Expansión de la CASEN sobre el CENSO (Región 16)

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 16.

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 == 16) 

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	16101011001	1	16101	52	2017
2	16101011002	1	16101	105	2017
3	16101011003	1	16101	142	2017
4	16101011004	1	16101	163	2017
5	16101021001	1	16101	10	2017
6	16101021002	1	16101	114	2017
7	16101021003	1	16101	87	2017
8	16101021004	1	16101	79	2017
9	16101031001	1	16101	335	2017
10	16101031002	1	16101	116	2017
11	16101031003	1	16101	125	2017
12	16101031004	1	16101	55	2017
13	16101041001	1	16101	119	2017
14	16101041002	1	16101	41	2017
15	16101041003	1	16101	67	2017
16	16101041004	1	16101	35	2017
17	16101042032	1	16101	1	2017
18	16101051001	1	16101	44	2017
19	16101051002	1	16101	382	2017
20	16101051003	1	16101	73	2017
21	16101051004	1	16101	16	2017
22	16101051005	1	16101	424	2017
23	16101052010	1	16101	7	2017
24	16101052026	1	16101	9	2017
25	16101052027	1	16101	9	2017
26	16101052028	1	16101	32	2017
27	16101061001	1	16101	5	2017
28	16101062003	1	16101	29	2017
29	16101062014	1	16101	18	2017
30	16101062024	1	16101	22	2017
31	16101062027	1	16101	18	2017
32	16101062901	1	16101	1	2017
33	16101071001	1	16101	61	2017
34	16101071002	1	16101	21	2017
35	16101072001	1	16101	18	2017
36	16101072901	1	16101	7	2017
37	16101081001	1	16101	37	2017
38	16101082007	1	16101	6	2017
39	16101082008	1	16101	4	2017
40	16101082011	1	16101	14	2017
41	16101082012	1	16101	7	2017
42	16101082015	1	16101	9	2017
43	16101082031	1	16101	4	2017
44	16101082037	1	16101	14	2017
45	16101092901	1	16101	5	2017
46	16101102004	1	16101	88	2017
47	16101112030	1	16101	2	2017
48	16101112901	1	16101	3	2017
49	16101121001	1	16101	154	2017
50	16101122002	1	16101	16	2017
51	16101122017	1	16101	49	2017
52	16101122020	1	16101	8	2017
53	16101122034	1	16101	24	2017
54	16101122035	1	16101	34	2017
55	16101131001	1	16101	866	2017
56	16101131002	1	16101	107	2017
57	16101131003	1	16101	103	2017
58	16101131004	1	16101	160	2017
59	16101141001	1	16101	446	2017
60	16101141002	1	16101	882	2017
61	16101141003	1	16101	243	2017
62	16101141004	1	16101	789	2017
63	16101142009	1	16101	95	2017
64	16101142018	1	16101	69	2017
65	16101142036	1	16101	33	2017
66	16101151001	1	16101	222	2017
67	16101151002	1	16101	34	2017
68	16101151003	1	16101	31	2017
69	16101151004	1	16101	76	2017
70	16101151005	1	16101	39	2017
71	16101151006	1	16101	65	2017
72	16101151007	1	16101	46	2017
73	16101151008	1	16101	28	2017
74	16101151009	1	16101	314	2017
75	16101151010	1	16101	294	2017
76	16101151011	1	16101	84	2017
77	16101151012	1	16101	51	2017
78	16101151013	1	16101	46	2017
79	16101151014	1	16101	24	2017
80	16101151015	1	16101	13	2017
81	16101152016	1	16101	16	2017
82	16101152025	1	16101	182	2017
83	16101152033	1	16101	9	2017
84	16101161001	1	16101	96	2017
85	16101161002	1	16101	97	2017
86	16101161003	1	16101	109	2017
87	16101161004	1	16101	186	2017
88	16101161005	1	16101	148	2017
89	16101171001	1	16101	76	2017
90	16101171002	1	16101	82	2017
91	16101171003	1	16101	90	2017
92	16101171004	1	16101	30	2017
93	16101991999	1	16101	9	2017
975	16102011001	1	16102	54	2017
976	16102011002	1	16102	84	2017
977	16102011003	1	16102	83	2017
978	16102012006	1	16102	6	2017
979	16102012044	1	16102	1	2017
980	16102021001	1	16102	6	2017
981	16102022005	1	16102	5	2017

Agregamos un cero a los códigos comunales de cuatro dígitos:

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

	zona	Freq	anio	código
1	16101011001	52	2017	16101
2	16101011002	105	2017	16101
3	16101011003	142	2017	16101
4	16101011004	163	2017	16101
5	16101021001	10	2017	16101
6	16101021002	114	2017	16101
7	16101021003	87	2017	16101
8	16101021004	79	2017	16101
9	16101031001	335	2017	16101
10	16101031002	116	2017	16101
11	16101031003	125	2017	16101
12	16101031004	55	2017	16101
13	16101041001	119	2017	16101
14	16101041002	41	2017	16101
15	16101041003	67	2017	16101
16	16101041004	35	2017	16101
17	16101042032	1	2017	16101
18	16101051001	44	2017	16101
19	16101051002	382	2017	16101
20	16101051003	73	2017	16101
21	16101051004	16	2017	16101
22	16101051005	424	2017	16101
23	16101052010	7	2017	16101
24	16101052026	9	2017	16101
25	16101052027	9	2017	16101
26	16101052028	32	2017	16101
27	16101061001	5	2017	16101
28	16101062003	29	2017	16101
29	16101062014	18	2017	16101
30	16101062024	22	2017	16101
31	16101062027	18	2017	16101
32	16101062901	1	2017	16101
33	16101071001	61	2017	16101
34	16101071002	21	2017	16101
35	16101072001	18	2017	16101
36	16101072901	7	2017	16101
37	16101081001	37	2017	16101
38	16101082007	6	2017	16101
39	16101082008	4	2017	16101
40	16101082011	14	2017	16101
41	16101082012	7	2017	16101
42	16101082015	9	2017	16101
43	16101082031	4	2017	16101
44	16101082037	14	2017	16101
45	16101092901	5	2017	16101
46	16101102004	88	2017	16101
47	16101112030	2	2017	16101
48	16101112901	3	2017	16101
49	16101121001	154	2017	16101
50	16101122002	16	2017	16101
51	16101122017	49	2017	16101
52	16101122020	8	2017	16101
53	16101122034	24	2017	16101
54	16101122035	34	2017	16101
55	16101131001	866	2017	16101
56	16101131002	107	2017	16101
57	16101131003	103	2017	16101
58	16101131004	160	2017	16101
59	16101141001	446	2017	16101
60	16101141002	882	2017	16101
61	16101141003	243	2017	16101
62	16101141004	789	2017	16101
63	16101142009	95	2017	16101
64	16101142018	69	2017	16101
65	16101142036	33	2017	16101
66	16101151001	222	2017	16101
67	16101151002	34	2017	16101
68	16101151003	31	2017	16101
69	16101151004	76	2017	16101
70	16101151005	39	2017	16101
71	16101151006	65	2017	16101
72	16101151007	46	2017	16101
73	16101151008	28	2017	16101
74	16101151009	314	2017	16101
75	16101151010	294	2017	16101
76	16101151011	84	2017	16101
77	16101151012	51	2017	16101
78	16101151013	46	2017	16101
79	16101151014	24	2017	16101
80	16101151015	13	2017	16101
81	16101152016	16	2017	16101
82	16101152025	182	2017	16101
83	16101152033	9	2017	16101
84	16101161001	96	2017	16101
85	16101161002	97	2017	16101
86	16101161003	109	2017	16101
87	16101161004	186	2017	16101
88	16101161005	148	2017	16101
89	16101171001	76	2017	16101
90	16101171002	82	2017	16101
91	16101171003	90	2017	16101
92	16101171004	30	2017	16101
93	16101991999	9	2017	16101
975	16102011001	54	2017	16102
976	16102011002	84	2017	16102
977	16102011003	83	2017	16102
978	16102012006	6	2017	16102
979	16102012044	1	2017	16102
980	16102021001	6	2017	16102
981	16102022005	5	2017	16102

