1 Variable CENSO

Necesitamos calcular las frecuencias a nivel censal de las respuestas correspondientes a la categoría: “Parquet, piso flotante, cerámico, madera, alfombra, flexit, cubrepiso u otro similar, sobre radier o vigas de madera” del campo P03C 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 Censo 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)

Hagamos un subset con la 1:

tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$REGION == 16) 
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA== 1)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave[,-c(1,2,4,5,6,7,8,9,10,11,12,14,15,16,17,18,19,20),drop=FALSE]

aterial de construcción del piso

names(tabla_con_clave_f)[2] <- "Tipo de piso"
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de piso` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de piso`
d <- tabla_con_clave_ff$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 406 2017
2 16101011002 1 16101 393 2017
3 16101011003 1 16101 813 2017
4 16101011004 1 16101 681 2017
5 16101021001 1 16101 407 2017
6 16101021002 1 16101 723 2017
7 16101021003 1 16101 699 2017
8 16101021004 1 16101 637 2017
9 16101031001 1 16101 1260 2017
10 16101031002 1 16101 756 2017
11 16101031003 1 16101 780 2017
12 16101031004 1 16101 589 2017
13 16101041001 1 16101 1072 2017
14 16101041002 1 16101 573 2017
15 16101041003 1 16101 1134 2017
16 16101041004 1 16101 744 2017
17 16101051001 1 16101 414 2017
18 16101051002 1 16101 1556 2017
19 16101051003 1 16101 677 2017
20 16101051004 1 16101 618 2017
21 16101051005 1 16101 1469 2017
22 16101061001 1 16101 297 2017
23 16101071001 1 16101 427 2017
24 16101071002 1 16101 289 2017
25 16101081001 1 16101 377 2017
26 16101121001 1 16101 1328 2017
27 16101121002 1 16101 49 2017
28 16101131001 1 16101 1728 2017
29 16101131002 1 16101 754 2017
30 16101131003 1 16101 763 2017
31 16101131004 1 16101 1217 2017
32 16101141001 1 16101 1825 2017
33 16101141002 1 16101 1756 2017
34 16101141003 1 16101 984 2017
35 16101141004 1 16101 1206 2017
36 16101151001 1 16101 949 2017
37 16101151002 1 16101 948 2017
38 16101151003 1 16101 524 2017
39 16101151004 1 16101 1161 2017
40 16101151005 1 16101 1396 2017
41 16101151006 1 16101 1113 2017
42 16101151007 1 16101 643 2017
43 16101151008 1 16101 911 2017
44 16101151009 1 16101 1303 2017
45 16101151010 1 16101 1294 2017
46 16101151011 1 16101 737 2017
47 16101151012 1 16101 627 2017
48 16101151013 1 16101 1196 2017
49 16101151014 1 16101 1093 2017
50 16101151015 1 16101 70 2017
51 16101161001 1 16101 637 2017
52 16101161002 1 16101 753 2017
53 16101161003 1 16101 1237 2017
54 16101161004 1 16101 781 2017
55 16101161005 1 16101 950 2017
56 16101171001 1 16101 1162 2017
57 16101171002 1 16101 1033 2017
58 16101171003 1 16101 678 2017
59 16101171004 1 16101 408 2017
60 16101991999 1 16101 59 2017
215 16102011001 1 16102 702 2017
216 16102011002 1 16102 1439 2017
217 16102011003 1 16102 994 2017
218 16102021001 1 16102 151 2017
219 16102041001 1 16102 442 2017
220 16102051001 1 16102 265 2017
221 16102071001 1 16102 37 2017
222 16102991999 1 16102 13 2017
377 16103041001 1 16103 1101 2017
378 16103041002 1 16103 1732 2017
379 16103041003 1 16103 1224 2017
380 16103041004 1 16103 1279 2017
381 16103041005 1 16103 876 2017
382 16103041006 1 16103 773 2017
383 16103041007 1 16103 1130 2017
384 16103991999 1 16103 18 2017
539 16104011001 1 16104 1308 2017
540 16104041001 1 16104 20 2017
541 16104991999 1 16104 1 2017
696 16105011001 1 16105 1090 2017
697 16105061001 1 16105 9 2017
698 16105091001 1 16105 40 2017
699 16105991999 1 16105 1 2017
854 16106011001 1 16106 728 2017
855 16106021001 1 16106 412 2017
856 16106051001 1 16106 298 2017
857 16106051002 1 16106 203 2017
858 16106991999 1 16106 29 2017
1013 16107011001 1 16107 1343 2017
1014 16107011002 1 16107 1054 2017
1015 16107011004 1 16107 473 2017
1016 16107051001 1 16107 31 2017
1017 16107061001 1 16107 52 2017
1018 16107991999 1 16107 59 2017
1173 16108011001 1 16108 834 2017
1174 16108051001 1 16108 442 2017
1175 16108051002 1 16108 199 2017
1176 16108061001 1 16108 363 2017
1177 16108061002 1 16108 108 2017
1178 16108991999 1 16108 7 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 406 2017 16101
2 16101011002 393 2017 16101
3 16101011003 813 2017 16101
4 16101011004 681 2017 16101
