Expansión de la CASEN sobre el CENSO (Región 14)

De ingresos sobre una categoría de respuesta

DataIntelligence

Abstract

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.

Haremos el análisis sobre la región 14.

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.

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:

tabla_con_clave <- readRDS("censo_viviendas_con_clave_17.rds")
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 <- filter(tabla_con_clave, tabla_con_clave$REGION == 14) 

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave[,-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	14101011001	1	14101	94	2017
2	14101021001	1	14101	577	2017
3	14101031001	1	14101	106	2017
4	14101041001	1	14101	77	2017
5	14101041002	1	14101	11	2017
6	14101041003	1	14101	202	2017
7	14101041004	1	14101	18	2017
8	14101041005	1	14101	167	2017
9	14101042005	1	14101	5	2017
10	14101042058	1	14101	3	2017
11	14101051001	1	14101	67	2017
12	14101051002	1	14101	61	2017
13	14101051003	1	14101	36	2017
14	14101051004	1	14101	38	2017
15	14101051005	1	14101	182	2017
16	14101061001	1	14101	67	2017
17	14101061002	1	14101	30	2017
18	14101061003	1	14101	77	2017
19	14101061004	1	14101	60	2017
20	14101061005	1	14101	58	2017
21	14101061006	1	14101	10	2017
22	14101061007	1	14101	1	2017
23	14101062011	1	14101	2	2017
24	14101062013	1	14101	2	2017
25	14101062021	1	14101	6	2017
26	14101062047	1	14101	1	2017
27	14101071001	1	14101	85	2017
28	14101071002	1	14101	65	2017
29	14101071003	1	14101	131	2017
30	14101071004	1	14101	47	2017
31	14101071005	1	14101	337	2017
32	14101071006	1	14101	61	2017
33	14101072002	1	14101	6	2017
34	14101072045	1	14101	15	2017
35	14101081001	1	14101	352	2017
36	14101081002	1	14101	198	2017
37	14101081003	1	14101	471	2017
38	14101081004	1	14101	99	2017
39	14101081005	1	14101	314	2017
40	14101081006	1	14101	38	2017
41	14101081007	1	14101	26	2017
42	14101081008	1	14101	65	2017
43	14101081009	1	14101	54	2017
44	14101081010	1	14101	136	2017
45	14101081011	1	14101	26	2017
46	14101082002	1	14101	54	2017
47	14101091001	1	14101	169	2017
48	14101091002	1	14101	152	2017
49	14101101001	1	14101	61	2017
50	14101101002	1	14101	104	2017
51	14101101003	1	14101	80	2017
52	14101112005	1	14101	24	2017
53	14101112010	1	14101	8	2017
54	14101112017	1	14101	35	2017
55	14101112037	1	14101	14	2017
56	14101112046	1	14101	6	2017
57	14101112058	1	14101	2	2017
58	14101122007	1	14101	26	2017
59	14101122009	1	14101	2	2017
60	14101122042	1	14101	5	2017
61	14101122058	1	14101	31	2017
62	14101132003	1	14101	9	2017
63	14101132050	1	14101	2	2017
64	14101132901	1	14101	5	2017
65	14101142014	1	14101	5	2017
66	14101142039	1	14101	1	2017
67	14101142059	1	14101	1	2017
68	14101152059	1	14101	1	2017
69	14101161001	1	14101	380	2017
70	14101162018	1	14101	2	2017
71	14101162019	1	14101	8	2017
72	14101162034	1	14101	1	2017
73	14101162053	1	14101	5	2017
74	14101162061	1	14101	6	2017
75	14101171001	1	14101	73	2017
76	14101172001	1	14101	42	2017
77	14101172016	1	14101	7	2017
78	14101172027	1	14101	3	2017
79	14101172036	1	14101	1	2017
80	14101172055	1	14101	1	2017
81	14101182004	1	14101	2	2017
82	14101182006	1	14101	2	2017
83	14101182015	1	14101	16	2017
84	14101182040	1	14101	4	2017
85	14101991999	1	14101	18	2017
612	14102011001	1	14102	51	2017
613	14102012011	1	14102	1	2017
614	14102012018	1	14102	2	2017
615	14102022008	1	14102	5	2017
616	14102022010	1	14102	6	2017
617	14102032001	1	14102	2	2017
618	14102032005	1	14102	1	2017
619	14102032019	1	14102	1	2017
620	14102032901	1	14102	1	2017
621	14102042006	1	14102	1	2017
622	14102042007	1	14102	1	2017
623	14102042015	1	14102	1	2017
624	14102042018	1	14102	4	2017
625	14102991999	1	14102	1	2017
1152	14103011001	1	14103	38	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	14101042005	5	2017	14101
10	14101042058	3	2017	14101
11	14101051001	67	2017	14101
12	14101051002	61	2017	14101
13	14101051003	36	2017	14101
14	14101051004	38	2017	14101
15	14101051005	182	2017	14101
16	14101061001	67	2017	14101
17	14101061002	30	2017	14101
18	14101061003	77	2017	14101
19	14101061004	60	2017	14101
20	14101061005	58	2017	14101
21	14101061006	10	2017	14101
22	14101061007	1	2017	14101
23	14101062011	2	2017	14101
24	14101062013	2	2017	14101
25	14101062021	6	2017	14101
26	14101062047	1	2017	14101
27	14101071001	85	2017	14101
28	14101071002	65	2017	14101
29	14101071003	131	2017	14101
30	14101071004	47	2017	14101
31	14101071005	337	2017	14101
32	14101071006	61	2017	14101
33	14101072002	6	2017	14101
34	14101072045	15	2017	14101
35	14101081001	352	2017	14101
36	14101081002	198	2017	14101
37	14101081003	471	2017	14101
38	14101081004	99	2017	14101
39	14101081005	314	2017	14101
40	14101081006	38	2017	14101
41	14101081007	26	2017	14101
42	14101081008	65	2017	14101
43	14101081009	54	2017	14101
44	14101081010	136	2017	14101
45	14101081011	26	2017	14101
46	14101082002	54	2017	14101
47	14101091001	169	2017	14101
48	14101091002	152	2017	14101
49	14101101001	61	2017	14101
50	14101101002	104	2017	14101
51	14101101003	80	2017	14101
52	14101112005	24	2017	14101
53	14101112010	8	2017	14101
54	14101112017	35	2017	14101
55	14101112037	14	2017	14101
56	14101112046	6	2017	14101
57	14101112058	2	2017	14101
58	14101122007	26	2017	14101
59	14101122009	2	2017	14101
60	14101122042	5	2017	14101
61	14101122058	31	2017	14101
62	14101132003	9	2017	14101
63	14101132050	2	2017	14101
64	14101132901	5	2017	14101
65	14101142014	5	2017	14101
66	14101142039	1	2017	14101
67	14101142059	1	2017	14101
68	14101152059	1	2017	14101
69	14101161001	380	2017	14101
70	14101162018	2	2017	14101
71	14101162019	8	2017	14101
72	14101162034	1	2017	14101
73	14101162053	5	2017	14101
74	14101162061	6	2017	14101
75	14101171001	73	2017	14101
76	14101172001	42	2017	14101
77	14101172016	7	2017	14101
78	14101172027	3	2017	14101
79	14101172036	1	2017	14101
80	14101172055	1	2017	14101
81	14101182004	2	2017	14101
82	14101182006	2	2017	14101
83	14101182015	16	2017	14101
84	14101182040	4	2017	14101
85	14101991999	18	2017	14101
612	14102011001	51	2017	14102
613	14102012011	1	2017	14102
614	14102012018	2	2017	14102
615	14102022008	5	2017	14102
616	14102022010	6	2017	14102
617	14102032001	2	2017	14102
618	14102032005	1	2017	14102
619	14102032019	1	2017	14102
620	14102032901	1	2017	14102
621	14102042006	1	2017	14102
622	14102042007	1	2017	14102
623	14102042015	1	2017	14102
624	14102042018	4	2017	14102
625	14102991999	1	2017	14102
1152	14103011001	38	2017	14103

