.0448387	12401	319538992
12402012004	12402	1	2017	NA	NA	NA	NA	NA	NA	NA	138	0.1141439	12402	NA

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -126518515  -32574370  -11433679   20038645  226787553 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 31290609   14369486   2.178   0.0383 *  
## Freq.x       4601923     699439   6.579 4.67e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 64770000 on 27 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.6159, Adjusted R-squared:  0.6016 
## F-statistic: 43.29 on 1 and 27 DF,  p-value: 4.667e-07

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.601645813337973"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.601645813337973"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.550309420040432"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.635030217587285"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.654833021220414"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.536300070318569"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.583654499183309"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.512253981515292"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 8       log-log 0.512253981515292
## 6      log-raíz 0.536300070318569
## 3   logarítmico 0.550309420040432
## 7      raíz-log 0.583654499183309
## 1    cuadrático 0.601645813337973
## 2        cúbico 0.601645813337973
## 4 raíz cuadrada 0.635030217587285
## 5     raíz-raíz 0.654833021220414
##                                                                     sintaxis
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 5
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = sqrt(multi_pob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3463.6 -2409.3  -379.6  1284.4  7208.0 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    3005.3      835.8   3.596  0.00128 ** 
## sqrt(Freq.x)   1833.8      249.3   7.357  6.5e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2833 on 27 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.6672, Adjusted R-squared:  0.6548 
## F-statistic: 54.12 on 1 and 27 DF,  p-value: 6.503e-08

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    3005.289

bb <- linearMod$coefficients[2]
bb

## sqrt(Freq.x) 
##     1833.798

9 Modelo raíz-raíz (raíz-raíz)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.6548 ).

9.1 Diagrama de dispersión sobre raíz-raíz

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=sqrt(h_y_m_comuna_corr_01$Freq.x), y=sqrt(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo raíz-raíz

Observemos nuevamente el resultado sobre raíz-raíz.

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = sqrt(multi_pob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3463.6 -2409.3  -379.6  1284.4  7208.0 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    3005.3      835.8   3.596  0.00128 ** 
## sqrt(Freq.x)   1833.8      249.3   7.357  6.5e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2833 on 27 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.6672, Adjusted R-squared:  0.6548 
## F-statistic: 54.12 on 1 and 27 DF,  p-value: 6.503e-08

ggplot(h_y_m_comuna_corr_01, aes(x = sqrt(Freq.x) , y = sqrt(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = {3005.289}^2 + 2 3005.289 1833.798 \sqrt{X}+ 1833.798^2 X \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- ((aa^2)+(2*(aa*bb)) * sqrt(h_y_m_comuna_corr_01$Freq.x)+((bb^2)*h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
1	12101092005	12101	66	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	671	0.0050991	12101	208498998	320522235
2	12101092006	12101	2	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	79	0.0006003	12101	24547572	31345118
3	12101092009	12101	4	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	95	0.0007219	12101	29519232	44527396
4	12101092016	12101	7	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	79	0.0006003	12101	24547572	61733433
5	12101092019	12101	24	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	1186	0.0090127	12101	368524309	143736794
6	12101092020	12101	41	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	491	0.0037312	12101	152567821	217483617
7	12101092032	12101	54	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	1064	0.0080856	12101	330615400	271619984
8	12101092901	12101	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	19	0.0001444	12101	5903846	23416764
9	12101102002	12101	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	180	0.0013679	12101	55931177	23416764
10	12101102015	12101	23	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	252	0.0019150	12101	78303647	139237062
11	12101102021	12101	24	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	567	0.0043088	12101	176183207	143736794
12	12101102029	12101	6	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	76	0.0005775	12101	23615386	56207386
13	12101102030	12101	7	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	85	0.0006459	12101	26411945	61733433
14	12101102033	12101	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	116	0.0008815	12101	36044536	23416764
15	12101102901	12101	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	59	0.0004484	12101	18332997	23416764
16	12101122001	12101	4	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	184	0.0013983	12101	57174092	44527396
17	12101142011	12101	3	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	217	0.0016490	12101	67428141	38211195
18	12102012012	12102	1	2017	NA	NA	NA	NA	NA	NA	NA	13	0.0474453	12102	NA	23416764
19	12102012014	12102	3	2017	NA	NA	NA	NA	NA	NA	NA	19	0.0693431	12102	NA	38211195
20	12102012015	12102	1	2017	NA	NA	NA	NA	NA	NA	NA	9	0.0328467	12102	NA	23416764
21	12102012017	12102	4	2017	NA	NA	NA	NA	NA	NA	NA	35	0.1277372	12102	NA	44527396
22	12102012020	12102	2	2017	NA	NA	NA	NA	NA	NA	NA	115	0.4197080	12102	NA	31345118
23	12102012901	12102	5	2017	NA	NA	NA	NA	NA	NA	NA	70	0.2554745	12102	NA	50492197
24	12103012014	12103	1	2017	NA	NA	NA	NA	NA	NA	NA	51	0.0826580	12103	NA	23416764
25	12103012019	12103	1	2017	NA	NA	NA	NA	NA	NA	NA	122	0.1977310	12103	NA	23416764
26	12103022009	12103	1	2017	NA	NA	NA	NA	NA	NA	NA	272	0.4408428	12103	NA	23416764
27	12104012008	12104	1	2017	NA	NA	NA	NA	NA	NA	NA	30	0.0375469	12104	NA	23416764
28	12104012010	12104	2	2017	NA	NA	NA	NA	NA	NA	NA	313	0.3917397	12104	NA	31345118
29	12104022901	12104	1	2017	NA	NA	NA	NA	NA	NA	NA	52	0.0650814	12104	NA	23416764
30	12201012901	12201	2	2017	NA	NA	NA	NA	NA	NA	NA	44	0.0213282	12201	NA	31345118
31	12301012005	12301	2	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	14	0.0020585	12301	6368619	31345118
32	12301012007	12301	5	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	112	0.0164682	12301	50948948	50492197
33	12301012008	12301	3	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	74	0.0108808	12301	33662698	38211195
34	12301022001	12301	1	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	23	0.0033819	12301	10462730	23416764
35	12301032003	12301	1	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	62	0.0091163	12301	28203882	23416764
36	12301032011	12301	1	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	285	0.0419056	12301	129646877	23416764
37	12302012005	12302	15	2017	NA	NA	NA	NA	NA	NA	NA	795	0.6865285	12302	NA	102162736
38	12302012901	12302	1	2017	NA	NA	NA	NA	NA	NA	NA	109	0.0941278	12302	NA	23416764
39	12303012005	12303	1	2017	NA	NA	NA	NA	NA	NA	NA	70	0.1728395	12303	NA	23416764
40	12303012009	12303	1	2017	NA	NA	NA	NA	NA	NA	NA	22	0.0543210	12303	NA	23416764
41	12303012010	12303	1	2017	NA	NA	NA	NA	NA	NA	NA	100	0.2469136	12303	NA	23416764
42	12303012012	12303	1	2017	NA	NA	NA	NA	NA	NA	NA	19	0.0469136	12303	NA	23416764
43	12303012901	12303	3	2017	NA	NA	NA	NA	NA	NA	NA	29	0.0716049	12303	NA	38211195
44	12401012010	12401	3	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	102	0.0047493	12401	33845252	38211195
45	12401012017	12401	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	39	0.0018159	12401	12940831	23416764
46	12401012022	12401	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	185	0.0086139	12401	61385995	23416764
47	12401022013	12401	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	10	0.0004656	12401	3318162	23416764
48	12401042021	12401	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	100	0.0046561	12401	33181619	23416764
49	12401052022	12401	37	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	963	0.0448387	12401	319538992	200501269
50	12402012004	12402	1	2017	NA	NA	NA	NA	NA	NA	NA	138	0.1141439	12402	NA	23416764
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.41	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.42	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.43	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.44	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.45	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.46	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.47	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.48	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.49	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
1	12101092005	12101	66	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	671	0.0050991	12101	208498998	320522235	477678.44
2	12101092006	12101	2	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	79	0.0006003	12101	24547572	31345118	396773.65
3	12101092009	12101	4	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	95	0.0007219	12101	29519232	44527396	468709.43
4	12101092016	12101	7	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	79	0.0006003	12101	24547572	61733433	781435.86
5	12101092019	12101	24	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	1186	0.0090127	12101	368524309	143736794	121194.60
6	12101092020	12101	41	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	491	0.0037312	12101	152567821	217483617	442940.16
7	12101092032	12101	54	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	1064	0.0080856	12101	330615400	271619984	255281.94
8	12101092901	12101	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	19	0.0001444	12101	5903846	23416764	1232461.26
9	12101102002	12101	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	180	0.0013679	12101	55931177	23416764	130093.13
10	12101102015	12101	23	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	252	0.0019150	12101	78303647	139237062	552528.02
11	12101102021	12101	24	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	567	0.0043088	12101	176183207	143736794	253504.05
12	12101102029	12101	6	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	76	0.0005775	12101	23615386	56207386	739570.87
13	12101102030	12101	7	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	85	0.0006459	12101	26411945	61733433	726275.69
14	12101102033	12101	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	116	0.0008815	12101	36044536	23416764	201868.66
15	12101102901	12101	1	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	59	0.0004484	12101	18332997	23416764	396894.30
16	12101122001	12101	4	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	184	0.0013983	12101	57174092	44527396	241996.72
17	12101142011	12101	3	2017	Punta Arenas	310728.8	2017	12101	131592	40889418945	Rural	217	0.0016490	12101	67428141	38211195	176088.46
18	12102012012	12102	1	2017	NA	NA	NA	NA	NA	NA	NA	13	0.0474453	12102	NA	23416764	NA
19	12102012014	12102	3	2017	NA	NA	NA	NA	NA	NA	NA	19	0.0693431	12102	NA	38211195	NA
20	12102012015	12102	1	2017	NA	NA	NA	NA	NA	NA	NA	9	0.0328467	12102	NA	23416764	NA
21	12102012017	12102	4	2017	NA	NA	NA	NA	NA	NA	NA	35	0.1277372	12102	NA	44527396	NA
22	12102012020	12102	2	2017	NA	NA	NA	NA	NA	NA	NA	115	0.4197080	12102	NA	31345118	NA
23	12102012901	12102	5	2017	NA	NA	NA	NA	NA	NA	NA	70	0.2554745	12102	NA	50492197	NA
24	12103012014	12103	1	2017	NA	NA	NA	NA	NA	NA	NA	51	0.0826580	12103	NA	23416764	NA
25	12103012019	12103	1	2017	NA	NA	NA	NA	NA	NA	NA	122	0.1977310	12103	NA	23416764	NA
26	12103022009	12103	1	2017	NA	NA	NA	NA	NA	NA	NA	272	0.4408428	12103	NA	23416764	NA
27	12104012008	12104	1	2017	NA	NA	NA	NA	NA	NA	NA	30	0.0375469	12104	NA	23416764	NA
28	12104012010	12104	2	2017	NA	NA	NA	NA	NA	NA	NA	313	0.3917397	12104	NA	31345118	NA
29	12104022901	12104	1	2017	NA	NA	NA	NA	NA	NA	NA	52	0.0650814	12104	NA	23416764	NA
30	12201012901	12201	2	2017	NA	NA	NA	NA	NA	NA	NA	44	0.0213282	12201	NA	31345118	NA
31	12301012005	12301	2	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	14	0.0020585	12301	6368619	31345118	2238937.02
32	12301012007	12301	5	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	112	0.0164682	12301	50948948	50492197	450823.18
33	12301012008	12301	3	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	74	0.0108808	12301	33662698	38211195	516367.50
34	12301022001	12301	1	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	23	0.0033819	12301	10462730	23416764	1018120.17
35	12301032003	12301	1	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	62	0.0091163	12301	28203882	23416764	377689.74
36	12301032011	12301	1	2017	Porvenir	454901.3	2017	12301	6801	3093783887	Rural	285	0.0419056	12301	129646877	23416764	82164.08
37	12302012005	12302	15	2017	NA	NA	NA	NA	NA	NA	NA	795	0.6865285	12302	NA	102162736	NA
38	12302012901	12302	1	2017	NA	NA	NA	NA	NA	NA	NA	109	0.0941278	12302	NA	23416764	NA
39	12303012005	12303	1	2017	NA	NA	NA	NA	NA	NA	NA	70	0.1728395	12303	NA	23416764	NA
40	12303012009	12303	1	2017	NA	NA	NA	NA	NA	NA	NA	22	0.0543210	12303	NA	23416764	NA
41	12303012010	12303	1	2017	NA	NA	NA	NA	NA	NA	NA	100	0.2469136	12303	NA	23416764	NA
42	12303012012	12303	1	2017	NA	NA	NA	NA	NA	NA	NA	19	0.0469136	12303	NA	23416764	NA
43	12303012901	12303	3	2017	NA	NA	NA	NA	NA	NA	NA	29	0.0716049	12303	NA	38211195	NA
44	12401012010	12401	3	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	102	0.0047493	12401	33845252	38211195	374619.56
45	12401012017	12401	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	39	0.0018159	12401	12940831	23416764	600429.85
46	12401012022	12401	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	185	0.0086139	12401	61385995	23416764	126577.10
47	12401022013	12401	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	10	0.0004656	12401	3318162	23416764	2341676.40
48	12401042021	12401	1	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	100	0.0046561	12401	33181619	23416764	234167.64
49	12401052022	12401	37	2017	Natales	331816.2	2017	12401	21477	7126416345	Rural	963	0.0448387	12401	319538992	200501269	208204.85
50	12402012004	12402	1	2017	NA	NA	NA	NA	NA	NA	NA	138	0.1141439	12402	NA	23416764	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.41	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.42	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.43	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.44	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.45	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.46	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.47	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.48	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.49	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_rural_nivel_nacional_17_r12.rds")

---

R-13

Urbano

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 1)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15201	1	1	1	1767	1	1	1	2	1	3	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	2	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	3	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	4	1	1	2	3	1	99	1	2	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	5	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	6	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	7	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	8	1	1	3	3	1	99	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	9	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	10	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	11	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	12	1	1	4	3	3	3	4	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	13	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	14	1	1	2	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	15	1	1	2	1	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	16	1	1	5	1	5	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	17	1	1	5	3	1	3	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	18	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	19	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	20	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	21	4	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	22	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	23	1	1	5	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	24	1	1	3	1	1	6	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	25	1	1	5	6	1	1	4	2	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	26	4	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	27	1	1	5	99	3	3	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	28	1	1	6	3	2	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	29	1	1	2	3	1	99	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	30	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	31	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	32	1	1	3	1	1	2	1	1	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	33	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	34	3	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	35	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	36	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	37	1	1	5	3	3	1	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	38	1	1	1	1	4	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	39	1	1	5	3	4	3	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	40	1	1	5	3	2	1	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	41	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	42	1	1	2	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	43	1	1	1	1	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	44	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	45	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	46	1	1	5	6	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	47	1	1	5	3	4	0	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	48	1	1	3	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	49	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	50	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	51	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	52	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	53	1	1	1	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	54	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	55	1	1	2	2	1	4	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	56	8	1	98	98	98	98	98	98	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	57	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	58	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	59	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	60	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	61	1	1	2	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	62	1	1	5	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	63	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	64	1	1	5	3	2	4	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	65	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	66	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	67	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	68	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	69	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	70	1	1	1	3	3	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	71	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	72	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	73	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	74	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	75	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	76	1	1	5	3	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	77	1	1	3	6	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	78	5	1	3	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	79	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	80	1	1	2	1	1	3	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	81	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	82	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	83	1	1	5	3	2	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	84	4	1	5	3	2	0	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	85	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	86	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	87	1	1	3	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	88	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	89	5	1	4	3	2	1	2	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	90	1	1	5	3	3	2	1	3	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	91	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	92	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	93	5	1	4	3	5	1	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	94	1	1	5	3	3	99	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	95	1	1	5	3	2	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	96	1	1	2	3	4	6	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	97	1	1	5	1	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	98	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	99	1	1	2	7	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	100	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 13:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 13)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	anio	código
13101011001	211	2017	13101
13101011002	241	2017	13101
13101011003	141	2017	13101
13101011004	114	2017	13101
13101011005	96	2017	13101
13101021001	80	2017	13101
13101021002	29	2017	13101
13101021003	190	2017	13101
13101021004	391	2017	13101
13101021005	139	2017	13101
13101021006	326	2017	13101
13101021007	99	2017	13101
13101021008	71	2017	13101
13101031001	122	2017	13101
13101031002	265	2017	13101
13101031003	348	2017	13101
13101031004	359	2017	13101
13101031005	139	2017	13101
13101031006	174	2017	13101
13101031007	307	2017	13101
13101041001	300	2017	13101
13101041002	219	2017	13101
13101041003	266	2017	13101
13101041004	153	2017	13101
13101041005	219	2017	13101
13101051001	253	2017	13101
13101051002	370	2017	13101
13101051003	165	2017	13101
13101061001	417	2017	13101
13101061002	418	2017	13101
13101061003	227	2017	13101
13101071001	390	2017	13101
13101071002	230	2017	13101
13101071003	218	2017	13101
13101081001	234	2017	13101
13101081002	433	2017	13101
13101081003	125	2017	13101
13101081004	216	2017	13101
13101091001	186	2017	13101
13101091002	394	2017	13101
13101091003	373	2017	13101
13101091004	267	2017	13101
13101101001	44	2017	13101
13101101002	68	2017	13101
13101101003	156	2017	13101
13101101004	101	2017	13101
13101101005	52	2017	13101
13101101006	42	2017	13101
13101101007	105	2017	13101
13101101008	114	2017	13101
13101101009	33	2017	13101
13101101010	149	2017	13101
13101111001	56	2017	13101
13101111002	409	2017	13101
13101111003	84	2017	13101
13101111004	93	2017	13101
13101111005	85	2017	13101
13101111006	60	2017	13101
13101111007	105	2017	13101
13101111008	94	2017	13101
13101111009	140	2017	13101
13101111010	59	2017	13101
13101111011	68	2017	13101
13101111012	147	2017	13101
13101111013	85	2017	13101
13101111014	69	2017	13101
13101111015	187	2017	13101
13101111016	74	2017	13101
13101111017	113	2017	13101
13101111018	71	2017	13101
13101111019	152	2017	13101
13101121001	123	2017	13101
13101121002	152	2017	13101
13101121003	63	2017	13101
13101121004	375	2017	13101
13101121005	165	2017	13101
13101121006	213	2017	13101
13101121007	164	2017	13101
13101121008	276	2017	13101
13101121009	320	2017	13101
13101131001	114	2017	13101
13101131002	79	2017	13101
13101131003	143	2017	13101
13101131004	158	2017	13101
13101131005	102	2017	13101
13101131006	40	2017	13101
13101131007	202	2017	13101
13101131008	205	2017	13101
13101131009	173	2017	13101
13101141001	228	2017	13101
13101141002	101	2017	13101
13101141003	196	2017	13101
13101151001	497	2017	13101
13101151002	316	2017	13101
13101151003	210	2017	13101
13101151004	175	2017	13101
13101161001	190	2017	13101
13101161002	350	2017	13101
13101161003	292	2017	13101
13101171001	365	2017	13101

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_urbano_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
01107	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
01401	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
01405	Pica	330061.1	2017	1405	9296	3068247619	Urbano
02101	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02102	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02104	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02201	Calama	416281.1	2017	2201	165731	68990679686	Urbano
02203	San Pedro de Atacama	437934.7	2017	2203	10996	4815529626	Urbano
02301	Tocopilla	271720.8	2017	2301	25186	6843559467	Urbano
02302	María Elena	466266.9	2017	2302	6457	3010685220	Urbano
03101	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
03102	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
03103	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
03201	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
03202	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
03301	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
03303	Freirina	289049.9	2017	3303	7041	2035200054	Urbano
03304	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
04101	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04102	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04103	Andacollo	251267.7	2017	4103	11044	2775000288	Urbano
04104	La Higuera	214257.0	2017	4104	4241	908664019	Urbano
04106	Vicuña	245957.4	2017	4106	27771	6830481918	Urbano
04201	Illapel	270316.5	2017	4201	30848	8338722128	Urbano
04202	Canela	233397.3	2017	4202	9093	2122281844	Urbano
04203	Los Vilos	282415.6	2017	4203	21382	6038609501	Urbano
04204	Salamanca	262056.9	2017	4204	29347	7690585032	Urbano
04301	Ovalle	274771.4	2017	4301	111272	30574361012	Urbano
04302	Combarbalá	228990.4	2017	4302	13322	3050610572	Urbano
04303	Monte Patria	225369.1	2017	4303	30751	6930326684	Urbano
04304	Punitaqui	212496.1	2017	4304	10956	2328107498	Urbano
05101	Valparaíso	297929.0	2017	5101	296655	88382118059	Urbano
05102	Casablanca	341641.8	2017	5102	26867	9178890241	Urbano
05103	Concón	318496.3	2017	5103	42152	13425257057	Urbano
05105	Puchuncaví	296035.5	2017	5105	18546	5490274928	Urbano
05107	Quintero	308224.7	2017	5107	31923	9839456903	Urbano
05109	Viña del Mar	337006.1	2017	5109	334248	112643604611	Urbano
05301	Los Andes	339720.2	2017	5301	66708	22662055502	Urbano
05302	Calle Larga	246387.3	2017	5302	14832	3654416747	Urbano
05303	Rinconada	273904.7	2017	5303	10207	2795744821	Urbano
05304	San Esteban	219571.6	2017	5304	18855	4140022481	Urbano
05401	La Ligua	250134.4	2017	5401	35390	8852256241	Urbano
05402	Cabildo	262745.9	2017	5402	19388	5094117762	Urbano
05403	Papudo	294355.2	2017	5403	6356	1870921373	Urbano
05404	Petorca	237510.8	2017	5404	9826	2333781007	Urbano
05405	Zapallar	294389.2	2017	5405	7339	2160521991	Urbano
05501	Quillota	286029.5	2017	5501	90517	25890529852	Urbano
05502	Calera	277181.9	2017	5502	50554	14012652087	Urbano
05503	Hijuelas	254094.0	2017	5503	17988	4570642363	Urbano

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
13101	13101011001	211	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101011002	241	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101011003	141	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101011004	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101011005	96	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101021001	80	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101021002	29	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101021003	190	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101021004	391	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101021005	139	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101021006	326	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101021007	99	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101021008	71	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101031001	122	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101031002	265	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101031003	348	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101031004	359	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101031005	139	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101031006	174	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101031007	307	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101041001	300	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101041002	219	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101041003	266	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101041004	153	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101041005	219	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101051001	253	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101051002	370	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101051003	165	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101061001	417	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101061002	418	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101061003	227	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101071001	390	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101071002	230	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101071003	218	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101081001	234	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101081002	433	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101081003	125	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101081004	216	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101091001	186	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101091002	394	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101091003	373	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101091004	267	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101101001	44	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101101002	68	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101101003	156	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101101004	101	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101101005	52	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101101006	42	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101101007	105	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101101008	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101101009	33	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101101010	149	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111001	56	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111002	409	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111003	84	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111004	93	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111005	85	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111006	60	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111007	105	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111008	94	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111009	140	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111010	59	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111011	68	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111012	147	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111013	85	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111014	69	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111015	187	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111016	74	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111017	113	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111018	71	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111019	152	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101121001	123	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101121002	152	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101121003	63	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101121004	375	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101121005	165	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101121006	213	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101121007	164	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101121008	276	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101121009	320	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101131001	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101131002	79	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101131003	143	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101131004	158	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101131005	102	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101131006	40	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101131007	202	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101131008	205	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101131009	173	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101141001	228	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101141002	101	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101141003	196	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101151001	497	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101151002	316	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101151003	210	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101151004	175	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101161001	190	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101161002	350	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101161003	292	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101171001	365	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
13101	13101011001	211	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101011002	241	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101011003	141	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101011004	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101011005	96	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101021001	80	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101021002	29	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101021003	190	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101021004	391	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101021005	139	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101021006	326	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101021007	99	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101021008	71	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101031001	122	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101031002	265	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101031003	348	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101031004	359	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101031005	139	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101031006	174	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101031007	307	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101041001	300	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101041002	219	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101041003	266	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101041004	153	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101041005	219	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101051001	253	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101051002	370	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101051003	165	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101061001	417	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101061002	418	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101061003	227	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101071001	390	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101071002	230	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101071003	218	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101081001	234	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101081002	433	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101081003	125	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101081004	216	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101091001	186	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101091002	394	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101091003	373	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101091004	267	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101101001	44	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101101002	68	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101101003	156	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101101004	101	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101101005	52	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101101006	42	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101101007	105	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101101008	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101101009	33	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101101010	149	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111001	56	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111002	409	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111003	84	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111004	93	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111005	85	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111006	60	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111007	105	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111008	94	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111009	140	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111010	59	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111011	68	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111012	147	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111013	85	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111014	69	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111015	187	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111016	74	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111017	113	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111018	71	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101111019	152	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101121001	123	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101121002	152	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101121003	63	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101121004	375	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101121005	165	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101121006	213	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101121007	164	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101121008	276	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101121009	320	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101131001	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101131002	79	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101131003	143	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101131004	158	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101131005	102	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101131006	40	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101131007	202	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101131008	205	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101131009	173	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101141001	228	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101141002	101	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101141003	196	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101151001	497	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101151002	316	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101151003	210	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101151004	175	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101161001	190	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101161002	350	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101161003	292	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano
13101	13101171001	365	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
13101011001	13101	211	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2174	0.0053746	13101
13101011002	13101	241	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2282	0.0056416	13101
13101011003	13101	141	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2209	0.0054611	13101
13101011004	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1821	0.0045019	13101
13101011005	13101	96	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1741	0.0043041	13101
13101021001	13101	80	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3448	0.0085242	13101
13101021002	13101	29	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2856	0.0070607	13101
13101021003	13101	190	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2768	0.0068431	13101
13101021004	13101	391	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3279	0.0081064	13101
13101021005	13101	139	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2258	0.0055823	13101
13101021006	13101	326	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2478	0.0061262	13101
13101021007	13101	99	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2326	0.0057504	13101
13101021008	13101	71	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1541	0.0038097	13101
13101031001	13101	122	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2313	0.0057182	13101
13101031002	13101	265	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5950	0.0147097	13101
13101031003	13101	348	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4955	0.0122498	13101
13101031004	13101	359	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3903	0.0096491	13101
13101031005	13101	139	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1791	0.0044277	13101
13101031006	13101	174	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1697	0.0041954	13101
13101031007	13101	307	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2973	0.0073499	13101
13101041001	13101	300	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2790	0.0068975	13101
13101041002	13101	219	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2668	0.0065959	13101
13101041003	13101	266	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2729	0.0067467	13101
13101041004	13101	153	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2828	0.0069914	13101
13101041005	13101	219	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2550	0.0063042	13101
13101051001	13101	253	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3135	0.0077504	13101
13101051002	13101	370	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4424	0.0109371	13101
13101051003	13101	165	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2355	0.0058221	13101
13101061001	13101	417	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3968	0.0098098	13101
13101061002	13101	418	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4747	0.0117356	13101
13101061003	13101	227	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2864	0.0070804	13101
13101071001	13101	390	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5009	0.0123833	13101
13101071002	13101	230	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3511	0.0086800	13101
13101071003	13101	218	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3368	0.0083264	13101
13101081001	13101	234	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3716	0.0091868	13101
13101081002	13101	433	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5244	0.0129643	13101
13101081003	13101	125	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3326	0.0082226	13101
13101081004	13101	216	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2794	0.0069074	13101
13101091001	13101	186	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3393	0.0083882	13101
13101091002	13101	394	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3321	0.0082102	13101
13101091003	13101	373	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3210	0.0079358	13101
13101091004	13101	267	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2956	0.0073079	13101
13101101001	13101	44	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2491	0.0061583	13101
13101101002	13101	68	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1820	0.0044994	13101
13101101003	13101	156	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2768	0.0068431	13101
13101101004	13101	101	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2423	0.0059902	13101
13101101005	13101	52	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1805	0.0044624	13101
13101101006	13101	42	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1296	0.0032040	13101
13101101007	13101	105	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2965	0.0073301	13101
13101101008	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1624	0.0040149	13101
13101101009	13101	33	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1345	0.0033251	13101
13101101010	13101	149	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2930	0.0072436	13101
13101111001	13101	56	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2555	0.0063165	13101
13101111002	13101	409	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2594	0.0064129	13101
13101111003	13101	84	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2269	0.0056095	13101
13101111004	13101	93	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1996	0.0049345	13101
13101111005	13101	85	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1960	0.0048455	13101
13101111006	13101	60	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1677	0.0041459	13101
13101111007	13101	105	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1859	0.0045959	13101
13101111008	13101	94	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2552	0.0063091	13101
13101111009	13101	140	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3455	0.0085415	13101
13101111010	13101	59	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2560	0.0063289	13101
13101111011	13101	68	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1550	0.0038319	13101
13101111012	13101	147	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4104	0.0101460	13101
13101111013	13101	85	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3191	0.0078888	13101
13101111014	13101	69	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1894	0.0046824	13101
13101111015	13101	187	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2747	0.0067912	13101
13101111016	13101	74	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2452	0.0060619	13101
13101111017	13101	113	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1798	0.0044450	13101
13101111018	13101	71	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2724	0.0067343	13101
13101111019	13101	152	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2430	0.0060075	13101
13101121001	13101	123	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2320	0.0057355	13101
13101121002	13101	152	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1774	0.0043857	13101
13101121003	13101	63	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2011	0.0049716	13101
13101121004	13101	375	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4425	0.0109396	13101
13101121005	13101	165	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2095	0.0051793	13101
13101121006	13101	213	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3272	0.0080891	13101
13101121007	13101	164	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3067	0.0075823	13101
13101121008	13101	276	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4682	0.0115749	13101
13101121009	13101	320	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4441	0.0109791	13101
13101131001	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1811	0.0044772	13101
13101131002	13101	79	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1759	0.0043486	13101
13101131003	13101	143	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3953	0.0097727	13101
13101131004	13101	158	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2442	0.0060372	13101
13101131005	13101	102	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3683	0.0091052	13101
13101131006	13101	40	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2197	0.0054315	13101
13101131007	13101	202	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2952	0.0072980	13101
13101131008	13101	205	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3025	0.0074785	13101
13101131009	13101	173	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3631	0.0089766	13101
13101141001	13101	228	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4373	0.0108110	13101
13101141002	13101	101	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3048	0.0075353	13101
13101141003	13101	196	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4067	0.0100545	13101
13101151001	13101	497	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4797	0.0118592	13101
13101151002	13101	316	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3915	0.0096787	13101
13101151003	13101	210	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3644	0.0090088	13101
13101151004	13101	175	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2038	0.0050384	13101
13101161001	13101	190	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2637	0.0065192	13101
13101161002	13101	350	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5341	0.0132041	13101
13101161003	13101	292	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5331	0.0131794	13101
13101171001	13101	365	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5638	0.0139384	13101

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
13101011001	13101	211	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2174	0.0053746	13101	925335221
13101011002	13101	241	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2282	0.0056416	13101	971304036
13101011003	13101	141	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2209	0.0054611	13101	940232522
13101011004	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1821	0.0045019	13101	775085298
13101011005	13101	96	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1741	0.0043041	13101	741034324
13101021001	13101	80	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3448	0.0085242	13101	1467596983
13101021002	13101	29	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2856	0.0070607	13101	1215619775
13101021003	13101	190	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2768	0.0068431	13101	1178163704
13101021004	13101	391	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3279	0.0081064	13101	1395664301
13101021005	13101	139	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2258	0.0055823	13101	961088744
13101021006	13101	326	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2478	0.0061262	13101	1054728923
13101021007	13101	99	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2326	0.0057504	13101	990032072
13101021008	13101	71	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1541	0.0038097	13101	655906888
13101031001	13101	122	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2313	0.0057182	13101	984498788
13101031002	13101	265	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5950	0.0147097	13101	2532541198
13101031003	13101	348	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4955	0.0122498	13101	2109032208
13101031004	13101	359	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3903	0.0096491	13101	1661261899
13101031005	13101	139	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1791	0.0044277	13101	762316183
13101031006	13101	174	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1697	0.0041954	13101	722306288
13101031007	13101	307	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2973	0.0073499	13101	1265419325
13101041001	13101	300	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2790	0.0068975	13101	1187527722
13101041002	13101	219	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2668	0.0065959	13101	1135599986
13101041003	13101	266	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2729	0.0067467	13101	1161563854
13101041004	13101	153	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2828	0.0069914	13101	1203701934
13101041005	13101	219	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2550	0.0063042	13101	1085374799
13101051001	13101	253	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3135	0.0077504	13101	1334372547
13101051002	13101	370	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4424	0.0109371	13101	1883018867
13101051003	13101	165	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2355	0.0058221	13101	1002375550
13101061001	13101	417	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3968	0.0098098	13101	1688928315
13101061002	13101	418	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4747	0.0117356	13101	2020499675
13101061003	13101	227	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2864	0.0070804	13101	1219024873
13101071001	13101	390	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5009	0.0123833	13101	2132016615
13101071002	13101	230	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3511	0.0086800	13101	1494412126
13101071003	13101	218	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3368	0.0083264	13101	1433546009
13101081001	13101	234	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3716	0.0091868	13101	1581667747
13101081002	13101	433	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5244	0.0129643	13101	2232041352
13101081003	13101	125	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3326	0.0082226	13101	1415669248
13101081004	13101	216	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2794	0.0069074	13101	1189230270
13101091001	13101	186	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3393	0.0083882	13101	1444186939
13101091002	13101	394	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3321	0.0082102	13101	1413541062
13101091003	13101	373	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3210	0.0079358	13101	1366295336
13101091004	13101	267	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2956	0.0073079	13101	1258183493
13101101001	13101	44	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2491	0.0061583	13101	1060262206
13101101002	13101	68	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1820	0.0044994	13101	774659661
13101101003	13101	156	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2768	0.0068431	13101	1178163704
13101101004	13101	101	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2423	0.0059902	13101	1031318878
13101101005	13101	52	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1805	0.0044624	13101	768275103
13101101006	13101	42	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1296	0.0032040	13101	551625780
13101101007	13101	105	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2965	0.0073301	13101	1262014227
13101101008	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1624	0.0040149	13101	691234774
13101101009	13101	33	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1345	0.0033251	13101	572482002
13101101010	13101	149	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2930	0.0072436	13101	1247116926
13101111001	13101	56	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2555	0.0063165	13101	1087502985
13101111002	13101	409	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2594	0.0064129	13101	1104102835
13101111003	13101	84	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2269	0.0056095	13101	965770753
13101111004	13101	93	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1996	0.0049345	13101	849571804
13101111005	13101	85	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1960	0.0048455	13101	834248865
13101111006	13101	60	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1677	0.0041459	13101	713793544
13101111007	13101	105	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1859	0.0045959	13101	791259511
13101111008	13101	94	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2552	0.0063091	13101	1086226074
13101111009	13101	140	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3455	0.0085415	13101	1470576444
13101111010	13101	59	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2560	0.0063289	13101	1089631171
13101111011	13101	68	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1550	0.0038319	13101	659737623
13101111012	13101	147	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4104	0.0101460	13101	1746814971
13101111013	13101	85	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3191	0.0078888	13101	1358208229
13101111014	13101	69	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1894	0.0046824	13101	806156812
13101111015	13101	187	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2747	0.0067912	13101	1169225323
13101111016	13101	74	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2452	0.0060619	13101	1043662356
13101111017	13101	113	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1798	0.0044450	13101	765295643
13101111018	13101	71	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2724	0.0067343	13101	1159435668
13101111019	13101	152	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2430	0.0060075	13101	1034298338
13101121001	13101	123	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2320	0.0057355	13101	987478249
13101121002	13101	152	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1774	0.0043857	13101	755080351
13101121003	13101	63	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2011	0.0049716	13101	855956361
13101121004	13101	375	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4425	0.0109396	13101	1883444505
13101121005	13101	165	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2095	0.0051793	13101	891709884
13101121006	13101	213	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3272	0.0080891	13101	1392684840
13101121007	13101	164	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3067	0.0075823	13101	1305429219
13101121008	13101	276	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4682	0.0115749	13101	1992833259
13101121009	13101	320	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4441	0.0109791	13101	1890254699
13101131001	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1811	0.0044772	13101	770828926
13101131002	13101	79	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1759	0.0043486	13101	748695793
13101131003	13101	143	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3953	0.0097727	13101	1682543757
13101131004	13101	158	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2442	0.0060372	13101	1039405984
13101131005	13101	102	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3683	0.0091052	13101	1567621720
13101131006	13101	40	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2197	0.0054315	13101	935124876
13101131007	13101	202	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2952	0.0072980	13101	1256480944
13101131008	13101	205	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3025	0.0074785	13101	1287552458
13101131009	13101	173	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3631	0.0089766	13101	1545488587
13101141001	13101	228	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4373	0.0108110	13101	1861311371
13101141002	13101	101	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3048	0.0075353	13101	1297342113
13101141003	13101	196	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4067	0.0100545	13101	1731066396
13101151001	13101	497	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4797	0.0118592	13101	2041781534
13101151002	13101	316	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3915	0.0096787	13101	1666369545
13101151003	13101	210	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3644	0.0090088	13101	1551021870
13101151004	13101	175	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2038	0.0050384	13101	867448565
13101161001	13101	190	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2637	0.0065192	13101	1122405234
13101161002	13101	350	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5341	0.0132041	13101	2273328158
13101161003	13101	292	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5331	0.0131794	13101	2269071786
13101171001	13101	365	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5638	0.0139384	13101	2399742399

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -958384948 -276686343   -9027691  269198148 3659449209 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 960293587   13614074   70.54   <2e-16 ***
## Freq.x         820227      33052   24.82   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 428600000 on 1908 degrees of freedom
## Multiple R-squared:  0.244,  Adjusted R-squared:  0.2436 
## F-statistic: 615.8 on 1 and 1908 DF,  p-value: < 2.2e-16

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.243610380425369"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.243610380425369"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.439212531657019"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.344254845359136"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.347062796142984"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.277545761636209"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.544139781572026"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.587424388061825"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 1    cuadrático 0.243610380425369
## 2        cúbico 0.243610380425369
## 6      log-raíz 0.277545761636209
## 4 raíz cuadrada 0.344254845359136
## 5     raíz-raíz 0.347062796142984
## 3   logarítmico 0.439212531657019
## 7      raíz-log 0.544139781572026
## 8       log-log 0.587424388061825
##                                                                     sintaxis
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 8
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1719 -0.2381  0.0675  0.3132  1.9973 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.99128    0.05393  333.63   <2e-16 ***
## log(Freq.x)  0.53036    0.01017   52.14   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.48 on 1908 degrees of freedom
## Multiple R-squared:  0.5876, Adjusted R-squared:  0.5874 
## F-statistic:  2719 on 1 and 1908 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    17.99128

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##   0.5303622

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.5874 ).

9.1 Diagrama de dispersión sobre log-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1719 -0.2381  0.0675  0.3132  1.9973 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.99128    0.05393  333.63   <2e-16 ***
## log(Freq.x)  0.53036    0.01017   52.14   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.48 on 1908 degrees of freedom
## Multiple R-squared:  0.5876, Adjusted R-squared:  0.5874 
## F-statistic:  2719 on 1 and 1908 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = e^{17.361982+0.641075 \cdot ln{X}} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
13101011001	13101	211	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2174	0.0053746	13101	925335221	1112305500
13101011002	13101	241	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2282	0.0056416	13101	971304036	1193560190
13101011003	13101	141	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2209	0.0054611	13101	940232522	898208403
13101011004	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1821	0.0045019	13101	775085298	802448414
13101011005	13101	96	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1741	0.0043041	13101	741034324	732544947
13101021001	13101	80	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3448	0.0085242	13101	1467596983	665027402
13101021002	13101	29	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2856	0.0070607	13101	1215619775	388251541
13101021003	13101	190	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2768	0.0068431	13101	1178163704	1052149138
13101021004	13101	391	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3279	0.0081064	13101	1395664301	1542783849
13101021005	13101	139	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2258	0.0055823	13101	961088744	891428636
13101021006	13101	326	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2478	0.0061262	13101	1054728923	1400967740
13101021007	13101	99	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2326	0.0057504	13101	990032072	744598258
13101021008	13101	71	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1541	0.0038097	13101	655906888	624237711
13101031001	13101	122	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2313	0.0057182	13101	984498788	831838332
13101031002	13101	265	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5950	0.0147097	13101	2532541198	1255193081
13101031003	13101	348	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4955	0.0122498	13101	2109032208	1450340843
13101031004	13101	359	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3903	0.0096491	13101	1661261899	1474477095
13101031005	13101	139	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1791	0.0044277	13101	762316183	891428636
13101031006	13101	174	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1697	0.0041954	13101	722306288	1004188309
13101031007	13101	307	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2973	0.0073499	13101	1265419325	1357052662
13101041001	13101	300	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2790	0.0068975	13101	1187527722	1340553033
13101041002	13101	219	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2668	0.0065959	13101	1135599986	1134476796
13101041003	13101	266	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2729	0.0067467	13101	1161563854	1257702961
13101041004	13101	153	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2828	0.0069914	13101	1203701934	937972895
13101041005	13101	219	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2550	0.0063042	13101	1085374799	1134476796
13101051001	13101	253	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3135	0.0077504	13101	1334372547	1224720003
13101051002	13101	370	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4424	0.0109371	13101	1883018867	1498268446
13101051003	13101	165	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2355	0.0058221	13101	1002375550	976297544
13101061001	13101	417	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3968	0.0098098	13101	1688928315	1596370291
13101061002	13101	418	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4747	0.0117356	13101	2020499675	1598399495
13101061003	13101	227	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2864	0.0070804	13101	1219024873	1156270868
13101071001	13101	390	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5009	0.0123833	13101	2132016615	1540689920
13101071002	13101	230	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3511	0.0086800	13101	1494412126	1164350402
13101071003	13101	218	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3368	0.0083264	13101	1433546009	1131726430
13101081001	13101	234	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3716	0.0091868	13101	1581667747	1175046520
13101081002	13101	433	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5244	0.0129643	13101	2232041352	1628568516
13101081003	13101	125	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3326	0.0082226	13101	1415669248	842625013
13101081004	13101	216	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2794	0.0069074	13101	1189230270	1126207863
13101091001	13101	186	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3393	0.0083882	13101	1444186939	1040342664
13101091002	13101	394	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3321	0.0082102	13101	1413541062	1549050591
13101091003	13101	373	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3210	0.0079358	13101	1366295336	1504699132
13101091004	13101	267	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2956	0.0073079	13101	1258183493	1260208413
13101101001	13101	44	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2491	0.0061583	13101	1060262206	484325948
13101101002	13101	68	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1820	0.0044994	13101	774659661	610107003
13101101003	13101	156	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2768	0.0068431	13101	1178163704	947682632
13101101004	13101	101	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2423	0.0059902	13101	1031318878	752538697
13101101005	13101	52	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1805	0.0044624	13101	768275103	529195163
13101101006	13101	42	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1296	0.0032040	13101	551625780	472522645
13101101007	13101	105	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2965	0.0073301	13101	1262014227	768201138
13101101008	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1624	0.0040149	13101	691234774	802448414
13101101009	13101	33	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1345	0.0033251	13101	572482002	415790881
13101101010	13101	149	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2930	0.0072436	13101	1247116926	924886384
13101111001	13101	56	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2555	0.0063165	13101	1087502985	550408844
13101111002	13101	409	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2594	0.0064129	13101	1104102835	1580053653
13101111003	13101	84	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2269	0.0056095	13101	965770753	682460536
13101111004	13101	93	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1996	0.0049345	13101	849571804	720313397
13101111005	13101	85	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1960	0.0048455	13101	834248865	686757503
13101111006	13101	60	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1677	0.0041459	13101	713793544	570921980
13101111007	13101	105	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1859	0.0045959	13101	791259511	768201138
13101111008	13101	94	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2552	0.0063091	13101	1086226074	724410897
13101111009	13101	140	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3455	0.0085415	13101	1470576444	894824205
13101111010	13101	59	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2560	0.0063289	13101	1089631171	565855476
13101111011	13101	68	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1550	0.0038319	13101	659737623	610107003
13101111012	13101	147	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4104	0.0101460	13101	1746814971	918281269
13101111013	13101	85	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3191	0.0078888	13101	1358208229	686757503
13101111014	13101	69	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1894	0.0046824	13101	806156812	614849183
13101111015	13101	187	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2747	0.0067912	13101	1169225323	1043305372
13101111016	13101	74	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2452	0.0060619	13101	1043662356	638090674
13101111017	13101	113	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1798	0.0044450	13101	765295643	798707461
13101111018	13101	71	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2724	0.0067343	13101	1159435668	624237711
13101111019	13101	152	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2430	0.0060075	13101	1034298338	934716481
13101121001	13101	123	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2320	0.0057355	13101	987478249	835447593
13101121002	13101	152	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1774	0.0043857	13101	755080351	934716481
13101121003	13101	63	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2011	0.0049716	13101	855956361	585888219
13101121004	13101	375	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4425	0.0109396	13101	1883444505	1508972769
13101121005	13101	165	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2095	0.0051793	13101	891709884	976297544
13101121006	13101	213	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3272	0.0080891	13101	1392684840	1117884816
13101121007	13101	164	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3067	0.0075823	13101	1305429219	973154935
13101121008	13101	276	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4682	0.0115749	13101	1992833259	1282562193
13101121009	13101	320	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4441	0.0109791	13101	1890254699	1387232880
13101131001	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1811	0.0044772	13101	770828926	802448414
13101131002	13101	79	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1759	0.0043486	13101	748695793	660605564
13101131003	13101	143	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3953	0.0097727	13101	1682543757	904943154
13101131004	13101	158	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2442	0.0060372	13101	1039405984	954107136
13101131005	13101	102	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3683	0.0091052	13101	1567621720	756481217
13101131006	13101	40	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2197	0.0054315	13101	935124876	460452276
13101131007	13101	202	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2952	0.0072980	13101	1256480944	1086885396
13101131008	13101	205	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3025	0.0074785	13101	1287552458	1095416788
13101131009	13101	173	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3631	0.0089766	13101	1545488587	1001123342
13101141001	13101	228	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4373	0.0108110	13101	1861311371	1158969587
13101141002	13101	101	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3048	0.0075353	13101	1297342113	752538697
13101141003	13101	196	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4067	0.0100545	13101	1731066396	1069642137
13101151001	13101	497	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4797	0.0118592	13101	2041781534	1752096480
13101151002	13101	316	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3915	0.0096787	13101	1666369545	1378009021
13101151003	13101	210	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3644	0.0090088	13101	1551021870	1109506530
13101151004	13101	175	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2038	0.0050384	13101	867448565	1007245014
13101161001	13101	190	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2637	0.0065192	13101	1122405234	1052149138
13101161002	13101	350	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5341	0.0132041	13101	2273328158	1454755618
13101161003	13101	292	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5331	0.0131794	13101	2269071786	1321473306
13101171001	13101	365	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5638	0.0139384	13101	2399742399	1487495970