5 16101021001 407 2017 16101
6 16101021002 723 2017 16101
7 16101021003 699 2017 16101
8 16101021004 637 2017 16101
9 16101031001 1260 2017 16101
10 16101031002 756 2017 16101
11 16101031003 780 2017 16101
12 16101031004 589 2017 16101
13 16101041001 1072 2017 16101
14 16101041002 573 2017 16101
15 16101041003 1134 2017 16101
16 16101041004 744 2017 16101
17 16101051001 414 2017 16101
18 16101051002 1556 2017 16101
19 16101051003 677 2017 16101
20 16101051004 618 2017 16101
21 16101051005 1469 2017 16101
22 16101061001 297 2017 16101
23 16101071001 427 2017 16101
24 16101071002 289 2017 16101
25 16101081001 377 2017 16101
26 16101121001 1328 2017 16101
27 16101121002 49 2017 16101
28 16101131001 1728 2017 16101
29 16101131002 754 2017 16101
30 16101131003 763 2017 16101
31 16101131004 1217 2017 16101
32 16101141001 1825 2017 16101
33 16101141002 1756 2017 16101
34 16101141003 984 2017 16101
35 16101141004 1206 2017 16101
36 16101151001 949 2017 16101
37 16101151002 948 2017 16101
38 16101151003 524 2017 16101
39 16101151004 1161 2017 16101
40 16101151005 1396 2017 16101
41 16101151006 1113 2017 16101
42 16101151007 643 2017 16101
43 16101151008 911 2017 16101
44 16101151009 1303 2017 16101
45 16101151010 1294 2017 16101
46 16101151011 737 2017 16101
47 16101151012 627 2017 16101
48 16101151013 1196 2017 16101
49 16101151014 1093 2017 16101
50 16101151015 70 2017 16101
51 16101161001 637 2017 16101
52 16101161002 753 2017 16101
53 16101161003 1237 2017 16101
54 16101161004 781 2017 16101
55 16101161005 950 2017 16101
56 16101171001 1162 2017 16101
57 16101171002 1033 2017 16101
58 16101171003 678 2017 16101
59 16101171004 408 2017 16101
60 16101991999 59 2017 16101
215 16102011001 702 2017 16102
216 16102011002 1439 2017 16102
217 16102011003 994 2017 16102
218 16102021001 151 2017 16102
219 16102041001 442 2017 16102
220 16102051001 265 2017 16102
221 16102071001 37 2017 16102
222 16102991999 13 2017 16102
377 16103041001 1101 2017 16103
378 16103041002 1732 2017 16103
379 16103041003 1224 2017 16103
380 16103041004 1279 2017 16103
381 16103041005 876 2017 16103
382 16103041006 773 2017 16103
383 16103041007 1130 2017 16103
384 16103991999 18 2017 16103
539 16104011001 1308 2017 16104
540 16104041001 20 2017 16104
541 16104991999 1 2017 16104
696 16105011001 1090 2017 16105
697 16105061001 9 2017 16105
698 16105091001 40 2017 16105
699 16105991999 1 2017 16105
854 16106011001 728 2017 16106
855 16106021001 412 2017 16106
856 16106051001 298 2017 16106
857 16106051002 203 2017 16106
858 16106991999 29 2017 16106
1013 16107011001 1343 2017 16107
1014 16107011002 1054 2017 16107
1015 16107011004 473 2017 16107
1016 16107051001 31 2017 16107
1017 16107061001 52 2017 16107
1018 16107991999 59 2017 16107
1173 16108011001 834 2017 16108
1174 16108051001 442 2017 16108
1175 16108051002 199 2017 16108
1176 16108061001 363 2017 16108
1177 16108061002 108 2017 16108
1178 16108991999 7 2017 16108


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_urbano_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 375676.9 2017 1101 191468 71930106513
01107 Alto Hospicio 311571.7 2017 1107 108375 33766585496
01401 Pozo Almonte 316138.5 2017 1401 15711 4966851883
01405 Pica 330061.1 2017 1405 9296 3068247619
02101 Antofagasta 368221.4 2017 2101 361873 133249367039
02102 Mejillones 369770.7 2017 2102 13467 4979702302
02104 Taltal 383666.2 2017 2104 13317 5109282942
02201 Calama 434325.1 2017 2201 165731 71981127235
02203 San Pedro de Atacama 442861.0 2017 2203 10996 4869699464
02301 Tocopilla 286187.2 2017 2301 25186 7207910819
02302 María Elena 477748.0 2017 2302 6457 3084818966
03101 Copiapó 343121.0 2017 3101 153937 52819016037
03102 Caldera 318653.2 2017 3102 17662 5628052276
03103 Tierra Amarilla 333194.9 2017 3103 14019 4671058718
03201 Chañaral 286389.3 2017 3201 12219 3499391196
03202 Diego de Almagro 351583.9 2017 3202 13925 4895805596
03301 Vallenar 315981.5 2017 3301 51917 16404810756
03303 Freirina 289049.9 2017 3303 7041 2035200054
03304 Huasco 337414.8 2017 3304 10149 3424422750
04101 La Serena 279340.1 2017 4101 221054 61749247282
04102 Coquimbo 269078.6 2017 4102 227730 61277269093
04103 Andacollo 258539.7 2017 4103 11044 2855312920
04104 La Higuera 214257.0 2017 4104 4241 908664019
04106 Vicuña 254177.0 2017 4106 27771 7058750373