2 Variable CASEN

2.1 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.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
01101	Iquique	354820.7	2017	1101	191468	67936815240
01107	Alto Hospicio	301933.4	2017	1107	108375	32722034397
01401	Pozo Almonte	285981.8	2017	1401	15711	4493059532
01402	Camiña	262850.3	2017	1402	1250	328562901
01404	Huara	253968.5	2017	1404	2730	693334131
01405	Pica	313007.5	2017	1405	9296	2909717399
02101	Antofagasta	347580.2	2017	2101	361873	125779893517
02102	Mejillones	369770.7	2017	2102	13467	4979702302
02103	Sierra Gorda	403458.5	2017	2103	10186	4109628188
02104	Taltal	364539.1	2017	2104	13317	4854566842
02201	Calama	409671.3	2017	2201	165731	67895226712
02203	San Pedro de Atacama	426592.0	2017	2203	10996	4690805471
02301	Tocopilla	246615.3	2017	2301	25186	6211253937
02302	María Elena	466266.9	2017	2302	6457	3010685220
03101	Copiapó	330075.2	2017	3101	153937	50810778473
03102	Caldera	299314.8	2017	3102	17662	5286498241
03103	Tierra Amarilla	314643.9	2017	3103	14019	4410992711
03201	Chañaral	286389.3	2017	3201	12219	3499391196
03202	Diego de Almagro	336256.8	2017	3202	13925	4682376047
03301	Vallenar	304336.7	2017	3301	51917	15800246795
03302	Alto del Carmen	227130.4	2017	3302	5299	1203563833
03303	Freirina	253086.7	2017	3303	7041	1781983257
03304	Huasco	287406.6	2017	3304	10149	2916889629
04101	La Serena	270221.9	2017	4101	221054	59733627577
04102	Coquimbo	261852.6	2017	4102	227730	59631700074
04103	Andacollo	248209.3	2017	4103	11044	2741223967
04104	La Higuera	228356.8	2017	4104	4241	968461330
04105	Paiguano	205942.1	2017	4105	4497	926121774
04106	Vicuña	211431.9	2017	4106	27771	5871675449
04201	Illapel	238674.4	2017	4201	30848	7362627007
04202	Canela	207933.6	2017	4202	9093	1890740321
04203	Los Vilos	255200.4	2017	4203	21382	5456695139
04204	Salamanca	242879.5	2017	4204	29347	7127783272
04301	Ovalle	266522.9	2017	4301	111272	29656533187
04302	Combarbalá	210409.7	2017	4302	13322	2803077721
04303	Monte Patria	211907.9	2017	4303	30751	6516380780
04304	Punitaqui	194997.8	2017	4304	10956	2136395349
04305	Río Hurtado	182027.2	2017	4305	4278	778712384
05101	Valparaíso	298720.7	2017	5101	296655	88616992249
05102	Casablanca	312802.7	2017	5102	26867	8404070481
05103	Concón	318496.3	2017	5103	42152	13425257057
05105	Puchuncaví	288737.2	2017	5105	18546	5354920887
05107	Quintero	316659.1	2017	5107	31923	10108709691
05109	Viña del Mar	337006.1	2017	5109	334248	112643604611
05301	Los Andes	338182.5	2017	5301	66708	22559476922
05302	Calle Larga	245165.4	2017	5302	14832	3636293159
05303	Rinconada	281633.2	2017	5303	10207	2874630315
05304	San Esteban	220958.4	2017	5304	18855	4166170587
05401	La Ligua	229623.7	2017	5401	35390	8126381563
05402	Cabildo	249717.7	2017	5402	19388	4841527150

3 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
14101	14101011001	94	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101021001	577	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101031001	106	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101041001	77	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101041002	11	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101041003	202	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101041004	18	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101041005	167	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101042005	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101042058	3	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101051001	67	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101051002	61	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101051003	36	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101051004	38	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101051005	182	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101061001	67	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101061002	30	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101061003	77	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101061004	60	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101061005	58	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101061006	10	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101061007	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101062011	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101062013	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101062021	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101062047	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101071001	85	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101071002	65	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101071003	131	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101071004	47	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101071005	337	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101071006	61	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101072002	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101072045	15	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101081001	352	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101081002	198	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101081003	471	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101081004	99	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101081005	314	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101081006	38	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101081007	26	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101081008	65	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101081009	54	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101081010	136	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101081011	26	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101082002	54	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101091001	169	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101091002	152	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101101001	61	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101101002	104	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101101003	80	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101112005	24	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101112010	8	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101112017	35	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101112037	14	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101112046	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101112058	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101122007	26	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101122009	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101122042	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101122058	31	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101132003	9	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101132050	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101132901	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101142014	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101142039	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101142059	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101152059	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101161001	380	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101162018	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101162019	8	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101162034	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101162053	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101162061	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101171001	73	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101172001	42	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101172016	7	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101172027	3	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101172036	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101172055	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101182004	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101182006	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101182015	16	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101182040	4	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101991999	18	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14102	14102011001	51	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102012011	1	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102012018	2	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102022008	5	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102022010	6	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102032001	2	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102032005	1	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102032019	1	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102032901	1	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102042006	1	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102042007	1	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102042015	1	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102042018	4	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102991999	1	2017	Corral	207459.9	2017	14102	5302	1099952396
14103	14103011001	38	2017	Lanco	217605.0	2017	14103	16752	3645318217