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)


r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
13101011001	13101	211	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2174	0.0053746	13101	925335221	1112305500	511640.1
13101011002	13101	241	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2282	0.0056416	13101	971304036	1193560190	523032.5
13101011003	13101	141	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2209	0.0054611	13101	940232522	898208403	406613.1
13101011004	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1821	0.0045019	13101	775085298	802448414	440663.6
13101011005	13101	96	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1741	0.0043041	13101	741034324	732544947	420761.0
13101021001	13101	80	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3448	0.0085242	13101	1467596983	665027402	192873.4
13101021002	13101	29	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2856	0.0070607	13101	1215619775	388251541	135942.4
13101021003	13101	190	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2768	0.0068431	13101	1178163704	1052149138	380111.7
13101021004	13101	391	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3279	0.0081064	13101	1395664301	1542783849	470504.4
13101021005	13101	139	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2258	0.0055823	13101	961088744	891428636	394786.8
13101021006	13101	326	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2478	0.0061262	13101	1054728923	1400967740	565362.3
13101021007	13101	99	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2326	0.0057504	13101	990032072	744598258	320119.6
13101021008	13101	71	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1541	0.0038097	13101	655906888	624237711	405086.1
13101031001	13101	122	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2313	0.0057182	13101	984498788	831838332	359636.1
13101031002	13101	265	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5950	0.0147097	13101	2532541198	1255193081	210956.8
13101031003	13101	348	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4955	0.0122498	13101	2109032208	1450340843	292702.5
13101031004	13101	359	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3903	0.0096491	13101	1661261899	1474477095	377780.4
13101031005	13101	139	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1791	0.0044277	13101	762316183	891428636	497726.8
13101031006	13101	174	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1697	0.0041954	13101	722306288	1004188309	591743.3
13101031007	13101	307	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2973	0.0073499	13101	1265419325	1357052662	456459.0
13101041001	13101	300	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2790	0.0068975	13101	1187527722	1340553033	480485.0
13101041002	13101	219	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2668	0.0065959	13101	1135599986	1134476796	425216.2
13101041003	13101	266	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2729	0.0067467	13101	1161563854	1257702961	460865.9
13101041004	13101	153	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2828	0.0069914	13101	1203701934	937972895	331673.6
13101041005	13101	219	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2550	0.0063042	13101	1085374799	1134476796	444892.9
13101051001	13101	253	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3135	0.0077504	13101	1334372547	1224720003	390660.3
13101051002	13101	370	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4424	0.0109371	13101	1883018867	1498268446	338668.3
13101051003	13101	165	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2355	0.0058221	13101	1002375550	976297544	414563.7
13101061001	13101	417	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3968	0.0098098	13101	1688928315	1596370291	402311.1
13101061002	13101	418	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4747	0.0117356	13101	2020499675	1598399495	336717.8
13101061003	13101	227	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2864	0.0070804	13101	1219024873	1156270868	403725.9
13101071001	13101	390	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5009	0.0123833	13101	2132016615	1540689920	307584.3
13101071002	13101	230	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3511	0.0086800	13101	1494412126	1164350402	331629.3
13101071003	13101	218	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3368	0.0083264	13101	1433546009	1131726430	336023.3
13101081001	13101	234	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3716	0.0091868	13101	1581667747	1175046520	316212.7
13101081002	13101	433	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5244	0.0129643	13101	2232041352	1628568516	310558.5
13101081003	13101	125	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3326	0.0082226	13101	1415669248	842625013	253344.9
13101081004	13101	216	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2794	0.0069074	13101	1189230270	1126207863	403080.8
13101091001	13101	186	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3393	0.0083882	13101	1444186939	1040342664	306614.4
13101091002	13101	394	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3321	0.0082102	13101	1413541062	1549050591	466441.0
13101091003	13101	373	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3210	0.0079358	13101	1366295336	1504699132	468753.6
13101091004	13101	267	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2956	0.0073079	13101	1258183493	1260208413	426322.2
13101101001	13101	44	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2491	0.0061583	13101	1060262206	484325948	194430.3
13101101002	13101	68	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1820	0.0044994	13101	774659661	610107003	335223.6
13101101003	13101	156	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2768	0.0068431	13101	1178163704	947682632	342370.9
13101101004	13101	101	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2423	0.0059902	13101	1031318878	752538697	310581.4
13101101005	13101	52	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1805	0.0044624	13101	768275103	529195163	293182.9
13101101006	13101	42	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1296	0.0032040	13101	551625780	472522645	364600.8
13101101007	13101	105	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2965	0.0073301	13101	1262014227	768201138	259089.8
13101101008	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1624	0.0040149	13101	691234774	802448414	494118.5
13101101009	13101	33	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1345	0.0033251	13101	572482002	415790881	309138.2
13101101010	13101	149	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2930	0.0072436	13101	1247116926	924886384	315660.9
13101111001	13101	56	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2555	0.0063165	13101	1087502985	550408844	215424.2
13101111002	13101	409	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2594	0.0064129	13101	1104102835	1580053653	609118.6
13101111003	13101	84	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2269	0.0056095	13101	965770753	682460536	300775.9
13101111004	13101	93	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1996	0.0049345	13101	849571804	720313397	360878.5
13101111005	13101	85	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1960	0.0048455	13101	834248865	686757503	350386.5
13101111006	13101	60	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1677	0.0041459	13101	713793544	570921980	340442.4
13101111007	13101	105	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1859	0.0045959	13101	791259511	768201138	413233.5
13101111008	13101	94	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2552	0.0063091	13101	1086226074	724410897	283860.1
13101111009	13101	140	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3455	0.0085415	13101	1470576444	894824205	258994.0
13101111010	13101	59	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2560	0.0063289	13101	1089631171	565855476	221037.3
13101111011	13101	68	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1550	0.0038319	13101	659737623	610107003	393617.4
13101111012	13101	147	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4104	0.0101460	13101	1746814971	918281269	223752.7
13101111013	13101	85	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3191	0.0078888	13101	1358208229	686757503	215217.0
13101111014	13101	69	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1894	0.0046824	13101	806156812	614849183	324630.0
13101111015	13101	187	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2747	0.0067912	13101	1169225323	1043305372	379798.1
13101111016	13101	74	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2452	0.0060619	13101	1043662356	638090674	260232.7
13101111017	13101	113	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1798	0.0044450	13101	765295643	798707461	444219.9
13101111018	13101	71	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2724	0.0067343	13101	1159435668	624237711	229162.2
13101111019	13101	152	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2430	0.0060075	13101	1034298338	934716481	384657.0
13101121001	13101	123	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2320	0.0057355	13101	987478249	835447593	360106.7
13101121002	13101	152	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1774	0.0043857	13101	755080351	934716481	526897.7
13101121003	13101	63	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2011	0.0049716	13101	855956361	585888219	291341.7
13101121004	13101	375	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4425	0.0109396	13101	1883444505	1508972769	341010.8
13101121005	13101	165	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2095	0.0051793	13101	891709884	976297544	466013.1
13101121006	13101	213	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3272	0.0080891	13101	1392684840	1117884816	341651.8
13101121007	13101	164	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3067	0.0075823	13101	1305429219	973154935	317298.6
13101121008	13101	276	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4682	0.0115749	13101	1992833259	1282562193	273934.7
13101121009	13101	320	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4441	0.0109791	13101	1890254699	1387232880	312369.5
13101131001	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1811	0.0044772	13101	770828926	802448414	443096.9
13101131002	13101	79	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	1759	0.0043486	13101	748695793	660605564	375557.5
13101131003	13101	143	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3953	0.0097727	13101	1682543757	904943154	228925.7
13101131004	13101	158	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2442	0.0060372	13101	1039405984	954107136	390707.3
13101131005	13101	102	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3683	0.0091052	13101	1567621720	756481217	205398.1
13101131006	13101	40	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2197	0.0054315	13101	935124876	460452276	209582.3
13101131007	13101	202	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2952	0.0072980	13101	1256480944	1086885396	368186.1
13101131008	13101	205	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3025	0.0074785	13101	1287552458	1095416788	362121.3
13101131009	13101	173	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3631	0.0089766	13101	1545488587	1001123342	275715.6
13101141001	13101	228	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4373	0.0108110	13101	1861311371	1158969587	265028.5
13101141002	13101	101	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3048	0.0075353	13101	1297342113	752538697	246895.9
13101141003	13101	196	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4067	0.0100545	13101	1731066396	1069642137	263005.2
13101151001	13101	497	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	4797	0.0118592	13101	2041781534	1752096480	365248.4
13101151002	13101	316	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3915	0.0096787	13101	1666369545	1378009021	351981.9
13101151003	13101	210	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	3644	0.0090088	13101	1551021870	1109506530	304474.9
13101151004	13101	175	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2038	0.0050384	13101	867448565	1007245014	494232.1
13101161001	13101	190	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	2637	0.0065192	13101	1122405234	1052149138	398994.7
13101161002	13101	350	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5341	0.0132041	13101	2273328158	1454755618	272375.1
13101161003	13101	292	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5331	0.0131794	13101	2269071786	1321473306	247884.7
13101171001	13101	365	2017	Santiago	425637.2	2017	13101	404495	172168109577	Urbano	5638	0.0139384	13101	2399742399	1487495970	263834.0

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_urbano_nivel_nacional_17_r13.rds")

---

Rural

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 2)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15202	1	2	6	13225	1	3	1	5	3	5	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	3	1	1	5	3	5	2	3	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	4	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	5	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	6	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	8	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	9	3	1	5	3	5	1	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	10	1	1	5	3	4	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	11	1	2	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	12	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	13	1	1	5	3	4	1	4	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	14	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	16	1	1	5	3	5	1	3	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	17	1	1	5	3	5	2	4	1	8	15	152	15202	15202012006
15	152	15202	1	2	6	13225	18	3	1	5	3	5	1	1	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	19	3	1	5	3	5	1	3	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	20	1	1	5	3	5	2	1	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	21	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	22	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	24	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	25	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	26	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	27	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	28	3	1	5	3	5	1	4	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	29	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	30	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	31	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	32	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	33	1	1	5	3	5	3	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	34	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	35	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	36	1	1	5	3	5	3	2	1	9	15	152	15202	15202012006
15	152	15202	1	2	6	13225	37	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	38	1	1	5	3	5	99	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	39	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	40	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	41	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	42	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	43	3	1	5	3	5	2	1	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	44	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	45	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	8	13910	1	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	3	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	4	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	5	3	1	5	3	5	2	3	1	3	15	152	15202	15202012008
15	152	15202	1	2	8	13910	6	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	7	1	1	5	99	5	2	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	8	3	1	5	3	5	3	3	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	9	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	10	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	11	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	12	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	13	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	14	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	16	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	18	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	20	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	21	1	1	5	3	5	1	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	22	3	1	5	3	5	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	23	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	24	3	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	25	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	26	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	27	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	28	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	29	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	30	1	1	5	1	4	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	12	8394	1	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	2	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	3	3	1	5	3	5	2	3	1	4	15	152	15202	15202012012
15	152	15202	1	2	12	8394	4	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	5	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	6	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	8	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	9	3	1	5	3	5	1	4	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	10	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	11	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	12	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	13	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	14	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	15	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	16	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	18	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	20	3	1	5	3	5	3	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	21	3	1	5	99	5	1	4	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	22	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	24	3	1	5	3	5	1	2	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	25	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 13:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 13)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Veamos los primeros 100 registros:

r3_100 <- d[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	unlist.c.	unlist.d.	Freq	anio
1	13107032901	1	13107	2	2017
471	13110072002	1	13110	15	2017
941	13115012005	1	13115	1	2017
942	13115022001	1	13115	16	2017
943	13115022901	1	13115	11	2017
944	13115032002	1	13115	12	2017
945	13115032003	1	13115	4	2017
946	13115032007	1	13115	15	2017
947	13115032008	1	13115	14	2017
948	13115032901	1	13115	3	2017
1418	13119072006	1	13119	30	2017
1419	13119132006	1	13119	14	2017
1420	13119132901	1	13119	1	2017
1421	13119142001	1	13119	8	2017
1422	13119142004	1	13119	28	2017
1892	13124062901	1	13124	1	2017
1893	13124072012	1	13124	3	2017
1894	13124072016	1	13124	13	2017
1895	13124072019	1	13124	102	2017
1896	13124082009	1	13124	2	2017
1897	13124082010	1	13124	27	2017
1898	13124082011	1	13124	10	2017
1899	13124082017	1	13124	2	2017
1900	13124082020	1	13124	7	2017
2370	13125022002	1	13125	3	2017
2840	13202012001	1	13202	306	2017
2841	13202012002	1	13202	132	2017
2842	13202012005	1	13202	110	2017
2843	13202012009	1	13202	115	2017
2844	13202022004	1	13202	60	2017
2845	13202022006	1	13202	45	2017
2846	13202022007	1	13202	31	2017
2847	13202022010	1	13202	49	2017
2848	13202022011	1	13202	3	2017
2849	13202022012	1	13202	100	2017
2850	13202022014	1	13202	177	2017
2851	13202032003	1	13202	308	2017
2852	13202032008	1	13202	258	2017
2853	13202032013	1	13202	178	2017
3323	13203012004	1	13203	11	2017
3324	13203012005	1	13203	34	2017
3325	13203012012	1	13203	2	2017
3326	13203012019	1	13203	6	2017
3327	13203012901	1	13203	4	2017
3328	13203022004	1	13203	2	2017
3329	13203032017	1	13203	3	2017
3330	13203032018	1	13203	18	2017
3331	13203042001	1	13203	4	2017
3332	13203042008	1	13203	1	2017
3333	13203042010	1	13203	9	2017
3334	13203052006	1	13203	26	2017
3335	13203052009	1	13203	11	2017
3336	13203052017	1	13203	2	2017
3337	13203062003	1	13203	46	2017
3338	13203062007	1	13203	193	2017
3808	13301012005	1	13301	165	2017
3809	13301012010	1	13301	1	2017
3810	13301012012	1	13301	302	2017
3811	13301012015	1	13301	42	2017
3812	13301012018	1	13301	155	2017
3813	13301012025	1	13301	41	2017
3814	13301012026	1	13301	25	2017
3815	13301012029	1	13301	7	2017
3816	13301022004	1	13301	682	2017
3817	13301022006	1	13301	150	2017
3818	13301022008	1	13301	29	2017
3819	13301032001	1	13301	35	2017
3820	13301032007	1	13301	343	2017
3821	13301032013	1	13301	41	2017
3822	13301032014	1	13301	23	2017
3823	13301032018	1	13301	156	2017
3824	13301032019	1	13301	6	2017
3825	13301032020	1	13301	147	2017
3826	13301032022	1	13301	56	2017
3827	13301032024	1	13301	466	2017
3828	13301032028	1	13301	146	2017
3829	13301042021	1	13301	13	2017
3830	13301052002	1	13301	66	2017
3831	13301052009	1	13301	14	2017
3832	13301052023	1	13301	54	2017
3833	13301052901	1	13301	2	2017
3834	13301062004	1	13301	189	2017
3835	13301062005	1	13301	27	2017
3836	13301062016	1	13301	234	2017
3837	13301062027	1	13301	26	2017
4307	13302012003	1	13302	86	2017
4308	13302012006	1	13302	20	2017
4309	13302012009	1	13302	52	2017
4310	13302012014	1	13302	33	2017
4311	13302012017	1	13302	17	2017
4312	13302012018	1	13302	4	2017
4313	13302012019	1	13302	17	2017
4314	13302012020	1	13302	20	2017
4315	13302012021	1	13302	7	2017
4316	13302012028	1	13302	42	2017
4317	13302022002	1	13302	10	2017
4318	13302022005	1	13302	49	2017
4319	13302022007	1	13302	2	2017
4320	13302022017	1	13302	87	2017
4321	13302032001	1	13302	137	2017

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	13107032901	2	2017	13107
471	13110072002	15	2017	13110
941	13115012005	1	2017	13115
942	13115022001	16	2017	13115
943	13115022901	11	2017	13115
944	13115032002	12	2017	13115
945	13115032003	4	2017	13115
946	13115032007	15	2017	13115
947	13115032008	14	2017	13115
948	13115032901	3	2017	13115
1418	13119072006	30	2017	13119
1419	13119132006	14	2017	13119
1420	13119132901	1	2017	13119
1421	13119142001	8	2017	13119
1422	13119142004	28	2017	13119
1892	13124062901	1	2017	13124
1893	13124072012	3	2017	13124
1894	13124072016	13	2017	13124
1895	13124072019	102	2017	13124
1896	13124082009	2	2017	13124
1897	13124082010	27	2017	13124
1898	13124082011	10	2017	13124
1899	13124082017	2	2017	13124
1900	13124082020	7	2017	13124
2370	13125022002	3	2017	13125
2840	13202012001	306	2017	13202
2841	13202012002	132	2017	13202
2842	13202012005	110	2017	13202
2843	13202012009	115	2017	13202
2844	13202022004	60	2017	13202
2845	13202022006	45	2017	13202
2846	13202022007	31	2017	13202
2847	13202022010	49	2017	13202
2848	13202022011	3	2017	13202
2849	13202022012	100	2017	13202
2850	13202022014	177	2017	13202
2851	13202032003	308	2017	13202
2852	13202032008	258	2017	13202
2853	13202032013	178	2017	13202
3323	13203012004	11	2017	13203
3324	13203012005	34	2017	13203
3325	13203012012	2	2017	13203
3326	13203012019	6	2017	13203
3327	13203012901	4	2017	13203
3328	13203022004	2	2017	13203
3329	13203032017	3	2017	13203
3330	13203032018	18	2017	13203
3331	13203042001	4	2017	13203
3332	13203042008	1	2017	13203
3333	13203042010	9	2017	13203
3334	13203052006	26	2017	13203
3335	13203052009	11	2017	13203
3336	13203052017	2	2017	13203
3337	13203062003	46	2017	13203
3338	13203062007	193	2017	13203
3808	13301012005	165	2017	13301
3809	13301012010	1	2017	13301
3810	13301012012	302	2017	13301
3811	13301012015	42	2017	13301
3812	13301012018	155	2017	13301
3813	13301012025	41	2017	13301
3814	13301012026	25	2017	13301
3815	13301012029	7	2017	13301
3816	13301022004	682	2017	13301
3817	13301022006	150	2017	13301
3818	13301022008	29	2017	13301
3819	13301032001	35	2017	13301
3820	13301032007	343	2017	13301
3821	13301032013	41	2017	13301
3822	13301032014	23	2017	13301
3823	13301032018	156	2017	13301
3824	13301032019	6	2017	13301
3825	13301032020	147	2017	13301
3826	13301032022	56	2017	13301
3827	13301032024	466	2017	13301
3828	13301032028	146	2017	13301
3829	13301042021	13	2017	13301
3830	13301052002	66	2017	13301
3831	13301052009	14	2017	13301
3832	13301052023	54	2017	13301
3833	13301052901	2	2017	13301
3834	13301062004	189	2017	13301
3835	13301062005	27	2017	13301
3836	13301062016	234	2017	13301
3837	13301062027	26	2017	13301
4307	13302012003	86	2017	13302
4308	13302012006	20	2017	13302
4309	13302012009	52	2017	13302
4310	13302012014	33	2017	13302
4311	13302012017	17	2017	13302
4312	13302012018	4	2017	13302
4313	13302012019	17	2017	13302
4314	13302012020	20	2017	13302
4315	13302012021	7	2017	13302
4316	13302012028	42	2017	13302
4317	13302022002	10	2017	13302
4318	13302022005	49	2017	13302
4319	13302022007	2	2017	13302
4320	13302022017	87	2017	13302
4321	13302032001	137	2017	13302

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_rural_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	289375.3	2017	1101	191468	55406102543	Rural
01401	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
01402	Camiña	262850.3	2017	1402	1250	328562901	Rural
01404	Huara	253968.5	2017	1404	2730	693334131	Rural
01405	Pica	290496.7	2017	1405	9296	2700457509	Rural
02103	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
02104	Taltal	345494.0	2017	2104	13317	4600943086	Rural
02201	Calama	310025.0	2017	2201	165731	51380756402	Rural
02203	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
02301	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
03101	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03103	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03202	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural
03301	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03302	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03303	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03304	Huasco	227560.7	2017	3304	10149	2309513927	Rural
04101	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04102	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04103	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04104	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04105	Paiguano	205942.1	2017	4105	4497	926121774	Rural
04106	Vicuña	176130.6	2017	4106	27771	4891322768	Rural
04201	Illapel	191976.8	2017	4201	30848	5922099530	Rural
04202	Canela	171370.3	2017	4202	9093	1558270441	Rural
04203	Los Vilos	173238.5	2017	4203	21382	3704185607	Rural
04204	Salamanca	223234.2	2017	4204	29347	6551254640	Rural
04301	Ovalle	241393.7	2017	4301	111272	26860360045	Rural
04302	Combarbalá	179139.6	2017	4302	13322	2386498044	Rural
04303	Monte Patria	201205.8	2017	4303	30751	6187280931	Rural
04304	Punitaqui	171931.7	2017	4304	10956	1883683880	Rural
04305	Río Hurtado	182027.2	2017	4305	4278	778712384	Rural
05101	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05102	Casablanca	268917.1	2017	5102	26867	7224996933	Rural
05105	Puchuncaví	279614.4	2017	5105	18546	5185728335	Rural
05107	Quintero	334628.2	2017	5107	31923	10682335196	Rural
05301	Los Andes	324402.1	2017	5301	66708	21640215030	Rural
05302	Calle Larga	242743.8	2017	5302	14832	3600375502	Rural
05303	Rinconada	326532.5	2017	5303	10207	3332917471	Rural
05304	San Esteban	223168.6	2017	5304	18855	4207844130	Rural
05401	La Ligua	181468.0	2017	5401	35390	6422154059	Rural
05402	Cabildo	231277.8	2017	5402	19388	4484014285	Rural
05404	Petorca	298208.9	2017	5404	9826	2930200178	Rural
05405	Zapallar	292882.3	2017	5405	7339	2149463129	Rural
05501	Quillota	220926.8	2017	5501	90517	19997628209	Rural
05502	Calera	226906.2	2017	5502	50554	11471016698	Rural
05503	Hijuelas	253739.9	2017	5503	17988	4564273201	Rural
05504	La Cruz	291124.1	2017	5504	22098	6433259569	Rural
05506	Nogales	264475.3	2017	5506	22120	5850194593	Rural
05601	San Antonio	266331.2	2017	5601	91350	24329353815	Rural

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
13107	13107032901	2	2017	NA	NA	NA	NA	NA	NA	NA
13110	13110072002	15	2017	NA	NA	NA	NA	NA	NA	NA
13115	13115012005	1	2017	NA	NA	NA	NA	NA	NA	NA
13115	13115022001	16	2017	NA	NA	NA	NA	NA	NA	NA
13115	13115022901	11	2017	NA	NA	NA	NA	NA	NA	NA
13115	13115032002	12	2017	NA	NA	NA	NA	NA	NA	NA
13115	13115032003	4	2017	NA	NA	NA	NA	NA	NA	NA
13115	13115032007	15	2017	NA	NA	NA	NA	NA	NA	NA
13115	13115032008	14	2017	NA	NA	NA	NA	NA	NA	NA
13115	13115032901	3	2017	NA	NA	NA	NA	NA	NA	NA
13119	13119072006	30	2017	NA	NA	NA	NA	NA	NA	NA
13119	13119132006	14	2017	NA	NA	NA	NA	NA	NA	NA
13119	13119132901	1	2017	NA	NA	NA	NA	NA	NA	NA
13119	13119142001	8	2017	NA	NA	NA	NA	NA	NA	NA
13119	13119142004	28	2017	NA	NA	NA	NA	NA	NA	NA
13124	13124062901	1	2017	NA	NA	NA	NA	NA	NA	NA
13124	13124072012	3	2017	NA	NA	NA	NA	NA	NA	NA
13124	13124072016	13	2017	NA	NA	NA	NA	NA	NA	NA
13124	13124072019	102	2017	NA	NA	NA	NA	NA	NA	NA
13124	13124082009	2	2017	NA	NA	NA	NA	NA	NA	NA
13124	13124082010	27	2017	NA	NA	NA	NA	NA	NA	NA
13124	13124082011	10	2017	NA	NA	NA	NA	NA	NA	NA
13124	13124082017	2	2017	NA	NA	NA	NA	NA	NA	NA
13124	13124082020	7	2017	NA	NA	NA	NA	NA	NA	NA
13125	13125022002	3	2017	NA	NA	NA	NA	NA	NA	NA
13202	13202012001	306	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202012002	132	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202012005	110	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202012009	115	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202022004	60	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202022006	45	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202022007	31	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202022010	49	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202022011	3	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202022012	100	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202022014	177	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202032003	308	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202032008	258	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202032013	178	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13203	13203012004	11	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203012005	34	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203012012	2	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203012019	6	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203012901	4	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203022004	2	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203032017	3	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203032018	18	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203042001	4	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203042008	1	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203042010	9	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203052006	26	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203052009	11	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203052017	2	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203062003	46	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203062007	193	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13301	13301012005	165	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301012010	1	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301012012	302	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301012015	42	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301012018	155	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301012025	41	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301012026	25	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301012029	7	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301022004	682	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301022006	150	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301022008	29	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301032001	35	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301032007	343	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301032013	41	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301032014	23	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301032018	156	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301032019	6	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301032020	147	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301032022	56	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301032024	466	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301032028	146	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301042021	13	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301052002	66	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301052009	14	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301052023	54	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301052901	2	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301062004	189	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301062005	27	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301062016	234	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301062027	26	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13302	13302012003	86	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302012006	20	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302012009	52	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302012014	33	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302012017	17	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302012018	4	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302012019	17	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302012020	20	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302012021	7	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302012028	42	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302022002	10	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302022005	49	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302022007	2	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302022017	87	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302032001	137	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
13107	13107032901	2	2017	NA	NA	NA	NA	NA	NA	NA
13110	13110072002	15	2017	NA	NA	NA	NA	NA	NA	NA
13115	13115012005	1	2017	NA	NA	NA	NA	NA	NA	NA
13115	13115022001	16	2017	NA	NA	NA	NA	NA	NA	NA
13115	13115022901	11	2017	NA	NA	NA	NA	NA	NA	NA
13115	13115032002	12	2017	NA	NA	NA	NA	NA	NA	NA
13115	13115032003	4	2017	NA	NA	NA	NA	NA	NA	NA
13115	13115032007	15	2017	NA	NA	NA	NA	NA	NA	NA
13115	13115032008	14	2017	NA	NA	NA	NA	NA	NA	NA
13115	13115032901	3	2017	NA	NA	NA	NA	NA	NA	NA
13119	13119072006	30	2017	NA	NA	NA	NA	NA	NA	NA
13119	13119132006	14	2017	NA	NA	NA	NA	NA	NA	NA
13119	13119132901	1	2017	NA	NA	NA	NA	NA	NA	NA
13119	13119142001	8	2017	NA	NA	NA	NA	NA	NA	NA
13119	13119142004	28	2017	NA	NA	NA	NA	NA	NA	NA
13124	13124062901	1	2017	NA	NA	NA	NA	NA	NA	NA
13124	13124072012	3	2017	NA	NA	NA	NA	NA	NA	NA
13124	13124072016	13	2017	NA	NA	NA	NA	NA	NA	NA
13124	13124072019	102	2017	NA	NA	NA	NA	NA	NA	NA
13124	13124082009	2	2017	NA	NA	NA	NA	NA	NA	NA
13124	13124082010	27	2017	NA	NA	NA	NA	NA	NA	NA
13124	13124082011	10	2017	NA	NA	NA	NA	NA	NA	NA
13124	13124082017	2	2017	NA	NA	NA	NA	NA	NA	NA
13124	13124082020	7	2017	NA	NA	NA	NA	NA	NA	NA
13125	13125022002	3	2017	NA	NA	NA	NA	NA	NA	NA
13202	13202012001	306	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202012002	132	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202012005	110	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202012009	115	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202022004	60	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202022006	45	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202022007	31	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202022010	49	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202022011	3	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202022012	100	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202022014	177	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202032003	308	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202032008	258	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13202	13202032013	178	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural
13203	13203012004	11	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203012005	34	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203012012	2	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203012019	6	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203012901	4	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203022004	2	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203032017	3	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203032018	18	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203042001	4	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203042008	1	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203042010	9	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203052006	26	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203052009	11	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203052017	2	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203062003	46	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13203	13203062007	193	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural
13301	13301012005	165	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301012010	1	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301012012	302	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301012015	42	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301012018	155	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301012025	41	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301012026	25	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301012029	7	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301022004	682	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301022006	150	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301022008	29	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301032001	35	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301032007	343	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301032013	41	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301032014	23	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301032018	156	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301032019	6	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301032020	147	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301032022	56	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301032024	466	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301032028	146	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301042021	13	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301052002	66	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301052009	14	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301052023	54	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301052901	2	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301062004	189	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301062005	27	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301062016	234	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13301	13301062027	26	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural
13302	13302012003	86	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302012006	20	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302012009	52	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302012014	33	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302012017	17	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302012018	4	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302012019	17	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302012020	20	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302012021	7	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302012028	42	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302022002	10	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302022005	49	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302022007	2	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302022017	87	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural
13302	13302032001	137	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
13107032901	13107	2	2017	NA	NA	NA	NA	NA	NA	NA	46	0.0004662	13107
13110072002	13110	15	2017	NA	NA	NA	NA	NA	NA	NA	117	0.0003189	13110
13115012005	13115	1	2017	NA	NA	NA	NA	NA	NA	NA	29	0.0002740	13115
13115022001	13115	16	2017	NA	NA	NA	NA	NA	NA	NA	89	0.0008409	13115
13115022901	13115	11	2017	NA	NA	NA	NA	NA	NA	NA	95	0.0008976	13115
13115032002	13115	12	2017	NA	NA	NA	NA	NA	NA	NA	153	0.0014457	13115
13115032003	13115	4	2017	NA	NA	NA	NA	NA	NA	NA	85	0.0008032	13115
13115032007	13115	15	2017	NA	NA	NA	NA	NA	NA	NA	219	0.0020693	13115
13115032008	13115	14	2017	NA	NA	NA	NA	NA	NA	NA	168	0.0015874	13115
13115032901	13115	3	2017	NA	NA	NA	NA	NA	NA	NA	253	0.0023906	13115
13119072006	13119	30	2017	NA	NA	NA	NA	NA	NA	NA	1289	0.0024711	13119
13119132006	13119	14	2017	NA	NA	NA	NA	NA	NA	NA	832	0.0015950	13119
13119132901	13119	1	2017	NA	NA	NA	NA	NA	NA	NA	120	0.0002300	13119
13119142001	13119	8	2017	NA	NA	NA	NA	NA	NA	NA	260	0.0004984	13119
13119142004	13119	28	2017	NA	NA	NA	NA	NA	NA	NA	897	0.0017196	13119
13124062901	13124	1	2017	NA	NA	NA	NA	NA	NA	NA	22	0.0000955	13124
13124072012	13124	3	2017	NA	NA	NA	NA	NA	NA	NA	112	0.0004863	13124
13124072016	13124	13	2017	NA	NA	NA	NA	NA	NA	NA	284	0.0012332	13124
13124072019	13124	102	2017	NA	NA	NA	NA	NA	NA	NA	1796	0.0077988	13124
13124082009	13124	2	2017	NA	NA	NA	NA	NA	NA	NA	40	0.0001737	13124
13124082010	13124	27	2017	NA	NA	NA	NA	NA	NA	NA	416	0.0018064	13124
13124082011	13124	10	2017	NA	NA	NA	NA	NA	NA	NA	470	0.0020409	13124
13124082017	13124	2	2017	NA	NA	NA	NA	NA	NA	NA	103	0.0004473	13124
13124082020	13124	7	2017	NA	NA	NA	NA	NA	NA	NA	844	0.0036649	13124
13125022002	13125	3	2017	NA	NA	NA	NA	NA	NA	NA	213	0.0010123	13125
13202012001	13202	306	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1610	0.0607066	13202
13202012002	13202	132	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1485	0.0559934	13202
13202012005	13202	110	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1482	0.0558802	13202
13202012009	13202	115	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	954	0.0359715	13202
13202022004	13202	60	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	343	0.0129331	13202
13202022006	13202	45	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	234	0.0088232	13202
13202022007	13202	31	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	168	0.0063346	13202
13202022010	13202	49	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	308	0.0116134	13202
13202022011	13202	3	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	58	0.0021869	13202
13202022012	13202	100	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	744	0.0280532	13202
13202022014	13202	177	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1477	0.0556917	13202
13202032003	13202	308	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1944	0.0733004	13202
13202032008	13202	258	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	2296	0.0865729	13202
13202032013	13202	178	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1748	0.0659100	13202
13203012004	13203	11	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	151	0.0083017	13203
13203012005	13203	34	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	618	0.0339766	13203
13203012012	13203	2	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	66	0.0036286	13203
13203012019	13203	6	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	537	0.0295233	13203
13203012901	13203	4	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	10	0.0005498	13203
13203022004	13203	2	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	23	0.0012645	13203
13203032017	13203	3	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	35	0.0019242	13203
13203032018	13203	18	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	1186	0.0652042	13203
13203042001	13203	4	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	40	0.0021991	13203
13203042008	13203	1	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	62	0.0034087	13203
13203042010	13203	9	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	358	0.0196822	13203
13203052006	13203	26	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	256	0.0140744	13203
13203052009	13203	11	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	218	0.0119853	13203
13203052017	13203	2	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	32	0.0017593	13203
13203062003	13203	46	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	306	0.0168234	13203
13203062007	13203	193	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	1864	0.1024795	13203
13301012005	13301	165	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1304	0.0089189	13301
13301012010	13301	1	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	121	0.0008276	13301
13301012012	13301	302	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1844	0.0126123	13301
13301012015	13301	42	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	620	0.0042406	13301
13301012018	13301	155	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	776	0.0053075	13301
13301012025	13301	41	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	259	0.0017715	13301
13301012026	13301	25	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	205	0.0014021	13301
13301012029	13301	7	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	304	0.0020792	13301
13301022004	13301	682	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	5042	0.0344854	13301
13301022006	13301	150	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1010	0.0069080	13301
13301022008	13301	29	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	185	0.0012653	13301
13301032001	13301	35	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	727	0.0049724	13301
13301032007	13301	343	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	2002	0.0136929	13301
13301032013	13301	41	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	303	0.0020724	13301
13301032014	13301	23	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	99	0.0006771	13301
13301032018	13301	156	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	802	0.0054854	13301
13301032019	13301	6	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	50	0.0003420	13301
13301032020	13301	147	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1068	0.0073047	13301
13301032022	13301	56	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	321	0.0021955	13301
13301032024	13301	466	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	4204	0.0287538	13301
13301032028	13301	146	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	896	0.0061283	13301
13301042021	13301	13	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	364	0.0024896	13301
13301052002	13301	66	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1273	0.0087068	13301
13301052009	13301	14	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	155	0.0010601	13301
13301052023	13301	54	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	967	0.0066139	13301
13301052901	13301	2	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	22	0.0001505	13301
13301062004	13301	189	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1532	0.0104783	13301
13301062005	13301	27	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	172	0.0011764	13301
13301062016	13301	234	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1331	0.0091035	13301
13301062027	13301	26	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	231	0.0015800	13301
13302012003	13302	86	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	808	0.0079189	13302
13302012006	13302	20	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	355	0.0034792	13302
13302012009	13302	52	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	968	0.0094870	13302
13302012014	13302	33	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	200	0.0019601	13302
13302012017	13302	17	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	344	0.0033714	13302
13302012018	13302	4	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	118	0.0011565	13302
13302012019	13302	17	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	500	0.0049003	13302
13302012020	13302	20	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	532	0.0052139	13302
13302012021	13302	7	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	206	0.0020189	13302
13302012028	13302	42	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	437	0.0042829	13302
13302022002	13302	10	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	255	0.0024992	13302
13302022005	13302	49	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	1376	0.0134857	13302
13302022007	13302	2	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	243	0.0023816	13302
13302022017	13302	87	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	1012	0.0099183	13302
13302032001	13302	137	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	1258	0.0123292	13302

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
13107032901	13107	2	2017	NA	NA	NA	NA	NA	NA	NA	46	0.0004662	13107	NA
13110072002	13110	15	2017	NA	NA	NA	NA	NA	NA	NA	117	0.0003189	13110	NA
13115012005	13115	1	2017	NA	NA	NA	NA	NA	NA	NA	29	0.0002740	13115	NA
13115022001	13115	16	2017	NA	NA	NA	NA	NA	NA	NA	89	0.0008409	13115	NA
13115022901	13115	11	2017	NA	NA	NA	NA	NA	NA	NA	95	0.0008976	13115	NA
13115032002	13115	12	2017	NA	NA	NA	NA	NA	NA	NA	153	0.0014457	13115	NA
13115032003	13115	4	2017	NA	NA	NA	NA	NA	NA	NA	85	0.0008032	13115	NA
13115032007	13115	15	2017	NA	NA	NA	NA	NA	NA	NA	219	0.0020693	13115	NA
13115032008	13115	14	2017	NA	NA	NA	NA	NA	NA	NA	168	0.0015874	13115	NA
13115032901	13115	3	2017	NA	NA	NA	NA	NA	NA	NA	253	0.0023906	13115	NA
13119072006	13119	30	2017	NA	NA	NA	NA	NA	NA	NA	1289	0.0024711	13119	NA
13119132006	13119	14	2017	NA	NA	NA	NA	NA	NA	NA	832	0.0015950	13119	NA
13119132901	13119	1	2017	NA	NA	NA	NA	NA	NA	NA	120	0.0002300	13119	NA
13119142001	13119	8	2017	NA	NA	NA	NA	NA	NA	NA	260	0.0004984	13119	NA
13119142004	13119	28	2017	NA	NA	NA	NA	NA	NA	NA	897	0.0017196	13119	NA
13124062901	13124	1	2017	NA	NA	NA	NA	NA	NA	NA	22	0.0000955	13124	NA
13124072012	13124	3	2017	NA	NA	NA	NA	NA	NA	NA	112	0.0004863	13124	NA
13124072016	13124	13	2017	NA	NA	NA	NA	NA	NA	NA	284	0.0012332	13124	NA
13124072019	13124	102	2017	NA	NA	NA	NA	NA	NA	NA	1796	0.0077988	13124	NA
13124082009	13124	2	2017	NA	NA	NA	NA	NA	NA	NA	40	0.0001737	13124	NA
13124082010	13124	27	2017	NA	NA	NA	NA	NA	NA	NA	416	0.0018064	13124	NA
13124082011	13124	10	2017	NA	NA	NA	NA	NA	NA	NA	470	0.0020409	13124	NA
13124082017	13124	2	2017	NA	NA	NA	NA	NA	NA	NA	103	0.0004473	13124	NA
13124082020	13124	7	2017	NA	NA	NA	NA	NA	NA	NA	844	0.0036649	13124	NA
13125022002	13125	3	2017	NA	NA	NA	NA	NA	NA	NA	213	0.0010123	13125	NA
13202012001	13202	306	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1610	0.0607066	13202	486372289
13202012002	13202	132	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1485	0.0559934	13202	448610466
13202012005	13202	110	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1482	0.0558802	13202	447704182
13202012009	13202	115	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	954	0.0359715	13202	288198239
13202022004	13202	60	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	343	0.0129331	13202	103618444
13202022006	13202	45	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	234	0.0088232	13202	70690134
13202022007	13202	31	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	168	0.0063346	13202	50751891
13202022010	13202	49	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	308	0.0116134	13202	93045134
13202022011	13202	3	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	58	0.0021869	13202	17521486
13202022012	13202	100	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	744	0.0280532	13202	224758375
13202022014	13202	177	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1477	0.0556917	13202	446193709
13202032003	13202	308	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1944	0.0733004	13202	587271882
13202032008	13202	258	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	2296	0.0865729	13202	693609178
13202032013	13202	178	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1748	0.0659100	13202	528061343
13203012004	13203	11	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	151	0.0083017	13203	57702282
13203012005	13203	34	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	618	0.0339766	13203	236159007
13203012012	13203	2	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	66	0.0036286	13203	25220865
13203012019	13203	6	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	537	0.0295233	13203	205206128
13203012901	13203	4	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	10	0.0005498	13203	3821343
13203022004	13203	2	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	23	0.0012645	13203	8789089
13203032017	13203	3	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	35	0.0019242	13203	13374701
13203032018	13203	18	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	1186	0.0652042	13203	453211299
13203042001	13203	4	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	40	0.0021991	13203	15285373
13203042008	13203	1	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	62	0.0034087	13203	23692328
13203042010	13203	9	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	358	0.0196822	13203	136804085
13203052006	13203	26	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	256	0.0140744	13203	97826385
13203052009	13203	11	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	218	0.0119853	13203	83305281
13203052017	13203	2	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	32	0.0017593	13203	12228298
13203062003	13203	46	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	306	0.0168234	13203	116933101
13203062007	13203	193	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	1864	0.1024795	13203	712298365
13301012005	13301	165	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1304	0.0089189	13301	351320614
13301012010	13301	1	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	121	0.0008276	13301	32599535
13301012012	13301	302	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1844	0.0126123	13301	496806144
13301012015	13301	42	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	620	0.0042406	13301	167038942
13301012018	13301	155	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	776	0.0053075	13301	209068095
13301012025	13301	41	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	259	0.0017715	13301	69779171
13301012026	13301	25	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	205	0.0014021	13301	55230618
13301012029	13301	7	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	304	0.0020792	13301	81902965
13301022004	13301	682	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	5042	0.0344854	13301	1358403784
13301022006	13301	150	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1010	0.0069080	13301	272111825
13301022008	13301	29	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	185	0.0012653	13301	49842265
13301032001	13301	35	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	727	0.0049724	13301	195866631
13301032007	13301	343	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	2002	0.0136929	13301	539374133
13301032013	13301	41	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	303	0.0020724	13301	81633548
13301032014	13301	23	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	99	0.0006771	13301	26672347
13301032018	13301	156	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	802	0.0054854	13301	216072954
13301032019	13301	6	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	50	0.0003420	13301	13470882
13301032020	13301	147	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1068	0.0073047	13301	287738049
13301032022	13301	56	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	321	0.0021955	13301	86483065
13301032024	13301	466	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	4204	0.0287538	13301	1132631795
13301032028	13301	146	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	896	0.0061283	13301	241398213
13301042021	13301	13	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	364	0.0024896	13301	98068024
13301052002	13301	66	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1273	0.0087068	13301	342968667
13301052009	13301	14	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	155	0.0010601	13301	41759736
13301052023	13301	54	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	967	0.0066139	13301	260526866
13301052901	13301	2	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	22	0.0001505	13301	5927188
13301062004	13301	189	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1532	0.0104783	13301	412747838
13301062005	13301	27	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	172	0.0011764	13301	46339836
13301062016	13301	234	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1331	0.0091035	13301	358594890
13301062027	13301	26	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	231	0.0015800	13301	62235477
13302012003	13302	86	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	808	0.0079189	13302	230487848
13302012006	13302	20	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	355	0.0034792	13302	101266320
13302012009	13302	52	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	968	0.0094870	13302	276129006
13302012014	13302	33	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	200	0.0019601	13302	57051448
13302012017	13302	17	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	344	0.0033714	13302	98128490
13302012018	13302	4	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	118	0.0011565	13302	33660354
13302012019	13302	17	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	500	0.0049003	13302	142628619
13302012020	13302	20	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	532	0.0052139	13302	151756851
13302012021	13302	7	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	206	0.0020189	13302	58762991
13302012028	13302	42	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	437	0.0042829	13302	124657413
13302022002	13302	10	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	255	0.0024992	13302	72740596
13302022005	13302	49	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	1376	0.0134857	13302	392513960
13302022007	13302	2	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	243	0.0023816	13302	69317509
13302022017	13302	87	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	1012	0.0099183	13302	288680325
13302032001	13302	137	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	1258	0.0123292	13302	358853606

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -321116724  -54912786  -28394055   35053964  989324452 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 57937982    6131692   9.449   <2e-16 ***
## Freq.x       1921284      63207  30.397   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 108400000 on 442 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.6764, Adjusted R-squared:  0.6757 
## F-statistic:   924 on 1 and 442 DF,  p-value: < 2.2e-16

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.675684116468626"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.675684116468626"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.517794548490782"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.683739338507383"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.729415568068399"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.602209596038431"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.681353889894007"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq               
## [1,] "log-log" "0.7131207393422"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 3   logarítmico 0.517794548490782
## 6      log-raíz 0.602209596038431
## 1    cuadrático 0.675684116468626
## 2        cúbico 0.675684116468626
## 7      raíz-log 0.681353889894007
## 4 raíz cuadrada 0.683739338507383
## 8       log-log   0.7131207393422
## 5     raíz-raíz 0.729415568068399
##                                                                     sintaxis
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 8
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.05331 -0.46740 -0.01647  0.46748  2.41717 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 15.98862    0.07440   214.9   <2e-16 ***
## log(Freq.x)  0.73617    0.02217    33.2   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6947 on 442 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.7138, Adjusted R-squared:  0.7131 
## F-statistic:  1102 on 1 and 442 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    15.98862

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##   0.7361705

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.7131 ).