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
16101	16101011001	52	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101011002	105	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101011003	142	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101011004	163	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101021001	10	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101021002	114	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101021003	87	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101021004	79	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101031001	335	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101031002	116	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101031003	125	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101031004	55	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101041001	119	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101041002	41	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101041003	67	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101041004	35	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101042032	1	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101051001	44	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101051002	382	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101051003	73	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101051004	16	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101051005	424	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101052010	7	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101052026	9	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101052027	9	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101052028	32	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101061001	5	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101062003	29	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101062014	18	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101062024	22	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101062027	18	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101062901	1	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101071001	61	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101071002	21	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101072001	18	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101072901	7	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101081001	37	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101082007	6	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101082008	4	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101082011	14	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101082012	7	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101082015	9	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101082031	4	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101082037	14	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101092901	5	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101102004	88	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101112030	2	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101112901	3	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101121001	154	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101122002	16	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101122017	49	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101122020	8	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101122034	24	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101122035	34	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101131001	866	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101131002	107	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101131003	103	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101131004	160	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101141001	446	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101141002	882	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101141003	243	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101141004	789	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101142009	95	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101142018	69	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101142036	33	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151001	222	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151002	34	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151003	31	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151004	76	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151005	39	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151006	65	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151007	46	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151008	28	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151009	314	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151010	294	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151011	84	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151012	51	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151013	46	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151014	24	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151015	13	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101152016	16	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101152025	182	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101152033	9	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101161001	96	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101161002	97	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101161003	109	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101161004	186	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101161005	148	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101171001	76	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101171002	82	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101171003	90	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101171004	30	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101991999	9	2017	Chillán	267443.2	2017	16101	184739	49407186552
16102	16102011001	54	2017	Bulnes	209128.5	2017	16102	21493	4494798610
16102	16102011002	84	2017	Bulnes	209128.5	2017	16102	21493	4494798610
16102	16102011003	83	2017	Bulnes	209128.5	2017	16102	21493	4494798610
16102	16102012006	6	2017	Bulnes	209128.5	2017	16102	21493	4494798610
16102	16102012044	1	2017	Bulnes	209128.5	2017	16102	21493	4494798610
16102	16102021001	6	2017	Bulnes	209128.5	2017	16102	21493	4494798610
16102	16102022005	5	2017	Bulnes	209128.5	2017	16102	21493	4494798610

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
16101	16101011001	52	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101011002	105	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101011003	142	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101011004	163	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101021001	10	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101021002	114	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101021003	87	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101021004	79	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101031001	335	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101031002	116	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101031003	125	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101031004	55	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101041001	119	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101041002	41	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101041003	67	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101041004	35	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101042032	1	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101051001	44	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101051002	382	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101051003	73	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101051004	16	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101051005	424	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101052010	7	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101052026	9	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101052027	9	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101052028	32	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101061001	5	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101062003	29	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101062014	18	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101062024	22	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101062027	18	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101062901	1	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101071001	61	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101071002	21	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101072001	18	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101072901	7	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101081001	37	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101082007	6	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101082008	4	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101082011	14	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101082012	7	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101082015	9	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101082031	4	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101082037	14	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101092901	5	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101102004	88	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101112030	2	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101112901	3	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101121001	154	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101122002	16	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101122017	49	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101122020	8	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101122034	24	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101122035	34	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101131001	866	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101131002	107	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101131003	103	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101131004	160	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101141001	446	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101141002	882	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101141003	243	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101141004	789	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101142009	95	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101142018	69	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101142036	33	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151001	222	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151002	34	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151003	31	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151004	76	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151005	39	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151006	65	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151007	46	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151008	28	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151009	314	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151010	294	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151011	84	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151012	51	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151013	46	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151014	24	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101151015	13	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101152016	16	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101152025	182	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101152033	9	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101161001	96	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101161002	97	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101161003	109	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101161004	186	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101161005	148	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101171001	76	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101171002	82	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101171003	90	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101171004	30	2017	Chillán	267443.2	2017	16101	184739	49407186552
16101	16101991999	9	2017	Chillán	267443.2	2017	16101	184739	49407186552
16102	16102011001	54	2017	Bulnes	209128.5	2017	16102	21493	4494798610
16102	16102011002	84	2017	Bulnes	209128.5	2017	16102	21493	4494798610
16102	16102011003	83	2017	Bulnes	209128.5	2017	16102	21493	4494798610
16102	16102012006	6	2017	Bulnes	209128.5	2017	16102	21493	4494798610
16102	16102012044	1	2017	Bulnes	209128.5	2017	16102	21493	4494798610
16102	16102021001	6	2017	Bulnes	209128.5	2017	16102	21493	4494798610
16102	16102022005	5	2017	Bulnes	209128.5	2017	16102	21493	4494798610