4 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

5 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
14101	14101011001	94	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101021001	577	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101031001	106	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101041001	77	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101041002	11	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101041003	202	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101041004	18	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101041005	167	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101042005	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101042058	3	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101051001	67	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101051002	61	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101051003	36	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101051004	38	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101051005	182	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101061001	67	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101061002	30	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101061003	77	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101061004	60	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101061005	58	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101061006	10	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101061007	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101062011	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101062013	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101062021	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101062047	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101071001	85	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101071002	65	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101071003	131	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101071004	47	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101071005	337	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101071006	61	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101072002	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101072045	15	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101081001	352	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101081002	198	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101081003	471	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101081004	99	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101081005	314	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101081006	38	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101081007	26	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101081008	65	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101081009	54	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101081010	136	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101081011	26	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101082002	54	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101091001	169	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101091002	152	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101101001	61	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101101002	104	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101101003	80	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101112005	24	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101112010	8	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101112017	35	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101112037	14	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101112046	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101112058	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101122007	26	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101122009	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101122042	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101122058	31	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101132003	9	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101132050	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101132901	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101142014	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101142039	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101142059	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101152059	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101161001	380	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101162018	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101162019	8	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101162034	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101162053	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101162061	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101171001	73	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101172001	42	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101172016	7	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101172027	3	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101172036	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101172055	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101182004	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101182006	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101182015	16	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101182040	4	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14101	14101991999	18	2017	Valdivia	283495.1	2017	14101	166080	47082873150
14102	14102011001	51	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102012011	1	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102012018	2	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102022008	5	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102022010	6	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102032001	2	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102032005	1	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102032019	1	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102032901	1	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102042006	1	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102042007	1	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102042015	1	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102042018	4	2017	Corral	207459.9	2017	14102	5302	1099952396
14102	14102991999	1	2017	Corral	207459.9	2017	14102	5302	1099952396
14103	14103011001	38	2017	Lanco	217605.0	2017	14103	16752	3645318217

6 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	Freq.y	p_poblacional	código.y
14101011001	14101	94	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3316	0.0199663	14101
14101021001	14101	577	2017	Valdivia	283495.1	2017	14101	166080	47082873150	5505	0.0331467	14101
14101031001	14101	106	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1916	0.0115366	14101
14101041001	14101	77	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3347	0.0201529	14101
14101041002	14101	11	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1217	0.0073278	14101
14101041003	14101	202	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3319	0.0199843	14101
14101041004	14101	18	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3079	0.0185393	14101
14101041005	14101	167	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4543	0.0273543	14101
14101042005	14101	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150	60	0.0003613	14101
14101042058	14101	3	2017	Valdivia	283495.1	2017	14101	166080	47082873150	40	0.0002408	14101
14101051001	14101	67	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3908	0.0235308	14101
14101051002	14101	61	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2924	0.0176060	14101
14101051003	14101	36	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3046	0.0183406	14101
14101051004	14101	38	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1117	0.0067257	14101
14101051005	14101	182	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3677	0.0221399	14101
14101061001	14101	67	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4741	0.0285465	14101
14101061002	14101	30	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2213	0.0133249	14101
14101061003	14101	77	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3643	0.0219352	14101
14101061004	14101	60	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4455	0.0268244	14101
14101061005	14101	58	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2502	0.0150650	14101
14101061006	14101	10	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1422	0.0085621	14101
14101061007	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	33	0.0001987	14101
14101062011	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	101	0.0006081	14101
14101062013	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	88	0.0005299	14101
14101062021	14101	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150	296	0.0017823	14101
14101062047	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	16	0.0000963	14101
14101071001	14101	85	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4214	0.0253733	14101
14101071002	14101	65	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3859	0.0232358	14101
14101071003	14101	131	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4649	0.0279925	14101
14101071004	14101	47	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1808	0.0108863	14101
14101071005	14101	337	2017	Valdivia	283495.1	2017	14101	166080	47082873150	6057	0.0364704	14101
14101071006	14101	61	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4766	0.0286970	14101
14101072002	14101	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150	294	0.0017702	14101
14101072045	14101	15	2017	Valdivia	283495.1	2017	14101	166080	47082873150	287	0.0017281	14101
14101081001	14101	352	2017	Valdivia	283495.1	2017	14101	166080	47082873150	5118	0.0308165	14101
14101081002	14101	198	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3425	0.0206226	14101
14101081003	14101	471	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4434	0.0266980	14101
14101081004	14101	99	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4941	0.0297507	14101
14101081005	14101	314	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3782	0.0227722	14101
14101081006	14101	38	2017	Valdivia	283495.1	2017	14101	166080	47082873150	5317	0.0320147	14101
14101081007	14101	26	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3719	0.0223928	14101
14101081008	14101	65	2017	Valdivia	283495.1	2017	14101	166080	47082873150	5423	0.0326529	14101
14101081009	14101	54	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3389	0.0204058	14101
14101081010	14101	136	2017	Valdivia	283495.1	2017	14101	166080	47082873150	5849	0.0352180	14101
14101081011	14101	26	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2888	0.0173892	14101
14101082002	14101	54	2017	Valdivia	283495.1	2017	14101	166080	47082873150	429	0.0025831	14101
14101091001	14101	169	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3024	0.0182081	14101
14101091002	14101	152	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4672	0.0281310	14101
14101101001	14101	61	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1538	0.0092606	14101
14101101002	14101	104	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2989	0.0179974	14101
14101101003	14101	80	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2463	0.0148302	14101
14101112005	14101	24	2017	Valdivia	283495.1	2017	14101	166080	47082873150	380	0.0022881	14101
14101112010	14101	8	2017	Valdivia	283495.1	2017	14101	166080	47082873150	176	0.0010597	14101
14101112017	14101	35	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1083	0.0065210	14101
14101112037	14101	14	2017	Valdivia	283495.1	2017	14101	166080	47082873150	535	0.0032213	14101
14101112046	14101	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150	103	0.0006202	14101
14101112058	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	59	0.0003553	14101
14101122007	14101	26	2017	Valdivia	283495.1	2017	14101	166080	47082873150	297	0.0017883	14101
14101122009	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	266	0.0016016	14101
14101122042	14101	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150	233	0.0014029	14101
14101122058	14101	31	2017	Valdivia	283495.1	2017	14101	166080	47082873150	509	0.0030648	14101
14101132003	14101	9	2017	Valdivia	283495.1	2017	14101	166080	47082873150	290	0.0017461	14101
14101132050	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	66	0.0003974	14101
14101132901	14101	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150	250	0.0015053	14101
14101142014	14101	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150	325	0.0019569	14101
14101142039	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	58	0.0003492	14101
14101142059	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	96	0.0005780	14101
14101152059	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	82	0.0004937	14101
14101161001	14101	380	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1801	0.0108442	14101
14101162018	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	97	0.0005841	14101
14101162019	14101	8	2017	Valdivia	283495.1	2017	14101	166080	47082873150	64	0.0003854	14101
14101162034	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	42	0.0002529	14101
14101162053	14101	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150	217	0.0013066	14101
14101162061	14101	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150	94	0.0005660	14101
14101171001	14101	73	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3989	0.0240185	14101
14101172001	14101	42	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1348	0.0081166	14101
14101172016	14101	7	2017	Valdivia	283495.1	2017	14101	166080	47082873150	148	0.0008911	14101
14101172027	14101	3	2017	Valdivia	283495.1	2017	14101	166080	47082873150	103	0.0006202	14101
14101172036	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	110	0.0006623	14101
14101172055	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	88	0.0005299	14101
14101182004	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	188	0.0011320	14101
14101182006	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	67	0.0004034	14101
14101182015	14101	16	2017	Valdivia	283495.1	2017	14101	166080	47082873150	455	0.0027396	14101
14101182040	14101	4	2017	Valdivia	283495.1	2017	14101	166080	47082873150	323	0.0019448	14101
14101991999	14101	18	2017	Valdivia	283495.1	2017	14101	166080	47082873150	679	0.0040884	14101
14102011001	14102	51	2017	Corral	207459.9	2017	14102	5302	1099952396	3469	0.6542814	14102
14102012011	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	22	0.0041494	14102
14102012018	14102	2	2017	Corral	207459.9	2017	14102	5302	1099952396	80	0.0150886	14102
14102022008	14102	5	2017	Corral	207459.9	2017	14102	5302	1099952396	507	0.0956243	14102
14102022010	14102	6	2017	Corral	207459.9	2017	14102	5302	1099952396	41	0.0077329	14102
14102032001	14102	2	2017	Corral	207459.9	2017	14102	5302	1099952396	68	0.0128253	14102
14102032005	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	64	0.0120709	14102
14102032019	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	62	0.0116937	14102
14102032901	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	31	0.0058469	14102
14102042006	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	257	0.0484723	14102
14102042007	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	115	0.0216899	14102
14102042015	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	9	0.0016975	14102
14102042018	14102	4	2017	Corral	207459.9	2017	14102	5302	1099952396	33	0.0062241	14102
14102991999	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	12	0.0022633	14102
14103011001	14103	38	2017	Lanco	217605.0	2017	14103	16752	3645318217	2488	0.1485196	14103