04201 Illapel 282139.3 2017 4201 30848 8703433491
04202 Canela 233397.3 2017 4202 9093 2122281844
04203 Los Vilos 285214.0 2017 4203 21382 6098444926
04204 Salamanca 262056.9 2017 4204 29347 7690585032
04301 Ovalle 280373.5 2017 4301 111272 31197719080
04302 Combarbalá 234537.3 2017 4302 13322 3124505460
04303 Monte Patria 225369.1 2017 4303 30751 6930326684
04304 Punitaqui 212496.1 2017 4304 10956 2328107498
05101 Valparaíso 306572.5 2017 5101 296655 90946261553
05102 Casablanca 348088.6 2017 5102 26867 9352095757
05103 Concón 333932.4 2017 5103 42152 14075920021
05105 Puchuncaví 296035.5 2017 5105 18546 5490274928
05107 Quintero 308224.7 2017 5107 31923 9839456903
05109 Viña del Mar 354715.9 2017 5109 334248 118563074323
05301 Los Andes 355446.2 2017 5301 66708 23711104774
05302 Calle Larga 246387.3 2017 5302 14832 3654416747
05303 Rinconada 279807.9 2017 5303 10207 2855998928
05304 San Esteban 219571.6 2017 5304 18855 4140022481
05401 La Ligua 259482.3 2017 5401 35390 9183080280
05402 Cabildo 262745.9 2017 5402 19388 5094117762
05403 Papudo 302317.1 2017 5403 6356 1921527704
05404 Petorca 237510.8 2017 5404 9826 2333781007
05405 Zapallar 294389.2 2017 5405 7339 2160521991
05501 Quillota 288694.2 2017 5501 90517 26131733924
05502 Calera 282823.6 2017 5502 50554 14297866792
05503 Hijuelas 268449.7 2017 5503 17988 4828872604

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)
comunas_con_ing_exp <-comunas_con_ing_exp[!(is.na(comunas_con_ing_exp$Ingresos_expandidos)),]
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 406 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101011002 393 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101011003 813 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101011004 681 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101021001 407 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101021002 723 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101021003 699 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101021004 637 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101031001 1260 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101031002 756 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101031003 780 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101031004 589 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101041001 1072 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101041002 573 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101041003 1134 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101041004 744 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101051001 414 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101051002 1556 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101051003 677 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101051004 618 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101051005 1469 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101061001 297 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101071001 427 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101071002 289 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101081001 377 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101121001 1328 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101121002 49 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101131001 1728 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101131002 754 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101131003 763 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101131004 1217 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101141001 1825 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101141002 1756 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101141003 984 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101141004 1206 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151001 949 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151002 948 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151003 524 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151004 1161 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151005 1396 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151006 1113 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151007 643 