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
16101011001	16101	52	2017	Chillán	267443.2	2017	16101	184739	49407186552	1080	0.0058461	16101
16101011002	16101	105	2017	Chillán	267443.2	2017	16101	184739	49407186552	1525	0.0082549	16101
16101011003	16101	142	2017	Chillán	267443.2	2017	16101	184739	49407186552	2051	0.0111021	16101
16101011004	16101	163	2017	Chillán	267443.2	2017	16101	184739	49407186552	1819	0.0098463	16101
16101021001	16101	10	2017	Chillán	267443.2	2017	16101	184739	49407186552	1345	0.0072805	16101
16101021002	16101	114	2017	Chillán	267443.2	2017	16101	184739	49407186552	1991	0.0107774	16101
16101021003	16101	87	2017	Chillán	267443.2	2017	16101	184739	49407186552	2007	0.0108640	16101
16101021004	16101	79	2017	Chillán	267443.2	2017	16101	184739	49407186552	1882	0.0101873	16101
16101031001	16101	335	2017	Chillán	267443.2	2017	16101	184739	49407186552	3622	0.0196060	16101
16101031002	16101	116	2017	Chillán	267443.2	2017	16101	184739	49407186552	2516	0.0136192	16101
16101031003	16101	125	2017	Chillán	267443.2	2017	16101	184739	49407186552	2184	0.0118221	16101
16101031004	16101	55	2017	Chillán	267443.2	2017	16101	184739	49407186552	1866	0.0101007	16101
16101041001	16101	119	2017	Chillán	267443.2	2017	16101	184739	49407186552	3315	0.0179442	16101
16101041002	16101	41	2017	Chillán	267443.2	2017	16101	184739	49407186552	1999	0.0108207	16101
16101041003	16101	67	2017	Chillán	267443.2	2017	16101	184739	49407186552	4799	0.0259772	16101
16101041004	16101	35	2017	Chillán	267443.2	2017	16101	184739	49407186552	2766	0.0149725	16101
16101042032	16101	1	2017	Chillán	267443.2	2017	16101	184739	49407186552	30	0.0001624	16101
16101051001	16101	44	2017	Chillán	267443.2	2017	16101	184739	49407186552	1466	0.0079355	16101
16101051002	16101	382	2017	Chillán	267443.2	2017	16101	184739	49407186552	4764	0.0257877	16101
16101051003	16101	73	2017	Chillán	267443.2	2017	16101	184739	49407186552	2374	0.0128506	16101
16101051004	16101	16	2017	Chillán	267443.2	2017	16101	184739	49407186552	2018	0.0109235	16101
16101051005	16101	424	2017	Chillán	267443.2	2017	16101	184739	49407186552	4539	0.0245698	16101
16101052010	16101	7	2017	Chillán	267443.2	2017	16101	184739	49407186552	112	0.0006063	16101
16101052026	16101	9	2017	Chillán	267443.2	2017	16101	184739	49407186552	355	0.0019216	16101
16101052027	16101	9	2017	Chillán	267443.2	2017	16101	184739	49407186552	362	0.0019595	16101
16101052028	16101	32	2017	Chillán	267443.2	2017	16101	184739	49407186552	345	0.0018675	16101
16101061001	16101	5	2017	Chillán	267443.2	2017	16101	184739	49407186552	941	0.0050937	16101
16101062003	16101	29	2017	Chillán	267443.2	2017	16101	184739	49407186552	638	0.0034535	16101
16101062014	16101	18	2017	Chillán	267443.2	2017	16101	184739	49407186552	348	0.0018837	16101
16101062024	16101	22	2017	Chillán	267443.2	2017	16101	184739	49407186552	451	0.0024413	16101
16101062027	16101	18	2017	Chillán	267443.2	2017	16101	184739	49407186552	620	0.0033561	16101
16101062901	16101	1	2017	Chillán	267443.2	2017	16101	184739	49407186552	373	0.0020191	16101
16101071001	16101	61	2017	Chillán	267443.2	2017	16101	184739	49407186552	1599	0.0086555	16101
16101071002	16101	21	2017	Chillán	267443.2	2017	16101	184739	49407186552	950	0.0051424	16101
16101072001	16101	18	2017	Chillán	267443.2	2017	16101	184739	49407186552	593	0.0032099	16101
16101072901	16101	7	2017	Chillán	267443.2	2017	16101	184739	49407186552	491	0.0026578	16101
16101081001	16101	37	2017	Chillán	267443.2	2017	16101	184739	49407186552	1276	0.0069070	16101
16101082007	16101	6	2017	Chillán	267443.2	2017	16101	184739	49407186552	184	0.0009960	16101
16101082008	16101	4	2017	Chillán	267443.2	2017	16101	184739	49407186552	147	0.0007957	16101
16101082011	16101	14	2017	Chillán	267443.2	2017	16101	184739	49407186552	408	0.0022085	16101
16101082012	16101	7	2017	Chillán	267443.2	2017	16101	184739	49407186552	139	0.0007524	16101
16101082015	16101	9	2017	Chillán	267443.2	2017	16101	184739	49407186552	313	0.0016943	16101
16101082031	16101	4	2017	Chillán	267443.2	2017	16101	184739	49407186552	104	0.0005630	16101
16101082037	16101	14	2017	Chillán	267443.2	2017	16101	184739	49407186552	451	0.0024413	16101
16101092901	16101	5	2017	Chillán	267443.2	2017	16101	184739	49407186552	87	0.0004709	16101
16101102004	16101	88	2017	Chillán	267443.2	2017	16101	184739	49407186552	3591	0.0194382	16101
16101112030	16101	2	2017	Chillán	267443.2	2017	16101	184739	49407186552	37	0.0002003	16101
16101112901	16101	3	2017	Chillán	267443.2	2017	16101	184739	49407186552	16	0.0000866	16101
16101121001	16101	154	2017	Chillán	267443.2	2017	16101	184739	49407186552	4876	0.0263940	16101
16101122002	16101	16	2017	Chillán	267443.2	2017	16101	184739	49407186552	280	0.0015157	16101
16101122017	16101	49	2017	Chillán	267443.2	2017	16101	184739	49407186552	682	0.0036917	16101
16101122020	16101	8	2017	Chillán	267443.2	2017	16101	184739	49407186552	102	0.0005521	16101
16101122034	16101	24	2017	Chillán	267443.2	2017	16101	184739	49407186552	171	0.0009256	16101
16101122035	16101	34	2017	Chillán	267443.2	2017	16101	184739	49407186552	534	0.0028906	16101
16101131001	16101	866	2017	Chillán	267443.2	2017	16101	184739	49407186552	5741	0.0310763	16101
16101131002	16101	107	2017	Chillán	267443.2	2017	16101	184739	49407186552	2211	0.0119682	16101
16101131003	16101	103	2017	Chillán	267443.2	2017	16101	184739	49407186552	2135	0.0115568	16101
16101131004	16101	160	2017	Chillán	267443.2	2017	16101	184739	49407186552	4141	0.0224154	16101
16101141001	16101	446	2017	Chillán	267443.2	2017	16101	184739	49407186552	5365	0.0290410	16101
16101141002	16101	882	2017	Chillán	267443.2	2017	16101	184739	49407186552	5814	0.0314714	16101
16101141003	16101	243	2017	Chillán	267443.2	2017	16101	184739	49407186552	3016	0.0163257	16101
16101141004	16101	789	2017	Chillán	267443.2	2017	16101	184739	49407186552	3759	0.0203476	16101
16101142009	16101	95	2017	Chillán	267443.2	2017	16101	184739	49407186552	607	0.0032857	16101
16101142018	16101	69	2017	Chillán	267443.2	2017	16101	184739	49407186552	625	0.0033832	16101
16101142036	16101	33	2017	Chillán	267443.2	2017	16101	184739	49407186552	443	0.0023980	16101
16101151001	16101	222	2017	Chillán	267443.2	2017	16101	184739	49407186552	3362	0.0181986	16101
16101151002	16101	34	2017	Chillán	267443.2	2017	16101	184739	49407186552	3634	0.0196710	16101
16101151003	16101	31	2017	Chillán	267443.2	2017	16101	184739	49407186552	1805	0.0097705	16101
16101151004	16101	76	2017	Chillán	267443.2	2017	16101	184739	49407186552	3489	0.0188861	16101
16101151005	16101	39	2017	Chillán	267443.2	2017	16101	184739	49407186552	4931	0.0266917	16101
16101151006	16101	65	2017	Chillán	267443.2	2017	16101	184739	49407186552	4103	0.0222097	16101
16101151007	16101	46	2017	Chillán	267443.2	2017	16101	184739	49407186552	2402	0.0130021	16101
16101151008	16101	28	2017	Chillán	267443.2	2017	16101	184739	49407186552	3208	0.0173650	16101
16101151009	16101	314	2017	Chillán	267443.2	2017	16101	184739	49407186552	4520	0.0244670	16101
16101151010	16101	294	2017	Chillán	267443.2	2017	16101	184739	49407186552	4906	0.0265564	16101
16101151011	16101	84	2017	Chillán	267443.2	2017	16101	184739	49407186552	2718	0.0147126	16101
16101151012	16101	51	2017	Chillán	267443.2	2017	16101	184739	49407186552	2161	0.0116976	16101
16101151013	16101	46	2017	Chillán	267443.2	2017	16101	184739	49407186552	3743	0.0202610	16101
16101151014	16101	24	2017	Chillán	267443.2	2017	16101	184739	49407186552	3883	0.0210188	16101
16101151015	16101	13	2017	Chillán	267443.2	2017	16101	184739	49407186552	248	0.0013424	16101
16101152016	16101	16	2017	Chillán	267443.2	2017	16101	184739	49407186552	521	0.0028202	16101
16101152025	16101	182	2017	Chillán	267443.2	2017	16101	184739	49407186552	1780	0.0096352	16101
16101152033	16101	9	2017	Chillán	267443.2	2017	16101	184739	49407186552	122	0.0006604	16101
16101161001	16101	96	2017	Chillán	267443.2	2017	16101	184739	49407186552	2140	0.0115839	16101
16101161002	16101	97	2017	Chillán	267443.2	2017	16101	184739	49407186552	2196	0.0118870	16101
16101161003	16101	109	2017	Chillán	267443.2	2017	16101	184739	49407186552	3884	0.0210243	16101
16101161004	16101	186	2017	Chillán	267443.2	2017	16101	184739	49407186552	2612	0.0141389	16101
16101161005	16101	148	2017	Chillán	267443.2	2017	16101	184739	49407186552	3326	0.0180038	16101
16101171001	16101	76	2017	Chillán	267443.2	2017	16101	184739	49407186552	4382	0.0237200	16101
16101171002	16101	82	2017	Chillán	267443.2	2017	16101	184739	49407186552	2900	0.0156978	16101
16101171003	16101	90	2017	Chillán	267443.2	2017	16101	184739	49407186552	2262	0.0122443	16101
16101171004	16101	30	2017	Chillán	267443.2	2017	16101	184739	49407186552	1590	0.0086067	16101
16101991999	16101	9	2017	Chillán	267443.2	2017	16101	184739	49407186552	304	0.0016456	16101
16102011001	16102	54	2017	Bulnes	209128.5	2017	16102	21493	4494798610	2452	0.1140837	16102
16102011002	16102	84	2017	Bulnes	209128.5	2017	16102	21493	4494798610	4765	0.2217001	16102
16102011003	16102	83	2017	Bulnes	209128.5	2017	16102	21493	4494798610	3184	0.1481413	16102
16102012006	16102	6	2017	Bulnes	209128.5	2017	16102	21493	4494798610	183	0.0085144	16102
16102012044	16102	1	2017	Bulnes	209128.5	2017	16102	21493	4494798610	32	0.0014889	16102
16102021001	16102	6	2017	Bulnes	209128.5	2017	16102	21493	4494798610	537	0.0249849	16102
16102022005	16102	5	2017	Bulnes	209128.5	2017	16102	21493	4494798610	102	0.0047457	16102