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	Freq.y	p_poblacional	código.y	multi_pob
14101011001	14101	94	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3316	0.0199663	14101	940069890
14101021001	14101	577	2017	Valdivia	283495.1	2017	14101	166080	47082873150	5505	0.0331467	14101	1560640756
14101031001	14101	106	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1916	0.0115366	14101	543176692
14101041001	14101	77	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3347	0.0201529	14101	948858240
14101041002	14101	11	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1217	0.0073278	14101	345013588
14101041003	14101	202	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3319	0.0199843	14101	940920376
14101041004	14101	18	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3079	0.0185393	14101	872881542
14101041005	14101	167	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4543	0.0273543	14101	1287918429
14101042005	14101	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150	60	0.0003613	14101	17009709
14101042058	14101	3	2017	Valdivia	283495.1	2017	14101	166080	47082873150	40	0.0002408	14101	11339806
14101051001	14101	67	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3908	0.0235308	14101	1107899014
14101051002	14101	61	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2924	0.0176060	14101	828939795
14101051003	14101	36	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3046	0.0183406	14101	863526202
14101051004	14101	38	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1117	0.0067257	14101	316664073
14101051005	14101	182	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3677	0.0221399	14101	1042411636
14101061001	14101	67	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4741	0.0285465	14101	1344050467
14101061002	14101	30	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2213	0.0133249	14101	627374749
14101061003	14101	77	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3643	0.0219352	14101	1032772802
14101061004	14101	60	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4455	0.0268244	14101	1262970857
14101061005	14101	58	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2502	0.0150650	14101	709304845
14101061006	14101	10	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1422	0.0085621	14101	403130092
14101061007	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	33	0.0001987	14101	9355340
14101062011	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	101	0.0006081	14101	28633009
14101062013	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	88	0.0005299	14101	24947572
14101062021	14101	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150	296	0.0017823	14101	83914562
14101062047	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	16	0.0000963	14101	4535922
14101071001	14101	85	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4214	0.0253733	14101	1194648528
14101071002	14101	65	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3859	0.0232358	14101	1094007752
14101071003	14101	131	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4649	0.0279925	14101	1317968914
14101071004	14101	47	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1808	0.0108863	14101	512559216
14101071005	14101	337	2017	Valdivia	283495.1	2017	14101	166080	47082873150	6057	0.0364704	14101	1717130074
14101071006	14101	61	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4766	0.0286970	14101	1351137846
14101072002	14101	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150	294	0.0017702	14101	83347572
14101072045	14101	15	2017	Valdivia	283495.1	2017	14101	166080	47082873150	287	0.0017281	14101	81363106
14101081001	14101	352	2017	Valdivia	283495.1	2017	14101	166080	47082873150	5118	0.0308165	14101	1450928136
14101081002	14101	198	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3425	0.0206226	14101	970970861
14101081003	14101	471	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4434	0.0266980	14101	1257017459
14101081004	14101	99	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4941	0.0297507	14101	1400749496
14101081005	14101	314	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3782	0.0227722	14101	1072178626
14101081006	14101	38	2017	Valdivia	283495.1	2017	14101	166080	47082873150	5317	0.0320147	14101	1507343669
14101081007	14101	26	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3719	0.0223928	14101	1054318432
14101081008	14101	65	2017	Valdivia	283495.1	2017	14101	166080	47082873150	5423	0.0326529	14101	1537394154
14101081009	14101	54	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3389	0.0204058	14101	960765036
14101081010	14101	136	2017	Valdivia	283495.1	2017	14101	166080	47082873150	5849	0.0352180	14101	1658163084
14101081011	14101	26	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2888	0.0173892	14101	818733970
14101082002	14101	54	2017	Valdivia	283495.1	2017	14101	166080	47082873150	429	0.0025831	14101	121619416
14101091001	14101	169	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3024	0.0182081	14101	857289309
14101091002	14101	152	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4672	0.0281310	14101	1324489302
14101101001	14101	61	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1538	0.0092606	14101	436015528
14101101002	14101	104	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2989	0.0179974	14101	847366979
14101101003	14101	80	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2463	0.0148302	14101	698248534
14101112005	14101	24	2017	Valdivia	283495.1	2017	14101	166080	47082873150	380	0.0022881	14101	107728154
14101112010	14101	8	2017	Valdivia	283495.1	2017	14101	166080	47082873150	176	0.0010597	14101	49895145
14101112017	14101	35	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1083	0.0065210	14101	307025239
14101112037	14101	14	2017	Valdivia	283495.1	2017	14101	166080	47082873150	535	0.0032213	14101	151669901
14101112046	14101	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150	103	0.0006202	14101	29200000
14101112058	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	59	0.0003553	14101	16726213
14101122007	14101	26	2017	Valdivia	283495.1	2017	14101	166080	47082873150	297	0.0017883	14101	84198057
14101122009	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	266	0.0016016	14101	75409708
14101122042	14101	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150	233	0.0014029	14101	66054368
14101122058	14101	31	2017	Valdivia	283495.1	2017	14101	166080	47082873150	509	0.0030648	14101	144299027
14101132003	14101	9	2017	Valdivia	283495.1	2017	14101	166080	47082873150	290	0.0017461	14101	82213591
14101132050	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	66	0.0003974	14101	18710679
14101132901	14101	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150	250	0.0015053	14101	70873785
14101142014	14101	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150	325	0.0019569	14101	92135921
14101142039	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	58	0.0003492	14101	16442718
14101142059	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	96	0.0005780	14101	27215534
14101152059	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	82	0.0004937	14101	23246602
14101161001	14101	380	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1801	0.0108442	14101	510574750
14101162018	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	97	0.0005841	14101	27499029
14101162019	14101	8	2017	Valdivia	283495.1	2017	14101	166080	47082873150	64	0.0003854	14101	18143689
14101162034	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	42	0.0002529	14101	11906796
14101162053	14101	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150	217	0.0013066	14101	61518446
14101162061	14101	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150	94	0.0005660	14101	26648543
14101171001	14101	73	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3989	0.0240185	14101	1130862121
14101172001	14101	42	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1348	0.0081166	14101	382151451
14101172016	14101	7	2017	Valdivia	283495.1	2017	14101	166080	47082873150	148	0.0008911	14101	41957281
14101172027	14101	3	2017	Valdivia	283495.1	2017	14101	166080	47082873150	103	0.0006202	14101	29200000
14101172036	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	110	0.0006623	14101	31184466
14101172055	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	88	0.0005299	14101	24947572
14101182004	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	188	0.0011320	14101	53297087
14101182006	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	67	0.0004034	14101	18994175
14101182015	14101	16	2017	Valdivia	283495.1	2017	14101	166080	47082873150	455	0.0027396	14101	128990290
14101182040	14101	4	2017	Valdivia	283495.1	2017	14101	166080	47082873150	323	0.0019448	14101	91568931
14101991999	14101	18	2017	Valdivia	283495.1	2017	14101	166080	47082873150	679	0.0040884	14101	192493201
14102011001	14102	51	2017	Corral	207459.9	2017	14102	5302	1099952396	3469	0.6542814	14102	719678397
14102012011	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	22	0.0041494	14102	4564118
14102012018	14102	2	2017	Corral	207459.9	2017	14102	5302	1099952396	80	0.0150886	14102	16596792
14102022008	14102	5	2017	Corral	207459.9	2017	14102	5302	1099952396	507	0.0956243	14102	105182170
14102022010	14102	6	2017	Corral	207459.9	2017	14102	5302	1099952396	41	0.0077329	14102	8505856
14102032001	14102	2	2017	Corral	207459.9	2017	14102	5302	1099952396	68	0.0128253	14102	14107273
14102032005	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	64	0.0120709	14102	13277434
14102032019	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	62	0.0116937	14102	12862514
14102032901	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	31	0.0058469	14102	6431257
14102042006	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	257	0.0484723	14102	53317195
14102042007	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	115	0.0216899	14102	23857889
14102042015	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	9	0.0016975	14102	1867139
14102042018	14102	4	2017	Corral	207459.9	2017	14102	5302	1099952396	33	0.0062241	14102	6846177
14102991999	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	12	0.0022633	14102	2489519
14103011001	14103	38	2017	Lanco	217605.0	2017	14103	16752	3645318217	2488	0.1485196	14103	541401130