9.1 Diagrama de dispersión sobre log-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.05331 -0.46740 -0.01647  0.46748  2.41717 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 15.98862    0.07440   214.9   <2e-16 ***
## log(Freq.x)  0.73617    0.02217    33.2   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6947 on 442 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.7138, Adjusted R-squared:  0.7131 
## F-statistic:  1102 on 1 and 442 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = e^{15.98862+0.7361705 \cdot ln{X}} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
13107032901	13107	2	2017	NA	NA	NA	NA	NA	NA	NA	46	0.0004662	13107	NA	14634516
13110072002	13110	15	2017	NA	NA	NA	NA	NA	NA	NA	117	0.0003189	13110	NA	64501915
13115012005	13115	1	2017	NA	NA	NA	NA	NA	NA	NA	29	0.0002740	13115	NA	8785550
13115022001	13115	16	2017	NA	NA	NA	NA	NA	NA	NA	89	0.0008409	13115	NA	67640456
13115022901	13115	11	2017	NA	NA	NA	NA	NA	NA	NA	95	0.0008976	13115	NA	51334756
13115032002	13115	12	2017	NA	NA	NA	NA	NA	NA	NA	153	0.0014457	13115	NA	54730614
13115032003	13115	4	2017	NA	NA	NA	NA	NA	NA	NA	85	0.0008032	13115	NA	24377420
13115032007	13115	15	2017	NA	NA	NA	NA	NA	NA	NA	219	0.0020693	13115	NA	64501915
13115032008	13115	14	2017	NA	NA	NA	NA	NA	NA	NA	168	0.0015874	13115	NA	61307636
13115032901	13115	3	2017	NA	NA	NA	NA	NA	NA	NA	253	0.0023906	13115	NA	19724752
13119072006	13119	30	2017	NA	NA	NA	NA	NA	NA	NA	1289	0.0024711	13119	NA	107443958
13119132006	13119	14	2017	NA	NA	NA	NA	NA	NA	NA	832	0.0015950	13119	NA	61307636
13119132901	13119	1	2017	NA	NA	NA	NA	NA	NA	NA	120	0.0002300	13119	NA	8785550
13119142001	13119	8	2017	NA	NA	NA	NA	NA	NA	NA	260	0.0004984	13119	NA	40606648
13119142004	13119	28	2017	NA	NA	NA	NA	NA	NA	NA	897	0.0017196	13119	NA	102123092
13124062901	13124	1	2017	NA	NA	NA	NA	NA	NA	NA	22	0.0000955	13124	NA	8785550
13124072012	13124	3	2017	NA	NA	NA	NA	NA	NA	NA	112	0.0004863	13124	NA	19724752
13124072016	13124	13	2017	NA	NA	NA	NA	NA	NA	NA	284	0.0012332	13124	NA	58052531
13124072019	13124	102	2017	NA	NA	NA	NA	NA	NA	NA	1796	0.0077988	13124	NA	264509378
13124082009	13124	2	2017	NA	NA	NA	NA	NA	NA	NA	40	0.0001737	13124	NA	14634516
13124082010	13124	27	2017	NA	NA	NA	NA	NA	NA	NA	416	0.0018064	13124	NA	99425248
13124082011	13124	10	2017	NA	NA	NA	NA	NA	NA	NA	470	0.0020409	13124	NA	47856334
13124082017	13124	2	2017	NA	NA	NA	NA	NA	NA	NA	103	0.0004473	13124	NA	14634516
13124082020	13124	7	2017	NA	NA	NA	NA	NA	NA	NA	844	0.0036649	13124	NA	36804861
13125022002	13125	3	2017	NA	NA	NA	NA	NA	NA	NA	213	0.0010123	13125	NA	19724752
13202012001	13202	306	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1610	0.0607066	13202	486372289	593859414
13202012002	13202	132	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1485	0.0559934	13202	448610466	319795867
13202012005	13202	110	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1482	0.0558802	13202	447704182	279628835
13202012009	13202	115	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	954	0.0359715	13202	288198239	288930799
13202022004	13202	60	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	343	0.0129331	13202	103618444	178974594
13202022006	13202	45	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	234	0.0088232	13202	70690134	144815544
13202022007	13202	31	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	168	0.0063346	13202	50751891	110069093
13202022010	13202	49	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	308	0.0116134	13202	93045134	154184739
13202022011	13202	3	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	58	0.0021869	13202	17521486	19724752
13202022012	13202	100	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	744	0.0280532	13202	224758375	260681302
13202022014	13202	177	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1477	0.0556917	13202	446193709	396881151
13202032003	13202	308	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1944	0.0733004	13202	587271882	596714354
13202032008	13202	258	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	2296	0.0865729	13202	693609178	523759783
13202032013	13202	178	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1748	0.0659100	13202	528061343	398530614
13203012004	13203	11	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	151	0.0083017	13203	57702282	51334756
13203012005	13203	34	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	618	0.0339766	13203	236159007	117814434
13203012012	13203	2	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	66	0.0036286	13203	25220865	14634516
13203012019	13203	6	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	537	0.0295233	13203	205206128	32856472
13203012901	13203	4	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	10	0.0005498	13203	3821343	24377420
13203022004	13203	2	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	23	0.0012645	13203	8789089	14634516
13203032017	13203	3	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	35	0.0019242	13203	13374701	19724752
13203032018	13203	18	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	1186	0.0652042	13203	453211299	73767235
13203042001	13203	4	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	40	0.0021991	13203	15285373	24377420
13203042008	13203	1	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	62	0.0034087	13203	23692328	8785550
13203042010	13203	9	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	358	0.0196822	13203	136804085	44284741
13203052006	13203	26	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	256	0.0140744	13203	97826385	96700906
13203052009	13203	11	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	218	0.0119853	13203	83305281	51334756
13203052017	13203	2	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	32	0.0017593	13203	12228298	14634516
13203062003	13203	46	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	306	0.0168234	13203	116933101	147177751
13203062007	13203	193	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	1864	0.1024795	13203	712298365	422988671
13301012005	13301	165	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1304	0.0089189	13301	351320614	376890452
13301012010	13301	1	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	121	0.0008276	13301	32599535	8785550
13301012012	13301	302	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1844	0.0126123	13301	496806144	588134710
13301012015	13301	42	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	620	0.0042406	13301	167038942	137643954
13301012018	13301	155	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	776	0.0053075	13301	209068095	359936968
13301012025	13301	41	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	259	0.0017715	13301	69779171	135223694
13301012026	13301	25	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	205	0.0014021	13301	55230618	93948771
13301012029	13301	7	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	304	0.0020792	13301	81902965	36804861
13301022004	13301	682	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	5042	0.0344854	13301	1358403784	1071315292
13301022006	13301	150	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1010	0.0069080	13301	272111825	351352513
13301022008	13301	29	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	185	0.0012653	13301	49842265	104795630
13301032001	13301	35	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	727	0.0049724	13301	195866631	120355584
13301032007	13301	343	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	2002	0.0136929	13301	539374133	645918313
13301032013	13301	41	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	303	0.0020724	13301	81633548	135223694
13301032014	13301	23	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	99	0.0006771	13301	26672347	88355334
13301032018	13301	156	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	802	0.0054854	13301	216072954	361645033
13301032019	13301	6	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	50	0.0003420	13301	13470882	32856472
13301032020	13301	147	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1068	0.0073047	13301	287738049	346165641
13301032022	13301	56	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	321	0.0021955	13301	86483065	170111372
13301032024	13301	466	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	4204	0.0287538	13301	1132631795	809386294
13301032028	13301	146	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	896	0.0061283	13301	241398213	344430497
13301042021	13301	13	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	364	0.0024896	13301	98068024	58052531
13301052002	13301	66	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1273	0.0087068	13301	342968667	191983303
13301052009	13301	14	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	155	0.0010601	13301	41759736	61307636
13301052023	13301	54	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	967	0.0066139	13301	260526866	165617442
13301052901	13301	2	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	22	0.0001505	13301	5927188	14634516
13301062004	13301	189	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1532	0.0104783	13301	412747838	416517155
13301062005	13301	27	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	172	0.0011764	13301	46339836	99425248
13301062016	13301	234	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1331	0.0091035	13301	358594890	487433852
13301062027	13301	26	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	231	0.0015800	13301	62235477	96700906
13302012003	13302	86	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	808	0.0079189	13302	230487848	233286483
13302012006	13302	20	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	355	0.0034792	13302	101266320	79716609
13302012009	13302	52	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	968	0.0094870	13302	276129006	161079374
13302012014	13302	33	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	200	0.0019601	13302	57051448	115253486
13302012017	13302	17	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	344	0.0033714	13302	98128490	70727631
13302012018	13302	4	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	118	0.0011565	13302	33660354	24377420
13302012019	13302	17	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	500	0.0049003	13302	142628619	70727631
13302012020	13302	20	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	532	0.0052139	13302	151756851	79716609
13302012021	13302	7	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	206	0.0020189	13302	58762991	36804861
13302012028	13302	42	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	437	0.0042829	13302	124657413	137643954
13302022002	13302	10	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	255	0.0024992	13302	72740596	47856334
13302022005	13302	49	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	1376	0.0134857	13302	392513960	154184739
13302022007	13302	2	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	243	0.0023816	13302	69317509	14634516
13302022017	13302	87	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	1012	0.0099183	13302	288680325	235280396
13302032001	13302	137	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	1258	0.0123292	13302	358853606	328669590

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
13107032901	13107	2	2017	NA	NA	NA	NA	NA	NA	NA	46	0.0004662	13107	NA	14634516	NA
13110072002	13110	15	2017	NA	NA	NA	NA	NA	NA	NA	117	0.0003189	13110	NA	64501915	NA
13115012005	13115	1	2017	NA	NA	NA	NA	NA	NA	NA	29	0.0002740	13115	NA	8785550	NA
13115022001	13115	16	2017	NA	NA	NA	NA	NA	NA	NA	89	0.0008409	13115	NA	67640456	NA
13115022901	13115	11	2017	NA	NA	NA	NA	NA	NA	NA	95	0.0008976	13115	NA	51334756	NA
13115032002	13115	12	2017	NA	NA	NA	NA	NA	NA	NA	153	0.0014457	13115	NA	54730614	NA
13115032003	13115	4	2017	NA	NA	NA	NA	NA	NA	NA	85	0.0008032	13115	NA	24377420	NA
13115032007	13115	15	2017	NA	NA	NA	NA	NA	NA	NA	219	0.0020693	13115	NA	64501915	NA
13115032008	13115	14	2017	NA	NA	NA	NA	NA	NA	NA	168	0.0015874	13115	NA	61307636	NA
13115032901	13115	3	2017	NA	NA	NA	NA	NA	NA	NA	253	0.0023906	13115	NA	19724752	NA
13119072006	13119	30	2017	NA	NA	NA	NA	NA	NA	NA	1289	0.0024711	13119	NA	107443958	NA
13119132006	13119	14	2017	NA	NA	NA	NA	NA	NA	NA	832	0.0015950	13119	NA	61307636	NA
13119132901	13119	1	2017	NA	NA	NA	NA	NA	NA	NA	120	0.0002300	13119	NA	8785550	NA
13119142001	13119	8	2017	NA	NA	NA	NA	NA	NA	NA	260	0.0004984	13119	NA	40606648	NA
13119142004	13119	28	2017	NA	NA	NA	NA	NA	NA	NA	897	0.0017196	13119	NA	102123092	NA
13124062901	13124	1	2017	NA	NA	NA	NA	NA	NA	NA	22	0.0000955	13124	NA	8785550	NA
13124072012	13124	3	2017	NA	NA	NA	NA	NA	NA	NA	112	0.0004863	13124	NA	19724752	NA
13124072016	13124	13	2017	NA	NA	NA	NA	NA	NA	NA	284	0.0012332	13124	NA	58052531	NA
13124072019	13124	102	2017	NA	NA	NA	NA	NA	NA	NA	1796	0.0077988	13124	NA	264509378	NA
13124082009	13124	2	2017	NA	NA	NA	NA	NA	NA	NA	40	0.0001737	13124	NA	14634516	NA
13124082010	13124	27	2017	NA	NA	NA	NA	NA	NA	NA	416	0.0018064	13124	NA	99425248	NA
13124082011	13124	10	2017	NA	NA	NA	NA	NA	NA	NA	470	0.0020409	13124	NA	47856334	NA
13124082017	13124	2	2017	NA	NA	NA	NA	NA	NA	NA	103	0.0004473	13124	NA	14634516	NA
13124082020	13124	7	2017	NA	NA	NA	NA	NA	NA	NA	844	0.0036649	13124	NA	36804861	NA
13125022002	13125	3	2017	NA	NA	NA	NA	NA	NA	NA	213	0.0010123	13125	NA	19724752	NA
13202012001	13202	306	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1610	0.0607066	13202	486372289	593859414	368856.78
13202012002	13202	132	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1485	0.0559934	13202	448610466	319795867	215350.75
13202012005	13202	110	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1482	0.0558802	13202	447704182	279628835	188683.42
13202012009	13202	115	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	954	0.0359715	13202	288198239	288930799	302862.47
13202022004	13202	60	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	343	0.0129331	13202	103618444	178974594	521791.82
13202022006	13202	45	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	234	0.0088232	13202	70690134	144815544	618869.85
13202022007	13202	31	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	168	0.0063346	13202	50751891	110069093	655173.17
13202022010	13202	49	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	308	0.0116134	13202	93045134	154184739	500599.80
13202022011	13202	3	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	58	0.0021869	13202	17521486	19724752	340081.92
13202022012	13202	100	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	744	0.0280532	13202	224758375	260681302	350378.09
13202022014	13202	177	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1477	0.0556917	13202	446193709	396881151	268707.62
13202032003	13202	308	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1944	0.0733004	13202	587271882	596714354	306951.83
13202032008	13202	258	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	2296	0.0865729	13202	693609178	523759783	228118.37
13202032013	13202	178	2017	Pirque	302094.6	2017	13202	26521	8011850615	Rural	1748	0.0659100	13202	528061343	398530614	227992.34
13203012004	13203	11	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	151	0.0083017	13203	57702282	51334756	339965.27
13203012005	13203	34	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	618	0.0339766	13203	236159007	117814434	190638.24
13203012012	13203	2	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	66	0.0036286	13203	25220865	14634516	221735.09
13203012019	13203	6	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	537	0.0295233	13203	205206128	32856472	61185.24
13203012901	13203	4	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	10	0.0005498	13203	3821343	24377420	2437742.05
13203022004	13203	2	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	23	0.0012645	13203	8789089	14634516	636283.30
13203032017	13203	3	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	35	0.0019242	13203	13374701	19724752	563564.33
13203032018	13203	18	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	1186	0.0652042	13203	453211299	73767235	62198.34
13203042001	13203	4	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	40	0.0021991	13203	15285373	24377420	609435.51
13203042008	13203	1	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	62	0.0034087	13203	23692328	8785550	141702.42
13203042010	13203	9	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	358	0.0196822	13203	136804085	44284741	123700.40
13203052006	13203	26	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	256	0.0140744	13203	97826385	96700906	377737.92
13203052009	13203	11	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	218	0.0119853	13203	83305281	51334756	235480.53
13203052017	13203	2	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	32	0.0017593	13203	12228298	14634516	457328.62
13203062003	13203	46	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	306	0.0168234	13203	116933101	147177751	480973.04
13203062007	13203	193	2017	San José de Maipo	382134.3	2017	13203	18189	6950641070	Rural	1864	0.1024795	13203	712298365	422988671	226925.25
13301012005	13301	165	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1304	0.0089189	13301	351320614	376890452	289026.42
13301012010	13301	1	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	121	0.0008276	13301	32599535	8785550	72607.85
13301012012	13301	302	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1844	0.0126123	13301	496806144	588134710	318945.07
13301012015	13301	42	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	620	0.0042406	13301	167038942	137643954	222006.38
13301012018	13301	155	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	776	0.0053075	13301	209068095	359936968	463836.30
13301012025	13301	41	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	259	0.0017715	13301	69779171	135223694	522099.21
13301012026	13301	25	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	205	0.0014021	13301	55230618	93948771	458286.69
13301012029	13301	7	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	304	0.0020792	13301	81902965	36804861	121068.62
13301022004	13301	682	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	5042	0.0344854	13301	1358403784	1071315292	212478.24
13301022006	13301	150	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1010	0.0069080	13301	272111825	351352513	347873.78
13301022008	13301	29	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	185	0.0012653	13301	49842265	104795630	566462.86
13301032001	13301	35	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	727	0.0049724	13301	195866631	120355584	165551.01
13301032007	13301	343	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	2002	0.0136929	13301	539374133	645918313	322636.52
13301032013	13301	41	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	303	0.0020724	13301	81633548	135223694	446282.82
13301032014	13301	23	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	99	0.0006771	13301	26672347	88355334	892478.12
13301032018	13301	156	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	802	0.0054854	13301	216072954	361645033	450928.97
13301032019	13301	6	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	50	0.0003420	13301	13470882	32856472	657129.44
13301032020	13301	147	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1068	0.0073047	13301	287738049	346165641	324125.13
13301032022	13301	56	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	321	0.0021955	13301	86483065	170111372	529941.97
13301032024	13301	466	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	4204	0.0287538	13301	1132631795	809386294	192527.66
13301032028	13301	146	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	896	0.0061283	13301	241398213	344430497	384409.04
13301042021	13301	13	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	364	0.0024896	13301	98068024	58052531	159484.97
13301052002	13301	66	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1273	0.0087068	13301	342968667	191983303	150811.71
13301052009	13301	14	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	155	0.0010601	13301	41759736	61307636	395533.13
13301052023	13301	54	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	967	0.0066139	13301	260526866	165617442	171269.33
13301052901	13301	2	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	22	0.0001505	13301	5927188	14634516	665205.27
13301062004	13301	189	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1532	0.0104783	13301	412747838	416517155	271878.04
13301062005	13301	27	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	172	0.0011764	13301	46339836	99425248	578053.77
13301062016	13301	234	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	1331	0.0091035	13301	358594890	487433852	366216.27
13301062027	13301	26	2017	Colina	269417.6	2017	13301	146207	39390746156	Rural	231	0.0015800	13301	62235477	96700906	418618.64
13302012003	13302	86	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	808	0.0079189	13302	230487848	233286483	288720.90
13302012006	13302	20	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	355	0.0034792	13302	101266320	79716609	224553.83
13302012009	13302	52	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	968	0.0094870	13302	276129006	161079374	166404.31
13302012014	13302	33	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	200	0.0019601	13302	57051448	115253486	576267.43
13302012017	13302	17	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	344	0.0033714	13302	98128490	70727631	205603.58
13302012018	13302	4	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	118	0.0011565	13302	33660354	24377420	206588.31
13302012019	13302	17	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	500	0.0049003	13302	142628619	70727631	141455.26
13302012020	13302	20	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	532	0.0052139	13302	151756851	79716609	149843.25
13302012021	13302	7	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	206	0.0020189	13302	58762991	36804861	178664.37
13302012028	13302	42	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	437	0.0042829	13302	124657413	137643954	314974.72
13302022002	13302	10	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	255	0.0024992	13302	72740596	47856334	187671.90
13302022005	13302	49	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	1376	0.0134857	13302	392513960	154184739	112052.86
13302022007	13302	2	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	243	0.0023816	13302	69317509	14634516	60224.35
13302022017	13302	87	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	1012	0.0099183	13302	288680325	235280396	232490.51
13302032001	13302	137	2017	Lampa	285257.2	2017	13302	102034	29105937032	Rural	1258	0.0123292	13302	358853606	328669590	261263.59

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_rural_nivel_nacional_17_r13.rds")

---

R-14

Urbano

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 1)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15201	1	1	1	1767	1	1	1	2	1	3	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	2	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	3	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	4	1	1	2	3	1	99	1	2	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	5	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	6	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	7	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	8	1	1	3	3	1	99	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	9	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	10	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	11	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	12	1	1	4	3	3	3	4	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	13	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	14	1	1	2	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	15	1	1	2	1	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	16	1	1	5	1	5	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	17	1	1	5	3	1	3	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	18	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	19	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	20	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	21	4	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	22	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	23	1	1	5	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	24	1	1	3	1	1	6	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	25	1	1	5	6	1	1	4	2	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	26	4	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	27	1	1	5	99	3	3	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	28	1	1	6	3	2	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	29	1	1	2	3	1	99	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	30	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	31	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	32	1	1	3	1	1	2	1	1	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	33	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	34	3	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	35	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	36	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	37	1	1	5	3	3	1	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	38	1	1	1	1	4	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	39	1	1	5	3	4	3	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	40	1	1	5	3	2	1	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	41	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	42	1	1	2	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	43	1	1	1	1	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	44	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	45	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	46	1	1	5	6	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	47	1	1	5	3	4	0	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	48	1	1	3	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	49	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	50	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	51	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	52	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	53	1	1	1	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	54	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	55	1	1	2	2	1	4	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	56	8	1	98	98	98	98	98	98	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	57	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	58	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	59	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	60	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	61	1	1	2	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	62	1	1	5	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	63	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	64	1	1	5	3	2	4	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	65	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	66	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	67	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	68	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	69	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	70	1	1	1	3	3	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	71	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	72	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	73	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	74	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	75	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	76	1	1	5	3	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	77	1	1	3	6	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	78	5	1	3	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	79	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	80	1	1	2	1	1	3	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	81	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	82	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	83	1	1	5	3	2	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	84	4	1	5	3	2	0	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	85	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	86	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	87	1	1	3	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	88	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	89	5	1	4	3	2	1	2	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	90	1	1	5	3	3	2	1	3	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	91	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	92	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	93	5	1	4	3	5	1	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	94	1	1	5	3	3	99	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	95	1	1	5	3	2	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	96	1	1	2	3	4	6	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	97	1	1	5	1	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	98	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	99	1	1	2	7	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	100	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 14:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 14)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	14101011001	94	2017	14101
2	14101021001	577	2017	14101
3	14101031001	106	2017	14101
4	14101041001	77	2017	14101
5	14101041002	11	2017	14101
6	14101041003	202	2017	14101
7	14101041004	18	2017	14101
8	14101041005	167	2017	14101
9	14101051001	67	2017	14101
10	14101051002	61	2017	14101
11	14101051003	36	2017	14101
12	14101051004	38	2017	14101
13	14101051005	182	2017	14101
14	14101061001	67	2017	14101
15	14101061002	30	2017	14101
16	14101061003	77	2017	14101
17	14101061004	60	2017	14101
18	14101061005	58	2017	14101
19	14101061006	10	2017	14101
20	14101061007	1	2017	14101
21	14101071001	85	2017	14101
22	14101071002	65	2017	14101
23	14101071003	131	2017	14101
24	14101071004	47	2017	14101
25	14101071005	337	2017	14101
26	14101071006	61	2017	14101
27	14101081001	352	2017	14101
28	14101081002	198	2017	14101
29	14101081003	471	2017	14101
30	14101081004	99	2017	14101
31	14101081005	314	2017	14101
32	14101081006	38	2017	14101
33	14101081007	26	2017	14101
34	14101081008	65	2017	14101
35	14101081009	54	2017	14101
36	14101081010	136	2017	14101
37	14101081011	26	2017	14101
38	14101091001	169	2017	14101
39	14101091002	152	2017	14101
40	14101101001	61	2017	14101
41	14101101002	104	2017	14101
42	14101101003	80	2017	14101
43	14101161001	380	2017	14101
44	14101171001	73	2017	14101
45	14101991999	18	2017	14101
139	14102011001	51	2017	14102
140	14102991999	1	2017	14102
234	14103011001	38	2017	14103
235	14103011002	17	2017	14103
236	14103011003	26	2017	14103
237	14103031001	69	2017	14103
331	14104011001	27	2017	14104
332	14104051001	32	2017	14104
333	14104051002	42	2017	14104
427	14105011001	98	2017	14105
521	14106011001	55	2017	14106
522	14106011002	41	2017	14106
523	14106011003	58	2017	14106
524	14106991999	1	2017	14106
618	14107031001	34	2017	14107
619	14107031002	42	2017	14107
620	14107031003	32	2017	14107
621	14107051001	14	2017	14107
622	14107991999	3	2017	14107
716	14108011001	71	2017	14108
717	14108011002	51	2017	14108
718	14108011003	60	2017	14108
719	14108051001	27	2017	14108
720	14108111001	20	2017	14108
721	14108991999	11	2017	14108
815	14201011001	59	2017	14201
816	14201011002	41	2017	14201
817	14201011003	39	2017	14201
818	14201011004	92	2017	14201
819	14201021001	29	2017	14201
820	14201091001	101	2017	14201
821	14201091002	85	2017	14201
822	14201091003	59	2017	14201
823	14201091004	35	2017	14201
824	14201091005	124	2017	14201
825	14201991999	3	2017	14201
919	14202011001	63	2017	14202
920	14202011002	72	2017	14202
921	14202041001	16	2017	14202
1015	14203011001	46	2017	14203
1016	14203991999	1	2017	14203
1110	14204011001	113	2017	14204
1111	14204011002	150	2017	14204
1112	14204011003	106	2017	14204
1113	14204011004	66	2017	14204
1114	14204011005	57	2017	14204
1115	14204071001	11	2017	14204
1116	14204991999	2	2017	14204
NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA
NA.2	NA	NA	NA	NA
NA.3	NA	NA	NA	NA
NA.4	NA	NA	NA	NA
NA.5	NA	NA	NA	NA
NA.6	NA	NA	NA	NA

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_urbano_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
01107	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
01401	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
01405	Pica	330061.1	2017	1405	9296	3068247619	Urbano
02101	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02102	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02104	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02201	Calama	416281.1	2017	2201	165731	68990679686	Urbano
02203	San Pedro de Atacama	437934.7	2017	2203	10996	4815529626	Urbano
02301	Tocopilla	271720.8	2017	2301	25186	6843559467	Urbano
02302	María Elena	466266.9	2017	2302	6457	3010685220	Urbano
03101	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
03102	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
03103	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
03201	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
03202	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
03301	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
03303	Freirina	289049.9	2017	3303	7041	2035200054	Urbano
03304	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
04101	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04102	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04103	Andacollo	251267.7	2017	4103	11044	2775000288	Urbano
04104	La Higuera	214257.0	2017	4104	4241	908664019	Urbano
04106	Vicuña	245957.4	2017	4106	27771	6830481918	Urbano
04201	Illapel	270316.5	2017	4201	30848	8338722128	Urbano
04202	Canela	233397.3	2017	4202	9093	2122281844	Urbano
04203	Los Vilos	282415.6	2017	4203	21382	6038609501	Urbano
04204	Salamanca	262056.9	2017	4204	29347	7690585032	Urbano
04301	Ovalle	274771.4	2017	4301	111272	30574361012	Urbano
04302	Combarbalá	228990.4	2017	4302	13322	3050610572	Urbano
04303	Monte Patria	225369.1	2017	4303	30751	6930326684	Urbano
04304	Punitaqui	212496.1	2017	4304	10956	2328107498	Urbano
05101	Valparaíso	297929.0	2017	5101	296655	88382118059	Urbano
05102	Casablanca	341641.8	2017	5102	26867	9178890241	Urbano
05103	Concón	318496.3	2017	5103	42152	13425257057	Urbano
05105	Puchuncaví	296035.5	2017	5105	18546	5490274928	Urbano
05107	Quintero	308224.7	2017	5107	31923	9839456903	Urbano
05109	Viña del Mar	337006.1	2017	5109	334248	112643604611	Urbano
05301	Los Andes	339720.2	2017	5301	66708	22662055502	Urbano
05302	Calle Larga	246387.3	2017	5302	14832	3654416747	Urbano
05303	Rinconada	273904.7	2017	5303	10207	2795744821	Urbano
05304	San Esteban	219571.6	2017	5304	18855	4140022481	Urbano
05401	La Ligua	250134.4	2017	5401	35390	8852256241	Urbano
05402	Cabildo	262745.9	2017	5402	19388	5094117762	Urbano
05403	Papudo	294355.2	2017	5403	6356	1870921373	Urbano
05404	Petorca	237510.8	2017	5404	9826	2333781007	Urbano
05405	Zapallar	294389.2	2017	5405	7339	2160521991	Urbano
05501	Quillota	286029.5	2017	5501	90517	25890529852	Urbano
05502	Calera	277181.9	2017	5502	50554	14012652087	Urbano
05503	Hijuelas	254094.0	2017	5503	17988	4570642363	Urbano

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
1	14101	14101011001	94	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
2	14101	14101021001	577	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
3	14101	14101031001	106	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
4	14101	14101041001	77	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
5	14101	14101041002	11	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
6	14101	14101041003	202	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
7	14101	14101041004	18	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
8	14101	14101041005	167	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
9	14101	14101051001	67	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
10	14101	14101051002	61	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
11	14101	14101051003	36	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
12	14101	14101051004	38	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
13	14101	14101051005	182	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
14	14101	14101061001	67	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
15	14101	14101061002	30	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
16	14101	14101061003	77	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
17	14101	14101061004	60	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
18	14101	14101061005	58	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
19	14101	14101061006	10	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
20	14101	14101061007	1	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
21	14101	14101071001	85	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
22	14101	14101071002	65	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
23	14101	14101071003	131	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
24	14101	14101071004	47	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
25	14101	14101071005	337	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
26	14101	14101071006	61	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
27	14101	14101081001	352	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
28	14101	14101081002	198	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
29	14101	14101081003	471	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
30	14101	14101081004	99	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
31	14101	14101081005	314	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
32	14101	14101081006	38	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
33	14101	14101081007	26	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
34	14101	14101081008	65	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
35	14101	14101081009	54	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
36	14101	14101081010	136	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
37	14101	14101081011	26	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
38	14101	14101091001	169	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
39	14101	14101091002	152	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
40	14101	14101101001	61	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
41	14101	14101101002	104	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
42	14101	14101101003	80	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
43	14101	14101161001	380	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
44	14101	14101171001	73	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
45	14101	14101991999	18	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
46	14102	14102011001	51	2017	Corral	215990.7	2017	14102	5302	1145182670	Urbano
47	14102	14102991999	1	2017	Corral	215990.7	2017	14102	5302	1145182670	Urbano
48	14103	14103011001	38	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano
49	14103	14103011002	17	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano
50	14103	14103011003	26	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano
51	14103	14103031001	69	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano
52	14104	14104011001	27	2017	Los Lagos	211843.1	2017	14104	19634	4159328181	Urbano
53	14104	14104051001	32	2017	Los Lagos	211843.1	2017	14104	19634	4159328181	Urbano
54	14104	14104051002	42	2017	Los Lagos	211843.1	2017	14104	19634	4159328181	Urbano
55	14105	14105011001	98	2017	Máfil	315022.1	2017	14105	7095	2235081533	Urbano
56	14106	14106011001	55	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano
57	14106	14106011002	41	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano
58	14106	14106011003	58	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano
59	14106	14106991999	1	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano
60	14107	14107031001	34	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano
61	14107	14107031002	42	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano
62	14107	14107031003	32	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano
63	14107	14107051001	14	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano
64	14107	14107991999	3	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano
65	14108	14108011001	71	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano
66	14108	14108011002	51	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano
67	14108	14108011003	60	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano
68	14108	14108051001	27	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano
69	14108	14108111001	20	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano
70	14108	14108991999	11	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano
71	14201	14201011001	59	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano
72	14201	14201011002	41	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano
73	14201	14201011003	39	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano
74	14201	14201011004	92	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano
75	14201	14201021001	29	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano
76	14201	14201091001	101	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano
77	14201	14201091002	85	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano
78	14201	14201091003	59	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano
79	14201	14201091004	35	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano
80	14201	14201091005	124	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano
81	14201	14201991999	3	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano
82	14202	14202011001	63	2017	Futrono	247331.7	2017	14202	14665	3627119212	Urbano
83	14202	14202011002	72	2017	Futrono	247331.7	2017	14202	14665	3627119212	Urbano
84	14202	14202041001	16	2017	Futrono	247331.7	2017	14202	14665	3627119212	Urbano
85	14203	14203011001	46	2017	Lago Ranco	247154.2	2017	14203	9896	2445838259	Urbano
86	14203	14203991999	1	2017	Lago Ranco	247154.2	2017	14203	9896	2445838259	Urbano
87	14204	14204011001	113	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano
88	14204	14204011002	150	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano
89	14204	14204011003	106	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano
90	14204	14204011004	66	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano
91	14204	14204011005	57	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano
92	14204	14204071001	11	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano
93	14204	14204991999	2	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
1	14101	14101011001	94	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
2	14101	14101021001	577	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
3	14101	14101031001	106	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
4	14101	14101041001	77	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
5	14101	14101041002	11	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
6	14101	14101041003	202	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
7	14101	14101041004	18	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
8	14101	14101041005	167	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
9	14101	14101051001	67	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
10	14101	14101051002	61	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
11	14101	14101051003	36	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
12	14101	14101051004	38	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
13	14101	14101051005	182	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
14	14101	14101061001	67	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
15	14101	14101061002	30	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
16	14101	14101061003	77	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
17	14101	14101061004	60	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
18	14101	14101061005	58	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
19	14101	14101061006	10	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
20	14101	14101061007	1	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
21	14101	14101071001	85	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
22	14101	14101071002	65	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
23	14101	14101071003	131	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
24	14101	14101071004	47	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
25	14101	14101071005	337	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
26	14101	14101071006	61	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
27	14101	14101081001	352	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
28	14101	14101081002	198	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
29	14101	14101081003	471	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
30	14101	14101081004	99	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
31	14101	14101081005	314	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
32	14101	14101081006	38	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
33	14101	14101081007	26	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
34	14101	14101081008	65	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
35	14101	14101081009	54	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
36	14101	14101081010	136	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
37	14101	14101081011	26	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
38	14101	14101091001	169	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
39	14101	14101091002	152	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
40	14101	14101101001	61	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
41	14101	14101101002	104	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
42	14101	14101101003	80	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
43	14101	14101161001	380	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
44	14101	14101171001	73	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
45	14101	14101991999	18	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano
46	14102	14102011001	51	2017	Corral	215990.7	2017	14102	5302	1145182670	Urbano
47	14102	14102991999	1	2017	Corral	215990.7	2017	14102	5302	1145182670	Urbano
48	14103	14103011001	38	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano
49	14103	14103011002	17	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano
50	14103	14103011003	26	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano
51	14103	14103031001	69	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano
52	14104	14104011001	27	2017	Los Lagos	211843.1	2017	14104	19634	4159328181	Urbano
53	14104	14104051001	32	2017	Los Lagos	211843.1	2017	14104	19634	4159328181	Urbano
54	14104	14104051002	42	2017	Los Lagos	211843.1	2017	14104	19634	4159328181	Urbano
55	14105	14105011001	98	2017	Máfil	315022.1	2017	14105	7095	2235081533	Urbano
56	14106	14106011001	55	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano
57	14106	14106011002	41	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano
58	14106	14106011003	58	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano
59	14106	14106991999	1	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano
60	14107	14107031001	34	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano
61	14107	14107031002	42	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano
62	14107	14107031003	32	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano
63	14107	14107051001	14	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano
64	14107	14107991999	3	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano
65	14108	14108011001	71	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano
66	14108	14108011002	51	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano
67	14108	14108011003	60	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano
68	14108	14108051001	27	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano
69	14108	14108111001	20	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano
70	14108	14108991999	11	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano
71	14201	14201011001	59	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano
72	14201	14201011002	41	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano
73	14201	14201011003	39	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano
74	14201	14201011004	92	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano
75	14201	14201021001	29	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano
76	14201	14201091001	101	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano
77	14201	14201091002	85	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano
78	14201	14201091003	59	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano
79	14201	14201091004	35	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano
80	14201	14201091005	124	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano
81	14201	14201991999	3	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano
82	14202	14202011001	63	2017	Futrono	247331.7	2017	14202	14665	3627119212	Urbano
83	14202	14202011002	72	2017	Futrono	247331.7	2017	14202	14665	3627119212	Urbano
84	14202	14202041001	16	2017	Futrono	247331.7	2017	14202	14665	3627119212	Urbano
85	14203	14203011001	46	2017	Lago Ranco	247154.2	2017	14203	9896	2445838259	Urbano
86	14203	14203991999	1	2017	Lago Ranco	247154.2	2017	14203	9896	2445838259	Urbano
87	14204	14204011001	113	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano
88	14204	14204011002	150	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano
89	14204	14204011003	106	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano
90	14204	14204011004	66	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano
91	14204	14204011005	57	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano
92	14204	14204071001	11	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano
93	14204	14204991999	2	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
14101011001	14101	94	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3316	0.0199663	14101
14101021001	14101	577	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	5505	0.0331467	14101
14101031001	14101	106	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1916	0.0115366	14101
14101041001	14101	77	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3347	0.0201529	14101
14101041002	14101	11	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1217	0.0073278	14101
14101041003	14101	202	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3319	0.0199843	14101
14101041004	14101	18	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3079	0.0185393	14101
14101041005	14101	167	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4543	0.0273543	14101
14101051001	14101	67	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3908	0.0235308	14101
14101051002	14101	61	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2924	0.0176060	14101
14101051003	14101	36	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3046	0.0183406	14101
14101051004	14101	38	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1117	0.0067257	14101
14101051005	14101	182	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3677	0.0221399	14101
14101061001	14101	67	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4741	0.0285465	14101
14101061002	14101	30	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2213	0.0133249	14101
14101061003	14101	77	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3643	0.0219352	14101
14101061004	14101	60	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4455	0.0268244	14101
14101061005	14101	58	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2502	0.0150650	14101
14101061006	14101	10	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1422	0.0085621	14101
14101061007	14101	1	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	33	0.0001987	14101
14101071001	14101	85	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4214	0.0253733	14101
14101071002	14101	65	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3859	0.0232358	14101
14101071003	14101	131	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4649	0.0279925	14101
14101071004	14101	47	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1808	0.0108863	14101
14101071005	14101	337	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	6057	0.0364704	14101
14101071006	14101	61	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4766	0.0286970	14101
14101081001	14101	352	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	5118	0.0308165	14101
14101081002	14101	198	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3425	0.0206226	14101
14101081003	14101	471	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4434	0.0266980	14101
14101081004	14101	99	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4941	0.0297507	14101
14101081005	14101	314	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3782	0.0227722	14101
14101081006	14101	38	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	5317	0.0320147	14101
14101081007	14101	26	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3719	0.0223928	14101
14101081008	14101	65	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	5423	0.0326529	14101
14101081009	14101	54	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3389	0.0204058	14101
14101081010	14101	136	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	5849	0.0352180	14101
14101081011	14101	26	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2888	0.0173892	14101
14101091001	14101	169	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3024	0.0182081	14101
14101091002	14101	152	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4672	0.0281310	14101
14101101001	14101	61	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1538	0.0092606	14101
14101101002	14101	104	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2989	0.0179974	14101
14101101003	14101	80	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2463	0.0148302	14101
14101161001	14101	380	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1801	0.0108442	14101
14101171001	14101	73	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3989	0.0240185	14101
14101991999	14101	18	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	679	0.0040884	14101
14102011001	14102	51	2017	Corral	215990.7	2017	14102	5302	1145182670	Urbano	3469	0.6542814	14102
14102991999	14102	1	2017	Corral	215990.7	2017	14102	5302	1145182670	Urbano	12	0.0022633	14102
14103011001	14103	38	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano	2488	0.1485196	14103
14103011002	14103	17	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano	2058	0.1228510	14103
14103011003	14103	26	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano	3975	0.2372851	14103
14103031001	14103	69	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano	3061	0.1827245	14103
14104011001	14104	27	2017	Los Lagos	211843.1	2017	14104	19634	4159328181	Urbano	3294	0.1677702	14104
14104051001	14104	32	2017	Los Lagos	211843.1	2017	14104	19634	4159328181	Urbano	3514	0.1789752	14104
14104051002	14104	42	2017	Los Lagos	211843.1	2017	14104	19634	4159328181	Urbano	2938	0.1496384	14104
14105011001	14105	98	2017	Máfil	315022.1	2017	14105	7095	2235081533	Urbano	4239	0.5974630	14105
14106011001	14106	55	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano	3463	0.1627503	14106
14106011002	14106	41	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano	3400	0.1597895	14106
14106011003	14106	58	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano	2904	0.1364790	14106
14106991999	14106	1	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano	192	0.0090234	14106
14107031001	14107	34	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano	2834	0.1403804	14107
14107031002	14107	42	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano	4222	0.2091341	14107
14107031003	14107	32	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano	4219	0.2089855	14107
14107051001	14107	14	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano	1001	0.0495839	14107
14107991999	14107	3	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano	155	0.0076778	14107
14108011001	14108	71	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	5054	0.1463273	14108
14108011002	14108	51	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	4341	0.1256840	14108
14108011003	14108	60	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	1696	0.0491039	14108
14108051001	14108	27	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	1477	0.0427633	14108
14108111001	14108	20	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	1955	0.0566027	14108
14108991999	14108	11	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	750	0.0217146	14108
14201011001	14201	59	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	2522	0.0663056	14201
14201011002	14201	41	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	1283	0.0337312	14201
14201011003	14201	39	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	1697	0.0446156	14201
14201011004	14201	92	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	3020	0.0793985	14201
14201021001	14201	29	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	177	0.0046535	14201
14201091001	14201	101	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	1445	0.0379903	14201
14201091002	14201	85	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	3737	0.0982490	14201
14201091003	14201	59	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	2778	0.0730361	14201
14201091004	14201	35	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	4130	0.1085813	14201
14201091005	14201	124	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	5728	0.1505942	14201
14201991999	14201	3	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	145	0.0038122	14201
14202011001	14202	63	2017	Futrono	247331.7	2017	14202	14665	3627119212	Urbano	4140	0.2823048	14202
14202011002	14202	72	2017	Futrono	247331.7	2017	14202	14665	3627119212	Urbano	2955	0.2015002	14202
14202041001	14202	16	2017	Futrono	247331.7	2017	14202	14665	3627119212	Urbano	844	0.0575520	14202
14203011001	14203	46	2017	Lago Ranco	247154.2	2017	14203	9896	2445838259	Urbano	2146	0.2168553	14203
14203991999	14203	1	2017	Lago Ranco	247154.2	2017	14203	9896	2445838259	Urbano	123	0.0124293	14203
14204011001	14204	113	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	2356	0.0750988	14204
14204011002	14204	150	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	4161	0.1326342	14204
14204011003	14204	106	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	3509	0.1118513	14204
14204011004	14204	66	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	3988	0.1271197	14204
14204011005	14204	57	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	2512	0.0800714	14204
14204071001	14204	11	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	383	0.0122083	14204
14204991999	14204	2	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	234	0.0074589	14204

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
14101011001	14101	94	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3316	0.0199663	14101	980360328
14101021001	14101	577	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	5505	0.0331467	14101	1627528228
14101031001	14101	106	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1916	0.0115366	14101	566456691
14101041001	14101	77	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3347	0.0201529	14101	989525337
14101041002	14101	11	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1217	0.0073278	14101	359800518
14101041003	14101	202	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3319	0.0199843	14101	981247264
14101041004	14101	18	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3079	0.0185393	14101	910292355
14101041005	14101	167	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4543	0.0273543	14101	1343117301
14101051001	14101	67	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3908	0.0235308	14101	1155382437
14101051002	14101	61	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2924	0.0176060	14101	864467310
14101051003	14101	36	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3046	0.0183406	14101	900536055
14101051004	14101	38	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1117	0.0067257	14101	330235973
14101051005	14101	182	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3677	0.0221399	14101	1087088337
14101061001	14101	67	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4741	0.0285465	14101	1401655101
14101061002	14101	30	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2213	0.0133249	14101	654263391
14101061003	14101	77	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3643	0.0219352	14101	1077036392
14101061004	14101	60	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4455	0.0268244	14101	1317100501
14101061005	14101	58	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2502	0.0150650	14101	739704928
14101061006	14101	10	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1422	0.0085621	14101	420407837
14101061007	14101	1	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	33	0.0001987	14101	9756300
14101071001	14101	85	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4214	0.0253733	14101	1245849946
14101071002	14101	65	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3859	0.0232358	14101	1140895810
14101071003	14101	131	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4649	0.0279925	14101	1374455719
14101071004	14101	47	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1808	0.0108863	14101	534526982
14101071005	14101	337	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	6057	0.0364704	14101	1790724519
14101071006	14101	61	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4766	0.0286970	14101	1409046237
14101081001	14101	352	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	5118	0.0308165	14101	1513113437
14101081002	14101	198	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3425	0.0206226	14101	1012585683
14101081003	14101	471	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4434	0.0266980	14101	1310891946
14101081004	14101	99	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4941	0.0297507	14101	1460784192
14101081005	14101	314	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3782	0.0227722	14101	1118131110
14101081006	14101	38	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	5317	0.0320147	14101	1571946883
14101081007	14101	26	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3719	0.0223928	14101	1099505446
14101081008	14101	65	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	5423	0.0326529	14101	1603285301
14101081009	14101	54	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3389	0.0204058	14101	1001942446
14101081010	14101	136	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	5849	0.0352180	14101	1729230265
14101081011	14101	26	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2888	0.0173892	14101	853824073
14101091001	14101	169	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3024	0.0182081	14101	894031855
14101091002	14101	152	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4672	0.0281310	14101	1381255565
14101101001	14101	61	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1538	0.0092606	14101	454702709
14101101002	14101	104	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2989	0.0179974	14101	883684264
14101101003	14101	80	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2463	0.0148302	14101	728174755
14101161001	14101	380	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1801	0.0108442	14101	532457464
14101171001	14101	73	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3989	0.0240185	14101	1179329719
14101991999	14101	18	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	679	0.0040884	14101	200743264
14102011001	14102	51	2017	Corral	215990.7	2017	14102	5302	1145182670	Urbano	3469	0.6542814	14102	749271724
14102991999	14102	1	2017	Corral	215990.7	2017	14102	5302	1145182670	Urbano	12	0.0022633	14102	2591888
14103011001	14103	38	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano	2488	0.1485196	14103	632980714
14103011002	14103	17	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano	2058	0.1228510	14103	523582921
14103011003	14103	26	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano	3975	0.2372851	14103	1011293544
14103031001	14103	69	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano	3061	0.1827245	14103	778759632
14104011001	14104	27	2017	Los Lagos	211843.1	2017	14104	19634	4159328181	Urbano	3294	0.1677702	14104	697811298
14104051001	14104	32	2017	Los Lagos	211843.1	2017	14104	19634	4159328181	Urbano	3514	0.1789752	14104	744416789
14104051002	14104	42	2017	Los Lagos	211843.1	2017	14104	19634	4159328181	Urbano	2938	0.1496384	14104	622395141
14105011001	14105	98	2017	Máfil	315022.1	2017	14105	7095	2235081533	Urbano	4239	0.5974630	14105	1335378523
14106011001	14106	55	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano	3463	0.1627503	14106	820603610
14106011002	14106	41	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano	3400	0.1597895	14106	805674927
14106011003	14106	58	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano	2904	0.1364790	14106	688141173
14106991999	14106	1	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano	192	0.0090234	14106	45496937
14107031001	14107	34	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano	2834	0.1403804	14107	605377876
14107031002	14107	42	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano	4222	0.2091341	14107	901872051
14107031003	14107	32	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano	4219	0.2089855	14107	901231214
14107051001	14107	14	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano	1001	0.0495839	14107	213826131
14107991999	14107	3	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano	155	0.0076778	14107	33109940
14108011001	14108	71	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	5054	0.1463273	14108	1368979918
14108011002	14108	51	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	4341	0.1256840	14108	1175849194
14108011003	14108	60	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	1696	0.0491039	14108	459396506
14108051001	14108	27	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	1477	0.0427633	14108	400075849
14108111001	14108	20	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	1955	0.0566027	14108	529551987
14108991999	14108	11	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	750	0.0217146	14108	203152936
14201011001	14201	59	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	2522	0.0663056	14201	608008126
14201011002	14201	41	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	1283	0.0337312	14201	309307861
14201011003	14201	39	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	1697	0.0446156	14201	409115697
14201011004	14201	92	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	3020	0.0793985	14201	728066828
14201021001	14201	29	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	177	0.0046535	14201	42671466
14201091001	14201	101	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	1445	0.0379903	14201	348363101
14201091002	14201	85	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	3737	0.0982490	14201	900922429
14201091003	14201	59	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	2778	0.0730361	14201	669725049
14201091004	14201	35	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	4130	0.1085813	14201	995667549
14201091005	14201	124	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	5728	0.1505942	14201	1380916155
14201991999	14201	3	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	145	0.0038122	14201	34956851
14202011001	14202	63	2017	Futrono	247331.7	2017	14202	14665	3627119212	Urbano	4140	0.2823048	14202	1023953190
14202011002	14202	72	2017	Futrono	247331.7	2017	14202	14665	3627119212	Urbano	2955	0.2015002	14202	730865140
14202041001	14202	16	2017	Futrono	247331.7	2017	14202	14665	3627119212	Urbano	844	0.0575520	14202	208747945
14203011001	14203	46	2017	Lago Ranco	247154.2	2017	14203	9896	2445838259	Urbano	2146	0.2168553	14203	530392977
14203991999	14203	1	2017	Lago Ranco	247154.2	2017	14203	9896	2445838259	Urbano	123	0.0124293	14203	30399970
14204011001	14204	113	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	2356	0.0750988	14204	611283430
14204011002	14204	150	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	4161	0.1326342	14204	1079605413
14204011003	14204	106	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	3509	0.1118513	14204	910438691
14204011004	14204	66	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	3988	0.1271197	14204	1034719151
14204011005	14204	57	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	2512	0.0800714	14204	651758904
14204071001	14204	11	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	383	0.0122083	14204	99372476
14204991999	14204	2	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	234	0.0074589	14204	60713210

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -968992442 -281751894  -11373724  264997736  867475142 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 615918612   52340811  11.767  < 2e-16 ***
## Freq.x        2330346     405438   5.748 1.19e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 384700000 on 91 degrees of freedom
## Multiple R-squared:  0.2663, Adjusted R-squared:  0.2583 
## F-statistic: 33.04 on 1 and 91 DF,  p-value: 1.192e-07

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.258281772386418"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.258281772386418"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                
## [1,] "logarítmico" "0.51349834053722"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.416864060775169"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.419663407882544"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.346505760521134"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.632508230970549"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.680821590140021"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 1    cuadrático 0.258281772386418
## 2        cúbico 0.258281772386418
## 6      log-raíz 0.346505760521134
## 4 raíz cuadrada 0.416864060775169
## 5     raíz-raíz 0.419663407882544
## 3   logarítmico  0.51349834053722
## 7      raíz-log 0.632508230970549
## 8       log-log 0.680821590140021
##                                                                     sintaxis
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 8
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.57287 -0.33804  0.02958  0.42792  1.15147 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.34077    0.21271   81.52   <2e-16 ***
## log(Freq.x)  0.73936    0.05265   14.04   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6525 on 91 degrees of freedom
## Multiple R-squared:  0.6843, Adjusted R-squared:  0.6808 
## F-statistic: 197.2 on 1 and 91 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    17.34077

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##    0.739363

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.6808 ).