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
16101011001	16101	52	2017	Chillán	267443.2	2017	16101	184739	49407186552	1080	0.0058461	16101	288838640
16101011002	16101	105	2017	Chillán	267443.2	2017	16101	184739	49407186552	1525	0.0082549	16101	407850857
16101011003	16101	142	2017	Chillán	267443.2	2017	16101	184739	49407186552	2051	0.0111021	16101	548525972
16101011004	16101	163	2017	Chillán	267443.2	2017	16101	184739	49407186552	1819	0.0098463	16101	486479154
16101021001	16101	10	2017	Chillán	267443.2	2017	16101	184739	49407186552	1345	0.0072805	16101	359711084
16101021002	16101	114	2017	Chillán	267443.2	2017	16101	184739	49407186552	1991	0.0107774	16101	532479381
16101021003	16101	87	2017	Chillán	267443.2	2017	16101	184739	49407186552	2007	0.0108640	16101	536758472
16101021004	16101	79	2017	Chillán	267443.2	2017	16101	184739	49407186552	1882	0.0101873	16101	503328074
16101031001	16101	335	2017	Chillán	267443.2	2017	16101	184739	49407186552	3622	0.0196060	16101	968679216
16101031002	16101	116	2017	Chillán	267443.2	2017	16101	184739	49407186552	2516	0.0136192	16101	672887053
16101031003	16101	125	2017	Chillán	267443.2	2017	16101	184739	49407186552	2184	0.0118221	16101	584095916
16101031004	16101	55	2017	Chillán	267443.2	2017	16101	184739	49407186552	1866	0.0101007	16101	499048983
16101041001	16101	119	2017	Chillán	267443.2	2017	16101	184739	49407186552	3315	0.0179442	16101	886574158
16101041002	16101	41	2017	Chillán	267443.2	2017	16101	184739	49407186552	1999	0.0108207	16101	534618927
16101041003	16101	67	2017	Chillán	267443.2	2017	16101	184739	49407186552	4799	0.0259772	16101	1283459845
16101041004	16101	35	2017	Chillán	267443.2	2017	16101	184739	49407186552	2766	0.0149725	16101	739747850
16101042032	16101	1	2017	Chillán	267443.2	2017	16101	184739	49407186552	30	0.0001624	16101	8023296
16101051001	16101	44	2017	Chillán	267443.2	2017	16101	184739	49407186552	1466	0.0079355	16101	392071709
16101051002	16101	382	2017	Chillán	267443.2	2017	16101	184739	49407186552	4764	0.0257877	16101	1274099333
16101051003	16101	73	2017	Chillán	267443.2	2017	16101	184739	49407186552	2374	0.0128506	16101	634910121
16101051004	16101	16	2017	Chillán	267443.2	2017	16101	184739	49407186552	2018	0.0109235	16101	539700347
16101051005	16101	424	2017	Chillán	267443.2	2017	16101	184739	49407186552	4539	0.0245698	16101	1213924617
16101052010	16101	7	2017	Chillán	267443.2	2017	16101	184739	49407186552	112	0.0006063	16101	29953637
16101052026	16101	9	2017	Chillán	267443.2	2017	16101	184739	49407186552	355	0.0019216	16101	94942331
16101052027	16101	9	2017	Chillán	267443.2	2017	16101	184739	49407186552	362	0.0019595	16101	96814433
16101052028	16101	32	2017	Chillán	267443.2	2017	16101	184739	49407186552	345	0.0018675	16101	92267899
16101061001	16101	5	2017	Chillán	267443.2	2017	16101	184739	49407186552	941	0.0050937	16101	251664037
16101062003	16101	29	2017	Chillán	267443.2	2017	16101	184739	49407186552	638	0.0034535	16101	170628752
16101062014	16101	18	2017	Chillán	267443.2	2017	16101	184739	49407186552	348	0.0018837	16101	93070228
16101062024	16101	22	2017	Chillán	267443.2	2017	16101	184739	49407186552	451	0.0024413	16101	120616876
16101062027	16101	18	2017	Chillán	267443.2	2017	16101	184739	49407186552	620	0.0033561	16101	165814775
16101062901	16101	1	2017	Chillán	267443.2	2017	16101	184739	49407186552	373	0.0020191	16101	99756308
16101071001	16101	61	2017	Chillán	267443.2	2017	16101	184739	49407186552	1599	0.0086555	16101	427641653
16101071002	16101	21	2017	Chillán	267443.2	2017	16101	184739	49407186552	950	0.0051424	16101	254071026
16101072001	16101	18	2017	Chillán	267443.2	2017	16101	184739	49407186552	593	0.0032099	16101	158593809
16101072901	16101	7	2017	Chillán	267443.2	2017	16101	184739	49407186552	491	0.0026578	16101	131314604
16101081001	16101	37	2017	Chillán	267443.2	2017	16101	184739	49407186552	1276	0.0069070	16101	341257504
16101082007	16101	6	2017	Chillán	267443.2	2017	16101	184739	49407186552	184	0.0009960	16101	49209546
16101082008	16101	4	2017	Chillán	267443.2	2017	16101	184739	49407186552	147	0.0007957	16101	39314148
16101082011	16101	14	2017	Chillán	267443.2	2017	16101	184739	49407186552	408	0.0022085	16101	109116819
16101082012	16101	7	2017	Chillán	267443.2	2017	16101	184739	49407186552	139	0.0007524	16101	37174603
16101082015	16101	9	2017	Chillán	267443.2	2017	16101	184739	49407186552	313	0.0016943	16101	83709717
16101082031	16101	4	2017	Chillán	267443.2	2017	16101	184739	49407186552	104	0.0005630	16101	27814091
16101082037	16101	14	2017	Chillán	267443.2	2017	16101	184739	49407186552	451	0.0024413	16101	120616876
16101092901	16101	5	2017	Chillán	267443.2	2017	16101	184739	49407186552	87	0.0004709	16101	23267557
16101102004	16101	88	2017	Chillán	267443.2	2017	16101	184739	49407186552	3591	0.0194382	16101	960388477
16101112030	16101	2	2017	Chillán	267443.2	2017	16101	184739	49407186552	37	0.0002003	16101	9895398
16101112901	16101	3	2017	Chillán	267443.2	2017	16101	184739	49407186552	16	0.0000866	16101	4279091
16101121001	16101	154	2017	Chillán	267443.2	2017	16101	184739	49407186552	4876	0.0263940	16101	1304052970
16101122002	16101	16	2017	Chillán	267443.2	2017	16101	184739	49407186552	280	0.0015157	16101	74884092
16101122017	16101	49	2017	Chillán	267443.2	2017	16101	184739	49407186552	682	0.0036917	16101	182396252
16101122020	16101	8	2017	Chillán	267443.2	2017	16101	184739	49407186552	102	0.0005521	16101	27279205
16101122034	16101	24	2017	Chillán	267443.2	2017	16101	184739	49407186552	171	0.0009256	16101	45732785
16101122035	16101	34	2017	Chillán	267443.2	2017	16101	184739	49407186552	534	0.0028906	16101	142814661
16101131001	16101	866	2017	Chillán	267443.2	2017	16101	184739	49407186552	5741	0.0310763	16101	1535391325
16101131002	16101	107	2017	Chillán	267443.2	2017	16101	184739	49407186552	2211	0.0119682	16101	591316882
16101131003	16101	103	2017	Chillán	267443.2	2017	16101	184739	49407186552	2135	0.0115568	16101	570991200
16101131004	16101	160	2017	Chillán	267443.2	2017	16101	184739	49407186552	4141	0.0224154	16101	1107482229
16101141001	16101	446	2017	Chillán	267443.2	2017	16101	184739	49407186552	5365	0.0290410	16101	1434832687
16101141002	16101	882	2017	Chillán	267443.2	2017	16101	184739	49407186552	5814	0.0314714	16101	1554914678
16101141003	16101	243	2017	Chillán	267443.2	2017	16101	184739	49407186552	3016	0.0163257	16101	806608646
16101141004	16101	789	2017	Chillán	267443.2	2017	16101	184739	49407186552	3759	0.0203476	16101	1005318932
16101142009	16101	95	2017	Chillán	267443.2	2017	16101	184739	49407186552	607	0.0032857	16101	162338013
16101142018	16101	69	2017	Chillán	267443.2	2017	16101	184739	49407186552	625	0.0033832	16101	167151991
16101142036	16101	33	2017	Chillán	267443.2	2017	16101	184739	49407186552	443	0.0023980	16101	118477331
16101151001	16101	222	2017	Chillán	267443.2	2017	16101	184739	49407186552	3362	0.0181986	16101	899143988
16101151002	16101	34	2017	Chillán	267443.2	2017	16101	184739	49407186552	3634	0.0196710	16101	971888534
16101151003	16101	31	2017	Chillán	267443.2	2017	16101	184739	49407186552	1805	0.0097705	16101	482734949
16101151004	16101	76	2017	Chillán	267443.2	2017	16101	184739	49407186552	3489	0.0188861	16101	933109272
16101151005	16101	39	2017	Chillán	267443.2	2017	16101	184739	49407186552	4931	0.0266917	16101	1318762345
16101151006	16101	65	2017	Chillán	267443.2	2017	16101	184739	49407186552	4103	0.0222097	16101	1097319388
16101151007	16101	46	2017	Chillán	267443.2	2017	16101	184739	49407186552	2402	0.0130021	16101	642398530
16101151008	16101	28	2017	Chillán	267443.2	2017	16101	184739	49407186552	3208	0.0173650	16101	857957737
16101151009	16101	314	2017	Chillán	267443.2	2017	16101	184739	49407186552	4520	0.0244670	16101	1208843196
16101151010	16101	294	2017	Chillán	267443.2	2017	16101	184739	49407186552	4906	0.0265564	16101	1312076266
16101151011	16101	84	2017	Chillán	267443.2	2017	16101	184739	49407186552	2718	0.0147126	16101	726910577
16101151012	16101	51	2017	Chillán	267443.2	2017	16101	184739	49407186552	2161	0.0116976	16101	577944723
16101151013	16101	46	2017	Chillán	267443.2	2017	16101	184739	49407186552	3743	0.0202610	16101	1001039841
16101151014	16101	24	2017	Chillán	267443.2	2017	16101	184739	49407186552	3883	0.0210188	16101	1038481887
16101151015	16101	13	2017	Chillán	267443.2	2017	16101	184739	49407186552	248	0.0013424	16101	66325910
16101152016	16101	16	2017	Chillán	267443.2	2017	16101	184739	49407186552	521	0.0028202	16101	139337899
16101152025	16101	182	2017	Chillán	267443.2	2017	16101	184739	49407186552	1780	0.0096352	16101	476048869
16101152033	16101	9	2017	Chillán	267443.2	2017	16101	184739	49407186552	122	0.0006604	16101	32628069
16101161001	16101	96	2017	Chillán	267443.2	2017	16101	184739	49407186552	2140	0.0115839	16101	572328416
16101161002	16101	97	2017	Chillán	267443.2	2017	16101	184739	49407186552	2196	0.0118870	16101	587305234
16101161003	16101	109	2017	Chillán	267443.2	2017	16101	184739	49407186552	3884	0.0210243	16101	1038749331
16101161004	16101	186	2017	Chillán	267443.2	2017	16101	184739	49407186552	2612	0.0141389	16101	698561599
16101161005	16101	148	2017	Chillán	267443.2	2017	16101	184739	49407186552	3326	0.0180038	16101	889516033
16101171001	16101	76	2017	Chillán	267443.2	2017	16101	184739	49407186552	4382	0.0237200	16101	1171936037
16101171002	16101	82	2017	Chillán	267443.2	2017	16101	184739	49407186552	2900	0.0156978	16101	775585236
16101171003	16101	90	2017	Chillán	267443.2	2017	16101	184739	49407186552	2262	0.0122443	16101	604956484
16101171004	16101	30	2017	Chillán	267443.2	2017	16101	184739	49407186552	1590	0.0086067	16101	425234664
16101991999	16101	9	2017	Chillán	267443.2	2017	16101	184739	49407186552	304	0.0016456	16101	81302728
16102011001	16102	54	2017	Bulnes	209128.5	2017	16102	21493	4494798610	2452	0.1140837	16102	512783055
16102011002	16102	84	2017	Bulnes	209128.5	2017	16102	21493	4494798610	4765	0.2217001	16102	996497249
16102011003	16102	83	2017	Bulnes	209128.5	2017	16102	21493	4494798610	3184	0.1481413	16102	665865108
16102012006	16102	6	2017	Bulnes	209128.5	2017	16102	21493	4494798610	183	0.0085144	16102	38270513
16102012044	16102	1	2017	Bulnes	209128.5	2017	16102	21493	4494798610	32	0.0014889	16102	6692112
16102021001	16102	6	2017	Bulnes	209128.5	2017	16102	21493	4494798610	537	0.0249849	16102	112301998
16102022005	16102	5	2017	Bulnes	209128.5	2017	16102	21493	4494798610	102	0.0047457	16102	21331106