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151008 911 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151009 1303 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151010 1294 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151011 737 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151012 627 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151013 1196 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151014 1093 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151015 70 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101161001 637 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101161002 753 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101161003 1237 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101161004 781 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101161005 950 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101171001 1162 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101171002 1033 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101171003 678 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101171004 408 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101991999 59 2017 Chillán 275879.2 2017 16101 184739 50965643906
16102 16102011001 702 2017 Bulnes 224694.9 2017 16102 21493 4829367278
16102 16102011002 1439 2017 Bulnes 224694.9 2017 16102 21493 4829367278
16102 16102011003 994 2017 Bulnes 224694.9 2017 16102 21493 4829367278
16102 16102021001 151 2017 Bulnes 224694.9 2017 16102 21493 4829367278
16102 16102041001 442 2017 Bulnes 224694.9 2017 16102 21493 4829367278
16102 16102051001 265 2017 Bulnes 224694.9 2017 16102 21493 4829367278
16102 16102071001 37 2017 Bulnes 224694.9 2017 16102 21493 4829367278
16102 16102991999 13 2017 Bulnes 224694.9 2017 16102 21493 4829367278
16103 16103041001 1101 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560
16103 16103041002 1732 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560
16103 16103041003 1224 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560
16103 16103041004 1279 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560
16103 16103041005 876 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560
16103 16103041006 773 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560
16103 16103041007 1130 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560
16103 16103991999 18 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560
16104 16104011001 1308 2017 El Carmen 215566.5 2017 16104 12044 2596282563
16104 16104041001 20 2017 El Carmen 215566.5 2017 16104 12044 2596282563
16104 16104991999 1 2017 El Carmen 215566.5 2017 16104 12044 2596282563
16105 16105011001 1090 2017 Pemuco 262037.4 2017 16105 8448 2213691761
16105 16105061001 9 2017 Pemuco 262037.4 2017 16105 8448 2213691761
16105 16105091001 40 2017 Pemuco 262037.4 2017 16105 8448 2213691761
16105 16105991999 1 2017 Pemuco 262037.4 2017 16105 8448 2213691761
16106 16106011001 728 2017 Pinto 175602.5 2017 16106 10827 1901248804
16106 16106021001 412 2017 Pinto 175602.5 2017 16106 10827 1901248804
16106 16106051001 298 2017 Pinto 175602.5 2017 16106 10827 1901248804
16106 16106051002 203 2017 Pinto 175602.5 2017 16106 10827 1901248804
16106 16106991999 29 2017 Pinto 175602.5 2017 16106 10827 1901248804
16107 16107011001 1343 2017 Quillón 256072.4 2017 16107 17485 4477425886
16107 16107011002 1054 2017 Quillón 256072.4 2017 16107 17485 4477425886
16107 16107011004 473 2017 Quillón 256072.4 2017 16107 17485 4477425886
16107 16107051001 31 2017 Quillón 256072.4 2017 16107 17485 4477425886
16107 16107061001 52 2017 Quillón 256072.4 2017 16107 17485 4477425886
16107 16107991999 59 2017 Quillón 256072.4 2017 16107 17485 4477425886
16108 16108011001 834 2017 San Ignacio 203331.5 2017 16108 16079 3269367252
16108 16108051001 442 2017 San Ignacio 203331.5 2017 16108 16079 3269367252
16108 16108051002 199 2017 San Ignacio 203331.5 2017 16108 16079 3269367252
16108 16108061001 363 2017 San Ignacio 203331.5 2017 16108 16079 3269367252
16108 16108061002 108 2017 San Ignacio 203331.5 2017 16108 16079 3269367252
16108 16108991999 7 2017 San Ignacio 203331.5 2017 16108 16079 3269367252