9.1 Diagrama de dispersión sobre log-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.57287 -0.33804  0.02958  0.42792  1.15147 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.34077    0.21271   81.52   <2e-16 ***
## log(Freq.x)  0.73936    0.05265   14.04   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6525 on 91 degrees of freedom
## Multiple R-squared:  0.6843, Adjusted R-squared:  0.6808 
## F-statistic: 197.2 on 1 and 91 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = e^{17.34077+0.739363 \cdot ln{X}} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
1	14101011001	14101	94	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3316	0.0199663	14101	980360328	976918454
2	14101021001	14101	577	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	5505	0.0331467	14101	1627528228	3736903062
3	14101031001	14101	106	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1916	0.0115366	14101	566456691	1067669425
4	14101041001	14101	77	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3347	0.0201529	14101	989525337	842950407
5	14101041002	14101	11	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1217	0.0073278	14101	359800518	199971354
6	14101041003	14101	202	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3319	0.0199843	14101	981247264	1719856037
7	14101041004	14101	18	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3079	0.0185393	14101	910292355	287807837
8	14101041005	14101	167	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4543	0.0273543	14101	1343117301	1494152436
9	14101051001	14101	67	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3908	0.0235308	14101	1155382437	760558678
10	14101051002	14101	61	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2924	0.0176060	14101	864467310	709589864
11	14101051003	14101	36	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3046	0.0183406	14101	900536055	480477508
12	14101051004	14101	38	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1117	0.0067257	14101	330235973	500073817
13	14101051005	14101	182	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3677	0.0221399	14101	1087088337	1592259143
14	14101061001	14101	67	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4741	0.0285465	14101	1401655101	760558678
15	14101061002	14101	30	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2213	0.0133249	14101	654263391	419884050
16	14101061003	14101	77	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3643	0.0219352	14101	1077036392	842950407
17	14101061004	14101	60	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4455	0.0268244	14101	1317100501	700970633
18	14101061005	14101	58	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2502	0.0150650	14101	739704928	683618791
19	14101061006	14101	10	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1422	0.0085621	14101	420407837	186364662
20	14101061007	14101	1	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	33	0.0001987	14101	9756300	33962570
21	14101071001	14101	85	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4214	0.0253733	14101	1245849946	906862719
22	14101071002	14101	65	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3859	0.0232358	14101	1140895810	743706606
23	14101071003	14101	131	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4649	0.0279925	14101	1374455719	1248626776
24	14101071004	14101	47	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1808	0.0108863	14101	534526982	585177834
25	14101071005	14101	337	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	6057	0.0364704	14101	1790724519	2510941654
26	14101071006	14101	61	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4766	0.0286970	14101	1409046237	709589864
27	14101081001	14101	352	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	5118	0.0308165	14101	1513113437	2593104498
28	14101081002	14101	198	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3425	0.0206226	14101	1012585683	1694610354
29	14101081003	14101	471	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4434	0.0266980	14101	1310891946	3216128218
30	14101081004	14101	99	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4941	0.0297507	14101	1460784192	1015077986
31	14101081005	14101	314	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3782	0.0227722	14101	1118131110	2383076531
32	14101081006	14101	38	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	5317	0.0320147	14101	1571946883	500073817
33	14101081007	14101	26	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3719	0.0223928	14101	1099505446	377728291
34	14101081008	14101	65	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	5423	0.0326529	14101	1603285301	743706606
35	14101081009	14101	54	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3389	0.0204058	14101	1001942446	648437955
36	14101081010	14101	136	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	5849	0.0352180	14101	1729230265	1283690465
37	14101081011	14101	26	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2888	0.0173892	14101	853824073	377728291
38	14101091001	14101	169	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3024	0.0182081	14101	894031855	1507362083
39	14101091002	14101	152	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4672	0.0281310	14101	1381255565	1393718321
40	14101101001	14101	61	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1538	0.0092606	14101	454702709	709589864
41	14101101002	14101	104	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2989	0.0179974	14101	883684264	1052738258
42	14101101003	14101	80	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2463	0.0148302	14101	728174755	867111418
43	14101161001	14101	380	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1801	0.0108442	14101	532457464	2744082317
44	14101171001	14101	73	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3989	0.0240185	14101	1179329719	810349861
45	14101991999	14101	18	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	679	0.0040884	14101	200743264	287807837
46	14102011001	14102	51	2017	Corral	215990.7	2017	14102	5302	1145182670	Urbano	3469	0.6542814	14102	749271724	621605413
47	14102991999	14102	1	2017	Corral	215990.7	2017	14102	5302	1145182670	Urbano	12	0.0022633	14102	2591888	33962570
48	14103011001	14103	38	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano	2488	0.1485196	14103	632980714	500073817
49	14103011002	14103	17	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano	2058	0.1228510	14103	523582921	275898269
50	14103011003	14103	26	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano	3975	0.2372851	14103	1011293544	377728291
51	14103031001	14103	69	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano	3061	0.1827245	14103	778759632	777280123
52	14104011001	14104	27	2017	Los Lagos	211843.1	2017	14104	19634	4159328181	Urbano	3294	0.1677702	14104	697811298	388416777
53	14104051001	14104	32	2017	Los Lagos	211843.1	2017	14104	19634	4159328181	Urbano	3514	0.1789752	14104	744416789	440405545
54	14104051002	14104	42	2017	Los Lagos	211843.1	2017	14104	19634	4159328181	Urbano	2938	0.1496384	14104	622395141	538481817
55	14105011001	14105	98	2017	Máfil	315022.1	2017	14105	7095	2235081533	Urbano	4239	0.5974630	14105	1335378523	1007487043
56	14106011001	14106	55	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano	3463	0.1627503	14106	820603610	657295046
57	14106011002	14106	41	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano	3400	0.1597895	14106	805674927	528972736
58	14106011003	14106	58	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano	2904	0.1364790	14106	688141173	683618791
59	14106991999	14106	1	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano	192	0.0090234	14106	45496937	33962570
60	14107031001	14107	34	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano	2834	0.1403804	14107	605377876	460595216
61	14107031002	14107	42	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano	4222	0.2091341	14107	901872051	538481817
62	14107031003	14107	32	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano	4219	0.2089855	14107	901231214	440405545
63	14107051001	14107	14	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano	1001	0.0495839	14107	213826131	239004034
64	14107991999	14107	3	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano	155	0.0076778	14107	33109940	76518484
65	14108011001	14108	71	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	5054	0.1463273	14108	1368979918	793875700
66	14108011002	14108	51	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	4341	0.1256840	14108	1175849194	621605413
67	14108011003	14108	60	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	1696	0.0491039	14108	459396506	700970633
68	14108051001	14108	27	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	1477	0.0427633	14108	400075849	388416777
69	14108111001	14108	20	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	1955	0.0566027	14108	529551987	311124357
70	14108991999	14108	11	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	750	0.0217146	14108	203152936	199971354
71	14201011001	14201	59	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	2522	0.0663056	14201	608008126	692313876
72	14201011002	14201	41	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	1283	0.0337312	14201	309307861	528972736
73	14201011003	14201	39	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	1697	0.0446156	14201	409115697	509770710
74	14201011004	14201	92	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	3020	0.0793985	14201	728066828	961507428
75	14201021001	14201	29	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	177	0.0046535	14201	42671466	409490231
76	14201091001	14201	101	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	1445	0.0379903	14201	348363101	1030200245
77	14201091002	14201	85	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	3737	0.0982490	14201	900922429	906862719
78	14201091003	14201	59	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	2778	0.0730361	14201	669725049	692313876
79	14201091004	14201	35	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	4130	0.1085813	14201	995667549	470573382
80	14201091005	14201	124	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	5728	0.1505942	14201	1380916155	1198944622
81	14201991999	14201	3	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	145	0.0038122	14201	34956851	76518484
82	14202011001	14202	63	2017	Futrono	247331.7	2017	14202	14665	3627119212	Urbano	4140	0.2823048	14202	1023953190	726718821
83	14202011002	14202	72	2017	Futrono	247331.7	2017	14202	14665	3627119212	Urbano	2955	0.2015002	14202	730865140	802127690
84	14202041001	14202	16	2017	Futrono	247331.7	2017	14202	14665	3627119212	Urbano	844	0.0575520	14202	208747945	263804581
85	14203011001	14203	46	2017	Lago Ranco	247154.2	2017	14203	9896	2445838259	Urbano	2146	0.2168553	14203	530392977	575946571
86	14203991999	14203	1	2017	Lago Ranco	247154.2	2017	14203	9896	2445838259	Urbano	123	0.0124293	14203	30399970	33962570
87	14204011001	14204	113	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	2356	0.0750988	14204	611283430	1119362679
88	14204011002	14204	150	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	4161	0.1326342	14204	1079605413	1380136208
89	14204011003	14204	106	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	3509	0.1118513	14204	910438691	1067669425
90	14204011004	14204	66	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	3988	0.1271197	14204	1034719151	752149280
91	14204011005	14204	57	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	2512	0.0800714	14204	651758904	674884542
92	14204071001	14204	11	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	383	0.0122083	14204	99372476	199971354
93	14204991999	14204	2	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	234	0.0074589	14204	60713210	56698425
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)


r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
1	14101011001	14101	94	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3316	0.0199663	14101	980360328	976918454	294607.50
2	14101021001	14101	577	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	5505	0.0331467	14101	1627528228	3736903062	678819.81
3	14101031001	14101	106	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1916	0.0115366	14101	566456691	1067669425	557238.74
4	14101041001	14101	77	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3347	0.0201529	14101	989525337	842950407	251852.53
5	14101041002	14101	11	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1217	0.0073278	14101	359800518	199971354	164315.00
6	14101041003	14101	202	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3319	0.0199843	14101	981247264	1719856037	518185.01
7	14101041004	14101	18	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3079	0.0185393	14101	910292355	287807837	93474.45
8	14101041005	14101	167	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4543	0.0273543	14101	1343117301	1494152436	328891.14
9	14101051001	14101	67	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3908	0.0235308	14101	1155382437	760558678	194615.83
10	14101051002	14101	61	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2924	0.0176060	14101	864467310	709589864	242677.79
11	14101051003	14101	36	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3046	0.0183406	14101	900536055	480477508	157740.48
12	14101051004	14101	38	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1117	0.0067257	14101	330235973	500073817	447693.66
13	14101051005	14101	182	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3677	0.0221399	14101	1087088337	1592259143	433032.13
14	14101061001	14101	67	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4741	0.0285465	14101	1401655101	760558678	160421.57
15	14101061002	14101	30	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2213	0.0133249	14101	654263391	419884050	189735.22
16	14101061003	14101	77	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3643	0.0219352	14101	1077036392	842950407	231389.08
17	14101061004	14101	60	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4455	0.0268244	14101	1317100501	700970633	157344.70
18	14101061005	14101	58	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2502	0.0150650	14101	739704928	683618791	273228.93
19	14101061006	14101	10	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1422	0.0085621	14101	420407837	186364662	131058.13
20	14101061007	14101	1	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	33	0.0001987	14101	9756300	33962570	1029168.79
21	14101071001	14101	85	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4214	0.0253733	14101	1245849946	906862719	215202.35
22	14101071002	14101	65	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3859	0.0232358	14101	1140895810	743706606	192720.03
23	14101071003	14101	131	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4649	0.0279925	14101	1374455719	1248626776	268579.65
24	14101071004	14101	47	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1808	0.0108863	14101	534526982	585177834	323660.31
25	14101071005	14101	337	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	6057	0.0364704	14101	1790724519	2510941654	414552.03
26	14101071006	14101	61	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4766	0.0286970	14101	1409046237	709589864	148885.83
27	14101081001	14101	352	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	5118	0.0308165	14101	1513113437	2593104498	506663.64
28	14101081002	14101	198	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3425	0.0206226	14101	1012585683	1694610354	494776.75
29	14101081003	14101	471	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4434	0.0266980	14101	1310891946	3216128218	725333.38
30	14101081004	14101	99	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4941	0.0297507	14101	1460784192	1015077986	205439.79
31	14101081005	14101	314	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3782	0.0227722	14101	1118131110	2383076531	630110.14
32	14101081006	14101	38	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	5317	0.0320147	14101	1571946883	500073817	94051.87
33	14101081007	14101	26	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3719	0.0223928	14101	1099505446	377728291	101567.17
34	14101081008	14101	65	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	5423	0.0326529	14101	1603285301	743706606	137139.33
35	14101081009	14101	54	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3389	0.0204058	14101	1001942446	648437955	191336.07
36	14101081010	14101	136	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	5849	0.0352180	14101	1729230265	1283690465	219471.78
37	14101081011	14101	26	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2888	0.0173892	14101	853824073	377728291	130792.34
38	14101091001	14101	169	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3024	0.0182081	14101	894031855	1507362083	498466.30
39	14101091002	14101	152	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	4672	0.0281310	14101	1381255565	1393718321	298313.00
40	14101101001	14101	61	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1538	0.0092606	14101	454702709	709589864	461371.82
41	14101101002	14101	104	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2989	0.0179974	14101	883684264	1052738258	352204.17
42	14101101003	14101	80	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	2463	0.0148302	14101	728174755	867111418	352054.98
43	14101161001	14101	380	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	1801	0.0108442	14101	532457464	2744082317	1523643.71
44	14101171001	14101	73	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	3989	0.0240185	14101	1179329719	810349861	203146.12
45	14101991999	14101	18	2017	Valdivia	295645.5	2017	14101	166080	49100797127	Urbano	679	0.0040884	14101	200743264	287807837	423870.16
46	14102011001	14102	51	2017	Corral	215990.7	2017	14102	5302	1145182670	Urbano	3469	0.6542814	14102	749271724	621605413	179188.65
47	14102991999	14102	1	2017	Corral	215990.7	2017	14102	5302	1145182670	Urbano	12	0.0022633	14102	2591888	33962570	2830214.17
48	14103011001	14103	38	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano	2488	0.1485196	14103	632980714	500073817	200994.30
49	14103011002	14103	17	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano	2058	0.1228510	14103	523582921	275898269	134061.36
50	14103011003	14103	26	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano	3975	0.2372851	14103	1011293544	377728291	95025.99
51	14103031001	14103	69	2017	Lanco	254413.5	2017	14103	16752	4261934451	Urbano	3061	0.1827245	14103	778759632	777280123	253930.13
52	14104011001	14104	27	2017	Los Lagos	211843.1	2017	14104	19634	4159328181	Urbano	3294	0.1677702	14104	697811298	388416777	117916.45
53	14104051001	14104	32	2017	Los Lagos	211843.1	2017	14104	19634	4159328181	Urbano	3514	0.1789752	14104	744416789	440405545	125328.84
54	14104051002	14104	42	2017	Los Lagos	211843.1	2017	14104	19634	4159328181	Urbano	2938	0.1496384	14104	622395141	538481817	183281.76
55	14105011001	14105	98	2017	Máfil	315022.1	2017	14105	7095	2235081533	Urbano	4239	0.5974630	14105	1335378523	1007487043	237670.92
56	14106011001	14106	55	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano	3463	0.1627503	14106	820603610	657295046	189805.10
57	14106011002	14106	41	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano	3400	0.1597895	14106	805674927	528972736	155580.22
58	14106011003	14106	58	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano	2904	0.1364790	14106	688141173	683618791	235405.92
59	14106991999	14106	1	2017	Mariquina	236963.2	2017	14106	21278	5042103267	Urbano	192	0.0090234	14106	45496937	33962570	176888.39
60	14107031001	14107	34	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano	2834	0.1403804	14107	605377876	460595216	162524.78
61	14107031002	14107	42	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano	4222	0.2091341	14107	901872051	538481817	127541.88
62	14107031003	14107	32	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano	4219	0.2089855	14107	901231214	440405545	104386.24
63	14107051001	14107	14	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano	1001	0.0495839	14107	213826131	239004034	238765.27
64	14107991999	14107	3	2017	Paillaco	213612.5	2017	14107	20188	4312409515	Urbano	155	0.0076778	14107	33109940	76518484	493667.64
65	14108011001	14108	71	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	5054	0.1463273	14108	1368979918	793875700	157078.69
66	14108011002	14108	51	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	4341	0.1256840	14108	1175849194	621605413	143194.06
67	14108011003	14108	60	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	1696	0.0491039	14108	459396506	700970633	413308.16
68	14108051001	14108	27	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	1477	0.0427633	14108	400075849	388416777	262976.83
69	14108111001	14108	20	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	1955	0.0566027	14108	529551987	311124357	159142.89
70	14108991999	14108	11	2017	Panguipulli	270870.6	2017	14108	34539	9355599011	Urbano	750	0.0217146	14108	203152936	199971354	266628.47
71	14201011001	14201	59	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	2522	0.0663056	14201	608008126	692313876	274509.86
72	14201011002	14201	41	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	1283	0.0337312	14201	309307861	528972736	412293.64
73	14201011003	14201	39	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	1697	0.0446156	14201	409115697	509770710	300395.23
74	14201011004	14201	92	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	3020	0.0793985	14201	728066828	961507428	318379.94
75	14201021001	14201	29	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	177	0.0046535	14201	42671466	409490231	2313504.13
76	14201091001	14201	101	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	1445	0.0379903	14201	348363101	1030200245	712941.35
77	14201091002	14201	85	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	3737	0.0982490	14201	900922429	906862719	242671.32
78	14201091003	14201	59	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	2778	0.0730361	14201	669725049	692313876	249213.06
79	14201091004	14201	35	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	4130	0.1085813	14201	995667549	470573382	113940.29
80	14201091005	14201	124	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	5728	0.1505942	14201	1380916155	1198944622	209312.96
81	14201991999	14201	3	2017	La Unión	241081.7	2017	14201	38036	9169784719	Urbano	145	0.0038122	14201	34956851	76518484	527713.68
82	14202011001	14202	63	2017	Futrono	247331.7	2017	14202	14665	3627119212	Urbano	4140	0.2823048	14202	1023953190	726718821	175535.95
83	14202011002	14202	72	2017	Futrono	247331.7	2017	14202	14665	3627119212	Urbano	2955	0.2015002	14202	730865140	802127690	271447.61
84	14202041001	14202	16	2017	Futrono	247331.7	2017	14202	14665	3627119212	Urbano	844	0.0575520	14202	208747945	263804581	312564.67
85	14203011001	14203	46	2017	Lago Ranco	247154.2	2017	14203	9896	2445838259	Urbano	2146	0.2168553	14203	530392977	575946571	268381.44
86	14203991999	14203	1	2017	Lago Ranco	247154.2	2017	14203	9896	2445838259	Urbano	123	0.0124293	14203	30399970	33962570	276118.46
87	14204011001	14204	113	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	2356	0.0750988	14204	611283430	1119362679	475111.49
88	14204011002	14204	150	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	4161	0.1326342	14204	1079605413	1380136208	331683.78
89	14204011003	14204	106	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	3509	0.1118513	14204	910438691	1067669425	304266.01
90	14204011004	14204	66	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	3988	0.1271197	14204	1034719151	752149280	188603.13
91	14204011005	14204	57	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	2512	0.0800714	14204	651758904	674884542	268664.23
92	14204071001	14204	11	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	383	0.0122083	14204	99372476	199971354	522118.42
93	14204991999	14204	2	2017	Río Bueno	259458.2	2017	14204	31372	8139721467	Urbano	234	0.0074589	14204	60713210	56698425	242300.96
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_urbano_nivel_nacional_17_r14.rds")

---

Rural

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 2)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15202	1	2	6	13225	1	3	1	5	3	5	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	3	1	1	5	3	5	2	3	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	4	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	5	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	6	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	8	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	9	3	1	5	3	5	1	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	10	1	1	5	3	4	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	11	1	2	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	12	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	13	1	1	5	3	4	1	4	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	14	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	16	1	1	5	3	5	1	3	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	17	1	1	5	3	5	2	4	1	8	15	152	15202	15202012006
15	152	15202	1	2	6	13225	18	3	1	5	3	5	1	1	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	19	3	1	5	3	5	1	3	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	20	1	1	5	3	5	2	1	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	21	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	22	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	24	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	25	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	26	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	27	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	28	3	1	5	3	5	1	4	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	29	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	30	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	31	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	32	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	33	1	1	5	3	5	3	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	34	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	35	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	36	1	1	5	3	5	3	2	1	9	15	152	15202	15202012006
15	152	15202	1	2	6	13225	37	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	38	1	1	5	3	5	99	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	39	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	40	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	41	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	42	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	43	3	1	5	3	5	2	1	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	44	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	45	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	8	13910	1	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	3	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	4	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	5	3	1	5	3	5	2	3	1	3	15	152	15202	15202012008
15	152	15202	1	2	8	13910	6	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	7	1	1	5	99	5	2	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	8	3	1	5	3	5	3	3	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	9	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	10	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	11	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	12	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	13	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	14	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	16	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	18	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	20	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	21	1	1	5	3	5	1	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	22	3	1	5	3	5	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	23	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	24	3	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	25	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	26	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	27	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	28	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	29	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	30	1	1	5	1	4	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	12	8394	1	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	2	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	3	3	1	5	3	5	2	3	1	4	15	152	15202	15202012012
15	152	15202	1	2	12	8394	4	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	5	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	6	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	8	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	9	3	1	5	3	5	1	4	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	10	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	11	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	12	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	13	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	14	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	15	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	16	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	18	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	20	3	1	5	3	5	3	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	21	3	1	5	99	5	1	4	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	22	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	24	3	1	5	3	5	1	2	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	25	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 14:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 14)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Veamos los primeros 100 registros:

r3_100 <- d[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	unlist.c.	unlist.d.	Freq	anio
1	14101042005	1	14101	5	2017
2	14101042058	1	14101	3	2017
3	14101062011	1	14101	2	2017
4	14101062013	1	14101	2	2017
5	14101062021	1	14101	6	2017
6	14101062047	1	14101	1	2017
7	14101072002	1	14101	6	2017
8	14101072045	1	14101	15	2017
9	14101082002	1	14101	54	2017
10	14101112005	1	14101	24	2017
11	14101112010	1	14101	8	2017
12	14101112017	1	14101	35	2017
13	14101112037	1	14101	14	2017
14	14101112046	1	14101	6	2017
15	14101112058	1	14101	2	2017
16	14101122007	1	14101	26	2017
17	14101122009	1	14101	2	2017
18	14101122042	1	14101	5	2017
19	14101122058	1	14101	31	2017
20	14101132003	1	14101	9	2017
21	14101132050	1	14101	2	2017
22	14101132901	1	14101	5	2017
23	14101142014	1	14101	5	2017
24	14101142039	1	14101	1	2017
25	14101142059	1	14101	1	2017
26	14101152059	1	14101	1	2017
27	14101162018	1	14101	2	2017
28	14101162019	1	14101	8	2017
29	14101162034	1	14101	1	2017
30	14101162053	1	14101	5	2017
31	14101162061	1	14101	6	2017
32	14101172001	1	14101	42	2017
33	14101172016	1	14101	7	2017
34	14101172027	1	14101	3	2017
35	14101172036	1	14101	1	2017
36	14101172055	1	14101	1	2017
37	14101182004	1	14101	2	2017
38	14101182006	1	14101	2	2017
39	14101182015	1	14101	16	2017
40	14101182040	1	14101	4	2017
474	14102012011	1	14102	1	2017
475	14102012018	1	14102	2	2017
476	14102022008	1	14102	5	2017
477	14102022010	1	14102	6	2017
478	14102032001	1	14102	2	2017
479	14102032005	1	14102	1	2017
480	14102032019	1	14102	1	2017
481	14102032901	1	14102	1	2017
482	14102042006	1	14102	1	2017
483	14102042007	1	14102	1	2017
484	14102042015	1	14102	1	2017
485	14102042018	1	14102	4	2017
919	14103012004	1	14103	3	2017
920	14103012027	1	14103	1	2017
921	14103012040	1	14103	1	2017
922	14103022030	1	14103	4	2017
923	14103022039	1	14103	5	2017
924	14103022042	1	14103	1	2017
925	14103022044	1	14103	1	2017
926	14103032001	1	14103	1	2017
927	14103032002	1	14103	4	2017
928	14103032015	1	14103	2	2017
929	14103032019	1	14103	1	2017
930	14103032034	1	14103	1	2017
931	14103032041	1	14103	2	2017
932	14103032042	1	14103	1	2017
933	14103032901	1	14103	4	2017
934	14103052003	1	14103	3	2017
935	14103052035	1	14103	5	2017
936	14103052040	1	14103	3	2017
937	14103062021	1	14103	4	2017
938	14103062047	1	14103	1	2017
939	14103062048	1	14103	2	2017
940	14103072010	1	14103	3	2017
941	14103072020	1	14103	2	2017
942	14103072033	1	14103	1	2017
1376	14104012003	1	14104	5	2017
1377	14104012009	1	14104	2	2017
1378	14104012010	1	14104	1	2017
1379	14104012901	1	14104	1	2017
1380	14104022002	1	14104	8	2017
1381	14104022005	1	14104	3	2017
1382	14104022018	1	14104	6	2017
1383	14104022038	1	14104	1	2017
1384	14104022040	1	14104	3	2017
1385	14104022050	1	14104	2	2017
1386	14104022076	1	14104	15	2017
1387	14104032047	1	14104	5	2017
1388	14104042025	1	14104	8	2017
1389	14104042028	1	14104	3	2017
1390	14104042043	1	14104	1	2017
1391	14104042052	1	14104	3	2017
1392	14104042060	1	14104	1	2017
1393	14104042068	1	14104	9	2017
1394	14104042901	1	14104	1	2017
1395	14104052011	1	14104	1	2017
1396	14104052013	1	14104	2	2017
1397	14104052020	1	14104	9	2017
1398	14104052034	1	14104	3	2017
1399	14104052036	1	14104	2	2017

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	14101042005	5	2017	14101
2	14101042058	3	2017	14101
3	14101062011	2	2017	14101
4	14101062013	2	2017	14101
5	14101062021	6	2017	14101
6	14101062047	1	2017	14101
7	14101072002	6	2017	14101
8	14101072045	15	2017	14101
9	14101082002	54	2017	14101
10	14101112005	24	2017	14101
11	14101112010	8	2017	14101
12	14101112017	35	2017	14101
13	14101112037	14	2017	14101
14	14101112046	6	2017	14101
15	14101112058	2	2017	14101
16	14101122007	26	2017	14101
17	14101122009	2	2017	14101
18	14101122042	5	2017	14101
19	14101122058	31	2017	14101
20	14101132003	9	2017	14101
21	14101132050	2	2017	14101
22	14101132901	5	2017	14101
23	14101142014	5	2017	14101
24	14101142039	1	2017	14101
25	14101142059	1	2017	14101
26	14101152059	1	2017	14101
27	14101162018	2	2017	14101
28	14101162019	8	2017	14101
29	14101162034	1	2017	14101
30	14101162053	5	2017	14101
31	14101162061	6	2017	14101
32	14101172001	42	2017	14101
33	14101172016	7	2017	14101
34	14101172027	3	2017	14101
35	14101172036	1	2017	14101
36	14101172055	1	2017	14101
37	14101182004	2	2017	14101
38	14101182006	2	2017	14101
39	14101182015	16	2017	14101
40	14101182040	4	2017	14101
474	14102012011	1	2017	14102
475	14102012018	2	2017	14102
476	14102022008	5	2017	14102
477	14102022010	6	2017	14102
478	14102032001	2	2017	14102
479	14102032005	1	2017	14102
480	14102032019	1	2017	14102
481	14102032901	1	2017	14102
482	14102042006	1	2017	14102
483	14102042007	1	2017	14102
484	14102042015	1	2017	14102
485	14102042018	4	2017	14102
919	14103012004	3	2017	14103
920	14103012027	1	2017	14103
921	14103012040	1	2017	14103
922	14103022030	4	2017	14103
923	14103022039	5	2017	14103
924	14103022042	1	2017	14103
925	14103022044	1	2017	14103
926	14103032001	1	2017	14103
927	14103032002	4	2017	14103
928	14103032015	2	2017	14103
929	14103032019	1	2017	14103
930	14103032034	1	2017	14103
931	14103032041	2	2017	14103
932	14103032042	1	2017	14103
933	14103032901	4	2017	14103
934	14103052003	3	2017	14103
935	14103052035	5	2017	14103
936	14103052040	3	2017	14103
937	14103062021	4	2017	14103
938	14103062047	1	2017	14103
939	14103062048	2	2017	14103
940	14103072010	3	2017	14103
941	14103072020	2	2017	14103
942	14103072033	1	2017	14103
1376	14104012003	5	2017	14104
1377	14104012009	2	2017	14104
1378	14104012010	1	2017	14104
1379	14104012901	1	2017	14104
1380	14104022002	8	2017	14104
1381	14104022005	3	2017	14104
1382	14104022018	6	2017	14104
1383	14104022038	1	2017	14104
1384	14104022040	3	2017	14104
1385	14104022050	2	2017	14104
1386	14104022076	15	2017	14104
1387	14104032047	5	2017	14104
1388	14104042025	8	2017	14104
1389	14104042028	3	2017	14104
1390	14104042043	1	2017	14104
1391	14104042052	3	2017	14104
1392	14104042060	1	2017	14104
1393	14104042068	9	2017	14104
1394	14104042901	1	2017	14104
1395	14104052011	1	2017	14104
1396	14104052013	2	2017	14104
1397	14104052020	9	2017	14104
1398	14104052034	3	2017	14104
1399	14104052036	2	2017	14104

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_rural_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	289375.3	2017	1101	191468	55406102543	Rural
01401	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
01402	Camiña	262850.3	2017	1402	1250	328562901	Rural
01404	Huara	253968.5	2017	1404	2730	693334131	Rural
01405	Pica	290496.7	2017	1405	9296	2700457509	Rural
02103	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
02104	Taltal	345494.0	2017	2104	13317	4600943086	Rural
02201	Calama	310025.0	2017	2201	165731	51380756402	Rural
02203	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
02301	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
03101	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03103	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03202	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural
03301	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03302	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03303	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03304	Huasco	227560.7	2017	3304	10149	2309513927	Rural
04101	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04102	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04103	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04104	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04105	Paiguano	205942.1	2017	4105	4497	926121774	Rural
04106	Vicuña	176130.6	2017	4106	27771	4891322768	Rural
04201	Illapel	191976.8	2017	4201	30848	5922099530	Rural
04202	Canela	171370.3	2017	4202	9093	1558270441	Rural
04203	Los Vilos	173238.5	2017	4203	21382	3704185607	Rural
04204	Salamanca	223234.2	2017	4204	29347	6551254640	Rural
04301	Ovalle	241393.7	2017	4301	111272	26860360045	Rural
04302	Combarbalá	179139.6	2017	4302	13322	2386498044	Rural
04303	Monte Patria	201205.8	2017	4303	30751	6187280931	Rural
04304	Punitaqui	171931.7	2017	4304	10956	1883683880	Rural
04305	Río Hurtado	182027.2	2017	4305	4278	778712384	Rural
05101	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05102	Casablanca	268917.1	2017	5102	26867	7224996933	Rural
05105	Puchuncaví	279614.4	2017	5105	18546	5185728335	Rural
05107	Quintero	334628.2	2017	5107	31923	10682335196	Rural
05301	Los Andes	324402.1	2017	5301	66708	21640215030	Rural
05302	Calle Larga	242743.8	2017	5302	14832	3600375502	Rural
05303	Rinconada	326532.5	2017	5303	10207	3332917471	Rural
05304	San Esteban	223168.6	2017	5304	18855	4207844130	Rural
05401	La Ligua	181468.0	2017	5401	35390	6422154059	Rural
05402	Cabildo	231277.8	2017	5402	19388	4484014285	Rural
05404	Petorca	298208.9	2017	5404	9826	2930200178	Rural
05405	Zapallar	292882.3	2017	5405	7339	2149463129	Rural
05501	Quillota	220926.8	2017	5501	90517	19997628209	Rural
05502	Calera	226906.2	2017	5502	50554	11471016698	Rural
05503	Hijuelas	253739.9	2017	5503	17988	4564273201	Rural
05504	La Cruz	291124.1	2017	5504	22098	6433259569	Rural
05506	Nogales	264475.3	2017	5506	22120	5850194593	Rural
05601	San Antonio	266331.2	2017	5601	91350	24329353815	Rural

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
14101	14101042005	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101042058	3	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101062011	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101062013	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101062021	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101062047	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101072002	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101072045	15	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101082002	54	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101112005	24	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101112010	8	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101112017	35	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101112037	14	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101112046	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101112058	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101122007	26	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101122009	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101122042	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101122058	31	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101132003	9	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101132050	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101132901	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101142014	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101142039	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101142059	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101152059	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101162018	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101162019	8	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101162034	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101162053	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101162061	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101172001	42	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101172016	7	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101172027	3	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101172036	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101172055	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101182004	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101182006	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101182015	16	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101182040	4	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14102	14102012011	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14102	14102012018	2	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14102	14102022008	5	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14102	14102022010	6	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14102	14102032001	2	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14102	14102032005	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14102	14102032019	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14102	14102032901	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14102	14102042006	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14102	14102042007	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14102	14102042015	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14102	14102042018	4	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14103	14103012004	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103012027	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103012040	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103022030	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103022039	5	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103022042	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103022044	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103032001	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103032002	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103032015	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103032019	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103032034	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103032041	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103032042	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103032901	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103052003	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103052035	5	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103052040	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103062021	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103062047	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103062048	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103072010	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103072020	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103072033	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14104	14104012003	5	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104012009	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104012010	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104012901	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104022002	8	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104022005	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104022018	6	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104022038	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104022040	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104022050	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104022076	15	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104032047	5	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104042025	8	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104042028	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104042043	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104042052	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104042060	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104042068	9	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104042901	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104052011	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104052013	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104052020	9	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104052034	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104052036	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
14101	14101042005	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101042058	3	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101062011	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101062013	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101062021	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101062047	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101072002	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101072045	15	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101082002	54	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101112005	24	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101112010	8	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101112017	35	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101112037	14	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101112046	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101112058	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101122007	26	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101122009	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101122042	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101122058	31	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101132003	9	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101132050	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101132901	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101142014	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101142039	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101142059	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101152059	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101162018	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101162019	8	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101162034	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101162053	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101162061	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101172001	42	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101172016	7	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101172027	3	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101172036	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101172055	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101182004	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101182006	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101182015	16	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14101	14101182040	4	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural
14102	14102012011	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14102	14102012018	2	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14102	14102022008	5	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14102	14102022010	6	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14102	14102032001	2	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14102	14102032005	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14102	14102032019	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14102	14102032901	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14102	14102042006	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14102	14102042007	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14102	14102042015	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14102	14102042018	4	2017	Corral	184771.6	2017	14102	5302	979659113	Rural
14103	14103012004	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103012027	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103012040	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103022030	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103022039	5	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103022042	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103022044	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103032001	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103032002	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103032015	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103032019	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103032034	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103032041	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103032042	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103032901	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103052003	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103052035	5	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103052040	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103062021	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103062047	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103062048	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103072010	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103072020	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14103	14103072033	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural
14104	14104012003	5	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104012009	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104012010	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104012901	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104022002	8	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104022005	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104022018	6	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104022038	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104022040	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104022050	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104022076	15	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104032047	5	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104042025	8	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104042028	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104042043	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104042052	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104042060	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104042068	9	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104042901	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104052011	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104052013	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104052020	9	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104052034	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural
14104	14104052036	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
14101042005	14101	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	60	0.0003613	14101
14101042058	14101	3	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	40	0.0002408	14101
14101062011	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	101	0.0006081	14101
14101062013	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	88	0.0005299	14101
14101062021	14101	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	296	0.0017823	14101
14101062047	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	16	0.0000963	14101
14101072002	14101	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	294	0.0017702	14101
14101072045	14101	15	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	287	0.0017281	14101
14101082002	14101	54	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	429	0.0025831	14101
14101112005	14101	24	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	380	0.0022881	14101
14101112010	14101	8	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	176	0.0010597	14101
14101112017	14101	35	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	1083	0.0065210	14101
14101112037	14101	14	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	535	0.0032213	14101
14101112046	14101	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	103	0.0006202	14101
14101112058	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	59	0.0003553	14101
14101122007	14101	26	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	297	0.0017883	14101
14101122009	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	266	0.0016016	14101
14101122042	14101	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	233	0.0014029	14101
14101122058	14101	31	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	509	0.0030648	14101
14101132003	14101	9	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	290	0.0017461	14101
14101132050	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	66	0.0003974	14101
14101132901	14101	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	250	0.0015053	14101
14101142014	14101	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	325	0.0019569	14101
14101142039	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	58	0.0003492	14101
14101142059	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	96	0.0005780	14101
14101152059	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	82	0.0004937	14101
14101162018	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	97	0.0005841	14101
14101162019	14101	8	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	64	0.0003854	14101
14101162034	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	42	0.0002529	14101
14101162053	14101	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	217	0.0013066	14101
14101162061	14101	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	94	0.0005660	14101
14101172001	14101	42	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	1348	0.0081166	14101
14101172016	14101	7	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	148	0.0008911	14101
14101172027	14101	3	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	103	0.0006202	14101
14101172036	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	110	0.0006623	14101
14101172055	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	88	0.0005299	14101
14101182004	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	188	0.0011320	14101
14101182006	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	67	0.0004034	14101
14101182015	14101	16	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	455	0.0027396	14101
14101182040	14101	4	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	323	0.0019448	14101
14102012011	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	22	0.0041494	14102
14102012018	14102	2	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	80	0.0150886	14102
14102022008	14102	5	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	507	0.0956243	14102
14102022010	14102	6	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	41	0.0077329	14102
14102032001	14102	2	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	68	0.0128253	14102
14102032005	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	64	0.0120709	14102
14102032019	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	62	0.0116937	14102
14102032901	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	31	0.0058469	14102
14102042006	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	257	0.0484723	14102
14102042007	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	115	0.0216899	14102
14102042015	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	9	0.0016975	14102
14102042018	14102	4	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	33	0.0062241	14102
14103012004	14103	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	197	0.0117598	14103
14103012027	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	105	0.0062679	14103
14103012040	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	106	0.0063276	14103
14103022030	14103	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	199	0.0118792	14103
14103022039	14103	5	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	379	0.0226242	14103
14103022042	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	39	0.0023281	14103
14103022044	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	96	0.0057307	14103
14103032001	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	100	0.0059694	14103
14103032002	14103	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	273	0.0162966	14103
14103032015	14103	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	96	0.0057307	14103
14103032019	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	55	0.0032832	14103
14103032034	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	59	0.0035220	14103
14103032041	14103	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	159	0.0094914	14103
14103032042	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	304	0.0181471	14103
14103032901	14103	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	179	0.0106853	14103
14103052003	14103	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	250	0.0149236	14103
14103052035	14103	5	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	173	0.0103271	14103
14103052040	14103	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	268	0.0159981	14103
14103062021	14103	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	226	0.0134909	14103
14103062047	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	31	0.0018505	14103
14103062048	14103	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	8	0.0004776	14103
14103072010	14103	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	322	0.0192216	14103
14103072020	14103	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	123	0.0073424	14103
14103072033	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	50	0.0029847	14103
14104012003	14104	5	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	980	0.0499134	14104
14104012009	14104	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	34	0.0017317	14104
14104012010	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	105	0.0053479	14104
14104012901	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	88	0.0044820	14104
14104022002	14104	8	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	286	0.0145666	14104
14104022005	14104	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	39	0.0019864	14104
14104022018	14104	6	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	226	0.0115106	14104
14104022038	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	20	0.0010186	14104
14104022040	14104	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	152	0.0077417	14104
14104022050	14104	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	98	0.0049913	14104
14104022076	14104	15	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	470	0.0239381	14104
14104032047	14104	5	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	129	0.0065702	14104
14104042025	14104	8	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	160	0.0081491	14104
14104042028	14104	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	164	0.0083529	14104
14104042043	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	96	0.0048895	14104
14104042052	14104	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	259	0.0131914	14104
14104042060	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	15	0.0007640	14104
14104042068	14104	9	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	274	0.0139554	14104
14104042901	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	33	0.0016808	14104
14104052011	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	69	0.0035143	14104
14104052013	14104	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	215	0.0109504	14104
14104052020	14104	9	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	146	0.0074361	14104
14104052034	14104	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	124	0.0063156	14104
14104052036	14104	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	168	0.0085566	14104

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
14101042005	14101	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	60	0.0003613	14101	13822927
14101042058	14101	3	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	40	0.0002408	14101	9215285
14101062011	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	101	0.0006081	14101	23268594
14101062013	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	88	0.0005299	14101	20273626
14101062021	14101	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	296	0.0017823	14101	68193106
14101062047	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	16	0.0000963	14101	3686114
14101072002	14101	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	294	0.0017702	14101	67732342
14101072045	14101	15	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	287	0.0017281	14101	66119667
14101082002	14101	54	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	429	0.0025831	14101	98833928
14101112005	14101	24	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	380	0.0022881	14101	87545204
14101112010	14101	8	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	176	0.0010597	14101	40547252
14101112017	14101	35	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	1083	0.0065210	14101	249503831
14101112037	14101	14	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	535	0.0032213	14101	123254432
14101112046	14101	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	103	0.0006202	14101	23729358
14101112058	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	59	0.0003553	14101	13592545
14101122007	14101	26	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	297	0.0017883	14101	68423488
14101122009	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	266	0.0016016	14101	61281643
14101122042	14101	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	233	0.0014029	14101	53679033
14101122058	14101	31	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	509	0.0030648	14101	117264497
14101132003	14101	9	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	290	0.0017461	14101	66810814
14101132050	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	66	0.0003974	14101	15205220
14101132901	14101	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	250	0.0015053	14101	57595529
14101142014	14101	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	325	0.0019569	14101	74874188
14101142039	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	58	0.0003492	14101	13362163
14101142059	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	96	0.0005780	14101	22116683
14101152059	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	82	0.0004937	14101	18891333
14101162018	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	97	0.0005841	14101	22347065
14101162019	14101	8	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	64	0.0003854	14101	14744455
14101162034	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	42	0.0002529	14101	9676049
14101162053	14101	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	217	0.0013066	14101	49992919
14101162061	14101	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	94	0.0005660	14101	21655919
14101172001	14101	42	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	1348	0.0081166	14101	310555092
14101172016	14101	7	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	148	0.0008911	14101	34096553
14101172027	14101	3	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	103	0.0006202	14101	23729358
14101172036	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	110	0.0006623	14101	25342033
14101172055	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	88	0.0005299	14101	20273626
14101182004	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	188	0.0011320	14101	43311838
14101182006	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	67	0.0004034	14101	15435602
14101182015	14101	16	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	455	0.0027396	14101	104823863
14101182040	14101	4	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	323	0.0019448	14101	74413423
14102012011	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	22	0.0041494	14102	4064976
14102012018	14102	2	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	80	0.0150886	14102	14781729
14102022008	14102	5	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	507	0.0956243	14102	93679210
14102022010	14102	6	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	41	0.0077329	14102	7575636
14102032001	14102	2	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	68	0.0128253	14102	12564470
14102032005	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	64	0.0120709	14102	11825383
14102032019	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	62	0.0116937	14102	11455840
14102032901	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	31	0.0058469	14102	5727920
14102042006	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	257	0.0484723	14102	47486306
14102042007	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	115	0.0216899	14102	21248736
14102042015	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	9	0.0016975	14102	1662945
14102042018	14102	4	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	33	0.0062241	14102	6097463
14103012004	14103	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	197	0.0117598	14103	36391845
14103012027	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	105	0.0062679	14103	19396668
14103012040	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	106	0.0063276	14103	19581399
14103022030	14103	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	199	0.0118792	14103	36761305
14103022039	14103	5	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	379	0.0226242	14103	70012737
14103022042	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	39	0.0023281	14103	7204477
14103022044	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	96	0.0057307	14103	17734097
14103032001	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	100	0.0059694	14103	18473018
14103032002	14103	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	273	0.0162966	14103	50431338
14103032015	14103	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	96	0.0057307	14103	17734097
14103032019	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	55	0.0032832	14103	10160160
14103032034	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	59	0.0035220	14103	10899080
14103032041	14103	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	159	0.0094914	14103	29372098
14103032042	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	304	0.0181471	14103	56157973
14103032901	14103	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	179	0.0106853	14103	33066701
14103052003	14103	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	250	0.0149236	14103	46182544
14103052035	14103	5	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	173	0.0103271	14103	31958320
14103052040	14103	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	268	0.0159981	14103	49507687
14103062021	14103	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	226	0.0134909	14103	41749020
14103062047	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	31	0.0018505	14103	5726635
14103062048	14103	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	8	0.0004776	14103	1477841
14103072010	14103	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	322	0.0192216	14103	59483117
14103072020	14103	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	123	0.0073424	14103	22721812
14103072033	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	50	0.0029847	14103	9236509
14104012003	14104	5	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	980	0.0499134	14104	197630052
14104012009	14104	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	34	0.0017317	14104	6856553
14104012010	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	105	0.0053479	14104	21174648
14104012901	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	88	0.0044820	14104	17746372
14104022002	14104	8	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	286	0.0145666	14104	57675709
14104022005	14104	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	39	0.0019864	14104	7864869
14104022018	14104	6	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	226	0.0115106	14104	45575910
14104022038	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	20	0.0010186	14104	4033266
14104022040	14104	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	152	0.0077417	14104	30652824
14104022050	14104	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	98	0.0049913	14104	19763005
14104022076	14104	15	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	470	0.0239381	14104	94781760
14104032047	14104	5	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	129	0.0065702	14104	26014568
14104042025	14104	8	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	160	0.0081491	14104	32266131
14104042028	14104	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	164	0.0083529	14104	33072784
14104042043	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	96	0.0048895	14104	19359679
14104042052	14104	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	259	0.0131914	14104	52230799
14104042060	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	15	0.0007640	14104	3024950
14104042068	14104	9	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	274	0.0139554	14104	55255749
14104042901	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	33	0.0016808	14104	6654890
14104052011	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	69	0.0035143	14104	13914769
14104052013	14104	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	215	0.0109504	14104	43357613
14104052020	14104	9	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	146	0.0074361	14104	29442844
14104052034	14104	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	124	0.0063156	14104	25006251
14104052036	14104	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	168	0.0085566	14104	33879437