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 
## -1.355e+09 -6.366e+07 -5.075e+07 -1.821e+07  1.139e+09 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 66716935    6156145   10.84   <2e-16 ***
## Freq.x       2906369      91396   31.80   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 174400000 on 879 degrees of freedom
## Multiple R-squared:  0.535,  Adjusted R-squared:  0.5344 
## F-statistic:  1011 on 1 and 879 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 
## -1.355e+09 -6.366e+07 -5.075e+07 -1.821e+07  1.139e+09 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 66716935    6156145   10.84   <2e-16 ***
## Freq.x       2906369      91396   31.80   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 174400000 on 879 degrees of freedom
## Multiple R-squared:  0.535,  Adjusted R-squared:  0.5344 
## F-statistic:  1011 on 1 and 879 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 
## -1.355e+09 -6.366e+07 -5.075e+07 -1.821e+07  1.139e+09 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 66716935    6156145   10.84   <2e-16 ***
## Freq.x       2906369      91396   31.80   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 174400000 on 879 degrees of freedom
## Multiple R-squared:  0.535,  Adjusted R-squared:  0.5344 
## F-statistic:  1011 on 1 and 879 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 
## -398660297 -106101772  -24519545  107024332  913907217 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -102613225    8378144  -12.25   <2e-16 ***
## log(Freq.x)  138517760    3852106   35.96   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 162700000 on 879 degrees of freedom
## Multiple R-squared:  0.5953, Adjusted R-squared:  0.5949 
## F-statistic:  1293 on 1 and 879 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 
## -837014900  -45589952   -4175876   26767814  977025428 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -87267028    5761736  -15.15   <2e-16 ***
## sqrt(Freq.x)  68695610    1283600   53.52   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 123900000 on 879 degrees of freedom
## Multiple R-squared:  0.7652, Adjusted R-squared:  0.7649 
## F-statistic:  2864 on 1 and 879 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 
## -28087  -1916   -613   1095  21670 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1736.48     174.03   9.978   <2e-16 ***
## sqrt(Freq.x)  2066.91      38.77  53.311   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3742 on 879 degrees of freedom
## Multiple R-squared:  0.7638, Adjusted R-squared:  0.7635 
## F-statistic:  2842 on 1 and 879 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 
## -5.6818 -0.6101  0.1052  0.6797  2.7577 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  16.25640    0.04708   345.3   <2e-16 ***
## sqrt(Freq.x)  0.35659    0.01049    34.0   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.012 on 879 degrees of freedom
## Multiple R-squared:  0.5681, Adjusted R-squared:  0.5676 
## F-statistic:  1156 on 1 and 879 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 
## -11481.3  -2916.9   -338.5   2299.9  18848.0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   522.10     205.39   2.542   0.0112 *  
## log(Freq.x)  4625.19      94.43  48.978   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3987 on 879 degrees of freedom
## Multiple R-squared:  0.7318, Adjusted R-squared:  0.7315 
## F-statistic:  2399 on 1 and 879 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 
## -2.93398 -0.50305  0.03724  0.51433  2.60261 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 15.84901    0.04193  378.02   <2e-16 ***
## log(Freq.x)  0.91823    0.01928   47.63   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.814 on 879 degrees of freedom
## Multiple R-squared:  0.7208, Adjusted R-squared:  0.7204 
## F-statistic:  2269 on 1 and 879 DF,  p-value: < 2.2e-16

9 Modelo raíz cuadrada (r_sqrt)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.7649).

9.1 Diagrama de dispersión sobre r_sqrt

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

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

9.2 Modelo r_sqrt

Observemos nuevamente el resultado sobre r_sqrt.

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 
## -837014900  -45589952   -4175876   26767814  977025428 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -87267028    5761736  -15.15   <2e-16 ***
## sqrt(Freq.x)  68695610    1283600   53.52   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 123900000 on 879 degrees of freedom
## Multiple R-squared:  0.7652, Adjusted R-squared:  0.7649 
## F-statistic:  2864 on 1 and 879 DF,  p-value: < 2.2e-16

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

9.3 Análisis de residuos

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

9.4 Ecuación del modelo

\[ \hat Y = -87267028 + 68695610 \cdot \sqrt {X} \]

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

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- -87267028 + 68695610 * sqrt (h_y_m_comuna_corr_01$Freq.x)