7 Análisis de regresión

Aplicaremos un análisis de regresión donde:

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

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

7.1 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) 

7.2 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.

7.3 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 
## -1.330e+09 -8.326e+07 -6.729e+07 -2.597e+07  1.244e+09 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 88535556   10532176   8.406 4.02e-16 ***
## Freq.x       4610622     192241  23.984  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 226700000 on 524 degrees of freedom
## Multiple R-squared:  0.5233, Adjusted R-squared:  0.5224 
## F-statistic: 575.2 on 1 and 524 DF,  p-value: < 2.2e-16

7.4 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)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x^2), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -1.330e+09 -8.326e+07 -6.729e+07 -2.597e+07  1.244e+09 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 88535556   10532176   8.406 4.02e-16 ***
## Freq.x       4610622     192241  23.984  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 226700000 on 524 degrees of freedom
## Multiple R-squared:  0.5233, Adjusted R-squared:  0.5224 
## F-statistic: 575.2 on 1 and 524 DF,  p-value: < 2.2e-16

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)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x^3), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -1.330e+09 -8.326e+07 -6.729e+07 -2.597e+07  1.244e+09 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 88535556   10532176   8.406 4.02e-16 ***
## Freq.x       4610622     192241  23.984  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 226700000 on 524 degrees of freedom
## Multiple R-squared:  0.5233, Adjusted R-squared:  0.5224 
## F-statistic: 575.2 on 1 and 524 DF,  p-value: < 2.2e-16

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)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -478462706 -146140175   -3700162  118698814  969532506 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -106802780   13119383  -8.141 2.88e-15 ***
## log(Freq.x)  177209258    6078300  29.154  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 202700000 on 524 degrees of freedom
## Multiple R-squared:  0.6186, Adjusted R-squared:  0.6179 
## F-statistic:   850 on 1 and 524 DF,  p-value: < 2.2e-16

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)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -1.149e+09 -6.744e+07 -8.353e+06  2.860e+07  1.050e+09 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -98817629   10355548  -9.542   <2e-16 ***
## sqrt(Freq.x)  90227545    2380136  37.909   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 169700000 on 524 degrees of freedom
## Multiple R-squared:  0.7328, Adjusted R-squared:  0.7323 
## F-statistic:  1437 on 1 and 524 DF,  p-value: < 2.2e-16

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)
summary(linearMod) 

## 
## Call:
## lm(formula = sqrt(multi_pob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -27816  -2211   -662   1473  21317 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   2290.24     278.80   8.215 1.67e-15 ***
## sqrt(Freq.x)  2468.58      64.08  38.524  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4569 on 524 degrees of freedom
## Multiple R-squared:  0.7391, Adjusted R-squared:  0.7386 
## F-statistic:  1484 on 1 and 524 DF,  p-value: < 2.2e-16

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)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3572 -0.5430  0.1022  0.6764  2.3903 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   16.6250     0.0609  272.99   <2e-16 ***
## sqrt(Freq.x)   0.3705     0.0140   26.47   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.998 on 524 degrees of freedom
## Multiple R-squared:  0.5722, Adjusted R-squared:  0.5713 
## F-statistic: 700.7 on 1 and 524 DF,  p-value: < 2.2e-16

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)
summary(linearMod) 

## 
## Call:
## lm(formula = sqrt(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12624.8  -3210.2    -15.7   2832.6  18396.9 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1510.0      307.4   4.912 1.21e-06 ***
## log(Freq.x)   5200.6      142.4  36.513  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4751 on 524 degrees of freedom
## Multiple R-squared:  0.7179, Adjusted R-squared:  0.7173 
## F-statistic:  1333 on 1 and 524 DF,  p-value: < 2.2e-16

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)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.6070 -0.5026  0.0535  0.6295  2.2808 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.38046    0.05626  291.17   <2e-16 ***
## log(Freq.x)  0.86052    0.02606   33.02   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8694 on 524 degrees of freedom
## Multiple R-squared:  0.6753, Adjusted R-squared:  0.6747 
## F-statistic:  1090 on 1 and 524 DF,  p-value: < 2.2e-16

9 Modelo raíz-raíz (r-r)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.7386).

9.1 Diagrama de dispersión sobre r-r

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 r-r

Observemos nuevamente el resultado sobre r-r.