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -175748690  -19701826   -7543052   11489671  223873036 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 21891925    2265220   9.664   <2e-16 ***
## Freq.x       4679457     277392  16.869   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 37060000 on 431 degrees of freedom
## Multiple R-squared:  0.3977, Adjusted R-squared:  0.3963 
## F-statistic: 284.6 on 1 and 431 DF,  p-value: < 2.2e-16

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.396293789329632"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.396293789329632"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.347372986648198"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.406483573267162"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.402052505800242"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.321363506955278"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.376728280932506"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.332094029896672"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 6      log-raíz 0.321363506955278
## 8       log-log 0.332094029896672
## 3   logarítmico 0.347372986648198
## 7      raíz-log 0.376728280932506
## 1    cuadrático 0.396293789329632
## 2        cúbico 0.396293789329632
## 5     raíz-raíz 0.402052505800242
## 4 raíz cuadrada 0.406483573267162
##                                                                     sintaxis
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 4
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = multi_pob ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -98738532 -17922535  -7005300  11578867 216189686 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -10272710    3687611  -2.786  0.00558 ** 
## sqrt(Freq.x)  28284145    1641585  17.230  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36750000 on 431 degrees of freedom
## Multiple R-squared:  0.4079, Adjusted R-squared:  0.4065 
## F-statistic: 296.9 on 1 and 431 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##   -10272710

bb <- linearMod$coefficients[2]
bb

## sqrt(Freq.x) 
##     28284145

9 Modelo raíz cuadrada (raíz cuadrada)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.4065 ).

9.1 Diagrama de dispersión sobre raíz cuadrada

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=(h_y_m_comuna_corr_01$Freq.x), y=sqrt(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo raíz cuadrada

Observemos nuevamente el resultado sobre raíz cuadrada.

linearMod <- lm(( multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = (multi_pob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -98738532 -17922535  -7005300  11578867 216189686 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -10272710    3687611  -2.786  0.00558 ** 
## sqrt(Freq.x)  28284145    1641585  17.230  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36750000 on 431 degrees of freedom
## Multiple R-squared:  0.4079, Adjusted R-squared:  0.4065 
## F-statistic: 296.9 on 1 and 431 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = (Freq.x) , y = sqrt(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = 10272710 + 28284145\cdot \sqrt {X} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- aa+bb * sqrt(h_y_m_comuna_corr_01$Freq.x)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
14101042005	14101	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	60	0.0003613	14101	13822927	52972561
14101042058	14101	3	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	40	0.0002408	14101	9215285	38716866
14101062011	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	101	0.0006081	14101	23268594	29727112
14101062013	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	88	0.0005299	14101	20273626	29727112
14101062021	14101	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	296	0.0017823	14101	68193106	59009013
14101062047	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	16	0.0000963	14101	3686114	18011435
14101072002	14101	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	294	0.0017702	14101	67732342	59009013
14101072045	14101	15	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	287	0.0017281	14101	66119667	99271313
14101082002	14101	54	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	429	0.0025831	14101	98833928	197572460
14101112005	14101	24	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	380	0.0022881	14101	87545204	128290737
14101112010	14101	8	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	176	0.0010597	14101	40547252	69726933
14101112017	14101	35	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	1083	0.0065210	14101	249503831	157058549
14101112037	14101	14	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	535	0.0032213	14101	123254432	95556870
14101112046	14101	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	103	0.0006202	14101	23729358	59009013
14101112058	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	59	0.0003553	14101	13592545	29727112
14101122007	14101	26	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	297	0.0017883	14101	68423488	133948698
14101122009	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	266	0.0016016	14101	61281643	29727112
14101122042	14101	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	233	0.0014029	14101	53679033	52972561
14101122058	14101	31	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	509	0.0030648	14101	117264497	147206745
14101132003	14101	9	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	290	0.0017461	14101	66810814	74579725
14101132050	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	66	0.0003974	14101	15205220	29727112
14101132901	14101	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	250	0.0015053	14101	57595529	52972561
14101142014	14101	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	325	0.0019569	14101	74874188	52972561
14101142039	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	58	0.0003492	14101	13362163	18011435
14101142059	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	96	0.0005780	14101	22116683	18011435
14101152059	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	82	0.0004937	14101	18891333	18011435
14101162018	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	97	0.0005841	14101	22347065	29727112
14101162019	14101	8	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	64	0.0003854	14101	14744455	69726933
14101162034	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	42	0.0002529	14101	9676049	18011435
14101162053	14101	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	217	0.0013066	14101	49992919	52972561
14101162061	14101	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	94	0.0005660	14101	21655919	59009013
14101172001	14101	42	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	1348	0.0081166	14101	310555092	173029500
14101172016	14101	7	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	148	0.0008911	14101	34096553	64560104
14101172027	14101	3	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	103	0.0006202	14101	23729358	38716866
14101172036	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	110	0.0006623	14101	25342033	18011435
14101172055	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	88	0.0005299	14101	20273626	18011435
14101182004	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	188	0.0011320	14101	43311838	29727112
14101182006	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	67	0.0004034	14101	15435602	29727112
14101182015	14101	16	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	455	0.0027396	14101	104823863	102863870
14101182040	14101	4	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	323	0.0019448	14101	74413423	46295580
14102012011	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	22	0.0041494	14102	4064976	18011435
14102012018	14102	2	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	80	0.0150886	14102	14781729	29727112
14102022008	14102	5	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	507	0.0956243	14102	93679210	52972561
14102022010	14102	6	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	41	0.0077329	14102	7575636	59009013
14102032001	14102	2	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	68	0.0128253	14102	12564470	29727112
14102032005	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	64	0.0120709	14102	11825383	18011435
14102032019	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	62	0.0116937	14102	11455840	18011435
14102032901	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	31	0.0058469	14102	5727920	18011435
14102042006	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	257	0.0484723	14102	47486306	18011435
14102042007	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	115	0.0216899	14102	21248736	18011435
14102042015	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	9	0.0016975	14102	1662945	18011435
14102042018	14102	4	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	33	0.0062241	14102	6097463	46295580
14103012004	14103	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	197	0.0117598	14103	36391845	38716866
14103012027	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	105	0.0062679	14103	19396668	18011435
14103012040	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	106	0.0063276	14103	19581399	18011435
14103022030	14103	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	199	0.0118792	14103	36761305	46295580
14103022039	14103	5	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	379	0.0226242	14103	70012737	52972561
14103022042	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	39	0.0023281	14103	7204477	18011435
14103022044	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	96	0.0057307	14103	17734097	18011435
14103032001	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	100	0.0059694	14103	18473018	18011435
14103032002	14103	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	273	0.0162966	14103	50431338	46295580
14103032015	14103	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	96	0.0057307	14103	17734097	29727112
14103032019	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	55	0.0032832	14103	10160160	18011435
14103032034	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	59	0.0035220	14103	10899080	18011435
14103032041	14103	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	159	0.0094914	14103	29372098	29727112
14103032042	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	304	0.0181471	14103	56157973	18011435
14103032901	14103	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	179	0.0106853	14103	33066701	46295580
14103052003	14103	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	250	0.0149236	14103	46182544	38716866
14103052035	14103	5	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	173	0.0103271	14103	31958320	52972561
14103052040	14103	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	268	0.0159981	14103	49507687	38716866
14103062021	14103	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	226	0.0134909	14103	41749020	46295580
14103062047	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	31	0.0018505	14103	5726635	18011435
14103062048	14103	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	8	0.0004776	14103	1477841	29727112
14103072010	14103	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	322	0.0192216	14103	59483117	38716866
14103072020	14103	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	123	0.0073424	14103	22721812	29727112
14103072033	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	50	0.0029847	14103	9236509	18011435
14104012003	14104	5	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	980	0.0499134	14104	197630052	52972561
14104012009	14104	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	34	0.0017317	14104	6856553	29727112
14104012010	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	105	0.0053479	14104	21174648	18011435
14104012901	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	88	0.0044820	14104	17746372	18011435
14104022002	14104	8	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	286	0.0145666	14104	57675709	69726933
14104022005	14104	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	39	0.0019864	14104	7864869	38716866
14104022018	14104	6	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	226	0.0115106	14104	45575910	59009013
14104022038	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	20	0.0010186	14104	4033266	18011435
14104022040	14104	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	152	0.0077417	14104	30652824	38716866
14104022050	14104	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	98	0.0049913	14104	19763005	29727112
14104022076	14104	15	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	470	0.0239381	14104	94781760	99271313
14104032047	14104	5	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	129	0.0065702	14104	26014568	52972561
14104042025	14104	8	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	160	0.0081491	14104	32266131	69726933
14104042028	14104	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	164	0.0083529	14104	33072784	38716866
14104042043	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	96	0.0048895	14104	19359679	18011435
14104042052	14104	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	259	0.0131914	14104	52230799	38716866
14104042060	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	15	0.0007640	14104	3024950	18011435
14104042068	14104	9	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	274	0.0139554	14104	55255749	74579725
14104042901	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	33	0.0016808	14104	6654890	18011435
14104052011	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	69	0.0035143	14104	13914769	18011435
14104052013	14104	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	215	0.0109504	14104	43357613	29727112
14104052020	14104	9	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	146	0.0074361	14104	29442844	74579725
14104052034	14104	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	124	0.0063156	14104	25006251	38716866
14104052036	14104	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	168	0.0085566	14104	33879437	29727112

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
14101042005	14101	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	60	0.0003613	14101	13822927	52972561	882876.02
14101042058	14101	3	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	40	0.0002408	14101	9215285	38716866	967921.66
14101062011	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	101	0.0006081	14101	23268594	29727112	294327.84
14101062013	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	88	0.0005299	14101	20273626	29727112	337808.09
14101062021	14101	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	296	0.0017823	14101	68193106	59009013	199354.77
14101062047	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	16	0.0000963	14101	3686114	18011435	1125714.69
14101072002	14101	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	294	0.0017702	14101	67732342	59009013	200710.93
14101072045	14101	15	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	287	0.0017281	14101	66119667	99271313	345893.08
14101082002	14101	54	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	429	0.0025831	14101	98833928	197572460	460541.86
14101112005	14101	24	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	380	0.0022881	14101	87545204	128290737	337607.20
14101112010	14101	8	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	176	0.0010597	14101	40547252	69726933	396175.76
14101112017	14101	35	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	1083	0.0065210	14101	249503831	157058549	145021.74
14101112037	14101	14	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	535	0.0032213	14101	123254432	95556870	178610.97
14101112046	14101	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	103	0.0006202	14101	23729358	59009013	572903.04
14101112058	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	59	0.0003553	14101	13592545	29727112	503849.35
14101122007	14101	26	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	297	0.0017883	14101	68423488	133948698	451005.72
14101122009	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	266	0.0016016	14101	61281643	29727112	111756.06
14101122042	14101	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	233	0.0014029	14101	53679033	52972561	227350.05
14101122058	14101	31	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	509	0.0030648	14101	117264497	147206745	289207.75
14101132003	14101	9	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	290	0.0017461	14101	66810814	74579725	257171.47
14101132050	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	66	0.0003974	14101	15205220	29727112	450410.78
14101132901	14101	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	250	0.0015053	14101	57595529	52972561	211890.24
14101142014	14101	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	325	0.0019569	14101	74874188	52972561	162992.50
14101142039	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	58	0.0003492	14101	13362163	18011435	310541.98
14101142059	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	96	0.0005780	14101	22116683	18011435	187619.11
14101152059	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	82	0.0004937	14101	18891333	18011435	219651.65
14101162018	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	97	0.0005841	14101	22347065	29727112	306465.07
14101162019	14101	8	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	64	0.0003854	14101	14744455	69726933	1089483.33
14101162034	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	42	0.0002529	14101	9676049	18011435	428843.69
14101162053	14101	5	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	217	0.0013066	14101	49992919	52972561	244113.18
14101162061	14101	6	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	94	0.0005660	14101	21655919	59009013	627755.46
14101172001	14101	42	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	1348	0.0081166	14101	310555092	173029500	128360.16
14101172016	14101	7	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	148	0.0008911	14101	34096553	64560104	436216.92
14101172027	14101	3	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	103	0.0006202	14101	23729358	38716866	375891.91
14101172036	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	110	0.0006623	14101	25342033	18011435	163740.32
14101172055	14101	1	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	88	0.0005299	14101	20273626	18011435	204675.40
14101182004	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	188	0.0011320	14101	43311838	29727112	158122.93
14101182006	14101	2	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	67	0.0004034	14101	15435602	29727112	443688.23
14101182015	14101	16	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	455	0.0027396	14101	104823863	102863870	226074.44
14101182040	14101	4	2017	Valdivia	230382.1	2017	14101	166080	38261861778	Rural	323	0.0019448	14101	74413423	46295580	143329.97
14102012011	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	22	0.0041494	14102	4064976	18011435	818701.59
14102012018	14102	2	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	80	0.0150886	14102	14781729	29727112	371588.89
14102022008	14102	5	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	507	0.0956243	14102	93679210	52972561	104482.37
14102022010	14102	6	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	41	0.0077329	14102	7575636	59009013	1439244.23
14102032001	14102	2	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	68	0.0128253	14102	12564470	29727112	437163.40
14102032005	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	64	0.0120709	14102	11825383	18011435	281428.67
14102032019	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	62	0.0116937	14102	11455840	18011435	290507.02
14102032901	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	31	0.0058469	14102	5727920	18011435	581014.03
14102042006	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	257	0.0484723	14102	47486306	18011435	70083.40
14102042007	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	115	0.0216899	14102	21248736	18011435	156621.17
14102042015	14102	1	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	9	0.0016975	14102	1662945	18011435	2001270.56
14102042018	14102	4	2017	Corral	184771.6	2017	14102	5302	979659113	Rural	33	0.0062241	14102	6097463	46295580	1402896.37
14103012004	14103	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	197	0.0117598	14103	36391845	38716866	196532.32
14103012027	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	105	0.0062679	14103	19396668	18011435	171537.48
14103012040	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	106	0.0063276	14103	19581399	18011435	169919.20
14103022030	14103	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	199	0.0118792	14103	36761305	46295580	232641.11
14103022039	14103	5	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	379	0.0226242	14103	70012737	52972561	139769.29
14103022042	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	39	0.0023281	14103	7204477	18011435	461831.67
14103022044	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	96	0.0057307	14103	17734097	18011435	187619.11
14103032001	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	100	0.0059694	14103	18473018	18011435	180114.35
14103032002	14103	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	273	0.0162966	14103	50431338	46295580	169580.88
14103032015	14103	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	96	0.0057307	14103	17734097	29727112	309657.41
14103032019	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	55	0.0032832	14103	10160160	18011435	327480.64
14103032034	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	59	0.0035220	14103	10899080	18011435	305278.56
14103032041	14103	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	159	0.0094914	14103	29372098	29727112	186962.97
14103032042	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	304	0.0181471	14103	56157973	18011435	59248.14
14103032901	14103	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	179	0.0106853	14103	33066701	46295580	258634.53
14103052003	14103	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	250	0.0149236	14103	46182544	38716866	154867.47
14103052035	14103	5	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	173	0.0103271	14103	31958320	52972561	306199.77
14103052040	14103	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	268	0.0159981	14103	49507687	38716866	144465.92
14103062021	14103	4	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	226	0.0134909	14103	41749020	46295580	204847.70
14103062047	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	31	0.0018505	14103	5726635	18011435	581014.03
14103062048	14103	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	8	0.0004776	14103	1477841	29727112	3715888.94
14103072010	14103	3	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	322	0.0192216	14103	59483117	38716866	120238.72
14103072020	14103	2	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	123	0.0073424	14103	22721812	29727112	241683.83
14103072033	14103	1	2017	Lanco	184730.2	2017	14103	16752	3094599901	Rural	50	0.0029847	14103	9236509	18011435	360228.70
14104012003	14104	5	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	980	0.0499134	14104	197630052	52972561	54053.63
14104012009	14104	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	34	0.0017317	14104	6856553	29727112	874326.81
14104012010	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	105	0.0053479	14104	21174648	18011435	171537.48
14104012901	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	88	0.0044820	14104	17746372	18011435	204675.40
14104022002	14104	8	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	286	0.0145666	14104	57675709	69726933	243800.47
14104022005	14104	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	39	0.0019864	14104	7864869	38716866	992740.16
14104022018	14104	6	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	226	0.0115106	14104	45575910	59009013	261101.83
14104022038	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	20	0.0010186	14104	4033266	18011435	900571.75
14104022040	14104	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	152	0.0077417	14104	30652824	38716866	254716.23
14104022050	14104	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	98	0.0049913	14104	19763005	29727112	303337.87
14104022076	14104	15	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	470	0.0239381	14104	94781760	99271313	211215.56
14104032047	14104	5	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	129	0.0065702	14104	26014568	52972561	410640.01
14104042025	14104	8	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	160	0.0081491	14104	32266131	69726933	435793.33
14104042028	14104	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	164	0.0083529	14104	33072784	38716866	236078.45
14104042043	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	96	0.0048895	14104	19359679	18011435	187619.11
14104042052	14104	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	259	0.0131914	14104	52230799	38716866	149485.97
14104042060	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	15	0.0007640	14104	3024950	18011435	1200762.33
14104042068	14104	9	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	274	0.0139554	14104	55255749	74579725	272188.78
14104042901	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	33	0.0016808	14104	6654890	18011435	545801.06
14104052011	14104	1	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	69	0.0035143	14104	13914769	18011435	261035.29
14104052013	14104	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	215	0.0109504	14104	43357613	29727112	138265.64
14104052020	14104	9	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	146	0.0074361	14104	29442844	74579725	510820.04
14104052034	14104	3	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	124	0.0063156	14104	25006251	38716866	312232.79
14104052036	14104	2	2017	Los Lagos	201663.3	2017	14104	19634	3959457593	Rural	168	0.0085566	14104	33879437	29727112	176947.09

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_rural_nivel_nacional_17_r14.rds")

---

R-15

Urbano

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 1)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15201	1	1	1	1767	1	1	1	2	1	3	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	2	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	3	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	4	1	1	2	3	1	99	1	2	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	5	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	6	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	7	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	8	1	1	3	3	1	99	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	9	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	10	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	11	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	12	1	1	4	3	3	3	4	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	13	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	14	1	1	2	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	15	1	1	2	1	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	16	1	1	5	1	5	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	17	1	1	5	3	1	3	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	18	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	19	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	20	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	21	4	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	22	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	23	1	1	5	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	24	1	1	3	1	1	6	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	25	1	1	5	6	1	1	4	2	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	26	4	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	27	1	1	5	99	3	3	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	28	1	1	6	3	2	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	29	1	1	2	3	1	99	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	30	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	31	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	32	1	1	3	1	1	2	1	1	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	33	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	34	3	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	35	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	36	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	37	1	1	5	3	3	1	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	38	1	1	1	1	4	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	39	1	1	5	3	4	3	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	40	1	1	5	3	2	1	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	41	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	42	1	1	2	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	43	1	1	1	1	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	44	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	45	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	46	1	1	5	6	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	47	1	1	5	3	4	0	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	48	1	1	3	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	49	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	50	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	51	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	52	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	53	1	1	1	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	54	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	55	1	1	2	2	1	4	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	56	8	1	98	98	98	98	98	98	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	57	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	58	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	59	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	60	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	61	1	1	2	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	62	1	1	5	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	63	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	64	1	1	5	3	2	4	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	65	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	66	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	67	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	68	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	69	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	70	1	1	1	3	3	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	71	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	72	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	73	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	74	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	75	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	76	1	1	5	3	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	77	1	1	3	6	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	78	5	1	3	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	79	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	80	1	1	2	1	1	3	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	81	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	82	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	83	1	1	5	3	2	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	84	4	1	5	3	2	0	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	85	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	86	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	87	1	1	3	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	88	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	89	5	1	4	3	2	1	2	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	90	1	1	5	3	3	2	1	3	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	91	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	92	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	93	5	1	4	3	5	1	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	94	1	1	5	3	3	99	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	95	1	1	5	3	2	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	96	1	1	2	3	4	6	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	97	1	1	5	1	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	98	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	99	1	1	2	7	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	100	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 15:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 15)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	15101011001	150	2017	15101
2	15101011002	113	2017	15101
3	15101021001	427	2017	15101
4	15101031001	308	2017	15101
5	15101031002	188	2017	15101
6	15101031003	49	2017	15101
7	15101031004	490	2017	15101
8	15101031005	89	2017	15101
9	15101031006	206	2017	15101
10	15101031007	146	2017	15101
11	15101041001	210	2017	15101
12	15101041002	178	2017	15101
13	15101041003	345	2017	15101
14	15101041004	312	2017	15101
15	15101051001	156	2017	15101
16	15101051002	137	2017	15101
17	15101051003	280	2017	15101
18	15101051004	189	2017	15101
19	15101061001	403	2017	15101
20	15101061002	234	2017	15101
21	15101061003	87	2017	15101
22	15101061004	139	2017	15101
23	15101061005	46	2017	15101
24	15101071001	158	2017	15101
25	15101071002	115	2017	15101
26	15101071003	234	2017	15101
27	15101071004	138	2017	15101
28	15101081001	284	2017	15101
29	15101081002	430	2017	15101
30	15101081003	297	2017	15101
31	15101081004	198	2017	15101
32	15101091001	375	2017	15101
33	15101091002	187	2017	15101
34	15101101001	235	2017	15101
35	15101101002	231	2017	15101
36	15101101003	227	2017	15101
37	15101111001	198	2017	15101
38	15101111002	251	2017	15101
39	15101111003	162	2017	15101
40	15101121001	288	2017	15101
41	15101121002	364	2017	15101
42	15101121003	395	2017	15101
43	15101121004	214	2017	15101
44	15101121005	339	2017	15101
45	15101121006	33	2017	15101
46	15101121007	292	2017	15101
47	15101121008	346	2017	15101
48	15101121009	251	2017	15101
49	15101121010	303	2017	15101
50	15101141001	220	2017	15101
51	15101141002	230	2017	15101
52	15101171001	385	2017	15101
53	15101171002	119	2017	15101
54	15101171003	135	2017	15101
55	15101171004	210	2017	15101
56	15101171005	144	2017	15101
57	15101171006	457	2017	15101
58	15101171007	196	2017	15101
59	15101171008	228	2017	15101
60	15101171009	362	2017	15101
61	15101171010	156	2017	15101
62	15101181001	328	2017	15101
63	15101181002	384	2017	15101
64	15101181003	297	2017	15101
65	15101181004	154	2017	15101
66	15101181005	380	2017	15101
67	15101181006	380	2017	15101
68	15101191001	227	2017	15101
69	15101191002	386	2017	15101
70	15101191003	350	2017	15101
71	15101191004	235	2017	15101
72	15101991999	36	2017	15101
146	15201011001	34	2017	15201
NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA
NA.2	NA	NA	NA	NA
NA.3	NA	NA	NA	NA
NA.4	NA	NA	NA	NA
NA.5	NA	NA	NA	NA
NA.6	NA	NA	NA	NA
NA.7	NA	NA	NA	NA
NA.8	NA	NA	NA	NA
NA.9	NA	NA	NA	NA
NA.10	NA	NA	NA	NA
NA.11	NA	NA	NA	NA
NA.12	NA	NA	NA	NA
NA.13	NA	NA	NA	NA
NA.14	NA	NA	NA	NA
NA.15	NA	NA	NA	NA
NA.16	NA	NA	NA	NA
NA.17	NA	NA	NA	NA
NA.18	NA	NA	NA	NA
NA.19	NA	NA	NA	NA
NA.20	NA	NA	NA	NA
NA.21	NA	NA	NA	NA
NA.22	NA	NA	NA	NA
NA.23	NA	NA	NA	NA
NA.24	NA	NA	NA	NA
NA.25	NA	NA	NA	NA
NA.26	NA	NA	NA	NA

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_urbano_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
01107	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
01401	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
01405	Pica	330061.1	2017	1405	9296	3068247619	Urbano
02101	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02102	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02104	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02201	Calama	416281.1	2017	2201	165731	68990679686	Urbano
02203	San Pedro de Atacama	437934.7	2017	2203	10996	4815529626	Urbano
02301	Tocopilla	271720.8	2017	2301	25186	6843559467	Urbano
02302	María Elena	466266.9	2017	2302	6457	3010685220	Urbano
03101	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
03102	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
03103	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
03201	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
03202	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
03301	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
03303	Freirina	289049.9	2017	3303	7041	2035200054	Urbano
03304	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
04101	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04102	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04103	Andacollo	251267.7	2017	4103	11044	2775000288	Urbano
04104	La Higuera	214257.0	2017	4104	4241	908664019	Urbano
04106	Vicuña	245957.4	2017	4106	27771	6830481918	Urbano
04201	Illapel	270316.5	2017	4201	30848	8338722128	Urbano
04202	Canela	233397.3	2017	4202	9093	2122281844	Urbano
04203	Los Vilos	282415.6	2017	4203	21382	6038609501	Urbano
04204	Salamanca	262056.9	2017	4204	29347	7690585032	Urbano
04301	Ovalle	274771.4	2017	4301	111272	30574361012	Urbano
04302	Combarbalá	228990.4	2017	4302	13322	3050610572	Urbano
04303	Monte Patria	225369.1	2017	4303	30751	6930326684	Urbano
04304	Punitaqui	212496.1	2017	4304	10956	2328107498	Urbano
05101	Valparaíso	297929.0	2017	5101	296655	88382118059	Urbano
05102	Casablanca	341641.8	2017	5102	26867	9178890241	Urbano
05103	Concón	318496.3	2017	5103	42152	13425257057	Urbano
05105	Puchuncaví	296035.5	2017	5105	18546	5490274928	Urbano
05107	Quintero	308224.7	2017	5107	31923	9839456903	Urbano
05109	Viña del Mar	337006.1	2017	5109	334248	112643604611	Urbano
05301	Los Andes	339720.2	2017	5301	66708	22662055502	Urbano
05302	Calle Larga	246387.3	2017	5302	14832	3654416747	Urbano
05303	Rinconada	273904.7	2017	5303	10207	2795744821	Urbano
05304	San Esteban	219571.6	2017	5304	18855	4140022481	Urbano
05401	La Ligua	250134.4	2017	5401	35390	8852256241	Urbano
05402	Cabildo	262745.9	2017	5402	19388	5094117762	Urbano
05403	Papudo	294355.2	2017	5403	6356	1870921373	Urbano
05404	Petorca	237510.8	2017	5404	9826	2333781007	Urbano
05405	Zapallar	294389.2	2017	5405	7339	2160521991	Urbano
05501	Quillota	286029.5	2017	5501	90517	25890529852	Urbano
05502	Calera	277181.9	2017	5502	50554	14012652087	Urbano
05503	Hijuelas	254094.0	2017	5503	17988	4570642363	Urbano

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
1	15101	15101011001	150	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
2	15101	15101011002	113	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
3	15101	15101021001	427	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
4	15101	15101031001	308	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
5	15101	15101031002	188	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
6	15101	15101031003	49	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
7	15101	15101031004	490	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
8	15101	15101031005	89	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
9	15101	15101031006	206	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
10	15101	15101031007	146	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
11	15101	15101041001	210	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
12	15101	15101041002	178	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
13	15101	15101041003	345	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
14	15101	15101041004	312	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
15	15101	15101051001	156	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
16	15101	15101051002	137	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
17	15101	15101051003	280	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
18	15101	15101051004	189	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
19	15101	15101061001	403	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
20	15101	15101061002	234	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
21	15101	15101061003	87	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
22	15101	15101061004	139	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
23	15101	15101061005	46	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
24	15101	15101071001	158	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
25	15101	15101071002	115	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
26	15101	15101071003	234	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
27	15101	15101071004	138	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
28	15101	15101081001	284	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
29	15101	15101081002	430	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
30	15101	15101081003	297	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
31	15101	15101081004	198	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
32	15101	15101091001	375	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
33	15101	15101091002	187	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
34	15101	15101101001	235	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
35	15101	15101101002	231	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
36	15101	15101101003	227	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
37	15101	15101111001	198	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
38	15101	15101111002	251	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
39	15101	15101111003	162	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
40	15101	15101121001	288	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
41	15101	15101121002	364	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
42	15101	15101121003	395	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
43	15101	15101121004	214	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
44	15101	15101121005	339	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
45	15101	15101121006	33	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
46	15101	15101121007	292	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
47	15101	15101121008	346	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
48	15101	15101121009	251	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
49	15101	15101121010	303	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
50	15101	15101141001	220	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
51	15101	15101141002	230	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
52	15101	15101171001	385	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
53	15101	15101171002	119	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
54	15101	15101171003	135	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
55	15101	15101171004	210	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
56	15101	15101171005	144	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
57	15101	15101171006	457	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
58	15101	15101171007	196	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
59	15101	15101171008	228	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
60	15101	15101171009	362	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
61	15101	15101171010	156	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
62	15101	15101181001	328	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
63	15101	15101181002	384	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
64	15101	15101181003	297	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
65	15101	15101181004	154	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
66	15101	15101181005	380	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
67	15101	15101181006	380	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
68	15101	15101191001	227	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
69	15101	15101191002	386	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
70	15101	15101191003	350	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
71	15101	15101191004	235	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
72	15101	15101991999	36	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
73	15201	15201011001	34	2017	Putre	283661.5	2017	15201	2765	784324030	Urbano
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
1	15101	15101011001	150	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
2	15101	15101011002	113	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
3	15101	15101021001	427	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
4	15101	15101031001	308	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
5	15101	15101031002	188	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
6	15101	15101031003	49	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
7	15101	15101031004	490	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
8	15101	15101031005	89	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
9	15101	15101031006	206	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
10	15101	15101031007	146	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
11	15101	15101041001	210	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
12	15101	15101041002	178	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
13	15101	15101041003	345	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
14	15101	15101041004	312	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
15	15101	15101051001	156	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
16	15101	15101051002	137	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
17	15101	15101051003	280	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
18	15101	15101051004	189	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
19	15101	15101061001	403	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
20	15101	15101061002	234	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
21	15101	15101061003	87	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
22	15101	15101061004	139	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
23	15101	15101061005	46	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
24	15101	15101071001	158	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
25	15101	15101071002	115	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
26	15101	15101071003	234	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
27	15101	15101071004	138	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
28	15101	15101081001	284	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
29	15101	15101081002	430	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
30	15101	15101081003	297	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
31	15101	15101081004	198	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
32	15101	15101091001	375	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
33	15101	15101091002	187	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
34	15101	15101101001	235	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
35	15101	15101101002	231	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
36	15101	15101101003	227	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
37	15101	15101111001	198	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
38	15101	15101111002	251	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
39	15101	15101111003	162	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
40	15101	15101121001	288	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
41	15101	15101121002	364	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
42	15101	15101121003	395	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
43	15101	15101121004	214	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
44	15101	15101121005	339	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
45	15101	15101121006	33	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
46	15101	15101121007	292	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
47	15101	15101121008	346	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
48	15101	15101121009	251	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
49	15101	15101121010	303	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
50	15101	15101141001	220	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
51	15101	15101141002	230	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
52	15101	15101171001	385	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
53	15101	15101171002	119	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
54	15101	15101171003	135	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
55	15101	15101171004	210	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
56	15101	15101171005	144	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
57	15101	15101171006	457	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
58	15101	15101171007	196	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
59	15101	15101171008	228	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
60	15101	15101171009	362	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
61	15101	15101171010	156	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
62	15101	15101181001	328	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
63	15101	15101181002	384	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
64	15101	15101181003	297	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
65	15101	15101181004	154	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
66	15101	15101181005	380	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
67	15101	15101181006	380	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
68	15101	15101191001	227	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
69	15101	15101191002	386	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
70	15101	15101191003	350	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
71	15101	15101191004	235	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
72	15101	15101991999	36	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano
73	15201	15201011001	34	2017	Putre	283661.5	2017	15201	2765	784324030	Urbano
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
15101011001	15101	150	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1545	0.0069795	15101
15101011002	15101	113	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1001	0.0045220	15101
15101021001	15101	427	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4454	0.0201207	15101
15101031001	15101	308	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	6208	0.0280443	15101
15101031002	15101	188	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3166	0.0143022	15101
15101031003	15101	49	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	623	0.0028144	15101
15101031004	15101	490	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4041	0.0182550	15101
15101031005	15101	89	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1010	0.0045626	15101
15101031006	15101	206	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2848	0.0128657	15101
15101031007	15101	146	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2762	0.0124772	15101
15101041001	15101	210	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2911	0.0131503	15101
15101041002	15101	178	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2836	0.0128115	15101
15101041003	15101	345	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3316	0.0149799	15101
15101041004	15101	312	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2869	0.0129606	15101
15101051001	15101	156	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2041	0.0092201	15101
15101051002	15101	137	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1655	0.0074764	15101
15101051003	15101	280	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2345	0.0105934	15101
15101051004	15101	189	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2196	0.0099203	15101
15101061001	15101	403	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	5901	0.0266575	15101
15101061002	15101	234	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2066	0.0093330	15101
15101061003	15101	87	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	615	0.0027782	15101
15101061004	15101	139	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	723	0.0032661	15101
15101061005	15101	46	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1001	0.0045220	15101
15101071001	15101	158	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2416	0.0109142	15101
15101071002	15101	115	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2443	0.0110361	15101
15101071003	15101	234	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3686	0.0166513	15101
15101071004	15101	138	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2455	0.0110903	15101
15101081001	15101	284	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2735	0.0123552	15101
15101081002	15101	430	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2496	0.0112755	15101
15101081003	15101	297	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2823	0.0127528	15101
15101081004	15101	198	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1853	0.0083708	15101
15101091001	15101	375	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2582	0.0116640	15101
15101091002	15101	187	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1634	0.0073815	15101
15101101001	15101	235	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1944	0.0087819	15101
15101101002	15101	231	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1732	0.0078242	15101
15101101003	15101	227	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1542	0.0069659	15101
15101111001	15101	198	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1879	0.0084883	15101
15101111002	15101	251	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1971	0.0089039	15101
15101111003	15101	162	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1656	0.0074809	15101
15101121001	15101	288	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3627	0.0163848	15101
15101121002	15101	364	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4645	0.0209835	15101
15101121003	15101	395	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4990	0.0225421	15101
15101121004	15101	214	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3686	0.0166513	15101
15101121005	15101	339	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4822	0.0217831	15101
15101121006	15101	33	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	263	0.0011881	15101
15101121007	15101	292	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3635	0.0164209	15101
15101121008	15101	346	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3757	0.0169720	15101
15101121009	15101	251	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2707	0.0122287	15101
15101121010	15101	303	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3363	0.0151922	15101
15101141001	15101	220	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1387	0.0062657	15101
15101141002	15101	230	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1641	0.0074131	15101
15101171001	15101	385	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4176	0.0188649	15101
15101171002	15101	119	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2174	0.0098209	15101
15101171003	15101	135	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2503	0.0113072	15101
15101171004	15101	210	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2478	0.0111942	15101
15101171005	15101	144	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2371	0.0107109	15101
15101171006	15101	457	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4924	0.0222439	15101
15101171007	15101	196	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2627	0.0118673	15101
15101171008	15101	228	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2763	0.0124817	15101
15101171009	15101	362	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3821	0.0172612	15101
15101171010	15101	156	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3487	0.0157523	15101
15101181001	15101	328	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3763	0.0169992	15101
15101181002	15101	384	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4627	0.0209022	15101
15101181003	15101	297	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	5211	0.0235404	15101
15101181004	15101	154	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2346	0.0105979	15101
15101181005	15101	380	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3954	0.0178620	15101
15101181006	15101	380	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	7629	0.0344636	15101
15101191001	15101	227	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2482	0.0112123	15101
15101191002	15101	386	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3364	0.0151967	15101
15101191003	15101	350	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3067	0.0138550	15101
15101191004	15101	235	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2862	0.0129289	15101
15101991999	15101	36	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1947	0.0087955	15101
15201011001	15201	34	2017	Putre	283661.5	2017	15201	2765	784324030	Urbano	1716	0.6206148	15201

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
15101011001	15101	150	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1545	0.0069795	15101	455009113
15101011002	15101	113	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1001	0.0045220	15101	294798785
15101021001	15101	427	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4454	0.0201207	15101	1311722065
15101031001	15101	308	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	6208	0.0280443	15101	1828282573
15101031002	15101	188	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3166	0.0143022	15101	932400552
15101031003	15101	49	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	623	0.0028144	15101	183476167
15101031004	15101	490	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4041	0.0182550	15101	1190091797
15101031005	15101	89	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1010	0.0045626	15101	297449323
15101031006	15101	206	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2848	0.0128657	15101	838748191
15101031007	15101	146	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2762	0.0124772	15101	813420823
15101041001	15101	210	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2911	0.0131503	15101	857301960
15101041002	15101	178	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2836	0.0128115	15101	835214139
15101041003	15101	345	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3316	0.0149799	15101	976576194
15101041004	15101	312	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2869	0.0129606	15101	844932781
15101051001	15101	156	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2041	0.0092201	15101	601083236
15101051002	15101	137	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1655	0.0074764	15101	487404584
15101051003	15101	280	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2345	0.0105934	15101	690612538
15101051004	15101	189	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2196	0.0099203	15101	646731400
15101061001	15101	403	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	5901	0.0266575	15101	1737869759
15101061002	15101	234	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2066	0.0093330	15101	608445843
15101061003	15101	87	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	615	0.0027782	15101	181120132
15101061004	15101	139	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	723	0.0032661	15101	212926595
15101061005	15101	46	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1001	0.0045220	15101	294798785
15101071001	15101	158	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2416	0.0109142	15101	711522341
15101071002	15101	115	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2443	0.0110361	15101	719473957
15101071003	15101	234	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3686	0.0166513	15101	1085542778
15101071004	15101	138	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2455	0.0110903	15101	723008008
15101081001	15101	284	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2735	0.0123552	15101	805469207
15101081002	15101	430	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2496	0.0112755	15101	735082684
15101081003	15101	297	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2823	0.0127528	15101	831385584
15101081004	15101	198	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1853	0.0083708	15101	545716432
15101091001	15101	375	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2582	0.0116640	15101	760410052
15101091002	15101	187	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1634	0.0073815	15101	481219994
15101101001	15101	235	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1944	0.0087819	15101	572516321
15101101002	15101	231	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1732	0.0078242	15101	510081414
15101101003	15101	227	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1542	0.0069659	15101	454125600
15101111001	15101	198	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1879	0.0084883	15101	553373543
15101111002	15101	251	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1971	0.0089039	15101	580467937
15101111003	15101	162	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1656	0.0074809	15101	487699088
15101121001	15101	288	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3627	0.0163848	15101	1068167025
15101121002	15101	364	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4645	0.0209835	15101	1367972383
15101121003	15101	395	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4990	0.0225421	15101	1469576359
15101121004	15101	214	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3686	0.0166513	15101	1085542778
15101121005	15101	339	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4822	0.0217831	15101	1420099640
15101121006	15101	33	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	263	0.0011881	15101	77454626
15101121007	15101	292	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3635	0.0164209	15101	1070523059
15101121008	15101	346	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3757	0.0169720	15101	1106452582
15101121009	15101	251	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2707	0.0122287	15101	797223087
15101121010	15101	303	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3363	0.0151922	15101	990417895
15101141001	15101	220	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1387	0.0062657	15101	408477437
15101141002	15101	230	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1641	0.0074131	15101	483281524
15101171001	15101	385	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4176	0.0188649	15101	1229849875
15101171002	15101	119	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2174	0.0098209	15101	640252306
15101171003	15101	135	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2503	0.0113072	15101	737144214
15101171004	15101	210	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2478	0.0111942	15101	729781607
15101171005	15101	144	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2371	0.0107109	15101	698269649
15101171006	15101	457	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4924	0.0222439	15101	1450139077
15101171007	15101	196	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2627	0.0118673	15101	773662745
15101171008	15101	228	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2763	0.0124817	15101	813715327
15101171009	15101	362	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3821	0.0172612	15101	1125300855
15101171010	15101	156	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3487	0.0157523	15101	1026936426
15101181001	15101	328	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3763	0.0169992	15101	1108219607
15101181002	15101	384	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4627	0.0209022	15101	1362671306
15101181003	15101	297	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	5211	0.0235404	15101	1534661805
15101181004	15101	154	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2346	0.0105979	15101	690907042
15101181005	15101	380	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3954	0.0178620	15101	1164469925
15101181006	15101	380	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	7629	0.0344636	15101	2246773155
15101191001	15101	227	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2482	0.0112123	15101	730959624
15101191002	15101	386	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3364	0.0151967	15101	990712399
15101191003	15101	350	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3067	0.0138550	15101	903244628
15101191004	15101	235	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2862	0.0129289	15101	842871251
15101991999	15101	36	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1947	0.0087955	15101	573399834
15201011001	15201	34	2017	Putre	283661.5	2017	15201	2765	784324030	Urbano	1716	0.6206148	15201	486763123

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -614861539 -185975005  -16925167  137097219 1032013864 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 187353815   76642315   2.445    0.017 *  
## Freq.x        2703699     291179   9.285 6.99e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 273400000 on 71 degrees of freedom
## Multiple R-squared:  0.5484, Adjusted R-squared:  0.542 
## F-statistic: 86.22 on 1 and 71 DF,  p-value: 6.988e-14

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.542036425813549"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.542036425813549"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.460540058443155"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.523576052773076"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.576153182545774"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.573529962826021"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.534105713411017"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.568275385010322"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 3   logarítmico 0.460540058443155
## 4 raíz cuadrada 0.523576052773076
## 7      raíz-log 0.534105713411017
## 1    cuadrático 0.542036425813549
## 2        cúbico 0.542036425813549
## 8       log-log 0.568275385010322
## 6      log-raíz 0.573529962826021
## 5     raíz-raíz 0.576153182545774
##                                                                     sintaxis
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 8
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.94806 -0.24435  0.03673  0.22809  1.00242 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  16.6186     0.3896  42.652  < 2e-16 ***
## log(Freq.x)   0.7105     0.0726   9.786 8.42e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3755 on 71 degrees of freedom
## Multiple R-squared:  0.5743, Adjusted R-squared:  0.5683 
## F-statistic: 95.77 on 1 and 71 DF,  p-value: 8.416e-15

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##     16.6186

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##    0.710495

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.5683 ).