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 406 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101011002 393 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101011003 813 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101011004 681 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101021001 407 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101021002 723 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101021003 699 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101021004 637 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101031001 1260 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101031002 756 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101031003 780 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101031004 589 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101041001 1072 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101041002 573 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101041003 1134 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101041004 744 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101051001 414 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101051002 1556 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101051003 677 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101051004 618 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101051005 1469 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101061001 297 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101071001 427 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101071002 289 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101081001 377 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101121001 1328 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101121002 49 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101131001 1728 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101131002 754 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101131003 763 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101131004 1217 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101141001 1825 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101141002 1756 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101141003 984 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101141004 1206 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151001 949 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151002 948 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151003 524 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151004 1161 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151005 1396 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151006 1113 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151007 643 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151008 911 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151009 1303 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151010 1294 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151011 737 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151012 627 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151013 1196 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151014 1093 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101151015 70 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101161001 637 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101161002 753 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101161003 1237 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101161004 781 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101161005 950 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101171001 1162 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101171002 1033 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101171003 678 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101171004 408 2017 Chillán 275879.2 2017 16101 184739 50965643906
16101 16101991999 59 2017 Chillán 275879.2 2017 16101 184739 50965643906
16102 16102011001 702 2017 Bulnes 224694.9 2017 16102 21493 4829367278
16102 16102011002 1439 2017 Bulnes 224694.9 2017 16102 21493 4829367278
16102 16102011003 994 2017 Bulnes 224694.9 2017 16102 21493 4829367278
16102 16102021001 151 2017 Bulnes 224694.9 2017 16102 21493 4829367278
16102 16102041001 442 2017 Bulnes 224694.9 2017 16102 21493 4829367278
16102 16102051001 265 2017 Bulnes 224694.9 2017 16102 21493 4829367278