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 
## -27816  -2211   -662   1473  21317 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   2290.24     278.80   8.215 1.67e-15 ***
## sqrt(Freq.x)  2468.58      64.08  38.524  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4569 on 524 degrees of freedom
## Multiple R-squared:  0.7391, Adjusted R-squared:  0.7386 
## F-statistic:  1484 on 1 and 524 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

\[ \hat Y = 2290.24^2 + 2 \cdot 2290.24 \cdot 2468.58 \sqrt{X} + 2468.58 ^2 \cdot X \]

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$est_ing <- 2290.24^2 + 2290.24* 2468.58* sqrt(h_y_m_comuna_corr_01$Freq.x)+2468.58 ^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	Freq.y	p_poblacional	código.y	multi_pob	est_ing
14101011001	14101	94	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3316	0.0199663	14101	940069890	632884677
14101021001	14101	577	2017	Valdivia	283495.1	2017	14101	166080	47082873150	5505	0.0331467	14101	1560640756	3657223232
14101031001	14101	106	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1916	0.0115366	14101	543176692	709405037
14101041001	14101	77	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3347	0.0201529	14101	948858240	524085010
14101041002	14101	11	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1217	0.0073278	14101	345013588	91028963
14101041003	14101	202	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3319	0.0199843	14101	940920376	1316563748
14101041004	14101	18	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3079	0.0185393	14101	872881542	138921535
14101041005	14101	167	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4543	0.0273543	14101	1287918429	1095985503
14101042005	14101	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150	60	0.0003613	14101	17009709	48356560
14101042058	14101	3	2017	Valdivia	283495.1	2017	14101	166080	47082873150	40	0.0002408	14101	11339806	33319254
14101051001	14101	67	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3908	0.0235308	14101	1107899014	459812686
14101051002	14101	61	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2924	0.0176060	14101	828939795	421128665
14101051003	14101	36	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3046	0.0183406	14101	863526202	258546983
14101051004	14101	38	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1117	0.0067257	14101	316664073	271664295
14101051005	14101	182	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3677	0.0221399	14101	1042411636	1190604455
14101061001	14101	67	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4741	0.0285465	14101	1344050467	459812686
14101061002	14101	30	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2213	0.0133249	14101	627374749	219028081
14101061003	14101	77	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3643	0.0219352	14101	1032772802	524085010
14101061004	14101	60	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4455	0.0268244	14101	1262970857	414671344
14101061005	14101	58	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2502	0.0150650	14101	709304845	401747502
14101061006	14101	10	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1422	0.0085621	14101	403130092	84062453
14101061007	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	33	0.0001987	14101	9355340	16992727
14101062011	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	101	0.0006081	14101	28633009	25428429
14101062013	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	88	0.0005299	14101	24947572	25428429
14101062021	14101	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150	296	0.0017823	14101	83914562	55657057
14101062047	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	16	0.0000963	14101	4535922	16992727
14101071001	14101	85	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4214	0.0253733	14101	1194648528	575349604
14101071002	14101	65	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3859	0.0232358	14101	1094007752	446928977
14101071003	14101	131	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4649	0.0279925	14101	1317968914	868253300
14101071004	14101	47	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1808	0.0108863	14101	512559216	330417306
14101071005	14101	337	2017	Valdivia	283495.1	2017	14101	166080	47082873150	6057	0.0364704	14101	1717130074	2162672237
14101071006	14101	61	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4766	0.0286970	14101	1351137846	421128665
14101072002	14101	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150	294	0.0017702	14101	83347572	55657057
14101072045	14101	15	2017	Valdivia	283495.1	2017	14101	166080	47082873150	287	0.0017281	14101	81363106	118549964
14101081001	14101	352	2017	Valdivia	283495.1	2017	14101	166080	47082873150	5118	0.0308165	14101	1450928136	2256365200
14101081002	14101	198	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3425	0.0206226	14101	970970861	1291388644
14101081003	14101	471	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4434	0.0266980	14101	1257017459	2998164409
14101081004	14101	99	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4941	0.0297507	14101	1400749496	664793048
14101081005	14101	314	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3782	0.0227722	14101	1072178626	2018908553
14101081006	14101	38	2017	Valdivia	283495.1	2017	14101	166080	47082873150	5317	0.0320147	14101	1507343669	271664295
14101081007	14101	26	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3719	0.0223928	14101	1054318432	192514291
14101081008	14101	65	2017	Valdivia	283495.1	2017	14101	166080	47082873150	5423	0.0326529	14101	1537394154	446928977
14101081009	14101	54	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3389	0.0204058	14101	960765036	375860713
14101081010	14101	136	2017	Valdivia	283495.1	2017	14101	166080	47082873150	5849	0.0352180	14101	1658163084	899946074
14101081011	14101	26	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2888	0.0173892	14101	818733970	192514291
14101082002	14101	54	2017	Valdivia	283495.1	2017	14101	166080	47082873150	429	0.0025831	14101	121619416	375860713
14101091001	14101	169	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3024	0.0182081	14101	857289309	1108609467
14101091002	14101	152	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4672	0.0281310	14101	1324489302	1001218819
14101101001	14101	61	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1538	0.0092606	14101	436015528	421128665
14101101002	14101	104	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2989	0.0179974	14101	847366979	696665518
14101101003	14101	80	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2463	0.0148302	14101	698248534	543323876
14101112005	14101	24	2017	Valdivia	283495.1	2017	14101	166080	47082873150	380	0.0022881	14101	107728154	179195562
14101112010	14101	8	2017	Valdivia	283495.1	2017	14101	166080	47082873150	176	0.0010597	14101	49895145	69987208
14101112017	14101	35	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1083	0.0065210	14101	307025239	251978641
14101112037	14101	14	2017	Valdivia	283495.1	2017	14101	166080	47082873150	535	0.0032213	14101	151669901	111713607
14101112046	14101	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150	103	0.0006202	14101	29200000	55657057
14101112058	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	59	0.0003553	14101	16726213	25428429
14101122007	14101	26	2017	Valdivia	283495.1	2017	14101	166080	47082873150	297	0.0017883	14101	84198057	192514291
14101122009	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	266	0.0016016	14101	75409708	25428429
14101122042	14101	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150	233	0.0014029	14101	66054368	48356560
14101122058	14101	31	2017	Valdivia	283495.1	2017	14101	166080	47082873150	509	0.0030648	14101	144299027	225633842
14101132003	14101	9	2017	Valdivia	283495.1	2017	14101	166080	47082873150	290	0.0017461	14101	82213591	77051106
14101132050	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	66	0.0003974	14101	18710679	25428429
14101132901	14101	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150	250	0.0015053	14101	70873785	48356560
14101142014	14101	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150	325	0.0019569	14101	92135921	48356560
14101142039	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	58	0.0003492	14101	16442718	16992727
14101142059	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	96	0.0005780	14101	27215534	16992727
14101152059	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	82	0.0004937	14101	23246602	16992727
14101161001	14101	380	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1801	0.0108442	14101	510574750	2431132087
14101162018	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	97	0.0005841	14101	27499029	25428429
14101162019	14101	8	2017	Valdivia	283495.1	2017	14101	166080	47082873150	64	0.0003854	14101	18143689	69987208
14101162034	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	42	0.0002529	14101	11906796	16992727
14101162053	14101	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150	217	0.0013066	14101	61518446	48356560
14101162061	14101	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150	94	0.0005660	14101	26648543	55657057
14101171001	14101	73	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3989	0.0240185	14101	1130862121	498403693
14101172001	14101	42	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1348	0.0081166	14101	382151451	297828241
14101172016	14101	7	2017	Valdivia	283495.1	2017	14101	166080	47082873150	148	0.0008911	14101	41957281	62860537
14101172027	14101	3	2017	Valdivia	283495.1	2017	14101	166080	47082873150	103	0.0006202	14101	29200000	33319254
14101172036	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	110	0.0006623	14101	31184466	16992727
14101172055	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	88	0.0005299	14101	24947572	16992727
14101182004	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	188	0.0011320	14101	53297087	25428429
14101182006	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	67	0.0004034	14101	18994175	25428429
14101182015	14101	16	2017	Valdivia	283495.1	2017	14101	166080	47082873150	455	0.0027396	14101	128990290	125361957
14101182040	14101	4	2017	Valdivia	283495.1	2017	14101	166080	47082873150	323	0.0019448	14101	91568931	40928029
14101991999	14101	18	2017	Valdivia	283495.1	2017	14101	166080	47082873150	679	0.0040884	14101	192493201	138921535
14102011001	14102	51	2017	Corral	207459.9	2017	14102	5302	1099952396	3469	0.6542814	14102	719678397	356408517
14102012011	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	22	0.0041494	14102	4564118	16992727
14102012018	14102	2	2017	Corral	207459.9	2017	14102	5302	1099952396	80	0.0150886	14102	16596792	25428429
14102022008	14102	5	2017	Corral	207459.9	2017	14102	5302	1099952396	507	0.0956243	14102	105182170	48356560
14102022010	14102	6	2017	Corral	207459.9	2017	14102	5302	1099952396	41	0.0077329	14102	8505856	55657057
14102032001	14102	2	2017	Corral	207459.9	2017	14102	5302	1099952396	68	0.0128253	14102	14107273	25428429
14102032005	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	64	0.0120709	14102	13277434	16992727
14102032019	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	62	0.0116937	14102	12862514	16992727
14102032901	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	31	0.0058469	14102	6431257	16992727
14102042006	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	257	0.0484723	14102	53317195	16992727
14102042007	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	115	0.0216899	14102	23857889	16992727
14102042015	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	9	0.0016975	14102	1867139	16992727
14102042018	14102	4	2017	Corral	207459.9	2017	14102	5302	1099952396	33	0.0062241	14102	6846177	40928029
14102991999	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	12	0.0022633	14102	2489519	16992727
14103011001	14103	38	2017	Lanco	217605.0	2017	14103	16752	3645318217	2488	0.1485196	14103	541401130	271664295