9.1 Diagrama de dispersión sobre log-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.94806 -0.24435  0.03673  0.22809  1.00242 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  16.6186     0.3896  42.652  < 2e-16 ***
## log(Freq.x)   0.7105     0.0726   9.786 8.42e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3755 on 71 degrees of freedom
## Multiple R-squared:  0.5743, Adjusted R-squared:  0.5683 
## F-statistic: 95.77 on 1 and 71 DF,  p-value: 8.416e-15

ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = e^{16.6186+0.710495 \cdot ln{X}} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
1	15101011001	15101	150	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1545	0.0069795	15101	455009113	580055572
2	15101011002	15101	113	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1001	0.0045220	15101	294798785	474318011
3	15101021001	15101	427	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4454	0.0201207	15101	1311722065	1219757467
4	15101031001	15101	308	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	6208	0.0280443	15101	1828282573	967097983
5	15101031002	15101	188	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3166	0.0143022	15101	932400552	680997351
6	15101031003	15101	49	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	623	0.0028144	15101	183476167	261965151
7	15101031004	15101	490	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4041	0.0182550	15101	1190091797	1345050204
8	15101031005	15101	89	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1010	0.0045626	15101	297449323	400312824
9	15101031006	15101	206	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2848	0.0128657	15101	838748191	726705976
10	15101031007	15101	146	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2762	0.0124772	15101	813420823	569022615
11	15101041001	15101	210	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2911	0.0131503	15101	857301960	736703678
12	15101041002	15101	178	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2836	0.0128115	15101	835214139	655058071
13	15101041003	15101	345	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3316	0.0149799	15101	976576194	1048275444
14	15101041004	15101	312	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2869	0.0129606	15101	844932781	976004914
15	15101051001	15101	156	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2041	0.0092201	15101	601083236	596446790
16	15101051002	15101	137	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1655	0.0074764	15101	487404584	543872280
17	15101051003	15101	280	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2345	0.0105934	15101	690612538	903776787
18	15101051004	15101	189	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2196	0.0099203	15101	646731400	683569019
19	15101061001	15101	403	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	5901	0.0266575	15101	1737869759	1170641300
20	15101061002	15101	234	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2066	0.0093330	15101	608445843	795579639
21	15101061003	15101	87	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	615	0.0027782	15101	181120132	393900364
22	15101061004	15101	139	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	723	0.0032661	15101	212926595	549501580
23	15101061005	15101	46	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1001	0.0045220	15101	294798785	250466002
24	15101071001	15101	158	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2416	0.0109142	15101	711522341	601869742
25	15101071002	15101	115	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2443	0.0110361	15101	719473957	480267457
26	15101071003	15101	234	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3686	0.0166513	15101	1085542778	795579639
27	15101071004	15101	138	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2455	0.0110903	15101	723008008	546689883
28	15101081001	15101	284	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2735	0.0123552	15101	805469207	912931203
29	15101081002	15101	430	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2496	0.0112755	15101	735082684	1225840040
30	15101081003	15101	297	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2823	0.0127528	15101	831385584	942429192
31	15101081004	15101	198	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1853	0.0083708	15101	545716432	706540035
32	15101091001	15101	375	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2582	0.0116640	15101	760410052	1112254008
33	15101091002	15101	187	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1634	0.0073815	15101	481219994	678421721
34	15101101001	15101	235	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1944	0.0087819	15101	572516321	797993768
35	15101101002	15101	231	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1732	0.0078242	15101	510081414	788319252
36	15101101003	15101	227	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1542	0.0069659	15101	454125600	778596112
37	15101111001	15101	198	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1879	0.0084883	15101	553373543	706540035
38	15101111002	15101	251	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1971	0.0089039	15101	580467937	836226294
39	15101111003	15101	162	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1656	0.0074809	15101	487699088	612656455
40	15101121001	15101	288	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3627	0.0163848	15101	1068167025	922048366
41	15101121002	15101	364	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4645	0.0209835	15101	1367972383	1088973626
42	15101121003	15101	395	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4990	0.0225421	15101	1469576359	1154082582
43	15101121004	15101	214	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3686	0.0166513	15101	1085542778	746646398
44	15101121005	15101	339	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4822	0.0217831	15101	1420099640	1035289643
45	15101121006	15101	33	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	263	0.0011881	15101	77454626	197817279
46	15101121007	15101	292	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3635	0.0164209	15101	1070523059	931128942
47	15101121008	15101	346	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3757	0.0169720	15101	1106452582	1050433364
48	15101121009	15101	251	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2707	0.0122287	15101	797223087	836226294
49	15101121010	15101	303	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3363	0.0151922	15101	990417895	955917069
50	15101141001	15101	220	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1387	0.0062657	15101	408477437	761460259
51	15101141002	15101	230	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1641	0.0074131	15101	483281524	785893068
52	15101171001	15101	385	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4176	0.0188649	15101	1229849875	1133246939
53	15101171002	15101	119	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2174	0.0098209	15101	640252306	492077361
54	15101171003	15101	135	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2503	0.0113072	15101	737144214	538219138
55	15101171004	15101	210	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2478	0.0111942	15101	729781607	736703678
56	15101171005	15101	144	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2371	0.0107109	15101	698269649	563473380
57	15101171006	15101	457	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4924	0.0222439	15101	1450139077	1280043687
58	15101171007	15101	196	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2627	0.0118673	15101	773662745	701461951
59	15101171008	15101	228	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2763	0.0124817	15101	813715327	781031516
60	15101171009	15101	362	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3821	0.0172612	15101	1125300855	1084719082
61	15101171010	15101	156	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3487	0.0157523	15101	1026936426	596446790
62	15101181001	15101	328	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3763	0.0169992	15101	1108219607	1011307951
63	15101181002	15101	384	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4627	0.0209022	15101	1362671306	1131154810
64	15101181003	15101	297	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	5211	0.0235404	15101	1534661805	942429192
65	15101181004	15101	154	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2346	0.0105979	15101	690907042	591003671
66	15101181005	15101	380	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3954	0.0178620	15101	1164469925	1122770466
67	15101181006	15101	380	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	7629	0.0344636	15101	2246773155	1122770466
68	15101191001	15101	227	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2482	0.0112123	15101	730959624	778596112
69	15101191002	15101	386	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3364	0.0151967	15101	990712399	1135337494
70	15101191003	15101	350	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3067	0.0138550	15101	903244628	1059047062
71	15101191004	15101	235	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2862	0.0129289	15101	842871251	797993768
72	15101991999	15101	36	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1947	0.0087955	15101	573399834	210432496
73	15201011001	15201	34	2017	Putre	283661.5	2017	15201	2765	784324030	Urbano	1716	0.6206148	15201	486763123	202057872
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)


r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
1	15101011001	15101	150	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1545	0.0069795	15101	455009113	580055572	375440.5
2	15101011002	15101	113	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1001	0.0045220	15101	294798785	474318011	473844.2
3	15101021001	15101	427	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4454	0.0201207	15101	1311722065	1219757467	273856.6
4	15101031001	15101	308	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	6208	0.0280443	15101	1828282573	967097983	155782.5
5	15101031002	15101	188	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3166	0.0143022	15101	932400552	680997351	215097.1
6	15101031003	15101	49	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	623	0.0028144	15101	183476167	261965151	420489.8
7	15101031004	15101	490	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4041	0.0182550	15101	1190091797	1345050204	332850.8
8	15101031005	15101	89	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1010	0.0045626	15101	297449323	400312824	396349.3
9	15101031006	15101	206	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2848	0.0128657	15101	838748191	726705976	255163.6
10	15101031007	15101	146	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2762	0.0124772	15101	813420823	569022615	206018.3
11	15101041001	15101	210	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2911	0.0131503	15101	857301960	736703678	253075.8
12	15101041002	15101	178	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2836	0.0128115	15101	835214139	655058071	230979.6
13	15101041003	15101	345	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3316	0.0149799	15101	976576194	1048275444	316126.5
14	15101041004	15101	312	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2869	0.0129606	15101	844932781	976004914	340189.9
15	15101051001	15101	156	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2041	0.0092201	15101	601083236	596446790	292232.6
16	15101051002	15101	137	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1655	0.0074764	15101	487404584	543872280	328623.7
17	15101051003	15101	280	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2345	0.0105934	15101	690612538	903776787	385405.9
18	15101051004	15101	189	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2196	0.0099203	15101	646731400	683569019	311279.2
19	15101061001	15101	403	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	5901	0.0266575	15101	1737869759	1170641300	198380.2
20	15101061002	15101	234	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2066	0.0093330	15101	608445843	795579639	385082.1
21	15101061003	15101	87	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	615	0.0027782	15101	181120132	393900364	640488.4
22	15101061004	15101	139	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	723	0.0032661	15101	212926595	549501580	760029.8
23	15101061005	15101	46	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1001	0.0045220	15101	294798785	250466002	250215.8
24	15101071001	15101	158	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2416	0.0109142	15101	711522341	601869742	249118.3
25	15101071002	15101	115	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2443	0.0110361	15101	719473957	480267457	196589.2
26	15101071003	15101	234	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3686	0.0166513	15101	1085542778	795579639	215838.2
27	15101071004	15101	138	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2455	0.0110903	15101	723008008	546689883	222684.3
28	15101081001	15101	284	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2735	0.0123552	15101	805469207	912931203	333795.7
29	15101081002	15101	430	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2496	0.0112755	15101	735082684	1225840040	491121.8
30	15101081003	15101	297	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2823	0.0127528	15101	831385584	942429192	333839.6
31	15101081004	15101	198	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1853	0.0083708	15101	545716432	706540035	381295.2
32	15101091001	15101	375	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2582	0.0116640	15101	760410052	1112254008	430772.3
33	15101091002	15101	187	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1634	0.0073815	15101	481219994	678421721	415190.8
34	15101101001	15101	235	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1944	0.0087819	15101	572516321	797993768	410490.6
35	15101101002	15101	231	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1732	0.0078242	15101	510081414	788319252	455149.7
36	15101101003	15101	227	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1542	0.0069659	15101	454125600	778596112	504926.1
37	15101111001	15101	198	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1879	0.0084883	15101	553373543	706540035	376019.2
38	15101111002	15101	251	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1971	0.0089039	15101	580467937	836226294	424265.0
39	15101111003	15101	162	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1656	0.0074809	15101	487699088	612656455	369961.6
40	15101121001	15101	288	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3627	0.0163848	15101	1068167025	922048366	254217.9
41	15101121002	15101	364	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4645	0.0209835	15101	1367972383	1088973626	234440.0
42	15101121003	15101	395	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4990	0.0225421	15101	1469576359	1154082582	231279.1
43	15101121004	15101	214	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3686	0.0166513	15101	1085542778	746646398	202562.8
44	15101121005	15101	339	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4822	0.0217831	15101	1420099640	1035289643	214701.3
45	15101121006	15101	33	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	263	0.0011881	15101	77454626	197817279	752157.0
46	15101121007	15101	292	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3635	0.0164209	15101	1070523059	931128942	256156.5
47	15101121008	15101	346	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3757	0.0169720	15101	1106452582	1050433364	279593.7
48	15101121009	15101	251	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2707	0.0122287	15101	797223087	836226294	308912.6
49	15101121010	15101	303	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3363	0.0151922	15101	990417895	955917069	284245.3
50	15101141001	15101	220	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1387	0.0062657	15101	408477437	761460259	548998.0
51	15101141002	15101	230	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1641	0.0074131	15101	483281524	785893068	478911.1
52	15101171001	15101	385	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4176	0.0188649	15101	1229849875	1133246939	271371.4
53	15101171002	15101	119	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2174	0.0098209	15101	640252306	492077361	226346.5
54	15101171003	15101	135	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2503	0.0113072	15101	737144214	538219138	215029.6
55	15101171004	15101	210	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2478	0.0111942	15101	729781607	736703678	297297.7
56	15101171005	15101	144	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2371	0.0107109	15101	698269649	563473380	237652.2
57	15101171006	15101	457	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4924	0.0222439	15101	1450139077	1280043687	259960.1
58	15101171007	15101	196	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2627	0.0118673	15101	773662745	701461951	267020.2
59	15101171008	15101	228	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2763	0.0124817	15101	813715327	781031516	282675.2
60	15101171009	15101	362	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3821	0.0172612	15101	1125300855	1084719082	283883.6
61	15101171010	15101	156	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3487	0.0157523	15101	1026936426	596446790	171048.7
62	15101181001	15101	328	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3763	0.0169992	15101	1108219607	1011307951	268750.5
63	15101181002	15101	384	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	4627	0.0209022	15101	1362671306	1131154810	244468.3
64	15101181003	15101	297	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	5211	0.0235404	15101	1534661805	942429192	180853.8
65	15101181004	15101	154	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2346	0.0105979	15101	690907042	591003671	251919.7
66	15101181005	15101	380	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3954	0.0178620	15101	1164469925	1122770466	283958.1
67	15101181006	15101	380	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	7629	0.0344636	15101	2246773155	1122770466	147171.4
68	15101191001	15101	227	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2482	0.0112123	15101	730959624	778596112	313697.1
69	15101191002	15101	386	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3364	0.0151967	15101	990712399	1135337494	337496.3
70	15101191003	15101	350	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	3067	0.0138550	15101	903244628	1059047062	345303.9
71	15101191004	15101	235	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	2862	0.0129289	15101	842871251	797993768	278823.8
72	15101991999	15101	36	2017	Arica	294504.3	2017	15101	221364	65192645531	Urbano	1947	0.0087955	15101	573399834	210432496	108080.4
73	15201011001	15201	34	2017	Putre	283661.5	2017	15201	2765	784324030	Urbano	1716	0.6206148	15201	486763123	202057872	117749.3
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_urbano_nivel_nacional_17_r15.rds")

---

Rural

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 2)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15202	1	2	6	13225	1	3	1	5	3	5	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	3	1	1	5	3	5	2	3	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	4	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	5	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	6	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	8	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	9	3	1	5	3	5	1	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	10	1	1	5	3	4	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	11	1	2	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	12	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	13	1	1	5	3	4	1	4	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	14	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	16	1	1	5	3	5	1	3	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	17	1	1	5	3	5	2	4	1	8	15	152	15202	15202012006
15	152	15202	1	2	6	13225	18	3	1	5	3	5	1	1	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	19	3	1	5	3	5	1	3	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	20	1	1	5	3	5	2	1	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	21	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	22	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	24	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	25	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	26	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	27	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	28	3	1	5	3	5	1	4	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	29	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	30	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	31	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	32	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	33	1	1	5	3	5	3	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	34	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	35	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	36	1	1	5	3	5	3	2	1	9	15	152	15202	15202012006
15	152	15202	1	2	6	13225	37	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	38	1	1	5	3	5	99	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	39	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	40	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	41	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	42	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	43	3	1	5	3	5	2	1	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	44	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	45	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	8	13910	1	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	3	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	4	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	5	3	1	5	3	5	2	3	1	3	15	152	15202	15202012008
15	152	15202	1	2	8	13910	6	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	7	1	1	5	99	5	2	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	8	3	1	5	3	5	3	3	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	9	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	10	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	11	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	12	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	13	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	14	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	16	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	18	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	20	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	21	1	1	5	3	5	1	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	22	3	1	5	3	5	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	23	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	24	3	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	25	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	26	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	27	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	28	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	29	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	30	1	1	5	1	4	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	12	8394	1	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	2	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	3	3	1	5	3	5	2	3	1	4	15	152	15202	15202012012
15	152	15202	1	2	12	8394	4	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	5	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	6	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	8	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	9	3	1	5	3	5	1	4	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	10	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	11	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	12	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	13	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	14	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	15	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	16	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	18	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	20	3	1	5	3	5	3	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	21	3	1	5	99	5	1	4	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	22	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	24	3	1	5	3	5	1	2	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	25	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 15:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 15)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Veamos los primeros 100 registros:

r3_100 <- d[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	unlist.c.	unlist.d.	Freq	anio
1	15101032003	1	15101	10	2017
2	15101062011	1	15101	38	2017
3	15101062014	1	15101	705	2017
4	15101132015	1	15101	17	2017
5	15101152005	1	15101	79	2017
6	15101152007	1	15101	5	2017
7	15101152016	1	15101	128	2017
8	15101152901	1	15101	24	2017
9	15101162016	1	15101	31	2017
10	15101162901	1	15101	5	2017
46	15102012002	1	15102	2	2017
47	15102012003	1	15102	41	2017
48	15102022007	1	15102	2	2017
49	15102032005	1	15102	2	2017
50	15102032901	1	15102	8	2017
51	15102042001	1	15102	21	2017
52	15102052004	1	15102	24	2017
88	15201022002	1	15201	2	2017
89	15201032006	1	15201	6	2017
90	15201042021	1	15201	10	2017
91	15201052001	1	15201	1	2017
92	15201052012	1	15201	2	2017
93	15201052013	1	15201	2	2017
94	15201052020	1	15201	1	2017
95	15201052901	1	15201	2	2017
96	15201072901	1	15201	1	2017
132	15202012008	1	15202	1	2017
133	15202012015	1	15202	4	2017
134	15202022005	1	15202	5	2017
135	15202022009	1	15202	4	2017
136	15202022011	1	15202	3	2017
137	15202022901	1	15202	3	2017
138	15202032002	1	15202	1	2017
139	15202032010	1	15202	2	2017
140	15202992999	1	15202	1	2017
NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA
NA.41	NA	NA	NA	NA	NA
NA.42	NA	NA	NA	NA	NA
NA.43	NA	NA	NA	NA	NA
NA.44	NA	NA	NA	NA	NA
NA.45	NA	NA	NA	NA	NA
NA.46	NA	NA	NA	NA	NA
NA.47	NA	NA	NA	NA	NA
NA.48	NA	NA	NA	NA	NA
NA.49	NA	NA	NA	NA	NA
NA.50	NA	NA	NA	NA	NA
NA.51	NA	NA	NA	NA	NA
NA.52	NA	NA	NA	NA	NA
NA.53	NA	NA	NA	NA	NA
NA.54	NA	NA	NA	NA	NA
NA.55	NA	NA	NA	NA	NA
NA.56	NA	NA	NA	NA	NA
NA.57	NA	NA	NA	NA	NA
NA.58	NA	NA	NA	NA	NA
NA.59	NA	NA	NA	NA	NA
NA.60	NA	NA	NA	NA	NA
NA.61	NA	NA	NA	NA	NA
NA.62	NA	NA	NA	NA	NA
NA.63	NA	NA	NA	NA	NA
NA.64	NA	NA	NA	NA	NA

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	15101032003	10	2017	15101
2	15101062011	38	2017	15101
3	15101062014	705	2017	15101
4	15101132015	17	2017	15101
5	15101152005	79	2017	15101
6	15101152007	5	2017	15101
7	15101152016	128	2017	15101
8	15101152901	24	2017	15101
9	15101162016	31	2017	15101
10	15101162901	5	2017	15101
46	15102012002	2	2017	15102
47	15102012003	41	2017	15102
48	15102022007	2	2017	15102
49	15102032005	2	2017	15102
50	15102032901	8	2017	15102
51	15102042001	21	2017	15102
52	15102052004	24	2017	15102
88	15201022002	2	2017	15201
89	15201032006	6	2017	15201
90	15201042021	10	2017	15201
91	15201052001	1	2017	15201
92	15201052012	2	2017	15201
93	15201052013	2	2017	15201
94	15201052020	1	2017	15201
95	15201052901	2	2017	15201
96	15201072901	1	2017	15201
132	15202012008	1	2017	15202
133	15202012015	4	2017	15202
134	15202022005	5	2017	15202
135	15202022009	4	2017	15202
136	15202022011	3	2017	15202
137	15202022901	3	2017	15202
138	15202032002	1	2017	15202
139	15202032010	2	2017	15202
140	15202992999	1	2017	15202
NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA
NA.2	NA	NA	NA	NA
NA.3	NA	NA	NA	NA
NA.4	NA	NA	NA	NA
NA.5	NA	NA	NA	NA
NA.6	NA	NA	NA	NA
NA.7	NA	NA	NA	NA
NA.8	NA	NA	NA	NA
NA.9	NA	NA	NA	NA
NA.10	NA	NA	NA	NA
NA.11	NA	NA	NA	NA
NA.12	NA	NA	NA	NA
NA.13	NA	NA	NA	NA
NA.14	NA	NA	NA	NA
NA.15	NA	NA	NA	NA
NA.16	NA	NA	NA	NA
NA.17	NA	NA	NA	NA
NA.18	NA	NA	NA	NA
NA.19	NA	NA	NA	NA
NA.20	NA	NA	NA	NA
NA.21	NA	NA	NA	NA
NA.22	NA	NA	NA	NA
NA.23	NA	NA	NA	NA
NA.24	NA	NA	NA	NA
NA.25	NA	NA	NA	NA
NA.26	NA	NA	NA	NA
NA.27	NA	NA	NA	NA
NA.28	NA	NA	NA	NA
NA.29	NA	NA	NA	NA
NA.30	NA	NA	NA	NA
NA.31	NA	NA	NA	NA
NA.32	NA	NA	NA	NA
NA.33	NA	NA	NA	NA
NA.34	NA	NA	NA	NA
NA.35	NA	NA	NA	NA
NA.36	NA	NA	NA	NA
NA.37	NA	NA	NA	NA
NA.38	NA	NA	NA	NA
NA.39	NA	NA	NA	NA
NA.40	NA	NA	NA	NA
NA.41	NA	NA	NA	NA
NA.42	NA	NA	NA	NA
NA.43	NA	NA	NA	NA
NA.44	NA	NA	NA	NA
NA.45	NA	NA	NA	NA
NA.46	NA	NA	NA	NA
NA.47	NA	NA	NA	NA
NA.48	NA	NA	NA	NA
NA.49	NA	NA	NA	NA
NA.50	NA	NA	NA	NA
NA.51	NA	NA	NA	NA
NA.52	NA	NA	NA	NA
NA.53	NA	NA	NA	NA
NA.54	NA	NA	NA	NA
NA.55	NA	NA	NA	NA
NA.56	NA	NA	NA	NA
NA.57	NA	NA	NA	NA
NA.58	NA	NA	NA	NA
NA.59	NA	NA	NA	NA
NA.60	NA	NA	NA	NA
NA.61	NA	NA	NA	NA
NA.62	NA	NA	NA	NA
NA.63	NA	NA	NA	NA
NA.64	NA	NA	NA	NA

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_rural_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	289375.3	2017	1101	191468	55406102543	Rural
01401	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
01402	Camiña	262850.3	2017	1402	1250	328562901	Rural
01404	Huara	253968.5	2017	1404	2730	693334131	Rural
01405	Pica	290496.7	2017	1405	9296	2700457509	Rural
02103	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
02104	Taltal	345494.0	2017	2104	13317	4600943086	Rural
02201	Calama	310025.0	2017	2201	165731	51380756402	Rural
02203	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
02301	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
03101	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03103	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03202	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural
03301	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03302	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03303	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03304	Huasco	227560.7	2017	3304	10149	2309513927	Rural
04101	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04102	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04103	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04104	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04105	Paiguano	205942.1	2017	4105	4497	926121774	Rural
04106	Vicuña	176130.6	2017	4106	27771	4891322768	Rural
04201	Illapel	191976.8	2017	4201	30848	5922099530	Rural
04202	Canela	171370.3	2017	4202	9093	1558270441	Rural
04203	Los Vilos	173238.5	2017	4203	21382	3704185607	Rural
04204	Salamanca	223234.2	2017	4204	29347	6551254640	Rural
04301	Ovalle	241393.7	2017	4301	111272	26860360045	Rural
04302	Combarbalá	179139.6	2017	4302	13322	2386498044	Rural
04303	Monte Patria	201205.8	2017	4303	30751	6187280931	Rural
04304	Punitaqui	171931.7	2017	4304	10956	1883683880	Rural
04305	Río Hurtado	182027.2	2017	4305	4278	778712384	Rural
05101	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05102	Casablanca	268917.1	2017	5102	26867	7224996933	Rural
05105	Puchuncaví	279614.4	2017	5105	18546	5185728335	Rural
05107	Quintero	334628.2	2017	5107	31923	10682335196	Rural
05301	Los Andes	324402.1	2017	5301	66708	21640215030	Rural
05302	Calle Larga	242743.8	2017	5302	14832	3600375502	Rural
05303	Rinconada	326532.5	2017	5303	10207	3332917471	Rural
05304	San Esteban	223168.6	2017	5304	18855	4207844130	Rural
05401	La Ligua	181468.0	2017	5401	35390	6422154059	Rural
05402	Cabildo	231277.8	2017	5402	19388	4484014285	Rural
05404	Petorca	298208.9	2017	5404	9826	2930200178	Rural
05405	Zapallar	292882.3	2017	5405	7339	2149463129	Rural
05501	Quillota	220926.8	2017	5501	90517	19997628209	Rural
05502	Calera	226906.2	2017	5502	50554	11471016698	Rural
05503	Hijuelas	253739.9	2017	5503	17988	4564273201	Rural
05504	La Cruz	291124.1	2017	5504	22098	6433259569	Rural
05506	Nogales	264475.3	2017	5506	22120	5850194593	Rural
05601	San Antonio	266331.2	2017	5601	91350	24329353815	Rural

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
1	15101	15101032003	10	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural
2	15101	15101062011	38	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural
3	15101	15101132015	17	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural
4	15101	15101152005	79	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural
5	15101	15101152007	5	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural
6	15101	15101062014	705	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural
7	15101	15101152901	24	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural
8	15101	15101162016	31	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural
9	15101	15101162901	5	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural
10	15101	15101152016	128	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural
11	15102	15102012003	41	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural
12	15102	15102022007	2	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural
13	15102	15102032005	2	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural
14	15102	15102012002	2	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural
15	15102	15102042001	21	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural
16	15102	15102052004	24	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural
17	15102	15102032901	8	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural
18	15201	15201022002	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural
19	15201	15201042021	10	2017	Putre	205510.4	2017	15201	2765	568236174	Rural
20	15201	15201052001	1	2017	Putre	205510.4	2017	15201	2765	568236174	Rural
21	15201	15201052012	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural
22	15201	15201032006	6	2017	Putre	205510.4	2017	15201	2765	568236174	Rural
23	15201	15201052020	1	2017	Putre	205510.4	2017	15201	2765	568236174	Rural
24	15201	15201052901	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural
25	15201	15201072901	1	2017	Putre	205510.4	2017	15201	2765	568236174	Rural
26	15201	15201052013	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural
27	15202	15202022005	5	2017	NA	NA	NA	NA	NA	NA	NA
28	15202	15202022009	4	2017	NA	NA	NA	NA	NA	NA	NA
29	15202	15202012008	1	2017	NA	NA	NA	NA	NA	NA	NA
30	15202	15202012015	4	2017	NA	NA	NA	NA	NA	NA	NA
31	15202	15202032002	1	2017	NA	NA	NA	NA	NA	NA	NA
32	15202	15202032010	2	2017	NA	NA	NA	NA	NA	NA	NA
33	15202	15202022011	3	2017	NA	NA	NA	NA	NA	NA	NA
34	15202	15202022901	3	2017	NA	NA	NA	NA	NA	NA	NA
35	15202	15202992999	1	2017	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.41	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.42	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.43	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.44	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.45	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.46	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.47	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.48	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.49	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.50	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.51	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.52	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.53	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.54	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.55	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.56	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.57	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.58	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.59	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.60	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.61	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.62	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.63	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.64	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
1	15101	15101032003	10	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural
2	15101	15101062011	38	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural
3	15101	15101132015	17	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural
4	15101	15101152005	79	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural
5	15101	15101152007	5	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural
6	15101	15101062014	705	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural
7	15101	15101152901	24	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural
8	15101	15101162016	31	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural
9	15101	15101162901	5	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural
10	15101	15101152016	128	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural
11	15102	15102012003	41	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural
12	15102	15102022007	2	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural
13	15102	15102032005	2	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural
14	15102	15102012002	2	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural
15	15102	15102042001	21	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural
16	15102	15102052004	24	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural
17	15102	15102032901	8	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural
18	15201	15201022002	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural
19	15201	15201042021	10	2017	Putre	205510.4	2017	15201	2765	568236174	Rural
20	15201	15201052001	1	2017	Putre	205510.4	2017	15201	2765	568236174	Rural
21	15201	15201052012	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural
22	15201	15201032006	6	2017	Putre	205510.4	2017	15201	2765	568236174	Rural
23	15201	15201052020	1	2017	Putre	205510.4	2017	15201	2765	568236174	Rural
24	15201	15201052901	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural
25	15201	15201072901	1	2017	Putre	205510.4	2017	15201	2765	568236174	Rural
26	15201	15201052013	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural
27	15202	15202022005	5	2017	NA	NA	NA	NA	NA	NA	NA
28	15202	15202022009	4	2017	NA	NA	NA	NA	NA	NA	NA
29	15202	15202012008	1	2017	NA	NA	NA	NA	NA	NA	NA
30	15202	15202012015	4	2017	NA	NA	NA	NA	NA	NA	NA
31	15202	15202032002	1	2017	NA	NA	NA	NA	NA	NA	NA
32	15202	15202032010	2	2017	NA	NA	NA	NA	NA	NA	NA
33	15202	15202022011	3	2017	NA	NA	NA	NA	NA	NA	NA
34	15202	15202022901	3	2017	NA	NA	NA	NA	NA	NA	NA
35	15202	15202992999	1	2017	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.41	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.42	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.43	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.44	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.45	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.46	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.47	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.48	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.49	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.50	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.51	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.52	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.53	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.54	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.55	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.56	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.57	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.58	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.59	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.60	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.61	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.62	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.63	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.64	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
15101032003	15101	10	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	70	0.0003162	15101
15101062011	15101	38	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	2587	0.0116866	15101
15101062014	15101	705	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	8708	0.0393379	15101
15101132015	15101	17	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	338	0.0015269	15101
15101152005	15101	79	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	870	0.0039302	15101
15101152007	15101	5	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	130	0.0005873	15101
15101152016	15101	128	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	1759	0.0079462	15101
15101152901	15101	24	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	207	0.0009351	15101
15101162016	15101	31	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	393	0.0017754	15101
15101162901	15101	5	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	58	0.0002620	15101
15102012002	15102	2	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	25	0.0199203	15102
15102012003	15102	41	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	565	0.4501992	15102
15102022007	15102	2	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	39	0.0310757	15102
15102032005	15102	2	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	112	0.0892430	15102
15102032901	15102	8	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	30	0.0239044	15102
15102042001	15102	21	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	181	0.1442231	15102
15102052004	15102	24	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	259	0.2063745	15102
15201022002	15201	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	52	0.0188065	15201
15201032006	15201	6	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	31	0.0112116	15201
15201042021	15201	10	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	75	0.0271248	15201
15201052001	15201	1	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	63	0.0227848	15201
15201052012	15201	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	25	0.0090416	15201
15201052013	15201	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	17	0.0061483	15201
15201052020	15201	1	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	18	0.0065099	15201
15201052901	15201	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	7	0.0025316	15201
15201072901	15201	1	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	53	0.0191682	15201
15202012008	15202	1	2017	NA	NA	NA	NA	NA	NA	NA	12	0.0175439	15202
15202012015	15202	4	2017	NA	NA	NA	NA	NA	NA	NA	169	0.2470760	15202
15202022005	15202	5	2017	NA	NA	NA	NA	NA	NA	NA	15	0.0219298	15202
15202022009	15202	4	2017	NA	NA	NA	NA	NA	NA	NA	68	0.0994152	15202
15202022011	15202	3	2017	NA	NA	NA	NA	NA	NA	NA	87	0.1271930	15202
15202022901	15202	3	2017	NA	NA	NA	NA	NA	NA	NA	20	0.0292398	15202
15202032002	15202	1	2017	NA	NA	NA	NA	NA	NA	NA	27	0.0394737	15202
15202032010	15202	2	2017	NA	NA	NA	NA	NA	NA	NA	15	0.0219298	15202
15202992999	15202	1	2017	NA	NA	NA	NA	NA	NA	NA	1	0.0014620	15202

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
15101032003	15101	10	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	70	0.0003162	15101	19092784
15101062011	15101	38	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	2587	0.0116866	15101	705614736
15101062014	15101	705	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	8708	0.0393379	15101	2375142298
15101132015	15101	17	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	338	0.0015269	15101	92190870
15101152005	15101	79	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	870	0.0039302	15101	237296027
15101152007	15101	5	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	130	0.0005873	15101	35458027
15101152016	15101	128	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	1759	0.0079462	15101	479774380
15101152901	15101	24	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	207	0.0009351	15101	56460089
15101162016	15101	31	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	393	0.0017754	15101	107192343
15101162901	15101	5	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	58	0.0002620	15101	15819735
15102012002	15102	2	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	25	0.0199203	15102	5827095
15102012003	15102	41	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	565	0.4501992	15102	131692347
15102022007	15102	2	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	39	0.0310757	15102	9090268
15102032005	15102	2	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	112	0.0892430	15102	26105386
15102032901	15102	8	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	30	0.0239044	15102	6992514
15102042001	15102	21	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	181	0.1442231	15102	42188168
15102052004	15102	24	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	259	0.2063745	15102	60368704
15201022002	15201	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	52	0.0188065	15201	10686539
15201032006	15201	6	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	31	0.0112116	15201	6370821
15201042021	15201	10	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	75	0.0271248	15201	15413278
15201052001	15201	1	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	63	0.0227848	15201	12947153
15201052012	15201	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	25	0.0090416	15201	5137759
15201052013	15201	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	17	0.0061483	15201	3493676
15201052020	15201	1	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	18	0.0065099	15201	3699187
15201052901	15201	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	7	0.0025316	15201	1438573
15201072901	15201	1	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	53	0.0191682	15201	10892050
15202012008	15202	1	2017	NA	NA	NA	NA	NA	NA	NA	12	0.0175439	15202	NA
15202012015	15202	4	2017	NA	NA	NA	NA	NA	NA	NA	169	0.2470760	15202	NA
15202022005	15202	5	2017	NA	NA	NA	NA	NA	NA	NA	15	0.0219298	15202	NA
15202022009	15202	4	2017	NA	NA	NA	NA	NA	NA	NA	68	0.0994152	15202	NA
15202022011	15202	3	2017	NA	NA	NA	NA	NA	NA	NA	87	0.1271930	15202	NA
15202022901	15202	3	2017	NA	NA	NA	NA	NA	NA	NA	20	0.0292398	15202	NA
15202032002	15202	1	2017	NA	NA	NA	NA	NA	NA	NA	27	0.0394737	15202	NA
15202032010	15202	2	2017	NA	NA	NA	NA	NA	NA	NA	15	0.0219298	15202	NA
15202992999	15202	1	2017	NA	NA	NA	NA	NA	NA	NA	1	0.0014620	15202	NA

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -49547573 -35102589 -21662068 -14064602 556899385 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 20694047   24270379   0.853    0.402    
## Freq.x       3368982     170636  19.744  2.4e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 117400000 on 24 degrees of freedom
##   (9 observations deleted due to missingness)
## Multiple R-squared:  0.942,  Adjusted R-squared:  0.9396 
## F-statistic: 389.8 on 1 and 24 DF,  p-value: 2.4e-16

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.939586163182606"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.939586163182606"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.451360502128929"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.874848967466145"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.892366281671837"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.634556853379563"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.672813705280733"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.788944550889603"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 3   logarítmico 0.451360502128929
## 6      log-raíz 0.634556853379563
## 7      raíz-log 0.672813705280733
## 8       log-log 0.788944550889603
## 4 raíz cuadrada 0.874848967466145
## 5     raíz-raíz 0.892366281671837
## 1    cuadrático 0.939586163182606
## 2        cúbico 0.939586163182606
##                                                                     sintaxis
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)

metodo <- 4
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = multi_pob ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -326212942 -129984192   32217543   87299106  375533911 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -198209821   43345435  -4.573 0.000123 ***
## sqrt(Freq.x)   85700059    6464319  13.257 1.55e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.69e+08 on 24 degrees of freedom
##   (9 observations deleted due to missingness)
## Multiple R-squared:  0.8799, Adjusted R-squared:  0.8748 
## F-statistic: 175.8 on 1 and 24 DF,  p-value: 1.546e-12

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##  -198209821

bb <- linearMod$coefficients[2]
bb

## sqrt(Freq.x) 
##     85700059

9 Modelo raíz cuadrada (raíz cuadrada)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.8748 ).

9.1 Diagrama de dispersión sobre raíz cuadrada

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=(h_y_m_comuna_corr_01$Freq.x), y=sqrt(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo raíz cuadrada

Observemos nuevamente el resultado sobre raíz cuadrada.

linearMod <- lm(( multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = (multi_pob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -326212942 -129984192   32217543   87299106  375533911 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -198209821   43345435  -4.573 0.000123 ***
## sqrt(Freq.x)   85700059    6464319  13.257 1.55e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.69e+08 on 24 degrees of freedom
##   (9 observations deleted due to missingness)
## Multiple R-squared:  0.8799, Adjusted R-squared:  0.8748 
## F-statistic: 175.8 on 1 and 24 DF,  p-value: 1.546e-12

ggplot(h_y_m_comuna_corr_01, aes(x = (Freq.x) , y = sqrt(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = 198209821 + 85700059\cdot \sqrt {X} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- aa+bb * sqrt(h_y_m_comuna_corr_01$Freq.x)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
1	15101032003	15101	10	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	70	0.0003162	15101	19092784	72797562
2	15101062011	15101	38	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	2587	0.0116866	15101	705614736	330080825
3	15101062014	15101	705	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	8708	0.0393379	15101	2375142298	2077284110
4	15101132015	15101	17	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	338	0.0015269	15101	92190870	155140576
5	15101152005	15101	79	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	870	0.0039302	15101	237296027	563508969
6	15101152007	15101	5	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	130	0.0005873	15101	35458027	-6578662
7	15101152016	15101	128	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	1759	0.0079462	15101	479774380	771375670
8	15101152901	15101	24	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	207	0.0009351	15101	56460089	221633012
9	15101162016	15101	31	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	393	0.0017754	15101	107192343	278947916
10	15101162901	15101	5	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	58	0.0002620	15101	15819735	-6578662
11	15102012002	15102	2	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	25	0.0199203	15102	5827095	-77011635
12	15102012003	15102	41	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	565	0.4501992	15102	131692347	350538307
13	15102022007	15102	2	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	39	0.0310757	15102	9090268	-77011635
14	15102032005	15102	2	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	112	0.0892430	15102	26105386	-77011635
15	15102032901	15102	8	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	30	0.0239044	15102	6992514	44186552
16	15102042001	15102	21	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	181	0.1442231	15102	42188168	194517188
17	15102052004	15102	24	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	259	0.2063745	15102	60368704	221633012
18	15201022002	15201	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	52	0.0188065	15201	10686539	-77011635
19	15201032006	15201	6	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	31	0.0112116	15201	6370821	11711596
20	15201042021	15201	10	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	75	0.0271248	15201	15413278	72797562
21	15201052001	15201	1	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	63	0.0227848	15201	12947153	-112509762
22	15201052012	15201	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	25	0.0090416	15201	5137759	-77011635
23	15201052013	15201	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	17	0.0061483	15201	3493676	-77011635
24	15201052020	15201	1	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	18	0.0065099	15201	3699187	-112509762
25	15201052901	15201	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	7	0.0025316	15201	1438573	-77011635
26	15201072901	15201	1	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	53	0.0191682	15201	10892050	-112509762
27	15202012008	15202	1	2017	NA	NA	NA	NA	NA	NA	NA	12	0.0175439	15202	NA	-112509762
28	15202012015	15202	4	2017	NA	NA	NA	NA	NA	NA	NA	169	0.2470760	15202	NA	-26809702
29	15202022005	15202	5	2017	NA	NA	NA	NA	NA	NA	NA	15	0.0219298	15202	NA	-6578662
30	15202022009	15202	4	2017	NA	NA	NA	NA	NA	NA	NA	68	0.0994152	15202	NA	-26809702
31	15202022011	15202	3	2017	NA	NA	NA	NA	NA	NA	NA	87	0.1271930	15202	NA	-49772964
32	15202022901	15202	3	2017	NA	NA	NA	NA	NA	NA	NA	20	0.0292398	15202	NA	-49772964
33	15202032002	15202	1	2017	NA	NA	NA	NA	NA	NA	NA	27	0.0394737	15202	NA	-112509762
34	15202032010	15202	2	2017	NA	NA	NA	NA	NA	NA	NA	15	0.0219298	15202	NA	-77011635
35	15202992999	15202	1	2017	NA	NA	NA	NA	NA	NA	NA	1	0.0014620	15202	NA	-112509762
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.41	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.42	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.43	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.44	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.45	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.46	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.47	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.48	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.49	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.50	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.51	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.52	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.53	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.54	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.55	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.56	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.57	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.58	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.59	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.60	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.61	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.62	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.63	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.64	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
1	15101032003	15101	10	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	70	0.0003162	15101	19092784	72797562	1039965.2
2	15101062011	15101	38	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	2587	0.0116866	15101	705614736	330080825	127592.1
3	15101062014	15101	705	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	8708	0.0393379	15101	2375142298	2077284110	238548.9
4	15101132015	15101	17	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	338	0.0015269	15101	92190870	155140576	458995.8
5	15101152005	15101	79	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	870	0.0039302	15101	237296027	563508969	647711.5
6	15101152007	15101	5	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	130	0.0005873	15101	35458027	-6578662	-50605.1
7	15101152016	15101	128	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	1759	0.0079462	15101	479774380	771375670	438530.8
8	15101152901	15101	24	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	207	0.0009351	15101	56460089	221633012	1070690.9
9	15101162016	15101	31	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	393	0.0017754	15101	107192343	278947916	709791.1
10	15101162901	15101	5	2017	Arica	272754.1	2017	15101	221364	60377928296	Rural	58	0.0002620	15101	15819735	-6578662	-113425.2
11	15102012002	15102	2	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	25	0.0199203	15102	5827095	-77011635	-3080465.4
12	15102012003	15102	41	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	565	0.4501992	15102	131692347	350538307	620421.8
13	15102022007	15102	2	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	39	0.0310757	15102	9090268	-77011635	-1974657.3
14	15102032005	15102	2	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	112	0.0892430	15102	26105386	-77011635	-687603.9
15	15102032901	15102	8	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	30	0.0239044	15102	6992514	44186552	1472885.1
16	15102042001	15102	21	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	181	0.1442231	15102	42188168	194517188	1074680.6
17	15102052004	15102	24	2017	Camarones	233083.8	2017	15102	1255	292520169	Rural	259	0.2063745	15102	60368704	221633012	855725.9
18	15201022002	15201	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	52	0.0188065	15201	10686539	-77011635	-1480993.0
19	15201032006	15201	6	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	31	0.0112116	15201	6370821	11711596	377793.4
20	15201042021	15201	10	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	75	0.0271248	15201	15413278	72797562	970634.2
21	15201052001	15201	1	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	63	0.0227848	15201	12947153	-112509762	-1785869.2
22	15201052012	15201	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	25	0.0090416	15201	5137759	-77011635	-3080465.4
23	15201052013	15201	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	17	0.0061483	15201	3493676	-77011635	-4530096.2
24	15201052020	15201	1	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	18	0.0065099	15201	3699187	-112509762	-6250542.3
25	15201052901	15201	2	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	7	0.0025316	15201	1438573	-77011635	-11001662.1
26	15201072901	15201	1	2017	Putre	205510.4	2017	15201	2765	568236174	Rural	53	0.0191682	15201	10892050	-112509762	-2122825.7
27	15202012008	15202	1	2017	NA	NA	NA	NA	NA	NA	NA	12	0.0175439	15202	NA	-112509762	NA
28	15202012015	15202	4	2017	NA	NA	NA	NA	NA	NA	NA	169	0.2470760	15202	NA	-26809702	NA
29	15202022005	15202	5	2017	NA	NA	NA	NA	NA	NA	NA	15	0.0219298	15202	NA	-6578662	NA
30	15202022009	15202	4	2017	NA	NA	NA	NA	NA	NA	NA	68	0.0994152	15202	NA	-26809702	NA
31	15202022011	15202	3	2017	NA	NA	NA	NA	NA	NA	NA	87	0.1271930	15202	NA	-49772964	NA
32	15202022901	15202	3	2017	NA	NA	NA	NA	NA	NA	NA	20	0.0292398	15202	NA	-49772964	NA
33	15202032002	15202	1	2017	NA	NA	NA	NA	NA	NA	NA	27	0.0394737	15202	NA	-112509762	NA
34	15202032010	15202	2	2017	NA	NA	NA	NA	NA	NA	NA	15	0.0219298	15202	NA	-77011635	NA
35	15202992999	15202	1	2017	NA	NA	NA	NA	NA	NA	NA	1	0.0014620	15202	NA	-112509762	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.14	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.15	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.16	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.19	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.30	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.31	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.32	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.33	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.34	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.36	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.37	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.39	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.40	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.41	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.42	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.43	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.44	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.45	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.46	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.47	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.48	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.49	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.50	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.51	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.52	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.53	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.54	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.55	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.56	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.57	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.58	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.59	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.60	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.61	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.62	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.63	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA.64	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_rural_nivel_nacional_17_r15.rds")

---

R-16

Urbano

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 1)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15201	1	1	1	1767	1	1	1	2	1	3	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	2	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	3	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	4	1	1	2	3	1	99	1	2	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	5	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	6	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	7	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	8	1	1	3	3	1	99	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	9	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	10	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	11	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	12	1	1	4	3	3	3	4	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	13	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	14	1	1	2	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	15	1	1	2	1	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	16	1	1	5	1	5	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	17	1	1	5	3	1	3	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	18	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	19	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	20	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	21	4	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	22	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	23	1	1	5	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	24	1	1	3	1	1	6	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	25	1	1	5	6	1	1	4	2	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	26	4	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	27	1	1	5	99	3	3	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	28	1	1	6	3	2	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	29	1	1	2	3	1	99	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	30	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	31	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	32	1	1	3	1	1	2	1	1	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	33	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	34	3	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	35	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	36	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	37	1	1	5	3	3	1	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	38	1	1	1	1	4	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	39	1	1	5	3	4	3	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	40	1	1	5	3	2	1	1	1	7	15	152	15201	15201011001
15	152	15201	1	1	1	1767	41	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	42	1	1	2	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	43	1	1	1	1	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	44	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	45	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	46	1	1	5	6	1	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	47	1	1	5	3	4	0	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	48	1	1	3	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	49	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	50	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	51	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	52	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	53	1	1	1	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	54	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	55	1	1	2	2	1	4	1	1	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	56	8	1	98	98	98	98	98	98	4	15	152	15201	15201011001
15	152	15201	1	1	1	1767	57	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	58	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	59	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	60	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	61	1	1	2	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	62	1	1	5	3	1	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	63	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	64	1	1	5	3	2	4	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	65	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	66	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	67	5	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	68	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	69	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	70	1	1	1	3	3	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	71	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	72	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	73	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	74	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	75	1	1	5	3	4	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	76	1	1	5	3	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	77	1	1	3	6	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	78	5	1	3	3	1	1	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	79	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	80	1	1	2	1	1	3	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	81	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	82	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	83	1	1	5	3	2	1	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	84	4	1	5	3	2	0	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	85	8	1	98	98	98	98	98	98	8	15	152	15201	15201011001
15	152	15201	1	1	1	1767	86	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	87	1	1	3	3	1	2	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	88	1	2	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	89	5	1	4	3	2	1	2	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	90	1	1	5	3	3	2	1	3	6	15	152	15201	15201011001
15	152	15201	1	1	1	1767	91	1	1	2	3	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	92	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	93	5	1	4	3	5	1	1	1	5	15	152	15201	15201011001
15	152	15201	1	1	1	1767	94	1	1	5	3	3	99	1	1	3	15	152	15201	15201011001
15	152	15201	1	1	1	1767	95	1	1	5	3	2	2	1	1	2	15	152	15201	15201011001
15	152	15201	1	1	1	1767	96	1	1	2	3	4	6	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	97	1	1	5	1	1	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	98	1	4	98	98	98	98	98	0	0	15	152	15201	15201011001
15	152	15201	1	1	1	1767	99	1	1	2	7	4	1	1	1	1	15	152	15201	15201011001
15	152	15201	1	1	1	1767	100	1	3	98	98	98	98	98	0	0	15	152	15201	15201011001