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

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	Freq.y	p_poblacional	código.y	multi_pob	est_ing
16101011001	16101	52	2017	Chillán	267443.2	2017	16101	184739	49407186552	1080	0.0058461	16101	288838640	408104061
16101011002	16101	105	2017	Chillán	267443.2	2017	16101	184739	49407186552	1525	0.0082549	16101	407850857	616653506
16101011003	16101	142	2017	Chillán	267443.2	2017	16101	184739	49407186552	2051	0.0111021	16101	548525972	731335641
16101011004	16101	163	2017	Chillán	267443.2	2017	16101	184739	49407186552	1819	0.0098463	16101	486479154	789779809
16101021001	16101	10	2017	Chillán	267443.2	2017	16101	184739	49407186552	1345	0.0072805	16101	359711084	129967565
16101021002	16101	114	2017	Chillán	267443.2	2017	16101	184739	49407186552	1991	0.0107774	16101	532479381	646201376
16101021003	16101	87	2017	Chillán	267443.2	2017	16101	184739	49407186552	2007	0.0108640	16101	536758472	553482966
16101021004	16101	79	2017	Chillán	267443.2	2017	16101	184739	49407186552	1882	0.0101873	16101	503328074	523312909
16101031001	16101	335	2017	Chillán	267443.2	2017	16101	184739	49407186552	3622	0.0196060	16101	968679216	1170069080
16101031002	16101	116	2017	Chillán	267443.2	2017	16101	184739	49407186552	2516	0.0136192	16101	672887053	652607335
16101031003	16101	125	2017	Chillán	267443.2	2017	16101	184739	49407186552	2184	0.0118221	16101	584095916	680773241
16101031004	16101	55	2017	Chillán	267443.2	2017	16101	184739	49407186552	1866	0.0101007	16101	499048983	422193251
16101041001	16101	119	2017	Chillán	267443.2	2017	16101	184739	49407186552	3315	0.0179442	16101	886574158	662113605
16101041002	16101	41	2017	Chillán	267443.2	2017	16101	184739	49407186552	1999	0.0108207	16101	534618927	352599497
16101041003	16101	67	2017	Chillán	267443.2	2017	16101	184739	49407186552	4799	0.0259772	16101	1283459845	475030774
16101041004	16101	35	2017	Chillán	267443.2	2017	16101	184739	49407186552	2766	0.0149725	16101	739747850	319141682
16101042032	16101	1	2017	Chillán	267443.2	2017	16101	184739	49407186552	30	0.0001624	16101	8023296	-18571418
16101051001	16101	44	2017	Chillán	267443.2	2017	16101	184739	49407186552	1466	0.0079355	16101	392071709	368408098
16101051002	16101	382	2017	Chillán	267443.2	2017	16101	184739	49407186552	4764	0.0257877	16101	1274099333	1255376324
16101051003	16101	73	2017	Chillán	267443.2	2017	16101	184739	49407186552	2374	0.0128506	16101	634910121	499668521
16101051004	16101	16	2017	Chillán	267443.2	2017	16101	184739	49407186552	2018	0.0109235	16101	539700347	187515412
16101051005	16101	424	2017	Chillán	267443.2	2017	16101	184739	49407186552	4539	0.0245698	16101	1213924617	1327262158
16101052010	16101	7	2017	Chillán	267443.2	2017	16101	184739	49407186552	112	0.0006063	16101	29953637	94484472
16101052026	16101	9	2017	Chillán	267443.2	2017	16101	184739	49407186552	355	0.0019216	16101	94942331	118819802
16101052027	16101	9	2017	Chillán	267443.2	2017	16101	184739	49407186552	362	0.0019595	16101	96814433	118819802
16101052028	16101	32	2017	Chillán	267443.2	2017	16101	184739	49407186552	345	0.0018675	16101	92267899	301334025
16101061001	16101	5	2017	Chillán	267443.2	2017	16101	184739	49407186552	941	0.0050937	16101	251664037	66341026
16101062003	16101	29	2017	Chillán	267443.2	2017	16101	184739	49407186552	638	0.0034535	16101	170628752	282670153
16101062014	16101	18	2017	Chillán	267443.2	2017	16101	184739	49407186552	348	0.0018837	16101	93070228	204183762
16101062024	16101	22	2017	Chillán	267443.2	2017	16101	184739	49407186552	451	0.0024413	16101	120616876	234943944
16101062027	16101	18	2017	Chillán	267443.2	2017	16101	184739	49407186552	620	0.0033561	16101	165814775	204183762
16101062901	16101	1	2017	Chillán	267443.2	2017	16101	184739	49407186552	373	0.0020191	16101	99756308	-18571418
16101071001	16101	61	2017	Chillán	267443.2	2017	16101	184739	49407186552	1599	0.0086555	16101	427641653	449262838
16101071002	16101	21	2017	Chillán	267443.2	2017	16101	184739	49407186552	950	0.0051424	16101	254071026	227535805
16101072001	16101	18	2017	Chillán	267443.2	2017	16101	184739	49407186552	593	0.0032099	16101	158593809	204183762
16101072901	16101	7	2017	Chillán	267443.2	2017	16101	184739	49407186552	491	0.0026578	16101	131314604	94484472
16101081001	16101	37	2017	Chillán	267443.2	2017	16101	184739	49407186552	1276	0.0069070	16101	341257504	330592055
16101082007	16101	6	2017	Chillán	267443.2	2017	16101	184739	49407186552	184	0.0009960	16101	49209546	81002164
16101082008	16101	4	2017	Chillán	267443.2	2017	16101	184739	49407186552	147	0.0007957	16101	39314148	50124192
16101082011	16101	14	2017	Chillán	267443.2	2017	16101	184739	49407186552	408	0.0022085	16101	109116819	169768409
16101082012	16101	7	2017	Chillán	267443.2	2017	16101	184739	49407186552	139	0.0007524	16101	37174603	94484472
16101082015	16101	9	2017	Chillán	267443.2	2017	16101	184739	49407186552	313	0.0016943	16101	83709717	118819802
16101082031	16101	4	2017	Chillán	267443.2	2017	16101	184739	49407186552	104	0.0005630	16101	27814091	50124192
16101082037	16101	14	2017	Chillán	267443.2	2017	16101	184739	49407186552	451	0.0024413	16101	120616876	169768409
16101092901	16101	5	2017	Chillán	267443.2	2017	16101	184739	49407186552	87	0.0004709	16101	23267557	66341026
16101102004	16101	88	2017	Chillán	267443.2	2017	16101	184739	49407186552	3591	0.0194382	16101	960388477	557154916
16101112030	16101	2	2017	Chillán	267443.2	2017	16101	184739	49407186552	37	0.0002003	16101	9895398	9883235
16101112901	16101	3	2017	Chillán	267443.2	2017	16101	184739	49407186552	16	0.0000866	16101	4279091	31717259
16101121001	16101	154	2017	Chillán	267443.2	2017	16101	184739	49407186552	4876	0.0263940	16101	1304052970	765223073
16101122002	16101	16	2017	Chillán	267443.2	2017	16101	184739	49407186552	280	0.0015157	16101	74884092	187515412
16101122017	16101	49	2017	Chillán	267443.2	2017	16101	184739	49407186552	682	0.0036917	16101	182396252	393602242
16101122020	16101	8	2017	Chillán	267443.2	2017	16101	184739	49407186552	102	0.0005521	16101	27279205	107033499
16101122034	16101	24	2017	Chillán	267443.2	2017	16101	184739	49407186552	171	0.0009256	16101	45732785	249271356
16101122035	16101	34	2017	Chillán	267443.2	2017	16101	184739	49407186552	534	0.0028906	16101	142814661	313293769
16101131001	16101	866	2017	Chillán	267443.2	2017	16101	184739	49407186552	5741	0.0310763	16101	1535391325	1934298998
16101131002	16101	107	2017	Chillán	267443.2	2017	16101	184739	49407186552	2211	0.0119682	16101	591316882	623325887
16101131003	16101	103	2017	Chillán	267443.2	2017	16101	184739	49407186552	2135	0.0115568	16101	570991200	609917269
16101131004	16101	160	2017	Chillán	267443.2	2017	16101	184739	49407186552	4141	0.0224154	16101	1107482229	781671343
16101141001	16101	446	2017	Chillán	267443.2	2017	16101	184739	49407186552	5365	0.0290410	16101	1434832687	1363495781
16101141002	16101	882	2017	Chillán	267443.2	2017	16101	184739	49407186552	5814	0.0314714	16101	1554914678	1952888502
16101141003	16101	243	2017	Chillán	267443.2	2017	16101	184739	49407186552	3016	0.0163257	16101	806608646	983591553
16101141004	16101	789	2017	Chillán	267443.2	2017	16101	184739	49407186552	3759	0.0203476	16101	1005318932	1842333840
16101142009	16101	95	2017	Chillán	267443.2	2017	16101	184739	49407186552	607	0.0032857	16101	162338013	582294955
16101142018	16101	69	2017	Chillán	267443.2	2017	16101	184739	49407186552	625	0.0033832	16101	167151991	483361565
16101142036	16101	33	2017	Chillán	267443.2	2017	16101	184739	49407186552	443	0.0023980	16101	118477331	307359207
16101151001	16101	222	2017	Chillán	267443.2	2017	16101	184739	49407186552	3362	0.0181986	16101	899143988	936274509
16101151002	16101	34	2017	Chillán	267443.2	2017	16101	184739	49407186552	3634	0.0196710	16101	971888534	313293769
16101151003	16101	31	2017	Chillán	267443.2	2017	16101	184739	49407186552	1805	0.0097705	16101	482734949	295213941
16101151004	16101	76	2017	Chillán	267443.2	2017	16101	184739	49407186552	3489	0.0188861	16101	933109272	511607416
16101151005	16101	39	2017	Chillán	267443.2	2017	16101	184739	49407186552	4931	0.0266917	16101	1318762345	341736919
16101151006	16101	65	2017	Chillán	267443.2	2017	16101	184739	49407186552	4103	0.0222097	16101	1097319388	466574686
16101151007	16101	46	2017	Chillán	267443.2	2017	16101	184739	49407186552	2402	0.0130021	16101	642398530	378649267
16101151008	16101	28	2017	Chillán	267443.2	2017	16101	184739	49407186552	3208	0.0173650	16101	857957737	276235972
16101151009	16101	314	2017	Chillán	267443.2	2017	16101	184739	49407186552	4520	0.0244670	16101	1208843196	1130022283
16101151010	16101	294	2017	Chillán	267443.2	2017	16101	184739	49407186552	4906	0.0265564	16101	1312076266	1090617316
16101151011	16101	84	2017	Chillán	267443.2	2017	16101	184739	49407186552	2718	0.0147126	16101	726910577	542338637
16101151012	16101	51	2017	Chillán	267443.2	2017	16101	184739	49407186552	2161	0.0116976	16101	577944723	403317754
16101151013	16101	46	2017	Chillán	267443.2	2017	16101	184739	49407186552	3743	0.0202610	16101	1001039841	378649267
16101151014	16101	24	2017	Chillán	267443.2	2017	16101	184739	49407186552	3883	0.0210188	16101	1038481887	249271356
16101151015	16101	13	2017	Chillán	267443.2	2017	16101	184739	49407186552	248	0.0013424	16101	66325910	160418516
16101152016	16101	16	2017	Chillán	267443.2	2017	16101	184739	49407186552	521	0.0028202	16101	139337899	187515412
16101152025	16101	182	2017	Chillán	267443.2	2017	16101	184739	49407186552	1780	0.0096352	16101	476048869	839487418
16101152033	16101	9	2017	Chillán	267443.2	2017	16101	184739	49407186552	122	0.0006604	16101	32628069	118819802
16101161001	16101	96	2017	Chillán	267443.2	2017	16101	184739	49407186552	2140	0.0115839	16101	572328416	585809740
16101161002	16101	97	2017	Chillán	267443.2	2017	16101	184739	49407186552	2196	0.0118870	16101	587305234	589306266
16101161003	16101	109	2017	Chillán	267443.2	2017	16101	184739	49407186552	3884	0.0210243	16101	1038749331	629936196
16101161004	16101	186	2017	Chillán	267443.2	2017	16101	184739	49407186552	2612	0.0141389	16101	698561599	849616183
16101161005	16101	148	2017	Chillán	267443.2	2017	16101	184739	49407186552	3326	0.0180038	16101	889516033	748451137
16101171001	16101	76	2017	Chillán	267443.2	2017	16101	184739	49407186552	4382	0.0237200	16101	1171936037	511607416
16101171002	16101	82	2017	Chillán	267443.2	2017	16101	184739	49407186552	2900	0.0156978	16101	775585236	534798178
16101171003	16101	90	2017	Chillán	267443.2	2017	16101	184739	49407186552	2262	0.0122443	16101	604956484	564436751
16101171004	16101	30	2017	Chillán	267443.2	2017	16101	184739	49407186552	1590	0.0086067	16101	425234664	288994324
16101991999	16101	9	2017	Chillán	267443.2	2017	16101	184739	49407186552	304	0.0016456	16101	81302728	118819802
16102011001	16102	54	2017	Bulnes	209128.5	2017	16102	21493	4494798610	2452	0.1140837	16102	512783055	417540548
16102011002	16102	84	2017	Bulnes	209128.5	2017	16102	21493	4494798610	4765	0.2217001	16102	996497249	542338637
16102011003	16102	83	2017	Bulnes	209128.5	2017	16102	21493	4494798610	3184	0.1481413	16102	665865108	538579764
16102012006	16102	6	2017	Bulnes	209128.5	2017	16102	21493	4494798610	183	0.0085144	16102	38270513	81002164
16102012044	16102	1	2017	Bulnes	209128.5	2017	16102	21493	4494798610	32	0.0014889	16102	6692112	-18571418
16102021001	16102	6	2017	Bulnes	209128.5	2017	16102	21493	4494798610	537	0.0249849	16102	112301998	81002164
16102022005	16102	5	2017	Bulnes	209128.5	2017	16102	21493	4494798610	102	0.0047457	16102	21331106	66341026