16102 16102071001 37 2017 Bulnes 224694.9 2017 16102 21493 4829367278
16102 16102991999 13 2017 Bulnes 224694.9 2017 16102 21493 4829367278
16103 16103041001 1101 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560
16103 16103041002 1732 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560
16103 16103041003 1224 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560
16103 16103041004 1279 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560
16103 16103041005 876 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560
16103 16103041006 773 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560
16103 16103041007 1130 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560
16103 16103991999 18 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560
16104 16104011001 1308 2017 El Carmen 215566.5 2017 16104 12044 2596282563
16104 16104041001 20 2017 El Carmen 215566.5 2017 16104 12044 2596282563
16104 16104991999 1 2017 El Carmen 215566.5 2017 16104 12044 2596282563
16105 16105011001 1090 2017 Pemuco 262037.4 2017 16105 8448 2213691761
16105 16105061001 9 2017 Pemuco 262037.4 2017 16105 8448 2213691761
16105 16105091001 40 2017 Pemuco 262037.4 2017 16105 8448 2213691761
16105 16105991999 1 2017 Pemuco 262037.4 2017 16105 8448 2213691761
16106 16106011001 728 2017 Pinto 175602.5 2017 16106 10827 1901248804
16106 16106021001 412 2017 Pinto 175602.5 2017 16106 10827 1901248804
16106 16106051001 298 2017 Pinto 175602.5 2017 16106 10827 1901248804
16106 16106051002 203 2017 Pinto 175602.5 2017 16106 10827 1901248804
16106 16106991999 29 2017 Pinto 175602.5 2017 16106 10827 1901248804
16107 16107011001 1343 2017 Quillón 256072.4 2017 16107 17485 4477425886
16107 16107011002 1054 2017 Quillón 256072.4 2017 16107 17485 4477425886
16107 16107011004 473 2017 Quillón 256072.4 2017 16107 17485 4477425886
16107 16107051001 31 2017 Quillón 256072.4 2017 16107 17485 4477425886
16107 16107061001 52 2017 Quillón 256072.4 2017 16107 17485 4477425886
16107 16107991999 59 2017 Quillón 256072.4 2017 16107 17485 4477425886
16108 16108011001 834 2017 San Ignacio 203331.5 2017 16108 16079 3269367252
16108 16108051001 442 2017 San Ignacio 203331.5 2017 16108 16079 3269367252
16108 16108051002 199 2017 San Ignacio 203331.5 2017 16108 16079 3269367252
16108 16108061001 363 2017 San Ignacio 203331.5 2017 16108 16079 3269367252
16108 16108061002 108 2017 San Ignacio 203331.5 2017 16108 16079 3269367252
16108 16108991999 7 2017 San Ignacio 203331.5 2017 16108 16079 3269367252


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 406 2017 Chillán 275879.2 2017 16101 184739 50965643906 1080 0.0058461 16101
16101011002 16101 393 2017 Chillán 275879.2 2017 16101 184739 50965643906 1525 0.0082549 16101
16101011003 16101 813 2017 Chillán 275879.2 2017 16101 184739 50965643906 2051 0.0111021 16101
16101011004 16101 681 2017 Chillán 275879.2 2017 16101 184739 50965643906 1819 0.0098463 16101
16101021001 16101 407 2017 Chillán 275879.2 2017 16101 184739 50965643906 1345 0.0072805 16101
16101021002 16101 723 2017 Chillán 275879.2 2017 16101 184739 50965643906 1991 0.0107774 16101
16101021003 16101 699 2017 Chillán 275879.2 2017 16101 184739 50965643906 2007 0.0108640 16101
16101021004 16101 637 2017 Chillán 275879.2 2017 16101 184739 50965643906 1882 0.0101873 16101
16101031001 16101 1260 2017 Chillán 275879.2 2017 16101 184739 50965643906 3622 0.0196060 16101
16101031002 16101 756 2017 Chillán 275879.2 2017 16101 184739 50965643906 2516 0.0136192 16101
16101031003 16101 780 2017 Chillán 275879.2 2017 16101 184739 50965643906 2184 0.0118221 16101
16101031004 16101 589 2017 Chillán 275879.2 2017 16101 184739 50965643906 1866 0.0101007 16101
16101041001 16101 1072 2017 Chillán 275879.2 2017 16101 184739 50965643906 3315 0.0179442 16101
16101041002 16101 573 2017 Chillán 275879.2 2017 16101 184739 50965643906 1999 0.0108207 16101
16101041003 16101 1134 2017 Chillán 275879.2 2017 16101 184739 50965643906 4799 0.0259772 16101
16101041004 16101 744 2017 Chillán 275879.2 2017 16101 184739 50965643906 2766 0.0149725 16101
16101051001 16101 414 2017 Chillán 275879.2 2017 16101 184739 50965643906 1466 0.0079355 16101
16101051002 16101 1556 2017 Chillán 275879.2 2017 16101 184739 50965643906 4764 0.0257877 16101
16101051003 16101 677 2017 Chillán 275879.2 2017 16101 184739 50965643906 2374 0.0128506 16101