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 16:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 16)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	16101011001	52	2017	16101
2	16101011002	105	2017	16101
3	16101011003	142	2017	16101
4	16101011004	163	2017	16101
5	16101021001	10	2017	16101
6	16101021002	114	2017	16101
7	16101021003	87	2017	16101
8	16101021004	79	2017	16101
9	16101031001	335	2017	16101
10	16101031002	116	2017	16101
11	16101031003	125	2017	16101
12	16101031004	55	2017	16101
13	16101041001	119	2017	16101
14	16101041002	41	2017	16101
15	16101041003	67	2017	16101
16	16101041004	35	2017	16101
17	16101051001	44	2017	16101
18	16101051002	382	2017	16101
19	16101051003	73	2017	16101
20	16101051004	16	2017	16101
21	16101051005	424	2017	16101
22	16101061001	5	2017	16101
23	16101071001	61	2017	16101
24	16101071002	21	2017	16101
25	16101081001	37	2017	16101
26	16101121001	154	2017	16101
27	16101131001	866	2017	16101
28	16101131002	107	2017	16101
29	16101131003	103	2017	16101
30	16101131004	160	2017	16101
31	16101141001	446	2017	16101
32	16101141002	882	2017	16101
33	16101141003	243	2017	16101
34	16101141004	789	2017	16101
35	16101151001	222	2017	16101
36	16101151002	34	2017	16101
37	16101151003	31	2017	16101
38	16101151004	76	2017	16101
39	16101151005	39	2017	16101
40	16101151006	65	2017	16101
41	16101151007	46	2017	16101
42	16101151008	28	2017	16101
43	16101151009	314	2017	16101
44	16101151010	294	2017	16101
45	16101151011	84	2017	16101
46	16101151012	51	2017	16101
47	16101151013	46	2017	16101
48	16101151014	24	2017	16101
49	16101151015	13	2017	16101
50	16101161001	96	2017	16101
51	16101161002	97	2017	16101
52	16101161003	109	2017	16101
53	16101161004	186	2017	16101
54	16101161005	148	2017	16101
55	16101171001	76	2017	16101
56	16101171002	82	2017	16101
57	16101171003	90	2017	16101
58	16101171004	30	2017	16101
59	16101991999	9	2017	16101
203	16102011001	54	2017	16102
204	16102011002	84	2017	16102
205	16102011003	83	2017	16102
206	16102021001	6	2017	16102
207	16102041001	20	2017	16102
208	16102051001	26	2017	16102
209	16102071001	2	2017	16102
210	16102991999	2	2017	16102
354	16103041001	116	2017	16103
355	16103041002	122	2017	16103
356	16103041003	75	2017	16103
357	16103041004	185	2017	16103
358	16103041005	89	2017	16103
359	16103041006	87	2017	16103
360	16103041007	56	2017	16103
504	16104011001	103	2017	16104
648	16105011001	134	2017	16105
649	16105091001	3	2017	16105
793	16106011001	68	2017	16106
794	16106021001	32	2017	16106
795	16106051001	9	2017	16106
796	16106051002	7	2017	16106
797	16106991999	6	2017	16106
941	16107011001	99	2017	16107
942	16107011002	139	2017	16107
943	16107011004	28	2017	16107
944	16107051001	1	2017	16107
945	16107061001	5	2017	16107
946	16107991999	12	2017	16107
1090	16108011001	124	2017	16108
1091	16108051001	22	2017	16108
1092	16108051002	11	2017	16108
1093	16108061001	53	2017	16108
1094	16108061002	6	2017	16108
1095	16108991999	1	2017	16108
1239	16109011001	130	2017	16109
1240	16109011002	127	2017	16109
1241	16109021001	1	2017	16109
1242	16109031001	4	2017	16109
1243	16109041001	2	2017	16109
1244	16109081001	10	2017	16109

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_urbano_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	356487.6	2017	1101	191468	68255976664	Urbano
01107	Alto Hospicio	301933.4	2017	1107	108375	32722034397	Urbano
01401	Pozo Almonte	299998.6	2017	1401	15711	4713278189	Urbano
01405	Pica	330061.1	2017	1405	9296	3068247619	Urbano
02101	Antofagasta	347580.2	2017	2101	361873	125779893517	Urbano
02102	Mejillones	369770.7	2017	2102	13467	4979702302	Urbano
02104	Taltal	376328.9	2017	2104	13317	5011572025	Urbano
02201	Calama	416281.1	2017	2201	165731	68990679686	Urbano
02203	San Pedro de Atacama	437934.7	2017	2203	10996	4815529626	Urbano
02301	Tocopilla	271720.8	2017	2301	25186	6843559467	Urbano
02302	María Elena	466266.9	2017	2302	6457	3010685220	Urbano
03101	Copiapó	330574.6	2017	3101	153937	50887663717	Urbano
03102	Caldera	299314.8	2017	3102	17662	5286498241	Urbano
03103	Tierra Amarilla	315860.6	2017	3103	14019	4428049932	Urbano
03201	Chañaral	286389.3	2017	3201	12219	3499391196	Urbano
03202	Diego de Almagro	325861.5	2017	3202	13925	4537621312	Urbano
03301	Vallenar	311577.0	2017	3301	51917	16176145007	Urbano
03303	Freirina	289049.9	2017	3303	7041	2035200054	Urbano
03304	Huasco	337414.8	2017	3304	10149	3424422750	Urbano
04101	La Serena	272136.8	2017	4101	221054	60156924947	Urbano
04102	Coquimbo	264340.0	2017	4102	227730	60198159091	Urbano
04103	Andacollo	251267.7	2017	4103	11044	2775000288	Urbano
04104	La Higuera	214257.0	2017	4104	4241	908664019	Urbano
04106	Vicuña	245957.4	2017	4106	27771	6830481918	Urbano
04201	Illapel	270316.5	2017	4201	30848	8338722128	Urbano
04202	Canela	233397.3	2017	4202	9093	2122281844	Urbano
04203	Los Vilos	282415.6	2017	4203	21382	6038609501	Urbano
04204	Salamanca	262056.9	2017	4204	29347	7690585032	Urbano
04301	Ovalle	274771.4	2017	4301	111272	30574361012	Urbano
04302	Combarbalá	228990.4	2017	4302	13322	3050610572	Urbano
04303	Monte Patria	225369.1	2017	4303	30751	6930326684	Urbano
04304	Punitaqui	212496.1	2017	4304	10956	2328107498	Urbano
05101	Valparaíso	297929.0	2017	5101	296655	88382118059	Urbano
05102	Casablanca	341641.8	2017	5102	26867	9178890241	Urbano
05103	Concón	318496.3	2017	5103	42152	13425257057	Urbano
05105	Puchuncaví	296035.5	2017	5105	18546	5490274928	Urbano
05107	Quintero	308224.7	2017	5107	31923	9839456903	Urbano
05109	Viña del Mar	337006.1	2017	5109	334248	112643604611	Urbano
05301	Los Andes	339720.2	2017	5301	66708	22662055502	Urbano
05302	Calle Larga	246387.3	2017	5302	14832	3654416747	Urbano
05303	Rinconada	273904.7	2017	5303	10207	2795744821	Urbano
05304	San Esteban	219571.6	2017	5304	18855	4140022481	Urbano
05401	La Ligua	250134.4	2017	5401	35390	8852256241	Urbano
05402	Cabildo	262745.9	2017	5402	19388	5094117762	Urbano
05403	Papudo	294355.2	2017	5403	6356	1870921373	Urbano
05404	Petorca	237510.8	2017	5404	9826	2333781007	Urbano
05405	Zapallar	294389.2	2017	5405	7339	2160521991	Urbano
05501	Quillota	286029.5	2017	5501	90517	25890529852	Urbano
05502	Calera	277181.9	2017	5502	50554	14012652087	Urbano
05503	Hijuelas	254094.0	2017	5503	17988	4570642363	Urbano

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
16101	16101011001	52	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101011002	105	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101011003	142	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101011004	163	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101021001	10	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101021002	114	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101021003	87	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101021004	79	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101031001	335	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101031002	116	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101031003	125	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101031004	55	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101041001	119	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101041002	41	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101041003	67	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101041004	35	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101051001	44	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101051002	382	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101051003	73	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101051004	16	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101051005	424	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101061001	5	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101071001	61	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101071002	21	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101081001	37	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101121001	154	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101131001	866	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101131002	107	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101131003	103	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101131004	160	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101141001	446	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101141002	882	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101141003	243	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101141004	789	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151001	222	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151002	34	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151003	31	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151004	76	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151005	39	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151006	65	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151007	46	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151008	28	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151009	314	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151010	294	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151011	84	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151012	51	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151013	46	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151014	24	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151015	13	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101161001	96	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101161002	97	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101161003	109	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101161004	186	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101161005	148	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101171001	76	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101171002	82	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101171003	90	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101171004	30	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101991999	9	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16102	16102011001	54	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano
16102	16102011002	84	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano
16102	16102011003	83	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano
16102	16102021001	6	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano
16102	16102041001	20	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano
16102	16102051001	26	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano
16102	16102071001	2	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano
16102	16102991999	2	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano
16103	16103041001	116	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano
16103	16103041002	122	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano
16103	16103041003	75	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano
16103	16103041004	185	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano
16103	16103041005	89	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano
16103	16103041006	87	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano
16103	16103041007	56	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano
16104	16104011001	103	2017	El Carmen	205701.2	2017	16104	12044	2477465194	Urbano
16105	16105011001	134	2017	Pemuco	242752.9	2017	16105	8448	2050776137	Urbano
16105	16105091001	3	2017	Pemuco	242752.9	2017	16105	8448	2050776137	Urbano
16106	16106011001	68	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano
16106	16106021001	32	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano
16106	16106051001	9	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano
16106	16106051002	7	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano
16106	16106991999	6	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano
16107	16107011001	99	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano
16107	16107011002	139	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano
16107	16107011004	28	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano
16107	16107051001	1	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano
16107	16107061001	5	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano
16107	16107991999	12	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano
16108	16108011001	124	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano
16108	16108051001	22	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano
16108	16108051002	11	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano
16108	16108061001	53	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano
16108	16108061002	6	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano
16108	16108991999	1	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano
16109	16109011001	130	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano
16109	16109011002	127	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano
16109	16109021001	1	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano
16109	16109031001	4	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano
16109	16109041001	2	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano
16109	16109081001	10	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
16101	16101011001	52	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101011002	105	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101011003	142	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101011004	163	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101021001	10	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101021002	114	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101021003	87	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101021004	79	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101031001	335	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101031002	116	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101031003	125	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101031004	55	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101041001	119	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101041002	41	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101041003	67	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101041004	35	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101051001	44	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101051002	382	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101051003	73	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101051004	16	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101051005	424	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101061001	5	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101071001	61	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101071002	21	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101081001	37	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101121001	154	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101131001	866	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101131002	107	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101131003	103	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101131004	160	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101141001	446	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101141002	882	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101141003	243	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101141004	789	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151001	222	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151002	34	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151003	31	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151004	76	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151005	39	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151006	65	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151007	46	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151008	28	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151009	314	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151010	294	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151011	84	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151012	51	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151013	46	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151014	24	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101151015	13	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101161001	96	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101161002	97	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101161003	109	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101161004	186	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101161005	148	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101171001	76	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101171002	82	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101171003	90	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101171004	30	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16101	16101991999	9	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano
16102	16102011001	54	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano
16102	16102011002	84	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano
16102	16102011003	83	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano
16102	16102021001	6	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano
16102	16102041001	20	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano
16102	16102051001	26	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano
16102	16102071001	2	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano
16102	16102991999	2	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano
16103	16103041001	116	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano
16103	16103041002	122	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano
16103	16103041003	75	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano
16103	16103041004	185	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano
16103	16103041005	89	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano
16103	16103041006	87	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano
16103	16103041007	56	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano
16104	16104011001	103	2017	El Carmen	205701.2	2017	16104	12044	2477465194	Urbano
16105	16105011001	134	2017	Pemuco	242752.9	2017	16105	8448	2050776137	Urbano
16105	16105091001	3	2017	Pemuco	242752.9	2017	16105	8448	2050776137	Urbano
16106	16106011001	68	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano
16106	16106021001	32	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano
16106	16106051001	9	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano
16106	16106051002	7	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano
16106	16106991999	6	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano
16107	16107011001	99	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano
16107	16107011002	139	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano
16107	16107011004	28	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano
16107	16107051001	1	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano
16107	16107061001	5	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano
16107	16107991999	12	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano
16108	16108011001	124	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano
16108	16108051001	22	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano
16108	16108051002	11	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano
16108	16108061001	53	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano
16108	16108061002	6	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano
16108	16108991999	1	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano
16109	16109011001	130	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano
16109	16109011002	127	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano
16109	16109021001	1	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano
16109	16109031001	4	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano
16109	16109041001	2	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano
16109	16109081001	10	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
16101011001	16101	52	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1080	0.0058461	16101
16101011002	16101	105	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1525	0.0082549	16101
16101011003	16101	142	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2051	0.0111021	16101
16101011004	16101	163	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1819	0.0098463	16101
16101021001	16101	10	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1345	0.0072805	16101
16101021002	16101	114	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1991	0.0107774	16101
16101021003	16101	87	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2007	0.0108640	16101
16101021004	16101	79	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1882	0.0101873	16101
16101031001	16101	335	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3622	0.0196060	16101
16101031002	16101	116	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2516	0.0136192	16101
16101031003	16101	125	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2184	0.0118221	16101
16101031004	16101	55	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1866	0.0101007	16101
16101041001	16101	119	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3315	0.0179442	16101
16101041002	16101	41	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1999	0.0108207	16101
16101041003	16101	67	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4799	0.0259772	16101
16101041004	16101	35	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2766	0.0149725	16101
16101051001	16101	44	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1466	0.0079355	16101
16101051002	16101	382	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4764	0.0257877	16101
16101051003	16101	73	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2374	0.0128506	16101
16101051004	16101	16	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2018	0.0109235	16101
16101051005	16101	424	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4539	0.0245698	16101
16101061001	16101	5	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	941	0.0050937	16101
16101071001	16101	61	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1599	0.0086555	16101
16101071002	16101	21	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	950	0.0051424	16101
16101081001	16101	37	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1276	0.0069070	16101
16101121001	16101	154	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4876	0.0263940	16101
16101131001	16101	866	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	5741	0.0310763	16101
16101131002	16101	107	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2211	0.0119682	16101
16101131003	16101	103	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2135	0.0115568	16101
16101131004	16101	160	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4141	0.0224154	16101
16101141001	16101	446	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	5365	0.0290410	16101
16101141002	16101	882	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	5814	0.0314714	16101
16101141003	16101	243	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3016	0.0163257	16101
16101141004	16101	789	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3759	0.0203476	16101
16101151001	16101	222	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3362	0.0181986	16101
16101151002	16101	34	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3634	0.0196710	16101
16101151003	16101	31	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1805	0.0097705	16101
16101151004	16101	76	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3489	0.0188861	16101
16101151005	16101	39	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4931	0.0266917	16101
16101151006	16101	65	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4103	0.0222097	16101
16101151007	16101	46	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2402	0.0130021	16101
16101151008	16101	28	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3208	0.0173650	16101
16101151009	16101	314	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4520	0.0244670	16101
16101151010	16101	294	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4906	0.0265564	16101
16101151011	16101	84	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2718	0.0147126	16101
16101151012	16101	51	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2161	0.0116976	16101
16101151013	16101	46	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3743	0.0202610	16101
16101151014	16101	24	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3883	0.0210188	16101
16101151015	16101	13	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	248	0.0013424	16101
16101161001	16101	96	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2140	0.0115839	16101
16101161002	16101	97	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2196	0.0118870	16101
16101161003	16101	109	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3884	0.0210243	16101
16101161004	16101	186	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2612	0.0141389	16101
16101161005	16101	148	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3326	0.0180038	16101
16101171001	16101	76	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4382	0.0237200	16101
16101171002	16101	82	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2900	0.0156978	16101
16101171003	16101	90	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2262	0.0122443	16101
16101171004	16101	30	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1590	0.0086067	16101
16101991999	16101	9	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	304	0.0016456	16101
16102011001	16102	54	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	2452	0.1140837	16102
16102011002	16102	84	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	4765	0.2217001	16102
16102011003	16102	83	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	3184	0.1481413	16102
16102021001	16102	6	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	537	0.0249849	16102
16102041001	16102	20	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	1419	0.0660215	16102
16102051001	16102	26	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	949	0.0441539	16102
16102071001	16102	2	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	134	0.0062346	16102
16102991999	16102	2	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	51	0.0023729	16102
16103041001	16103	116	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	3503	0.1133400	16103
16103041002	16103	122	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	6143	0.1987576	16103
16103041003	16103	75	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	4173	0.1350180	16103
16103041004	16103	185	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	4071	0.1317177	16103
16103041005	16103	89	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	2991	0.0967742	16103
16103041006	16103	87	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	2508	0.0811467	16103
16103041007	16103	56	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	3970	0.1284499	16103
16104011001	16104	103	2017	El Carmen	205701.2	2017	16104	12044	2477465194	Urbano	4722	0.3920624	16104
16105011001	16105	134	2017	Pemuco	242752.9	2017	16105	8448	2050776137	Urbano	3963	0.4691051	16105
16105091001	16105	3	2017	Pemuco	242752.9	2017	16105	8448	2050776137	Urbano	164	0.0194129	16105
16106011001	16106	68	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano	2222	0.2052277	16106
16106021001	16106	32	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano	1544	0.1426064	16106
16106051001	16106	9	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano	956	0.0882978	16106
16106051002	16106	7	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano	651	0.0601275	16106
16106991999	16106	6	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano	85	0.0078507	16106
16107011001	16107	99	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	4657	0.2663426	16107
16107011002	16107	139	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	3783	0.2163569	16107
16107011004	16107	28	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	1550	0.0886474	16107
16107051001	16107	1	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	118	0.0067486	16107
16107061001	16107	5	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	171	0.0097798	16107
16107991999	16107	12	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	144	0.0082356	16107
16108011001	16108	124	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	2932	0.1823496	16108
16108051001	16108	22	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	1492	0.0927918	16108
16108051002	16108	11	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	685	0.0426022	16108
16108061001	16108	53	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	1300	0.0808508	16108
16108061002	16108	6	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	370	0.0230114	16108
16108991999	16108	1	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	23	0.0014304	16108
16109011001	16109	130	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	5212	0.2930230	16109
16109011002	16109	127	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	4188	0.2354529	16109
16109021001	16109	1	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	211	0.0118626	16109
16109031001	16109	4	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	367	0.0206330	16109
16109041001	16109	2	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	533	0.0299657	16109
16109081001	16109	10	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	373	0.0209704	16109

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
16101011001	16101	52	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1080	0.0058461	16101	289532747
16101011002	16101	105	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1525	0.0082549	16101	408830962
16101011003	16101	142	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2051	0.0111021	16101	549844133
16101011004	16101	163	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1819	0.0098463	16101	487648210
16101021001	16101	10	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1345	0.0072805	16101	360575504
16101021002	16101	114	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1991	0.0107774	16101	533758981
16101021003	16101	87	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2007	0.0108640	16101	538048355
16101021004	16101	79	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1882	0.0101873	16101	504537620
16101031001	16101	335	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3622	0.0196060	16101	971007045
16101031002	16101	116	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2516	0.0136192	16101	674504066
16101031003	16101	125	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2184	0.0118221	16101	585499555
16101031004	16101	55	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1866	0.0101007	16101	500248246
16101041001	16101	119	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3315	0.0179442	16101	888704681
16101041002	16101	41	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1999	0.0108207	16101	535903668
16101041003	16101	67	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4799	0.0259772	16101	1286544122
16101041004	16101	35	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2766	0.0149725	16101	741525535
16101051001	16101	44	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1466	0.0079355	16101	393013895
16101051002	16101	382	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4764	0.0257877	16101	1277161117
16101051003	16101	73	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2374	0.0128506	16101	636435871
16101051004	16101	16	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2018	0.0109235	16101	540997299
16101051005	16101	424	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4539	0.0245698	16101	1216841794
16101061001	16101	5	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	941	0.0050937	16101	252268810
16101071001	16101	61	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1599	0.0086555	16101	428669317
16101071002	16101	21	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	950	0.0051424	16101	254681583
16101081001	16101	37	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1276	0.0069070	16101	342077579
16101121001	16101	154	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4876	0.0263940	16101	1307186735
16101131001	16101	866	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	5741	0.0310763	16101	1539081018
16101131002	16101	107	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2211	0.0119682	16101	592737873
16101131003	16101	103	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2135	0.0115568	16101	572363347
16101131004	16101	160	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4141	0.0224154	16101	1110143615
16101141001	16101	446	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	5365	0.0290410	16101	1438280729
16101141002	16101	882	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	5814	0.0314714	16101	1558651287
16101141003	16101	243	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3016	0.0163257	16101	808547004
16101141004	16101	789	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3759	0.0203476	16101	1007734811
16101151001	16101	222	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3362	0.0181986	16101	901304717
16101151002	16101	34	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3634	0.0196710	16101	974224076
16101151003	16101	31	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1805	0.0097705	16101	483895007
16101151004	16101	76	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3489	0.0188861	16101	935351624
16101151005	16101	39	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4931	0.0266917	16101	1321931458
16101151006	16101	65	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4103	0.0222097	16101	1099956352
16101151007	16101	46	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2402	0.0130021	16101	643942276
16101151008	16101	28	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3208	0.0173650	16101	860019492
16101151009	16101	314	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4520	0.0244670	16101	1211748163
16101151010	16101	294	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4906	0.0265564	16101	1315229311
16101151011	16101	84	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2718	0.0147126	16101	728657413
16101151012	16101	51	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2161	0.0116976	16101	579333580
16101151013	16101	46	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3743	0.0202610	16101	1003445436
16101151014	16101	24	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3883	0.0210188	16101	1040977459
16101151015	16101	13	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	248	0.0013424	16101	66485297
16101161001	16101	96	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2140	0.0115839	16101	573703776
16101161002	16101	97	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2196	0.0118870	16101	588716585
16101161003	16101	109	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3884	0.0210243	16101	1041245545
16101161004	16101	186	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2612	0.0141389	16101	700240310
16101161005	16101	148	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3326	0.0180038	16101	891653626
16101171001	16101	76	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4382	0.0237200	16101	1174752312
16101171002	16101	82	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2900	0.0156978	16101	777449042
16101171003	16101	90	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2262	0.0122443	16101	606410253
16101171004	16101	30	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1590	0.0086067	16101	426256544
16101991999	16101	9	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	304	0.0016456	16101	81498107
16102011001	16102	54	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	2452	0.1140837	16102	526101569
16102011002	16102	84	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	4765	0.2217001	16102	1022379272
16102011003	16102	83	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	3184	0.1481413	16102	683159623
16102021001	16102	6	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	537	0.0249849	16102	115218818
16102041001	16102	20	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	1419	0.0660215	16102	304460900
16102051001	16102	26	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	949	0.0441539	16102	203617614
16102071001	16102	2	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	134	0.0062346	16102	28751065
16102991999	16102	2	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	51	0.0023729	16102	10942569
16103041001	16103	116	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	3503	0.1133400	16103	890773641
16103041002	16103	122	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	6143	0.1987576	16103	1562096055
16103041003	16103	75	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	4173	0.1350180	16103	1061147133
16103041004	16103	185	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	4071	0.1317177	16103	1035209676
16103041005	16103	89	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	2991	0.0967742	16103	760577779
16103041006	16103	87	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	2508	0.0811467	16103	637756292
16103041007	16103	56	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	3970	0.1284499	16103	1009526508
16104011001	16104	103	2017	El Carmen	205701.2	2017	16104	12044	2477465194	Urbano	4722	0.3920624	16104	971321043
16105011001	16105	134	2017	Pemuco	242752.9	2017	16105	8448	2050776137	Urbano	3963	0.4691051	16105	962029573
16105091001	16105	3	2017	Pemuco	242752.9	2017	16105	8448	2050776137	Urbano	164	0.0194129	16105	39811469
16106011001	16106	68	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano	2222	0.2052277	16106	390188865
16106021001	16106	32	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano	1544	0.1426064	16106	271130337
16106051001	16106	9	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano	956	0.0882978	16106	167876037
16106051002	16106	7	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano	651	0.0601275	16106	114317260
16106991999	16106	6	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano	85	0.0078507	16106	14926217
16107011001	16107	99	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	4657	0.2663426	16107	1192529159
16107011002	16107	139	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	3783	0.2163569	16107	968721883
16107011004	16107	28	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	1550	0.0886474	16107	396912217
16107051001	16107	1	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	118	0.0067486	16107	30216543
16107061001	16107	5	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	171	0.0097798	16107	43788380
16107991999	16107	12	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	144	0.0082356	16107	36874425
16108011001	16108	124	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	2932	0.1823496	16108	596167970
16108051001	16108	22	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	1492	0.0927918	16108	303370604
16108051002	16108	11	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	685	0.0426022	16108	139282080
16108061001	16108	53	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	1300	0.0808508	16108	264330955
16108061002	16108	6	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	370	0.0230114	16108	75232656
16108991999	16108	1	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	23	0.0014304	16108	4676625
16109011001	16109	130	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	5212	0.2930230	16109	1305827170
16109011002	16109	127	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	4188	0.2354529	16109	1049271717
16109021001	16109	1	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	211	0.0118626	16109	52864454
16109031001	16109	4	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	367	0.0206330	16109	91949074
16109041001	16109	2	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	533	0.0299657	16109	133539118
16109081001	16109	10	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	373	0.0209704	16109	93452328

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -856613025 -263931414  -46258194  220307068  913969783 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 425668954   33367797   12.76  < 2e-16 ***
## Freq.x        1823421     202384    9.01 1.31e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 330400000 on 141 degrees of freedom
## Multiple R-squared:  0.3654, Adjusted R-squared:  0.3609 
## F-statistic: 81.18 on 1 and 141 DF,  p-value: 1.312e-15

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.360864362024504"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.360864362024504"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.631386044549754"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.579696483423887"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                 
## [1,] "raíz-raíz" "0.566739136291337"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.442951412046716"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.747134813747916"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.748916059808116"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 1    cuadrático 0.360864362024504
## 2        cúbico 0.360864362024504
## 6      log-raíz 0.442951412046716
## 5     raíz-raíz 0.566739136291337
## 4 raíz cuadrada 0.579696483423887
## 3   logarítmico 0.631386044549754
## 7      raíz-log 0.747134813747916
## 8       log-log 0.748916059808116
##                                                                     sintaxis
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 8
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.3771 -0.3984  0.0570  0.4219  1.5052 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  16.6975     0.1564  106.78   <2e-16 ***
## log(Freq.x)   0.8057     0.0391   20.61   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6874 on 141 degrees of freedom
## Multiple R-squared:  0.7507, Adjusted R-squared:  0.7489 
## F-statistic: 424.5 on 1 and 141 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    16.69753

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##   0.8057413

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.7489 ).

9.1 Diagrama de dispersión sobre log-log

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.3771 -0.3984  0.0570  0.4219  1.5052 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  16.6975     0.1564  106.78   <2e-16 ***
## log(Freq.x)   0.8057     0.0391   20.61   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6874 on 141 degrees of freedom
## Multiple R-squared:  0.7507, Adjusted R-squared:  0.7489 
## F-statistic: 424.5 on 1 and 141 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = e^{16.69753+0.8057413 \cdot ln{X}} \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
16101011001	16101	52	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1080	0.0058461	16101	289532747	430825578
16101011002	16101	105	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1525	0.0082549	16101	408830962	758931181
16101011003	16101	142	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2051	0.0111021	16101	549844133	967908506
16101011004	16101	163	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1819	0.0098463	16101	487648210	1081677041
16101021001	16101	10	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1345	0.0072805	16101	360575504	114126906
16101021002	16101	114	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1991	0.0107774	16101	533758981	810923508
16101021003	16101	87	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2007	0.0108640	16101	538048355	652225043
16101021004	16101	79	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1882	0.0101873	16101	504537620	603452689
16101031001	16101	335	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3622	0.0196060	16101	971007045	1932767297
16101031002	16101	116	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2516	0.0136192	16101	674504066	822367171
16101031003	16101	125	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2184	0.0118221	16101	585499555	873401030
16101031004	16101	55	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1866	0.0101007	16101	500248246	450742812
16101041001	16101	119	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3315	0.0179442	16101	888704681	839461161
16101041002	16101	41	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1999	0.0108207	16101	535903668	355740474
16101041003	16101	67	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4799	0.0259772	16101	1286544122	528433809
16101041004	16101	35	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2766	0.0149725	16101	741525535	313159878
16101051001	16101	44	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1466	0.0079355	16101	393013895	376568869
16101051002	16101	382	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4764	0.0257877	16101	1277161117	2148432788
16101051003	16101	73	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2374	0.0128506	16101	636435871	566243060
16101051004	16101	16	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2018	0.0109235	16101	540997299	166669424
16101051005	16101	424	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4539	0.0245698	16101	1216841794	2336812463
16101061001	16101	5	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	941	0.0050937	16101	252268810	65288359
16101071001	16101	61	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1599	0.0086555	16101	428669317	489960077
16101071002	16101	21	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	950	0.0051424	16101	254681583	207497765
16101081001	16101	37	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1276	0.0069070	16101	342077579	327500235
16101121001	16101	154	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4876	0.0263940	16101	1307186735	1033290617
16101131001	16101	866	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	5741	0.0310763	16101	1539081018	4154570684
16101131002	16101	107	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2211	0.0119682	16101	592737873	770557454
16101131003	16101	103	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2135	0.0115568	16101	572363347	747261806
16101131004	16101	160	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4141	0.0224154	16101	1110143615	1065607322
16101141001	16101	446	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	5365	0.0290410	16101	1438280729	2434025880
16101141002	16101	882	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	5814	0.0314714	16101	1558651287	4216308246
16101141003	16101	243	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3016	0.0163257	16101	808547004	1492203962
16101141004	16101	789	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3759	0.0203476	16101	1007734811	3854262234
16101151001	16101	222	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3362	0.0181986	16101	901304717	1387395195
16101151002	16101	34	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3634	0.0196710	16101	974224076	305930330
16101151003	16101	31	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1805	0.0097705	16101	483895007	283986983
16101151004	16101	76	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3489	0.0188861	16101	935351624	584919227
16101151005	16101	39	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4931	0.0266917	16101	1321931458	341690720
16101151006	16101	65	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4103	0.0222097	16101	1099956352	515686635
16101151007	16101	46	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2402	0.0130021	16101	643942276	390300739
16101151008	16101	28	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3208	0.0173650	16101	860019492	261626492
16101151009	16101	314	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4520	0.0244670	16101	1211748163	1834535098
16101151010	16101	294	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4906	0.0265564	16101	1315229311	1739786964
16101151011	16101	84	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2718	0.0147126	16101	728657413	634041960
16101151012	16101	51	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2161	0.0116976	16101	579333580	424137360
16101151013	16101	46	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3743	0.0202610	16101	1003445436	390300739
16101151014	16101	24	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3883	0.0210188	16101	1040977459	231068065
16101151015	16101	13	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	248	0.0013424	16101	66485297	140992789
16101161001	16101	96	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2140	0.0115839	16101	573703776	706064709
16101161002	16101	97	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2196	0.0118870	16101	588716585	711984836
16101161003	16101	109	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3884	0.0210243	16101	1041245545	782141585
16101161004	16101	186	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2612	0.0141389	16101	700240310	1203059213
16101161005	16101	148	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3326	0.0180038	16101	891653626	1000728326
16101171001	16101	76	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4382	0.0237200	16101	1174752312	584919227
16101171002	16101	82	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2900	0.0156978	16101	777449042	621849899
16101171003	16101	90	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2262	0.0122443	16101	606410253	670286705
16101171004	16101	30	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1590	0.0086067	16101	426256544	276582262
16101991999	16101	9	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	304	0.0016456	16101	81498107	104838149
16102011001	16102	54	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	2452	0.1140837	16102	526101569	444127755
16102011002	16102	84	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	4765	0.2217001	16102	1022379272	634041960
16102011003	16102	83	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	3184	0.1481413	16102	683159623	627953064
16102021001	16102	6	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	537	0.0249849	16102	115218818	75619770
16102041001	16102	20	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	1419	0.0660215	16102	304460900	199498820
16102051001	16102	26	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	949	0.0441539	16102	203617614	246461559
16102071001	16102	2	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	134	0.0062346	16102	28751065	31203195
16102991999	16102	2	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	51	0.0023729	16102	10942569	31203195
16103041001	16103	116	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	3503	0.1133400	16103	890773641	822367171
16103041002	16103	122	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	6143	0.1987576	16103	1562096055	856471631
16103041003	16103	75	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	4173	0.1350180	16103	1061147133	578710029
16103041004	16103	185	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	4071	0.1317177	16103	1035209676	1197844902
16103041005	16103	89	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	2991	0.0967742	16103	760577779	664279337
16103041006	16103	87	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	2508	0.0811467	16103	637756292	652225043
16103041007	16103	56	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	3970	0.1284499	16103	1009526508	457334545
16104011001	16104	103	2017	El Carmen	205701.2	2017	16104	12044	2477465194	Urbano	4722	0.3920624	16104	971321043	747261806
16105011001	16105	134	2017	Pemuco	242752.9	2017	16105	8448	2050776137	Urbano	3963	0.4691051	16105	962029573	923725397
16105091001	16105	3	2017	Pemuco	242752.9	2017	16105	8448	2050776137	Urbano	164	0.0194129	16105	39811469	43259656
16106011001	16106	68	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano	2222	0.2052277	16106	390188865	534779590
16106021001	16106	32	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano	1544	0.1426064	16106	271130337	291345439
16106051001	16106	9	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano	956	0.0882978	16106	167876037	104838149
16106051002	16106	7	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano	651	0.0601275	16106	114317260	85620378
16106991999	16106	6	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano	85	0.0078507	16106	14926217	75619770
16107011001	16107	99	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	4657	0.2663426	16107	1192529159	723789702
16107011002	16107	139	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	3783	0.2163569	16107	968721883	951397989
16107011004	16107	28	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	1550	0.0886474	16107	396912217	261626492
16107051001	16107	1	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	118	0.0067486	16107	30216543	17850352
16107061001	16107	5	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	171	0.0097798	16107	43788380	65288359
16107991999	16107	12	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	144	0.0082356	16107	36874425	132186663
16108011001	16108	124	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	2932	0.1823496	16108	596167970	867766759
16108051001	16108	22	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	1492	0.0927918	16108	303370604	215423028
16108051002	16108	11	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	685	0.0426022	16108	139282080	123236637
16108061001	16108	53	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	1300	0.0808508	16108	264330955	437488856
16108061002	16108	6	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	370	0.0230114	16108	75232656	75619770
16108991999	16108	1	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	23	0.0014304	16108	4676625	17850352
16109011001	16109	130	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	5212	0.2930230	16109	1305827170	901442779
16109011002	16109	127	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	4188	0.2354529	16109	1049271717	884643406
16109021001	16109	1	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	211	0.0118626	16109	52864454	17850352
16109031001	16109	4	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	367	0.0206330	16109	91949074	54544549
16109041001	16109	2	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	533	0.0299657	16109	133539118	31203195
16109081001	16109	10	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	373	0.0209704	16109	93452328	114126906

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)


r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
16101011001	16101	52	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1080	0.0058461	16101	289532747	430825578	398912.57
16101011002	16101	105	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1525	0.0082549	16101	408830962	758931181	497659.79
16101011003	16101	142	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2051	0.0111021	16101	549844133	967908506	471920.29
16101011004	16101	163	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1819	0.0098463	16101	487648210	1081677041	594654.78
16101021001	16101	10	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1345	0.0072805	16101	360575504	114126906	84852.72
16101021002	16101	114	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1991	0.0107774	16101	533758981	810923508	407294.58
16101021003	16101	87	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2007	0.0108640	16101	538048355	652225043	324975.11
16101021004	16101	79	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1882	0.0101873	16101	504537620	603452689	320644.36
16101031001	16101	335	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3622	0.0196060	16101	971007045	1932767297	533618.80
16101031002	16101	116	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2516	0.0136192	16101	674504066	822367171	326855.00
16101031003	16101	125	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2184	0.0118221	16101	585499555	873401030	399908.90
16101031004	16101	55	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1866	0.0101007	16101	500248246	450742812	241555.63
16101041001	16101	119	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3315	0.0179442	16101	888704681	839461161	253231.12
16101041002	16101	41	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1999	0.0108207	16101	535903668	355740474	177959.22
16101041003	16101	67	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4799	0.0259772	16101	1286544122	528433809	110113.32
16101041004	16101	35	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2766	0.0149725	16101	741525535	313159878	113217.60
16101051001	16101	44	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1466	0.0079355	16101	393013895	376568869	256868.26
16101051002	16101	382	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4764	0.0257877	16101	1277161117	2148432788	450972.46
16101051003	16101	73	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2374	0.0128506	16101	636435871	566243060	238518.56
16101051004	16101	16	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2018	0.0109235	16101	540997299	166669424	82591.39
16101051005	16101	424	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4539	0.0245698	16101	1216841794	2336812463	514829.80
16101061001	16101	5	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	941	0.0050937	16101	252268810	65288359	69381.89
16101071001	16101	61	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1599	0.0086555	16101	428669317	489960077	306416.56
16101071002	16101	21	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	950	0.0051424	16101	254681583	207497765	218418.70
16101081001	16101	37	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1276	0.0069070	16101	342077579	327500235	256661.63
16101121001	16101	154	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4876	0.0263940	16101	1307186735	1033290617	211913.58
16101131001	16101	866	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	5741	0.0310763	16101	1539081018	4154570684	723666.73
16101131002	16101	107	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2211	0.0119682	16101	592737873	770557454	348510.83
16101131003	16101	103	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2135	0.0115568	16101	572363347	747261806	350005.53
16101131004	16101	160	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4141	0.0224154	16101	1110143615	1065607322	257330.92
16101141001	16101	446	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	5365	0.0290410	16101	1438280729	2434025880	453686.09
16101141002	16101	882	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	5814	0.0314714	16101	1558651287	4216308246	725199.22
16101141003	16101	243	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3016	0.0163257	16101	808547004	1492203962	494762.59
16101141004	16101	789	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3759	0.0203476	16101	1007734811	3854262234	1025342.44
16101151001	16101	222	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3362	0.0181986	16101	901304717	1387395195	412669.60
16101151002	16101	34	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3634	0.0196710	16101	974224076	305930330	84185.56
16101151003	16101	31	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1805	0.0097705	16101	483895007	283986983	157333.51
16101151004	16101	76	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3489	0.0188861	16101	935351624	584919227	167646.67
16101151005	16101	39	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4931	0.0266917	16101	1321931458	341690720	69294.41
16101151006	16101	65	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4103	0.0222097	16101	1099956352	515686635	125685.26
16101151007	16101	46	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2402	0.0130021	16101	643942276	390300739	162489.90
16101151008	16101	28	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3208	0.0173650	16101	860019492	261626492	81554.39
16101151009	16101	314	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4520	0.0244670	16101	1211748163	1834535098	405870.60
16101151010	16101	294	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4906	0.0265564	16101	1315229311	1739786964	354624.33
16101151011	16101	84	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2718	0.0147126	16101	728657413	634041960	233275.19
16101151012	16101	51	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2161	0.0116976	16101	579333580	424137360	196269.02
16101151013	16101	46	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3743	0.0202610	16101	1003445436	390300739	104274.84
16101151014	16101	24	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3883	0.0210188	16101	1040977459	231068065	59507.61
16101151015	16101	13	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	248	0.0013424	16101	66485297	140992789	568519.31
16101161001	16101	96	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2140	0.0115839	16101	573703776	706064709	329936.78
16101161002	16101	97	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2196	0.0118870	16101	588716585	711984836	324218.96
16101161003	16101	109	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3884	0.0210243	16101	1041245545	782141585	201375.28
16101161004	16101	186	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2612	0.0141389	16101	700240310	1203059213	460589.29
16101161005	16101	148	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	3326	0.0180038	16101	891653626	1000728326	300880.43
16101171001	16101	76	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	4382	0.0237200	16101	1174752312	584919227	133482.25
16101171002	16101	82	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2900	0.0156978	16101	777449042	621849899	214431.00
16101171003	16101	90	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	2262	0.0122443	16101	606410253	670286705	296324.80
16101171004	16101	30	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	1590	0.0086067	16101	426256544	276582262	173951.11
16101991999	16101	9	2017	Chillán	268085.9	2017	16101	184739	49525916776	Urbano	304	0.0016456	16101	81498107	104838149	344862.33
16102011001	16102	54	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	2452	0.1140837	16102	526101569	444127755	181128.77
16102011002	16102	84	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	4765	0.2217001	16102	1022379272	634041960	133062.32
16102011003	16102	83	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	3184	0.1481413	16102	683159623	627953064	197221.44
16102021001	16102	6	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	537	0.0249849	16102	115218818	75619770	140818.94
16102041001	16102	20	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	1419	0.0660215	16102	304460900	199498820	140591.13
16102051001	16102	26	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	949	0.0441539	16102	203617614	246461559	259706.60
16102071001	16102	2	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	134	0.0062346	16102	28751065	31203195	232859.67
16102991999	16102	2	2017	Bulnes	214560.2	2017	16102	21493	4611542013	Urbano	51	0.0023729	16102	10942569	31203195	611827.36
16103041001	16103	116	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	3503	0.1133400	16103	890773641	822367171	234760.83
16103041002	16103	122	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	6143	0.1987576	16103	1562096055	856471631	139422.37
16103041003	16103	75	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	4173	0.1350180	16103	1061147133	578710029	138679.61
16103041004	16103	185	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	4071	0.1317177	16103	1035209676	1197844902	294238.49
16103041005	16103	89	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	2991	0.0967742	16103	760577779	664279337	222092.72
16103041006	16103	87	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	2508	0.0811467	16103	637756292	652225043	260057.83
16103041007	16103	56	2017	Chillán Viejo	254288.8	2017	16103	30907	7859303721	Urbano	3970	0.1284499	16103	1009526508	457334545	115197.62
16104011001	16104	103	2017	El Carmen	205701.2	2017	16104	12044	2477465194	Urbano	4722	0.3920624	16104	971321043	747261806	158251.12
16105011001	16105	134	2017	Pemuco	242752.9	2017	16105	8448	2050776137	Urbano	3963	0.4691051	16105	962029573	923725397	233087.41
16105091001	16105	3	2017	Pemuco	242752.9	2017	16105	8448	2050776137	Urbano	164	0.0194129	16105	39811469	43259656	263778.39
16106011001	16106	68	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano	2222	0.2052277	16106	390188865	534779590	240674.88
16106021001	16106	32	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano	1544	0.1426064	16106	271130337	291345439	188695.23
16106051001	16106	9	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano	956	0.0882978	16106	167876037	104838149	109663.34
16106051002	16106	7	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano	651	0.0601275	16106	114317260	85620378	131521.32
16106991999	16106	6	2017	Pinto	175602.5	2017	16106	10827	1901248804	Urbano	85	0.0078507	16106	14926217	75619770	889644.35
16107011001	16107	99	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	4657	0.2663426	16107	1192529159	723789702	155419.73
16107011002	16107	139	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	3783	0.2163569	16107	968721883	951397989	251492.99
16107011004	16107	28	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	1550	0.0886474	16107	396912217	261626492	168791.29
16107051001	16107	1	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	118	0.0067486	16107	30216543	17850352	151274.17
16107061001	16107	5	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	171	0.0097798	16107	43788380	65288359	381803.27
16107991999	16107	12	2017	Quillón	256072.4	2017	16107	17485	4477425886	Urbano	144	0.0082356	16107	36874425	132186663	917962.94
16108011001	16108	124	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	2932	0.1823496	16108	596167970	867766759	295964.11
16108051001	16108	22	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	1492	0.0927918	16108	303370604	215423028	144385.41
16108051002	16108	11	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	685	0.0426022	16108	139282080	123236637	179907.50
16108061001	16108	53	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	1300	0.0808508	16108	264330955	437488856	336529.89
16108061002	16108	6	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	370	0.0230114	16108	75232656	75619770	204377.76
16108991999	16108	1	2017	San Ignacio	203331.5	2017	16108	16079	3269367252	Urbano	23	0.0014304	16108	4676625	17850352	776102.26
16109011001	16109	130	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	5212	0.2930230	16109	1305827170	901442779	172955.25
16109011002	16109	127	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	4188	0.2354529	16109	1049271717	884643406	211232.90
16109021001	16109	1	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	211	0.0118626	16109	52864454	17850352	84598.82
16109031001	16109	4	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	367	0.0206330	16109	91949074	54544549	148622.75
16109041001	16109	2	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	533	0.0299657	16109	133539118	31203195	58542.58
16109081001	16109	10	2017	Yungay	250542.4	2017	16109	17787	4456398287	Urbano	373	0.0209704	16109	93452328	114126906	305970.26

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_urbano_nivel_nacional_17_r16.rds")