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
16101011001	16101	52	2017	Chillán	267443.2	2017	16101	184739	49407186552	1080	0.0058461	16101	288838640	408104061	377874.13
16101011002	16101	105	2017	Chillán	267443.2	2017	16101	184739	49407186552	1525	0.0082549	16101	407850857	616653506	404362.95
16101011003	16101	142	2017	Chillán	267443.2	2017	16101	184739	49407186552	2051	0.0111021	16101	548525972	731335641	356575.15
16101011004	16101	163	2017	Chillán	267443.2	2017	16101	184739	49407186552	1819	0.0098463	16101	486479154	789779809	434183.51
16101021001	16101	10	2017	Chillán	267443.2	2017	16101	184739	49407186552	1345	0.0072805	16101	359711084	129967565	96630.16
16101021002	16101	114	2017	Chillán	267443.2	2017	16101	184739	49407186552	1991	0.0107774	16101	532479381	646201376	324561.21
16101021003	16101	87	2017	Chillán	267443.2	2017	16101	184739	49407186552	2007	0.0108640	16101	536758472	553482966	275776.27
16101021004	16101	79	2017	Chillán	267443.2	2017	16101	184739	49407186552	1882	0.0101873	16101	503328074	523312909	278062.12
16101031001	16101	335	2017	Chillán	267443.2	2017	16101	184739	49407186552	3622	0.0196060	16101	968679216	1170069080	323045.02
16101031002	16101	116	2017	Chillán	267443.2	2017	16101	184739	49407186552	2516	0.0136192	16101	672887053	652607335	259382.88
16101031003	16101	125	2017	Chillán	267443.2	2017	16101	184739	49407186552	2184	0.0118221	16101	584095916	680773241	311709.36
16101031004	16101	55	2017	Chillán	267443.2	2017	16101	184739	49407186552	1866	0.0101007	16101	499048983	422193251	226255.76
16101041001	16101	119	2017	Chillán	267443.2	2017	16101	184739	49407186552	3315	0.0179442	16101	886574158	662113605	199732.61
16101041002	16101	41	2017	Chillán	267443.2	2017	16101	184739	49407186552	1999	0.0108207	16101	534618927	352599497	176387.94
16101041003	16101	67	2017	Chillán	267443.2	2017	16101	184739	49407186552	4799	0.0259772	16101	1283459845	475030774	98985.37
16101041004	16101	35	2017	Chillán	267443.2	2017	16101	184739	49407186552	2766	0.0149725	16101	739747850	319141682	115380.22
16101042032	16101	1	2017	Chillán	267443.2	2017	16101	184739	49407186552	30	0.0001624	16101	8023296	-18571418	-619047.27
16101051001	16101	44	2017	Chillán	267443.2	2017	16101	184739	49407186552	1466	0.0079355	16101	392071709	368408098	251301.57
16101051002	16101	382	2017	Chillán	267443.2	2017	16101	184739	49407186552	4764	0.0257877	16101	1274099333	1255376324	263513.08
16101051003	16101	73	2017	Chillán	267443.2	2017	16101	184739	49407186552	2374	0.0128506	16101	634910121	499668521	210475.37
16101051004	16101	16	2017	Chillán	267443.2	2017	16101	184739	49407186552	2018	0.0109235	16101	539700347	187515412	92921.41
16101051005	16101	424	2017	Chillán	267443.2	2017	16101	184739	49407186552	4539	0.0245698	16101	1213924617	1327262158	292412.90
16101052010	16101	7	2017	Chillán	267443.2	2017	16101	184739	49407186552	112	0.0006063	16101	29953637	94484472	843611.36
16101052026	16101	9	2017	Chillán	267443.2	2017	16101	184739	49407186552	355	0.0019216	16101	94942331	118819802	334703.67
16101052027	16101	9	2017	Chillán	267443.2	2017	16101	184739	49407186552	362	0.0019595	16101	96814433	118819802	328231.50
16101052028	16101	32	2017	Chillán	267443.2	2017	16101	184739	49407186552	345	0.0018675	16101	92267899	301334025	873431.96
16101061001	16101	5	2017	Chillán	267443.2	2017	16101	184739	49407186552	941	0.0050937	16101	251664037	66341026	70500.56
16101062003	16101	29	2017	Chillán	267443.2	2017	16101	184739	49407186552	638	0.0034535	16101	170628752	282670153	443056.67
16101062014	16101	18	2017	Chillán	267443.2	2017	16101	184739	49407186552	348	0.0018837	16101	93070228	204183762	586734.95
16101062024	16101	22	2017	Chillán	267443.2	2017	16101	184739	49407186552	451	0.0024413	16101	120616876	234943944	520940.01
16101062027	16101	18	2017	Chillán	267443.2	2017	16101	184739	49407186552	620	0.0033561	16101	165814775	204183762	329328.65
16101062901	16101	1	2017	Chillán	267443.2	2017	16101	184739	49407186552	373	0.0020191	16101	99756308	-18571418	-49789.32
16101071001	16101	61	2017	Chillán	267443.2	2017	16101	184739	49407186552	1599	0.0086555	16101	427641653	449262838	280964.88
16101071002	16101	21	2017	Chillán	267443.2	2017	16101	184739	49407186552	950	0.0051424	16101	254071026	227535805	239511.37
16101072001	16101	18	2017	Chillán	267443.2	2017	16101	184739	49407186552	593	0.0032099	16101	158593809	204183762	344323.38
16101072901	16101	7	2017	Chillán	267443.2	2017	16101	184739	49407186552	491	0.0026578	16101	131314604	94484472	192432.73
16101081001	16101	37	2017	Chillán	267443.2	2017	16101	184739	49407186552	1276	0.0069070	16101	341257504	330592055	259084.68
16101082007	16101	6	2017	Chillán	267443.2	2017	16101	184739	49407186552	184	0.0009960	16101	49209546	81002164	440229.15
16101082008	16101	4	2017	Chillán	267443.2	2017	16101	184739	49407186552	147	0.0007957	16101	39314148	50124192	340980.90
16101082011	16101	14	2017	Chillán	267443.2	2017	16101	184739	49407186552	408	0.0022085	16101	109116819	169768409	416099.04
16101082012	16101	7	2017	Chillán	267443.2	2017	16101	184739	49407186552	139	0.0007524	16101	37174603	94484472	679744.40
16101082015	16101	9	2017	Chillán	267443.2	2017	16101	184739	49407186552	313	0.0016943	16101	83709717	118819802	379615.98
16101082031	16101	4	2017	Chillán	267443.2	2017	16101	184739	49407186552	104	0.0005630	16101	27814091	50124192	481963.38
16101082037	16101	14	2017	Chillán	267443.2	2017	16101	184739	49407186552	451	0.0024413	16101	120616876	169768409	376426.63
16101092901	16101	5	2017	Chillán	267443.2	2017	16101	184739	49407186552	87	0.0004709	16101	23267557	66341026	762540.53
16101102004	16101	88	2017	Chillán	267443.2	2017	16101	184739	49407186552	3591	0.0194382	16101	960388477	557154916	155153.14
16101112030	16101	2	2017	Chillán	267443.2	2017	16101	184739	49407186552	37	0.0002003	16101	9895398	9883235	267114.47
16101112901	16101	3	2017	Chillán	267443.2	2017	16101	184739	49407186552	16	0.0000866	16101	4279091	31717259	1982328.67
16101121001	16101	154	2017	Chillán	267443.2	2017	16101	184739	49407186552	4876	0.0263940	16101	1304052970	765223073	156936.64
16101122002	16101	16	2017	Chillán	267443.2	2017	16101	184739	49407186552	280	0.0015157	16101	74884092	187515412	669697.90