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	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
14101011001	14101	94	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3316	0.0199663	14101	940069890	632884677	190857.86
14101021001	14101	577	2017	Valdivia	283495.1	2017	14101	166080	47082873150	5505	0.0331467	14101	1560640756	3657223232	664345.73
14101031001	14101	106	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1916	0.0115366	14101	543176692	709405037	370253.15
14101041001	14101	77	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3347	0.0201529	14101	948858240	524085010	156583.51
14101041002	14101	11	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1217	0.0073278	14101	345013588	91028963	74797.83
14101041003	14101	202	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3319	0.0199843	14101	940920376	1316563748	396674.83
14101041004	14101	18	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3079	0.0185393	14101	872881542	138921535	45119.04
14101041005	14101	167	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4543	0.0273543	14101	1287918429	1095985503	241247.08
14101042005	14101	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150	60	0.0003613	14101	17009709	48356560	805942.67
14101042058	14101	3	2017	Valdivia	283495.1	2017	14101	166080	47082873150	40	0.0002408	14101	11339806	33319254	832981.34
14101051001	14101	67	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3908	0.0235308	14101	1107899014	459812686	117659.34
14101051002	14101	61	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2924	0.0176060	14101	828939795	421128665	144024.85
14101051003	14101	36	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3046	0.0183406	14101	863526202	258546983	84880.82
14101051004	14101	38	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1117	0.0067257	14101	316664073	271664295	243208.86
14101051005	14101	182	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3677	0.0221399	14101	1042411636	1190604455	323797.78
14101061001	14101	67	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4741	0.0285465	14101	1344050467	459812686	96986.43
14101061002	14101	30	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2213	0.0133249	14101	627374749	219028081	98973.38
14101061003	14101	77	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3643	0.0219352	14101	1032772802	524085010	143860.83
14101061004	14101	60	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4455	0.0268244	14101	1262970857	414671344	93079.99
14101061005	14101	58	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2502	0.0150650	14101	709304845	401747502	160570.54
14101061006	14101	10	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1422	0.0085621	14101	403130092	84062453	59115.65
14101061007	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	33	0.0001987	14101	9355340	16992727	514931.13
14101062011	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	101	0.0006081	14101	28633009	25428429	251766.62
14101062013	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	88	0.0005299	14101	24947572	25428429	288959.42
14101062021	14101	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150	296	0.0017823	14101	83914562	55657057	188030.60
14101062047	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	16	0.0000963	14101	4535922	16992727	1062045.45
14101071001	14101	85	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4214	0.0253733	14101	1194648528	575349604	136532.89
14101071002	14101	65	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3859	0.0232358	14101	1094007752	446928977	115814.71
14101071003	14101	131	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4649	0.0279925	14101	1317968914	868253300	186761.30
14101071004	14101	47	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1808	0.0108863	14101	512559216	330417306	182752.93
14101071005	14101	337	2017	Valdivia	283495.1	2017	14101	166080	47082873150	6057	0.0364704	14101	1717130074	2162672237	357053.37
14101071006	14101	61	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4766	0.0286970	14101	1351137846	421128665	88361.03
14101072002	14101	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150	294	0.0017702	14101	83347572	55657057	189309.72
14101072045	14101	15	2017	Valdivia	283495.1	2017	14101	166080	47082873150	287	0.0017281	14101	81363106	118549964	413066.08
14101081001	14101	352	2017	Valdivia	283495.1	2017	14101	166080	47082873150	5118	0.0308165	14101	1450928136	2256365200	440868.54
14101081002	14101	198	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3425	0.0206226	14101	970970861	1291388644	377047.78
14101081003	14101	471	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4434	0.0266980	14101	1257017459	2998164409	676176.01
14101081004	14101	99	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4941	0.0297507	14101	1400749496	664793048	134546.26
14101081005	14101	314	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3782	0.0227722	14101	1072178626	2018908553	533820.35
14101081006	14101	38	2017	Valdivia	283495.1	2017	14101	166080	47082873150	5317	0.0320147	14101	1507343669	271664295	51093.53
14101081007	14101	26	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3719	0.0223928	14101	1054318432	192514291	51765.07
14101081008	14101	65	2017	Valdivia	283495.1	2017	14101	166080	47082873150	5423	0.0326529	14101	1537394154	446928977	82413.60
14101081009	14101	54	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3389	0.0204058	14101	960765036	375860713	110906.08
14101081010	14101	136	2017	Valdivia	283495.1	2017	14101	166080	47082873150	5849	0.0352180	14101	1658163084	899946074	153863.24
14101081011	14101	26	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2888	0.0173892	14101	818733970	192514291	66660.07
14101082002	14101	54	2017	Valdivia	283495.1	2017	14101	166080	47082873150	429	0.0025831	14101	121619416	375860713	876132.20
14101091001	14101	169	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3024	0.0182081	14101	857289309	1108609467	366603.66
14101091002	14101	152	2017	Valdivia	283495.1	2017	14101	166080	47082873150	4672	0.0281310	14101	1324489302	1001218819	214301.97
14101101001	14101	61	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1538	0.0092606	14101	436015528	421128665	273815.78