---

Rural

tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 2)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15202	1	2	6	13225	1	3	1	5	3	5	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	3	1	1	5	3	5	2	3	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	4	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	5	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	6	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	8	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	9	3	1	5	3	5	1	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	10	1	1	5	3	4	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	11	1	2	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	12	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	13	1	1	5	3	4	1	4	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	14	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	16	1	1	5	3	5	1	3	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	17	1	1	5	3	5	2	4	1	8	15	152	15202	15202012006
15	152	15202	1	2	6	13225	18	3	1	5	3	5	1	1	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	19	3	1	5	3	5	1	3	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	20	1	1	5	3	5	2	1	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	21	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	22	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	24	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	25	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	26	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	27	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	28	3	1	5	3	5	1	4	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	29	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	30	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	31	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	32	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	33	1	1	5	3	5	3	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	34	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	35	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	36	1	1	5	3	5	3	2	1	9	15	152	15202	15202012006
15	152	15202	1	2	6	13225	37	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	38	1	1	5	3	5	99	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	39	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	40	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	41	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	42	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	43	3	1	5	3	5	2	1	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	44	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	45	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	8	13910	1	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	3	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	4	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	5	3	1	5	3	5	2	3	1	3	15	152	15202	15202012008
15	152	15202	1	2	8	13910	6	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	7	1	1	5	99	5	2	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	8	3	1	5	3	5	3	3	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	9	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	10	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	11	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	12	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	13	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	14	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	16	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	18	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	20	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	21	1	1	5	3	5	1	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	22	3	1	5	3	5	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	23	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	24	3	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	25	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	26	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	27	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	28	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	29	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	30	1	1	5	1	4	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	12	8394	1	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	2	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	3	3	1	5	3	5	2	3	1	4	15	152	15202	15202012012
15	152	15202	1	2	12	8394	4	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	5	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	6	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	8	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	9	3	1	5	3	5	1	4	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	10	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	11	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	12	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	13	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	14	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	15	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	16	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	18	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	20	3	1	5	3	5	3	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	21	3	1	5	99	5	1	4	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	22	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	24	3	1	5	3	5	1	2	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	25	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la 16:

tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 16)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE] 
names(tabla_con_clave_f)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Veamos los primeros 100 registros:

r3_100 <- d[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	unlist.c.	unlist.d.	Freq	anio
1	16101042032	1	16101	1	2017
2	16101052010	1	16101	7	2017
3	16101052026	1	16101	9	2017
4	16101052027	1	16101	9	2017
5	16101052028	1	16101	32	2017
6	16101062003	1	16101	29	2017
7	16101062014	1	16101	18	2017
8	16101062024	1	16101	22	2017
9	16101062027	1	16101	18	2017
10	16101062901	1	16101	1	2017
11	16101072001	1	16101	18	2017
12	16101072901	1	16101	7	2017
13	16101082007	1	16101	6	2017
14	16101082008	1	16101	4	2017
15	16101082011	1	16101	14	2017
16	16101082012	1	16101	7	2017
17	16101082015	1	16101	9	2017
18	16101082031	1	16101	4	2017
19	16101082037	1	16101	14	2017
20	16101092901	1	16101	5	2017
21	16101102004	1	16101	88	2017
22	16101112030	1	16101	2	2017
23	16101112901	1	16101	3	2017
24	16101122002	1	16101	16	2017
25	16101122017	1	16101	49	2017
26	16101122020	1	16101	8	2017
27	16101122034	1	16101	24	2017
28	16101122035	1	16101	34	2017
29	16101142009	1	16101	95	2017
30	16101142018	1	16101	69	2017
31	16101142036	1	16101	33	2017
32	16101152016	1	16101	16	2017
33	16101152025	1	16101	182	2017
34	16101152033	1	16101	9	2017
773	16102012006	1	16102	6	2017
774	16102012044	1	16102	1	2017
775	16102022005	1	16102	5	2017
776	16102022015	1	16102	3	2017
777	16102022019	1	16102	1	2017
778	16102022020	1	16102	12	2017
779	16102022034	1	16102	1	2017
780	16102022035	1	16102	4	2017
781	16102022050	1	16102	16	2017
782	16102032014	1	16102	14	2017
783	16102032017	1	16102	3	2017
784	16102032019	1	16102	7	2017
785	16102032037	1	16102	2	2017
786	16102032038	1	16102	9	2017
787	16102032901	1	16102	3	2017
788	16102042016	1	16102	1	2017
789	16102042023	1	16102	5	2017
790	16102042031	1	16102	10	2017
791	16102042049	1	16102	2	2017
792	16102052010	1	16102	4	2017
793	16102052011	1	16102	3	2017
794	16102052021	1	16102	1	2017
795	16102052036	1	16102	3	2017
796	16102052047	1	16102	3	2017
797	16102052049	1	16102	3	2017
798	16102052901	1	16102	3	2017
799	16102062008	1	16102	4	2017
800	16102062030	1	16102	4	2017
801	16102062033	1	16102	2	2017
802	16102062040	1	16102	9	2017
803	16102062048	1	16102	1	2017
804	16102062051	1	16102	6	2017
805	16102072003	1	16102	8	2017
806	16102072009	1	16102	2	2017
807	16102072018	1	16102	1	2017
808	16102072025	1	16102	1	2017
809	16102072028	1	16102	5	2017
810	16102072039	1	16102	1	2017
811	16102072041	1	16102	6	2017
812	16102072045	1	16102	1	2017
813	16102072046	1	16102	3	2017
814	16102072051	1	16102	8	2017
815	16102072901	1	16102	2	2017
816	16102082009	1	16102	5	2017
817	16102082027	1	16102	2	2017
818	16102082042	1	16102	3	2017
819	16102082045	1	16102	5	2017
1558	16103012003	1	16103	4	2017
1559	16103012012	1	16103	14	2017
1560	16103022004	1	16103	1	2017
1561	16103022010	1	16103	1	2017
1562	16103022014	1	16103	2	2017
1563	16103022015	1	16103	5	2017
1564	16103022016	1	16103	4	2017
1565	16103022028	1	16103	10	2017
1566	16103032007	1	16103	1	2017
1567	16103032011	1	16103	1	2017
1568	16103032020	1	16103	10	2017
1569	16103032025	1	16103	1	2017
1570	16103032032	1	16103	2	2017
1571	16103032901	1	16103	5	2017
1572	16103042027	1	16103	1	2017
1573	16103052006	1	16103	3	2017
1574	16103052021	1	16103	12	2017
1575	16103052034	1	16103	4	2017
1576	16103052901	1	16103	1	2017

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	16101042032	1	2017	16101
2	16101052010	7	2017	16101
3	16101052026	9	2017	16101
4	16101052027	9	2017	16101
5	16101052028	32	2017	16101
6	16101062003	29	2017	16101
7	16101062014	18	2017	16101
8	16101062024	22	2017	16101
9	16101062027	18	2017	16101
10	16101062901	1	2017	16101
11	16101072001	18	2017	16101
12	16101072901	7	2017	16101
13	16101082007	6	2017	16101
14	16101082008	4	2017	16101
15	16101082011	14	2017	16101
16	16101082012	7	2017	16101
17	16101082015	9	2017	16101
18	16101082031	4	2017	16101
19	16101082037	14	2017	16101
20	16101092901	5	2017	16101
21	16101102004	88	2017	16101
22	16101112030	2	2017	16101
23	16101112901	3	2017	16101
24	16101122002	16	2017	16101
25	16101122017	49	2017	16101
26	16101122020	8	2017	16101
27	16101122034	24	2017	16101
28	16101122035	34	2017	16101
29	16101142009	95	2017	16101
30	16101142018	69	2017	16101
31	16101142036	33	2017	16101
32	16101152016	16	2017	16101
33	16101152025	182	2017	16101
34	16101152033	9	2017	16101
773	16102012006	6	2017	16102
774	16102012044	1	2017	16102
775	16102022005	5	2017	16102
776	16102022015	3	2017	16102
777	16102022019	1	2017	16102
778	16102022020	12	2017	16102
779	16102022034	1	2017	16102
780	16102022035	4	2017	16102
781	16102022050	16	2017	16102
782	16102032014	14	2017	16102
783	16102032017	3	2017	16102
784	16102032019	7	2017	16102
785	16102032037	2	2017	16102
786	16102032038	9	2017	16102
787	16102032901	3	2017	16102
788	16102042016	1	2017	16102
789	16102042023	5	2017	16102
790	16102042031	10	2017	16102
791	16102042049	2	2017	16102
792	16102052010	4	2017	16102
793	16102052011	3	2017	16102
794	16102052021	1	2017	16102
795	16102052036	3	2017	16102
796	16102052047	3	2017	16102
797	16102052049	3	2017	16102
798	16102052901	3	2017	16102
799	16102062008	4	2017	16102
800	16102062030	4	2017	16102
801	16102062033	2	2017	16102
802	16102062040	9	2017	16102
803	16102062048	1	2017	16102
804	16102062051	6	2017	16102
805	16102072003	8	2017	16102
806	16102072009	2	2017	16102
807	16102072018	1	2017	16102
808	16102072025	1	2017	16102
809	16102072028	5	2017	16102
810	16102072039	1	2017	16102
811	16102072041	6	2017	16102
812	16102072045	1	2017	16102
813	16102072046	3	2017	16102
814	16102072051	8	2017	16102
815	16102072901	2	2017	16102
816	16102082009	5	2017	16102
817	16102082027	2	2017	16102
818	16102082042	3	2017	16102
819	16102082045	5	2017	16102
1558	16103012003	4	2017	16103
1559	16103012012	14	2017	16103
1560	16103022004	1	2017	16103
1561	16103022010	1	2017	16103
1562	16103022014	2	2017	16103
1563	16103022015	5	2017	16103
1564	16103022016	4	2017	16103
1565	16103022028	10	2017	16103
1566	16103032007	1	2017	16103
1567	16103032011	1	2017	16103
1568	16103032020	10	2017	16103
1569	16103032025	1	2017	16103
1570	16103032032	2	2017	16103
1571	16103032901	5	2017	16103
1572	16103042027	1	2017	16103
1573	16103052006	3	2017	16103
1574	16103052021	12	2017	16103
1575	16103052034	4	2017	16103
1576	16103052901	1	2017	16103

3.2 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_rural_nacional.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
01101	Iquique	289375.3	2017	1101	191468	55406102543	Rural
01401	Pozo Almonte	263069.6	2017	1401	15711	4133086727	Rural
01402	Camiña	262850.3	2017	1402	1250	328562901	Rural
01404	Huara	253968.5	2017	1404	2730	693334131	Rural
01405	Pica	290496.7	2017	1405	9296	2700457509	Rural
02103	Sierra Gorda	403458.5	2017	2103	10186	4109628188	Rural
02104	Taltal	345494.0	2017	2104	13317	4600943086	Rural
02201	Calama	310025.0	2017	2201	165731	51380756402	Rural
02203	San Pedro de Atacama	356147.9	2017	2203	10996	3916202829	Rural
02301	Tocopilla	180218.1	2017	2301	25186	4538972205	Rural
03101	Copiapó	308502.8	2017	3101	153937	47489990283	Rural
03103	Tierra Amarilla	312457.3	2017	3103	14019	4380339153	Rural
03202	Diego de Almagro	374511.6	2017	3202	13925	5215073473	Rural
03301	Vallenar	254290.6	2017	3301	51917	13202005308	Rural
03302	Alto del Carmen	227130.4	2017	3302	5299	1203563833	Rural
03303	Freirina	214803.3	2017	3303	7041	1512429891	Rural
03304	Huasco	227560.7	2017	3304	10149	2309513927	Rural
04101	La Serena	233184.2	2017	4101	221054	51546306303	Rural
04102	Coquimbo	231810.7	2017	4102	227730	52790242466	Rural
04103	Andacollo	242908.2	2017	4103	11044	2682678345	Rural
04104	La Higuera	250699.6	2017	4104	4241	1063217069	Rural
04105	Paiguano	205942.1	2017	4105	4497	926121774	Rural
04106	Vicuña	176130.6	2017	4106	27771	4891322768	Rural
04201	Illapel	191976.8	2017	4201	30848	5922099530	Rural
04202	Canela	171370.3	2017	4202	9093	1558270441	Rural
04203	Los Vilos	173238.5	2017	4203	21382	3704185607	Rural
04204	Salamanca	223234.2	2017	4204	29347	6551254640	Rural
04301	Ovalle	241393.7	2017	4301	111272	26860360045	Rural
04302	Combarbalá	179139.6	2017	4302	13322	2386498044	Rural
04303	Monte Patria	201205.8	2017	4303	30751	6187280931	Rural
04304	Punitaqui	171931.7	2017	4304	10956	1883683880	Rural
04305	Río Hurtado	182027.2	2017	4305	4278	778712384	Rural
05101	Valparaíso	331716.1	2017	5101	296655	98405237576	Rural
05102	Casablanca	268917.1	2017	5102	26867	7224996933	Rural
05105	Puchuncaví	279614.4	2017	5105	18546	5185728335	Rural
05107	Quintero	334628.2	2017	5107	31923	10682335196	Rural
05301	Los Andes	324402.1	2017	5301	66708	21640215030	Rural
05302	Calle Larga	242743.8	2017	5302	14832	3600375502	Rural
05303	Rinconada	326532.5	2017	5303	10207	3332917471	Rural
05304	San Esteban	223168.6	2017	5304	18855	4207844130	Rural
05401	La Ligua	181468.0	2017	5401	35390	6422154059	Rural
05402	Cabildo	231277.8	2017	5402	19388	4484014285	Rural
05404	Petorca	298208.9	2017	5404	9826	2930200178	Rural
05405	Zapallar	292882.3	2017	5405	7339	2149463129	Rural
05501	Quillota	220926.8	2017	5501	90517	19997628209	Rural
05502	Calera	226906.2	2017	5502	50554	11471016698	Rural
05503	Hijuelas	253739.9	2017	5503	17988	4564273201	Rural
05504	La Cruz	291124.1	2017	5504	22098	6433259569	Rural
05506	Nogales	264475.3	2017	5506	22120	5850194593	Rural
05601	San Antonio	266331.2	2017	5601	91350	24329353815	Rural

4 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
16101	16101042032	1	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101052010	7	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101052026	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101052027	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101052028	32	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101062003	29	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101062014	18	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101062024	22	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101062027	18	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101062901	1	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101072001	18	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101072901	7	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101082007	6	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101082008	4	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101082011	14	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101082012	7	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101082015	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101082031	4	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101082037	14	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101092901	5	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101102004	88	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101112030	2	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101112901	3	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101122002	16	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101122017	49	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101122020	8	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101122034	24	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101122035	34	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101142009	95	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101142018	69	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101142036	33	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101152016	16	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101152025	182	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101152033	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16102	16102012006	6	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102012044	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102022005	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102022015	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102022019	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102022020	12	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102022034	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102022035	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102022050	16	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102032014	14	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102032017	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102032019	7	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102032037	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102032038	9	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102032901	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102042016	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102042023	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102042031	10	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102042049	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102052010	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102052011	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102052021	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102052036	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102052047	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102052049	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102052901	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102062008	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102062030	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102062033	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102062040	9	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102062048	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102062051	6	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102072003	8	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102072009	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102072018	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102072025	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102072028	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102072039	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102072041	6	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102072045	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102072046	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102072051	8	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102072901	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102082009	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102082027	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102082042	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102082045	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16103	16103012003	4	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103012012	14	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103022004	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103022010	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103022014	2	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103022015	5	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103022016	4	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103022028	10	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103032007	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103032011	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103032020	10	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103032025	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103032032	2	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103032901	5	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103042027	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103052006	3	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103052021	12	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103052034	4	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103052901	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural

5 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

6 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo
16101	16101042032	1	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101052010	7	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101052026	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101052027	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101052028	32	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101062003	29	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101062014	18	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101062024	22	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101062027	18	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101062901	1	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101072001	18	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101072901	7	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101082007	6	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101082008	4	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101082011	14	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101082012	7	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101082015	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101082031	4	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101082037	14	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101092901	5	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101102004	88	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101112030	2	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101112901	3	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101122002	16	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101122017	49	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101122020	8	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101122034	24	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101122035	34	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101142009	95	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101142018	69	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101142036	33	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101152016	16	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101152025	182	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16101	16101152033	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural
16102	16102012006	6	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102012044	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102022005	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102022015	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102022019	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102022020	12	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102022034	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102022035	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102022050	16	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102032014	14	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102032017	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102032019	7	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102032037	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102032038	9	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102032901	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102042016	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102042023	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102042031	10	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102042049	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102052010	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102052011	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102052021	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102052036	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102052047	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102052049	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102052901	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102062008	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102062030	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102062033	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102062040	9	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102062048	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102062051	6	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102072003	8	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102072009	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102072018	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102072025	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102072028	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102072039	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102072041	6	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102072045	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102072046	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102072051	8	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102072901	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102082009	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102082027	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102082042	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16102	16102082045	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural
16103	16103012003	4	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103012012	14	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103022004	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103022010	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103022014	2	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103022015	5	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103022016	4	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103022028	10	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103032007	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103032011	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103032020	10	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103032025	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103032032	2	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103032901	5	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103042027	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103052006	3	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103052021	12	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103052034	4	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural
16103	16103052901	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural

7 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y
16101042032	16101	1	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	30	0.0001624	16101
16101052010	16101	7	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	112	0.0006063	16101
16101052026	16101	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	355	0.0019216	16101
16101052027	16101	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	362	0.0019595	16101
16101052028	16101	32	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	345	0.0018675	16101
16101062003	16101	29	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	638	0.0034535	16101
16101062014	16101	18	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	348	0.0018837	16101
16101062024	16101	22	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	451	0.0024413	16101
16101062027	16101	18	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	620	0.0033561	16101
16101062901	16101	1	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	373	0.0020191	16101
16101072001	16101	18	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	593	0.0032099	16101
16101072901	16101	7	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	491	0.0026578	16101
16101082007	16101	6	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	184	0.0009960	16101
16101082008	16101	4	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	147	0.0007957	16101
16101082011	16101	14	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	408	0.0022085	16101
16101082012	16101	7	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	139	0.0007524	16101
16101082015	16101	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	313	0.0016943	16101
16101082031	16101	4	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	104	0.0005630	16101
16101082037	16101	14	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	451	0.0024413	16101
16101092901	16101	5	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	87	0.0004709	16101
16101102004	16101	88	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	3591	0.0194382	16101
16101112030	16101	2	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	37	0.0002003	16101
16101112901	16101	3	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	16	0.0000866	16101
16101122002	16101	16	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	280	0.0015157	16101
16101122017	16101	49	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	682	0.0036917	16101
16101122020	16101	8	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	102	0.0005521	16101
16101122034	16101	24	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	171	0.0009256	16101
16101122035	16101	34	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	534	0.0028906	16101
16101142009	16101	95	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	607	0.0032857	16101
16101142018	16101	69	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	625	0.0033832	16101
16101142036	16101	33	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	443	0.0023980	16101
16101152016	16101	16	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	521	0.0028202	16101
16101152025	16101	182	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	1780	0.0096352	16101
16101152033	16101	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	122	0.0006604	16101
16102012006	16102	6	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	183	0.0085144	16102
16102012044	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	32	0.0014889	16102
16102022005	16102	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	102	0.0047457	16102
16102022015	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	160	0.0074443	16102
16102022019	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	59	0.0027451	16102
16102022020	16102	12	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	359	0.0167031	16102
16102022034	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	101	0.0046992	16102
16102022035	16102	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	46	0.0021402	16102
16102022050	16102	16	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	422	0.0196343	16102
16102032014	16102	14	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	372	0.0173080	16102
16102032017	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	31	0.0014423	16102
16102032019	16102	7	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	197	0.0091658	16102
16102032037	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	93	0.0043270	16102
16102032038	16102	9	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	358	0.0166566	16102
16102032901	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	24	0.0011166	16102
16102042016	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	219	0.0101894	16102
16102042023	16102	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	68	0.0031638	16102
16102042031	16102	10	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	152	0.0070721	16102
16102042049	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	41	0.0019076	16102
16102052010	16102	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	215	0.0100033	16102
16102052011	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	141	0.0065603	16102
16102052021	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	92	0.0042805	16102
16102052036	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	25	0.0011632	16102
16102052047	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	428	0.0199135	16102
16102052049	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	102	0.0047457	16102
16102052901	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	105	0.0048853	16102
16102062008	16102	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	81	0.0037687	16102
16102062030	16102	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	133	0.0061881	16102
16102062033	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	97	0.0045131	16102
16102062040	16102	9	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	274	0.0127483	16102
16102062048	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	70	0.0032569	16102
16102062051	16102	6	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	481	0.0223794	16102
16102072003	16102	8	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	66	0.0030708	16102
16102072009	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	43	0.0020007	16102
16102072018	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	116	0.0053971	16102
16102072025	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	20	0.0009305	16102
16102072028	16102	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	109	0.0050714	16102
16102072039	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	101	0.0046992	16102
16102072041	16102	6	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	84	0.0039082	16102
16102072045	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	50	0.0023263	16102
16102072046	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	152	0.0070721	16102
16102072051	16102	8	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	502	0.0233564	16102
16102072901	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	45	0.0020937	16102
16102082009	16102	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	456	0.0212162	16102
16102082027	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	72	0.0033499	16102
16102082042	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	102	0.0047457	16102
16102082045	16102	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	84	0.0039082	16102
16103012003	16103	4	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	78	0.0025237	16103
16103012012	16103	14	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	542	0.0175365	16103
16103022004	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	10	0.0003236	16103
16103022010	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	35	0.0011324	16103
16103022014	16103	2	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	90	0.0029120	16103
16103022015	16103	5	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	171	0.0055327	16103
16103022016	16103	4	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	43	0.0013913	16103
16103022028	16103	10	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	838	0.0271136	16103
16103032007	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	27	0.0008736	16103
16103032011	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	125	0.0040444	16103
16103032020	16103	10	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	386	0.0124891	16103
16103032025	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	21	0.0006795	16103
16103032032	16103	2	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	151	0.0048856	16103
16103032901	16103	5	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	59	0.0019090	16103
16103042027	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	48	0.0015530	16103
16103052006	16103	3	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	85	0.0027502	16103
16103052021	16103	12	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	498	0.0161129	16103
16103052034	16103	4	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	20	0.0006471	16103
16103052901	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	16	0.0005177	16103

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob
16101042032	16101	1	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	30	0.0001624	16101	6961248
16101052010	16101	7	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	112	0.0006063	16101	25988657
16101052026	16101	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	355	0.0019216	16101	82374762
16101052027	16101	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	362	0.0019595	16101	83999053
16101052028	16101	32	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	345	0.0018675	16101	80054346
16101062003	16101	29	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	638	0.0034535	16101	148042530
16101062014	16101	18	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	348	0.0018837	16101	80750471
16101062024	16101	22	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	451	0.0024413	16101	104650754
16101062027	16101	18	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	620	0.0033561	16101	143865782
16101062901	16101	1	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	373	0.0020191	16101	86551511
16101072001	16101	18	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	593	0.0032099	16101	137600659
16101072901	16101	7	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	491	0.0026578	16101	113932417
16101082007	16101	6	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	184	0.0009960	16101	42695651
16101082008	16101	4	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	147	0.0007957	16101	34110113
16101082011	16101	14	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	408	0.0022085	16101	94672966
16101082012	16101	7	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	139	0.0007524	16101	32253780
16101082015	16101	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	313	0.0016943	16101	72629016
16101082031	16101	4	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	104	0.0005630	16101	24132325
16101082037	16101	14	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	451	0.0024413	16101	104650754
16101092901	16101	5	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	87	0.0004709	16101	20187618
16101102004	16101	88	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	3591	0.0194382	16101	833261326
16101112030	16101	2	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	37	0.0002003	16101	8585539
16101112901	16101	3	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	16	0.0000866	16101	3712665
16101122002	16101	16	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	280	0.0015157	16101	64971643
16101122017	16101	49	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	682	0.0036917	16101	158252360
16101122020	16101	8	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	102	0.0005521	16101	23668242
16101122034	16101	24	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	171	0.0009256	16101	39679111
16101122035	16101	34	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	534	0.0028906	16101	123910206
16101142009	16101	95	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	607	0.0032857	16101	140849241
16101142018	16101	69	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	625	0.0033832	16101	145025990
16101142036	16101	33	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	443	0.0023980	16101	102794421
16101152016	16101	16	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	521	0.0028202	16101	120893665
16101152025	16101	182	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	1780	0.0096352	16101	413034018
16101152033	16101	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	122	0.0006604	16101	28309073
16102012006	16102	6	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	183	0.0085144	16102	36097913
16102012044	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	32	0.0014889	16102	6312203
16102022005	16102	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	102	0.0047457	16102	20120148
16102022015	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	160	0.0074443	16102	31561017
16102022019	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	59	0.0027451	16102	11638125
16102022020	16102	12	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	359	0.0167031	16102	70815032
16102022034	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	101	0.0046992	16102	19922892
16102022035	16102	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	46	0.0021402	16102	9073792
16102022050	16102	16	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	422	0.0196343	16102	83242183
16102032014	16102	14	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	372	0.0173080	16102	73379365
16102032017	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	31	0.0014423	16102	6114947
16102032019	16102	7	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	197	0.0091658	16102	38859502
16102032037	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	93	0.0043270	16102	18344841
16102032038	16102	9	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	358	0.0166566	16102	70617776
16102032901	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	24	0.0011166	16102	4734153
16102042016	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	219	0.0101894	16102	43199142
16102042023	16102	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	68	0.0031638	16102	13413432
16102042031	16102	10	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	152	0.0070721	16102	29982966
16102042049	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	41	0.0019076	16102	8087511
16102052010	16102	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	215	0.0100033	16102	42410117
16102052011	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	141	0.0065603	16102	27813146
16102052021	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	92	0.0042805	16102	18147585
16102052036	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	25	0.0011632	16102	4931409
16102052047	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	428	0.0199135	16102	84425721
16102052049	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	102	0.0047457	16102	20120148
16102052901	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	105	0.0048853	16102	20711918
16102062008	16102	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	81	0.0037687	16102	15977765
16102062030	16102	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	133	0.0061881	16102	26235096
16102062033	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	97	0.0045131	16102	19133867
16102062040	16102	9	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	274	0.0127483	16102	54048242
16102062048	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	70	0.0032569	16102	13807945
16102062051	16102	6	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	481	0.0223794	16102	94880308
16102072003	16102	8	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	66	0.0030708	16102	13018920
16102072009	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	43	0.0020007	16102	8482023
16102072018	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	116	0.0053971	16102	22881737
16102072025	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	20	0.0009305	16102	3945127
16102072028	16102	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	109	0.0050714	16102	21500943
16102072039	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	101	0.0046992	16102	19922892
16102072041	16102	6	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	84	0.0039082	16102	16569534
16102072045	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	50	0.0023263	16102	9862818
16102072046	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	152	0.0070721	16102	29982966
16102072051	16102	8	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	502	0.0233564	16102	99022691
16102072901	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	45	0.0020937	16102	8876536
16102082009	16102	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	456	0.0212162	16102	89948899
16102082027	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	72	0.0033499	16102	14202458
16102082042	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	102	0.0047457	16102	20120148
16102082045	16102	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	84	0.0039082	16102	16569534
16103012003	16103	4	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	78	0.0025237	16103	15286266
16103012012	16103	14	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	542	0.0175365	16103	106219949
16103022004	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	10	0.0003236	16103	1959778
16103022010	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	35	0.0011324	16103	6859222
16103022014	16103	2	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	90	0.0029120	16103	17637999
16103022015	16103	5	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	171	0.0055327	16103	33512198
16103022016	16103	4	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	43	0.0013913	16103	8427044
16103022028	16103	10	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	838	0.0271136	16103	164229368
16103032007	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	27	0.0008736	16103	5291400
16103032011	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	125	0.0040444	16103	24497221
16103032020	16103	10	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	386	0.0124891	16103	75647418
16103032025	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	21	0.0006795	16103	4115533
16103032032	16103	2	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	151	0.0048856	16103	29592643
16103032901	16103	5	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	59	0.0019090	16103	11562688
16103042027	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	48	0.0015530	16103	9406933
16103052006	16103	3	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	85	0.0027502	16103	16658110
16103052021	16103	12	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	498	0.0161129	16103	97596928
16103052034	16103	4	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	20	0.0006471	16103	3919555
16103052901	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	16	0.0005177	16103	3135644

8 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

8.1 Diagrama de dispersión

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

8.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

8.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -205226571  -15014111   -7655695    6544145  526841169 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 14484433    1505647    9.62   <2e-16 ***
## Freq.x       3317451     123450   26.87   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 35350000 on 736 degrees of freedom
## Multiple R-squared:  0.4952, Adjusted R-squared:  0.4946 
## F-statistic: 722.1 on 1 and 736 DF,  p-value: < 2.2e-16

8.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
modelos1 <- cbind(modelo,rq,sintaxis)
modelos1

##      modelo       rq                 
## [1,] "cuadrático" "0.494563936104179"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
modelos2 <- cbind(modelo,rq,sintaxis)
modelos2

##      modelo   rq                 
## [1,] "cúbico" "0.494563936104179"
##      sintaxis                                                            
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos3 <- cbind(modelo,rq,sintaxis)
modelos3

##      modelo        rq                 
## [1,] "logarítmico" "0.358464896288639"
##      sintaxis                                                             
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos5 <- cbind(modelo,rq,sintaxis)
modelos5

##      modelo          rq                 
## [1,] "raíz cuadrada" "0.496945820599638"
##      sintaxis                                                              
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos6 <- cbind(modelo,rq,sintaxis)
modelos6

##      modelo      rq                
## [1,] "raíz-raíz" "0.55174596792176"
##      sintaxis                                                                    
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos7 <- cbind(modelo,rq,sintaxis)
modelos7

##      modelo     rq                 
## [1,] "log-raíz" "0.430082428889594"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos8 <- cbind(modelo,rq,sintaxis)
modelos8

##      modelo     rq                 
## [1,] "raíz-log" "0.507692069836266"
##      sintaxis                                                                   
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
rq <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,rq,sintaxis)
modelos9

##      modelo    rq                 
## [1,] "log-log" "0.477014143707977"
##      sintaxis                                                                  
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"

modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$rq),]

##          modelo                rq
## 3   logarítmico 0.358464896288639
## 6      log-raíz 0.430082428889594
## 8       log-log 0.477014143707977
## 1    cuadrático 0.494563936104179
## 2        cúbico 0.494563936104179
## 4 raíz cuadrada 0.496945820599638
## 7      raíz-log 0.507692069836266
## 5     raíz-raíz  0.55174596792176
##                                                                     sintaxis
## 3        linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 6  linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 8   linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 1         linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2         linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 4       linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 7  linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)

metodo <- 5
switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)

## 
## Call:
## lm(formula = sqrt(multi_pob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7042.0 -1358.6  -261.4  1126.5 11115.1 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1385.93     143.41   9.664   <2e-16 ***
## sqrt(Freq.x)  1744.54      57.89  30.136   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1989 on 736 degrees of freedom
## Multiple R-squared:  0.5524, Adjusted R-squared:  0.5517 
## F-statistic: 908.2 on 1 and 736 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    1385.926

bb <- linearMod$coefficients[2]
bb

## sqrt(Freq.x) 
##     1744.536

9 Modelo raíz-raíz (raíz-raíz)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.5517 ).

9.1 Diagrama de dispersión sobre raíz-raíz

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=sqrt(h_y_m_comuna_corr_01$Freq.x), y=sqrt(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo raíz-raíz

Observemos nuevamente el resultado sobre raíz-raíz.

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = sqrt(multi_pob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7042.0 -1358.6  -261.4  1126.5 11115.1 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1385.93     143.41   9.664   <2e-16 ***
## sqrt(Freq.x)  1744.54      57.89  30.136   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1989 on 736 degrees of freedom
## Multiple R-squared:  0.5524, Adjusted R-squared:  0.5517 
## F-statistic: 908.2 on 1 and 736 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = sqrt(Freq.x) , y = sqrt(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = {1385.926}^2 + 2 1385.926 1744.536 \sqrt{X}+ 1744.536^2 X \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- ((aa^2)+(2*(aa*bb)) * sqrt(h_y_m_comuna_corr_01$Freq.x)+((bb^2)*h_y_m_comuna_corr_01$Freq.x))

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing
16101042032	16101	1	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	30	0.0001624	16101	6961248	9799791
16101052010	16101	7	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	112	0.0006063	16101	25988657	36018406
16101052026	16101	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	355	0.0019216	16101	82374762	43818218
16101052027	16101	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	362	0.0019595	16101	83999053	43818218
16101052028	16101	32	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	345	0.0018675	16101	80054346	126663997
16101062003	16101	29	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	638	0.0034535	16101	148042530	116220003
16101062014	16101	18	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	348	0.0018837	16101	80750471	77217768
16101062024	16101	22	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	451	0.0024413	16101	104650754	91556645
16101062027	16101	18	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	620	0.0033561	16101	143865782	77217768
16101062901	16101	1	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	373	0.0020191	16101	86551511	9799791
16101072001	16101	18	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	593	0.0032099	16101	137600659	77217768
16101072901	16101	7	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	491	0.0026578	16101	113932417	36018406
16101082007	16101	6	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	184	0.0009960	16101	42695651	32025960
16101082008	16101	4	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	147	0.0007957	16101	34110113	23765600
16101082011	16101	14	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	408	0.0022085	16101	94672966	62621596
16101082012	16101	7	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	139	0.0007524	16101	32253780	36018406
16101082015	16101	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	313	0.0016943	16101	72629016	43818218
16101082031	16101	4	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	104	0.0005630	16101	24132325	23765600
16101082037	16101	14	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	451	0.0024413	16101	104650754	62621596
16101092901	16101	5	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	87	0.0004709	16101	20187618	27950534
16101102004	16101	88	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	3591	0.0194382	16101	833261326	315102306
16101112030	16101	2	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	37	0.0002003	16101	8585539	14846165
16101112901	16101	3	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	16	0.0000866	16101	3712665	19426501
16101122002	16101	16	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	280	0.0015157	16101	64971643	69957646
16101122017	16101	49	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	682	0.0036917	16101	158252360	184896786
16101122020	16101	8	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	102	0.0005521	16101	23668242	39945157
16101122034	16101	24	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	171	0.0009256	16101	39679111	98651984
16101122035	16101	34	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	534	0.0028906	16101	123910206	133592672
16101142009	16101	95	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	607	0.0032857	16101	140849241	338175786
16101142018	16101	69	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	625	0.0033832	16101	145025990	252083183
16101142036	16101	33	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	443	0.0023980	16101	102794421	130131524
16101152016	16101	16	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	521	0.0028202	16101	120893665	69957646
16101152025	16101	182	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	1780	0.0096352	16101	413034018	621056187
16101152033	16101	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	122	0.0006604	16101	28309073	43818218
16102012006	16102	6	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	183	0.0085144	16102	36097913	32025960
16102012044	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	32	0.0014889	16102	6312203	9799791
16102022005	16102	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	102	0.0047457	16102	20120148	27950534
16102022015	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	160	0.0074443	16102	31561017	19426501
16102022019	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	59	0.0027451	16102	11638125	9799791
16102022020	16102	12	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	359	0.0167031	16102	70815032	55192640
16102022034	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	101	0.0046992	16102	19922892	9799791
16102022035	16102	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	46	0.0021402	16102	9073792	23765600
16102022050	16102	16	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	422	0.0196343	16102	83242183	69957646
16102032014	16102	14	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	372	0.0173080	16102	73379365	62621596
16102032017	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	31	0.0014423	16102	6114947	19426501
16102032019	16102	7	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	197	0.0091658	16102	38859502	36018406
16102032037	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	93	0.0043270	16102	18344841	14846165
16102032038	16102	9	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	358	0.0166566	16102	70617776	43818218
16102032901	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	24	0.0011166	16102	4734153	19426501
16102042016	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	219	0.0101894	16102	43199142	9799791
16102042023	16102	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	68	0.0031638	16102	13413432	27950534
16102042031	16102	10	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	152	0.0070721	16102	29982966	47646332
16102042049	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	41	0.0019076	16102	8087511	14846165
16102052010	16102	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	215	0.0100033	16102	42410117	23765600
16102052011	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	141	0.0065603	16102	27813146	19426501
16102052021	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	92	0.0042805	16102	18147585	9799791
16102052036	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	25	0.0011632	16102	4931409	19426501
16102052047	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	428	0.0199135	16102	84425721	19426501
16102052049	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	102	0.0047457	16102	20120148	19426501
16102052901	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	105	0.0048853	16102	20711918	19426501
16102062008	16102	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	81	0.0037687	16102	15977765	23765600
16102062030	16102	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	133	0.0061881	16102	26235096	23765600
16102062033	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	97	0.0045131	16102	19133867	14846165
16102062040	16102	9	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	274	0.0127483	16102	54048242	43818218
16102062048	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	70	0.0032569	16102	13807945	9799791
16102062051	16102	6	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	481	0.0223794	16102	94880308	32025960
16102072003	16102	8	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	66	0.0030708	16102	13018920	39945157
16102072009	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	43	0.0020007	16102	8482023	14846165
16102072018	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	116	0.0053971	16102	22881737	9799791
16102072025	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	20	0.0009305	16102	3945127	9799791
16102072028	16102	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	109	0.0050714	16102	21500943	27950534
16102072039	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	101	0.0046992	16102	19922892	9799791
16102072041	16102	6	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	84	0.0039082	16102	16569534	32025960
16102072045	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	50	0.0023263	16102	9862818	9799791
16102072046	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	152	0.0070721	16102	29982966	19426501
16102072051	16102	8	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	502	0.0233564	16102	99022691	39945157
16102072901	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	45	0.0020937	16102	8876536	14846165
16102082009	16102	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	456	0.0212162	16102	89948899	27950534
16102082027	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	72	0.0033499	16102	14202458	14846165
16102082042	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	102	0.0047457	16102	20120148	19426501
16102082045	16102	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	84	0.0039082	16102	16569534	27950534
16103012003	16103	4	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	78	0.0025237	16103	15286266	23765600
16103012012	16103	14	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	542	0.0175365	16103	106219949	62621596
16103022004	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	10	0.0003236	16103	1959778	9799791
16103022010	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	35	0.0011324	16103	6859222	9799791
16103022014	16103	2	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	90	0.0029120	16103	17637999	14846165
16103022015	16103	5	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	171	0.0055327	16103	33512198	27950534
16103022016	16103	4	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	43	0.0013913	16103	8427044	23765600
16103022028	16103	10	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	838	0.0271136	16103	164229368	47646332
16103032007	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	27	0.0008736	16103	5291400	9799791
16103032011	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	125	0.0040444	16103	24497221	9799791
16103032020	16103	10	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	386	0.0124891	16103	75647418	47646332
16103032025	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	21	0.0006795	16103	4115533	9799791
16103032032	16103	2	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	151	0.0048856	16103	29592643	14846165
16103032901	16103	5	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	59	0.0019090	16103	11562688	27950534
16103042027	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	48	0.0015530	16103	9406933	9799791
16103052006	16103	3	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	85	0.0027502	16103	16658110	19426501
16103052021	16103	12	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	498	0.0161129	16103	97596928	55192640
16103052034	16103	4	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	20	0.0006471	16103	3919555	23765600
16103052901	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	16	0.0005177	16103	3135644	9799791

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	tipo	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
16101042032	16101	1	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	30	0.0001624	16101	6961248	9799791	326659.69
16101052010	16101	7	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	112	0.0006063	16101	25988657	36018406	321592.91
16101052026	16101	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	355	0.0019216	16101	82374762	43818218	123431.60
16101052027	16101	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	362	0.0019595	16101	83999053	43818218	121044.80
16101052028	16101	32	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	345	0.0018675	16101	80054346	126663997	367142.02
16101062003	16101	29	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	638	0.0034535	16101	148042530	116220003	182163.01
16101062014	16101	18	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	348	0.0018837	16101	80750471	77217768	221890.14
16101062024	16101	22	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	451	0.0024413	16101	104650754	91556645	203008.08
16101062027	16101	18	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	620	0.0033561	16101	143865782	77217768	124544.79
16101062901	16101	1	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	373	0.0020191	16101	86551511	9799791	26272.90
16101072001	16101	18	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	593	0.0032099	16101	137600659	77217768	130215.46
16101072901	16101	7	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	491	0.0026578	16101	113932417	36018406	73357.24
16101082007	16101	6	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	184	0.0009960	16101	42695651	32025960	174054.13
16101082008	16101	4	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	147	0.0007957	16101	34110113	23765600	161670.75
16101082011	16101	14	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	408	0.0022085	16101	94672966	62621596	153484.31
16101082012	16101	7	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	139	0.0007524	16101	32253780	36018406	259125.22
16101082015	16101	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	313	0.0016943	16101	72629016	43818218	139994.31
16101082031	16101	4	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	104	0.0005630	16101	24132325	23765600	228515.38
16101082037	16101	14	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	451	0.0024413	16101	104650754	62621596	138850.55
16101092901	16101	5	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	87	0.0004709	16101	20187618	27950534	321270.50
16101102004	16101	88	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	3591	0.0194382	16101	833261326	315102306	87747.79
16101112030	16101	2	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	37	0.0002003	16101	8585539	14846165	401247.69
16101112901	16101	3	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	16	0.0000866	16101	3712665	19426501	1214156.34
16101122002	16101	16	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	280	0.0015157	16101	64971643	69957646	249848.74
16101122017	16101	49	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	682	0.0036917	16101	158252360	184896786	271109.66
16101122020	16101	8	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	102	0.0005521	16101	23668242	39945157	391619.18
16101122034	16101	24	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	171	0.0009256	16101	39679111	98651984	576912.19
16101122035	16101	34	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	534	0.0028906	16101	123910206	133592672	250173.54
16101142009	16101	95	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	607	0.0032857	16101	140849241	338175786	557126.50
16101142018	16101	69	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	625	0.0033832	16101	145025990	252083183	403333.09
16101142036	16101	33	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	443	0.0023980	16101	102794421	130131524	293750.62
16101152016	16101	16	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	521	0.0028202	16101	120893665	69957646	134275.71
16101152025	16101	182	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	1780	0.0096352	16101	413034018	621056187	348907.97
16101152033	16101	9	2017	Chillán	232041.6	2017	16101	184739	42867130063	Rural	122	0.0006604	16101	28309073	43818218	359165.72
16102012006	16102	6	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	183	0.0085144	16102	36097913	32025960	175005.24
16102012044	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	32	0.0014889	16102	6312203	9799791	306243.46
16102022005	16102	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	102	0.0047457	16102	20120148	27950534	274024.84
16102022015	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	160	0.0074443	16102	31561017	19426501	121415.63
16102022019	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	59	0.0027451	16102	11638125	9799791	166098.15
16102022020	16102	12	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	359	0.0167031	16102	70815032	55192640	153739.94
16102022034	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	101	0.0046992	16102	19922892	9799791	97027.63
16102022035	16102	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	46	0.0021402	16102	9073792	23765600	516643.47
16102022050	16102	16	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	422	0.0196343	16102	83242183	69957646	165776.41
16102032014	16102	14	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	372	0.0173080	16102	73379365	62621596	168337.62
16102032017	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	31	0.0014423	16102	6114947	19426501	626661.33
16102032019	16102	7	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	197	0.0091658	16102	38859502	36018406	182834.55
16102032037	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	93	0.0043270	16102	18344841	14846165	159636.18
16102032038	16102	9	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	358	0.0166566	16102	70617776	43818218	122397.26
16102032901	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	24	0.0011166	16102	4734153	19426501	809437.56
16102042016	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	219	0.0101894	16102	43199142	9799791	44747.90
16102042023	16102	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	68	0.0031638	16102	13413432	27950534	411037.26
16102042031	16102	10	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	152	0.0070721	16102	29982966	47646332	313462.71
16102042049	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	41	0.0019076	16102	8087511	14846165	362101.57
16102052010	16102	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	215	0.0100033	16102	42410117	23765600	110537.67
16102052011	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	141	0.0065603	16102	27813146	19426501	137776.61
16102052021	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	92	0.0042805	16102	18147585	9799791	106519.47
16102052036	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	25	0.0011632	16102	4931409	19426501	777060.06
16102052047	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	428	0.0199135	16102	84425721	19426501	45389.02
16102052049	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	102	0.0047457	16102	20120148	19426501	190455.90
16102052901	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	105	0.0048853	16102	20711918	19426501	185014.30
16102062008	16102	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	81	0.0037687	16102	15977765	23765600	293402.47
16102062030	16102	4	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	133	0.0061881	16102	26235096	23765600	178688.72
16102062033	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	97	0.0045131	16102	19133867	14846165	153053.24
16102062040	16102	9	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	274	0.0127483	16102	54048242	43818218	159920.50
16102062048	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	70	0.0032569	16102	13807945	9799791	139997.01
16102062051	16102	6	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	481	0.0223794	16102	94880308	32025960	66582.04
16102072003	16102	8	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	66	0.0030708	16102	13018920	39945157	605229.65
16102072009	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	43	0.0020007	16102	8482023	14846165	345259.64
16102072018	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	116	0.0053971	16102	22881737	9799791	84480.96
16102072025	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	20	0.0009305	16102	3945127	9799791	489989.54
16102072028	16102	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	109	0.0050714	16102	21500943	27950534	256426.91
16102072039	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	101	0.0046992	16102	19922892	9799791	97027.63
16102072041	16102	6	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	84	0.0039082	16102	16569534	32025960	381261.42
16102072045	16102	1	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	50	0.0023263	16102	9862818	9799791	195995.82
16102072046	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	152	0.0070721	16102	29982966	19426501	127805.93
16102072051	16102	8	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	502	0.0233564	16102	99022691	39945157	79572.03
16102072901	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	45	0.0020937	16102	8876536	14846165	329914.77
16102082009	16102	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	456	0.0212162	16102	89948899	27950534	61295.03
16102082027	16102	2	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	72	0.0033499	16102	14202458	14846165	206196.73
16102082042	16102	3	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	102	0.0047457	16102	20120148	19426501	190455.90
16102082045	16102	5	2017	Bulnes	197256.4	2017	16102	21493	4239630884	Rural	84	0.0039082	16102	16569534	27950534	332744.45
16103012003	16103	4	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	78	0.0025237	16103	15286266	23765600	304687.18
16103012012	16103	14	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	542	0.0175365	16103	106219949	62621596	115538.00
16103022004	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	10	0.0003236	16103	1959778	9799791	979979.08
16103022010	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	35	0.0011324	16103	6859222	9799791	279994.02
16103022014	16103	2	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	90	0.0029120	16103	17637999	14846165	164957.38
16103022015	16103	5	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	171	0.0055327	16103	33512198	27950534	163453.41
16103022016	16103	4	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	43	0.0013913	16103	8427044	23765600	552688.37
16103022028	16103	10	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	838	0.0271136	16103	164229368	47646332	56857.20
16103032007	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	27	0.0008736	16103	5291400	9799791	362955.21
16103032011	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	125	0.0040444	16103	24497221	9799791	78398.33
16103032020	16103	10	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	386	0.0124891	16103	75647418	47646332	123436.09
16103032025	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	21	0.0006795	16103	4115533	9799791	466656.70
16103032032	16103	2	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	151	0.0048856	16103	29592643	14846165	98318.97
16103032901	16103	5	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	59	0.0019090	16103	11562688	27950534	473737.86
16103042027	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	48	0.0015530	16103	9406933	9799791	204162.31
16103052006	16103	3	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	85	0.0027502	16103	16658110	19426501	228547.08
16103052021	16103	12	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	498	0.0161129	16103	97596928	55192640	110828.59
16103052034	16103	4	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	20	0.0006471	16103	3919555	23765600	1188279.99
16103052901	16103	1	2017	Chillán Viejo	195977.8	2017	16103	30907	6057084828	Rural	16	0.0005177	16103	3135644	9799791	612486.92

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_rural_nivel_nacional_17_r16.rds")

---

3 Referencias

https://rpubs.com/osoramirez/316691

https://dataintelligencechile.shinyapps.io/casenfinal

Manual_de_usuario_Censo_2017_16R.pdf

http://www.censo2017.cl/microdatos/

Censo de Población y Vivienda

https://www.ine.cl/estadisticas/sociales/censos-de-poblacion-y-vivienda/poblacion-y-vivienda