16101122017	16101	49	2017	Chillán	267443.2	2017	16101	184739	49407186552	682	0.0036917	16101	182396252	393602242	577129.39
16101122020	16101	8	2017	Chillán	267443.2	2017	16101	184739	49407186552	102	0.0005521	16101	27279205	107033499	1049348.03
16101122034	16101	24	2017	Chillán	267443.2	2017	16101	184739	49407186552	171	0.0009256	16101	45732785	249271356	1457727.23
16101122035	16101	34	2017	Chillán	267443.2	2017	16101	184739	49407186552	534	0.0028906	16101	142814661	313293769	586692.45
16101131001	16101	866	2017	Chillán	267443.2	2017	16101	184739	49407186552	5741	0.0310763	16101	1535391325	1934298998	336927.19
16101131002	16101	107	2017	Chillán	267443.2	2017	16101	184739	49407186552	2211	0.0119682	16101	591316882	623325887	281920.35
16101131003	16101	103	2017	Chillán	267443.2	2017	16101	184739	49407186552	2135	0.0115568	16101	570991200	609917269	285675.54
16101131004	16101	160	2017	Chillán	267443.2	2017	16101	184739	49407186552	4141	0.0224154	16101	1107482229	781671343	188763.91
16101141001	16101	446	2017	Chillán	267443.2	2017	16101	184739	49407186552	5365	0.0290410	16101	1434832687	1363495781	254146.46
16101141002	16101	882	2017	Chillán	267443.2	2017	16101	184739	49407186552	5814	0.0314714	16101	1554914678	1952888502	335894.14
16101141003	16101	243	2017	Chillán	267443.2	2017	16101	184739	49407186552	3016	0.0163257	16101	806608646	983591553	326124.52
16101141004	16101	789	2017	Chillán	267443.2	2017	16101	184739	49407186552	3759	0.0203476	16101	1005318932	1842333840	490112.75
16101142009	16101	95	2017	Chillán	267443.2	2017	16101	184739	49407186552	607	0.0032857	16101	162338013	582294955	959299.76
16101142018	16101	69	2017	Chillán	267443.2	2017	16101	184739	49407186552	625	0.0033832	16101	167151991	483361565	773378.50
16101142036	16101	33	2017	Chillán	267443.2	2017	16101	184739	49407186552	443	0.0023980	16101	118477331	307359207	693813.11
16101151001	16101	222	2017	Chillán	267443.2	2017	16101	184739	49407186552	3362	0.0181986	16101	899143988	936274509	278487.36
16101151002	16101	34	2017	Chillán	267443.2	2017	16101	184739	49407186552	3634	0.0196710	16101	971888534	313293769	86211.82
16101151003	16101	31	2017	Chillán	267443.2	2017	16101	184739	49407186552	1805	0.0097705	16101	482734949	295213941	163553.43
16101151004	16101	76	2017	Chillán	267443.2	2017	16101	184739	49407186552	3489	0.0188861	16101	933109272	511607416	146634.40
16101151005	16101	39	2017	Chillán	267443.2	2017	16101	184739	49407186552	4931	0.0266917	16101	1318762345	341736919	69303.78
16101151006	16101	65	2017	Chillán	267443.2	2017	16101	184739	49407186552	4103	0.0222097	16101	1097319388	466574686	113715.50
16101151007	16101	46	2017	Chillán	267443.2	2017	16101	184739	49407186552	2402	0.0130021	16101	642398530	378649267	157639.16
16101151008	16101	28	2017	Chillán	267443.2	2017	16101	184739	49407186552	3208	0.0173650	16101	857957737	276235972	86108.47
16101151009	16101	314	2017	Chillán	267443.2	2017	16101	184739	49407186552	4520	0.0244670	16101	1208843196	1130022283	250004.93
16101151010	16101	294	2017	Chillán	267443.2	2017	16101	184739	49407186552	4906	0.0265564	16101	1312076266	1090617316	222302.76
16101151011	16101	84	2017	Chillán	267443.2	2017	16101	184739	49407186552	2718	0.0147126	16101	726910577	542338637	199535.92
16101151012	16101	51	2017	Chillán	267443.2	2017	16101	184739	49407186552	2161	0.0116976	16101	577944723	403317754	186634.78
16101151013	16101	46	2017	Chillán	267443.2	2017	16101	184739	49407186552	3743	0.0202610	16101	1001039841	378649267	101161.97
16101151014	16101	24	2017	Chillán	267443.2	2017	16101	184739	49407186552	3883	0.0210188	16101	1038481887	249271356	64195.56
16101151015	16101	13	2017	Chillán	267443.2	2017	16101	184739	49407186552	248	0.0013424	16101	66325910	160418516	646848.86
16101152016	16101	16	2017	Chillán	267443.2	2017	16101	184739	49407186552	521	0.0028202	16101	139337899	187515412	359914.42
16101152025	16101	182	2017	Chillán	267443.2	2017	16101	184739	49407186552	1780	0.0096352	16101	476048869	839487418	471622.15
16101152033	16101	9	2017	Chillán	267443.2	2017	16101	184739	49407186552	122	0.0006604	16101	32628069	118819802	973932.80
16101161001	16101	96	2017	Chillán	267443.2	2017	16101	184739	49407186552	2140	0.0115839	16101	572328416	585809740	273742.87
16101161002	16101	97	2017	Chillán	267443.2	2017	16101	184739	49407186552	2196	0.0118870	16101	587305234	589306266	268354.40
16101161003	16101	109	2017	Chillán	267443.2	2017	16101	184739	49407186552	3884	0.0210243	16101	1038749331	629936196	162187.49
16101161004	16101	186	2017	Chillán	267443.2	2017	16101	184739	49407186552	2612	0.0141389	16101	698561599	849616183	325274.19
16101161005	16101	148	2017	Chillán	267443.2	2017	16101	184739	49407186552	3326	0.0180038	16101	889516033	748451137	225030.41
16101171001	16101	76	2017	Chillán	267443.2	2017	16101	184739	49407186552	4382	0.0237200	16101	1171936037	511607416	116752.03
16101171002	16101	82	2017	Chillán	267443.2	2017	16101	184739	49407186552	2900	0.0156978	16101	775585236	534798178	184413.16
16101171003	16101	90	2017	Chillán	267443.2	2017	16101	184739	49407186552	2262	0.0122443	16101	604956484	564436751	249529.95
16101171004	16101	30	2017	Chillán	267443.2	2017	16101	184739	49407186552	1590	0.0086067	16101	425234664	288994324	181757.44
16101991999	16101	9	2017	Chillán	267443.2	2017	16101	184739	49407186552	304	0.0016456	16101	81302728	118819802	390854.61
16102011001	16102	54	2017	Bulnes	209128.5	2017	16102	21493	4494798610	2452	0.1140837	16102	512783055	417540548	170285.70
16102011002	16102	84	2017	Bulnes	209128.5	2017	16102	21493	4494798610	4765	0.2217001	16102	996497249	542338637	113817.13
16102011003	16102	83	2017	Bulnes	209128.5	2017	16102	21493	4494798610	3184	0.1481413	16102	665865108	538579764	169151.94
16102012006	16102	6	2017	Bulnes	209128.5	2017	16102	21493	4494798610	183	0.0085144	16102	38270513	81002164	442634.78
16102012044	16102	1	2017	Bulnes	209128.5	2017	16102	21493	4494798610	32	0.0014889	16102	6692112	-18571418	-580356.81
16102021001	16102	6	2017	Bulnes	209128.5	2017	16102	21493	4494798610	537	0.0249849	16102	112301998	81002164	150842.02
16102022005	16102	5	2017	Bulnes	209128.5	2017	16102	21493	4494798610	102	0.0047457	16102	21331106	66341026	650402.21

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_region_16.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