14101101002	14101	104	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2989	0.0179974	14101	847366979	696665518	233076.45
14101101003	14101	80	2017	Valdivia	283495.1	2017	14101	166080	47082873150	2463	0.0148302	14101	698248534	543323876	220594.35
14101112005	14101	24	2017	Valdivia	283495.1	2017	14101	166080	47082873150	380	0.0022881	14101	107728154	179195562	471567.27
14101112010	14101	8	2017	Valdivia	283495.1	2017	14101	166080	47082873150	176	0.0010597	14101	49895145	69987208	397654.59
14101112017	14101	35	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1083	0.0065210	14101	307025239	251978641	232667.26
14101112037	14101	14	2017	Valdivia	283495.1	2017	14101	166080	47082873150	535	0.0032213	14101	151669901	111713607	208810.48
14101112046	14101	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150	103	0.0006202	14101	29200000	55657057	540359.78
14101112058	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	59	0.0003553	14101	16726213	25428429	430990.32
14101122007	14101	26	2017	Valdivia	283495.1	2017	14101	166080	47082873150	297	0.0017883	14101	84198057	192514291	648196.27
14101122009	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	266	0.0016016	14101	75409708	25428429	95595.60
14101122042	14101	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150	233	0.0014029	14101	66054368	48356560	207538.88
14101122058	14101	31	2017	Valdivia	283495.1	2017	14101	166080	47082873150	509	0.0030648	14101	144299027	225633842	443288.49
14101132003	14101	9	2017	Valdivia	283495.1	2017	14101	166080	47082873150	290	0.0017461	14101	82213591	77051106	265693.47
14101132050	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	66	0.0003974	14101	18710679	25428429	385279.23
14101132901	14101	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150	250	0.0015053	14101	70873785	48356560	193426.24
14101142014	14101	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150	325	0.0019569	14101	92135921	48356560	148789.42
14101142039	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	58	0.0003492	14101	16442718	16992727	292978.05
14101142059	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	96	0.0005780	14101	27215534	16992727	177007.57
14101152059	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	82	0.0004937	14101	23246602	16992727	207228.38
14101161001	14101	380	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1801	0.0108442	14101	510574750	2431132087	1349879.00
14101162018	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	97	0.0005841	14101	27499029	25428429	262148.75
14101162019	14101	8	2017	Valdivia	283495.1	2017	14101	166080	47082873150	64	0.0003854	14101	18143689	69987208	1093550.12
14101162034	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	42	0.0002529	14101	11906796	16992727	404588.74
14101162053	14101	5	2017	Valdivia	283495.1	2017	14101	166080	47082873150	217	0.0013066	14101	61518446	48356560	222841.29
14101162061	14101	6	2017	Valdivia	283495.1	2017	14101	166080	47082873150	94	0.0005660	14101	26648543	55657057	592096.35
14101171001	14101	73	2017	Valdivia	283495.1	2017	14101	166080	47082873150	3989	0.0240185	14101	1130862121	498403693	124944.52
14101172001	14101	42	2017	Valdivia	283495.1	2017	14101	166080	47082873150	1348	0.0081166	14101	382151451	297828241	220940.83
14101172016	14101	7	2017	Valdivia	283495.1	2017	14101	166080	47082873150	148	0.0008911	14101	41957281	62860537	424733.36
14101172027	14101	3	2017	Valdivia	283495.1	2017	14101	166080	47082873150	103	0.0006202	14101	29200000	33319254	323487.90
14101172036	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	110	0.0006623	14101	31184466	16992727	154479.34
14101172055	14101	1	2017	Valdivia	283495.1	2017	14101	166080	47082873150	88	0.0005299	14101	24947572	16992727	193099.17
14101182004	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	188	0.0011320	14101	53297087	25428429	135257.60
14101182006	14101	2	2017	Valdivia	283495.1	2017	14101	166080	47082873150	67	0.0004034	14101	18994175	25428429	379528.79
14101182015	14101	16	2017	Valdivia	283495.1	2017	14101	166080	47082873150	455	0.0027396	14101	128990290	125361957	275520.79
14101182040	14101	4	2017	Valdivia	283495.1	2017	14101	166080	47082873150	323	0.0019448	14101	91568931	40928029	126712.17
14101991999	14101	18	2017	Valdivia	283495.1	2017	14101	166080	47082873150	679	0.0040884	14101	192493201	138921535	204597.25
14102011001	14102	51	2017	Corral	207459.9	2017	14102	5302	1099952396	3469	0.6542814	14102	719678397	356408517	102741.00
14102012011	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	22	0.0041494	14102	4564118	16992727	772396.69
14102012018	14102	2	2017	Corral	207459.9	2017	14102	5302	1099952396	80	0.0150886	14102	16596792	25428429	317855.36
14102022008	14102	5	2017	Corral	207459.9	2017	14102	5302	1099952396	507	0.0956243	14102	105182170	48356560	95377.83
14102022010	14102	6	2017	Corral	207459.9	2017	14102	5302	1099952396	41	0.0077329	14102	8505856	55657057	1357489.20
14102032001	14102	2	2017	Corral	207459.9	2017	14102	5302	1099952396	68	0.0128253	14102	14107273	25428429	373947.49
14102032005	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	64	0.0120709	14102	13277434	16992727	265511.36
14102032019	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	62	0.0116937	14102	12862514	16992727	274076.24
14102032901	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	31	0.0058469	14102	6431257	16992727	548152.49
14102042006	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	257	0.0484723	14102	53317195	16992727	66119.56
14102042007	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	115	0.0216899	14102	23857889	16992727	147762.84
14102042015	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	9	0.0016975	14102	1867139	16992727	1888080.79
14102042018	14102	4	2017	Corral	207459.9	2017	14102	5302	1099952396	33	0.0062241	14102	6846177	40928029	1240243.32
14102991999	14102	1	2017	Corral	207459.9	2017	14102	5302	1099952396	12	0.0022633	14102	2489519	16992727	1416060.59
14103011001	14103	38	2017	Lanco	217605.0	2017	14103	16752	3645318217	2488	0.1485196	14103	541401130	271664295	109189.83

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_region_14.rds")

12 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

http://r-statistics.co/Linear-Regression.html