16101051004 16101 618 2017 Chillán 275879.2 2017 16101 184739 50965643906 2018 0.0109235 16101
16101051005 16101 1469 2017 Chillán 275879.2 2017 16101 184739 50965643906 4539 0.0245698 16101
16101061001 16101 297 2017 Chillán 275879.2 2017 16101 184739 50965643906 941 0.0050937 16101
16101071001 16101 427 2017 Chillán 275879.2 2017 16101 184739 50965643906 1599 0.0086555 16101
16101071002 16101 289 2017 Chillán 275879.2 2017 16101 184739 50965643906 950 0.0051424 16101
16101081001 16101 377 2017 Chillán 275879.2 2017 16101 184739 50965643906 1276 0.0069070 16101
16101121001 16101 1328 2017 Chillán 275879.2 2017 16101 184739 50965643906 4876 0.0263940 16101
16101121002 16101 49 2017 Chillán 275879.2 2017 16101 184739 50965643906 186 0.0010068 16101
16101131001 16101 1728 2017 Chillán 275879.2 2017 16101 184739 50965643906 5741 0.0310763 16101
16101131002 16101 754 2017 Chillán 275879.2 2017 16101 184739 50965643906 2211 0.0119682 16101
16101131003 16101 763 2017 Chillán 275879.2 2017 16101 184739 50965643906 2135 0.0115568 16101
16101131004 16101 1217 2017 Chillán 275879.2 2017 16101 184739 50965643906 4141 0.0224154 16101
16101141001 16101 1825 2017 Chillán 275879.2 2017 16101 184739 50965643906 5365 0.0290410 16101
16101141002 16101 1756 2017 Chillán 275879.2 2017 16101 184739 50965643906 5814 0.0314714 16101
16101141003 16101 984 2017 Chillán 275879.2 2017 16101 184739 50965643906 3016 0.0163257 16101
16101141004 16101 1206 2017 Chillán 275879.2 2017 16101 184739 50965643906 3759 0.0203476 16101
16101151001 16101 949 2017 Chillán 275879.2 2017 16101 184739 50965643906 3362 0.0181986 16101
16101151002 16101 948 2017 Chillán 275879.2 2017 16101 184739 50965643906 3634 0.0196710 16101
16101151003 16101 524 2017 Chillán 275879.2 2017 16101 184739 50965643906 1805 0.0097705 16101
16101151004 16101 1161 2017 Chillán 275879.2 2017 16101 184739 50965643906 3489 0.0188861 16101
16101151005 16101 1396 2017 Chillán 275879.2 2017 16101 184739 50965643906 4931 0.0266917 16101
16101151006 16101 1113 2017 Chillán 275879.2 2017 16101 184739 50965643906 4103 0.0222097 16101
16101151007 16101 643 2017 Chillán 275879.2 2017 16101 184739 50965643906 2402 0.0130021 16101
16101151008 16101 911 2017 Chillán 275879.2 2017 16101 184739 50965643906 3208 0.0173650 16101
16101151009 16101 1303 2017 Chillán 275879.2 2017 16101 184739 50965643906 4520 0.0244670 16101
16101151010 16101 1294 2017 Chillán 275879.2 2017 16101 184739 50965643906 4906 0.0265564 16101
16101151011 16101 737 2017 Chillán 275879.2 2017 16101 184739 50965643906 2718 0.0147126 16101
16101151012 16101 627 2017 Chillán 275879.2 2017 16101 184739 50965643906 2161 0.0116976 16101
16101151013 16101 1196 2017 Chillán 275879.2 2017 16101 184739 50965643906 3743 0.0202610 16101
16101151014 16101 1093 2017 Chillán 275879.2 2017 16101 184739 50965643906 3883 0.0210188 16101
16101151015 16101 70 2017 Chillán 275879.2 2017 16101 184739 50965643906 248 0.0013424 16101
16101161001 16101 637 2017 Chillán 275879.2 2017 16101 184739 50965643906 2140 0.0115839 16101
16101161002 16101 753 2017 Chillán 275879.2 2017 16101 184739 50965643906 2196 0.0118870 16101
16101161003 16101 1237 2017 Chillán 275879.2 2017 16101 184739 50965643906 3884 0.0210243 16101
16101161004 16101 781 2017 Chillán 275879.2 2017 16101 184739 50965643906 2612 0.0141389 16101
16101161005 16101 950 2017 Chillán 275879.2 2017 16101 184739 50965643906 3326 0.0180038 16101
16101171001 16101 1162 2017 Chillán 275879.2 2017 16101 184739 50965643906 4382 0.0237200 16101
16101171002 16101 1033 2017 Chillán 275879.2 2017 16101 184739 50965643906 2900 0.0156978 16101
16101171003 16101 678 2017 Chillán 275879.2 2017 16101 184739 50965643906 2262 0.0122443 16101
16101171004 16101 408 2017 Chillán 275879.2 2017 16101 184739 50965643906 1590 0.0086067 16101
16101991999 16101 59 2017 Chillán 275879.2 2017 16101 184739 50965643906 304 0.0016456 16101
16102011001 16102 702 2017 Bulnes 224694.9 2017 16102 21493 4829367278 2452 0.1140837 16102
16102011002 16102 1439 2017 Bulnes 224694.9 2017 16102 21493 4829367278 4765 0.2217001 16102
16102011003 16102 994 2017 Bulnes 224694.9 2017 16102 21493 4829367278 3184 0.1481413 16102
16102021001 16102 151 2017 Bulnes 224694.9 2017 16102 21493 4829367278 537 0.0249849 16102
16102041001 16102 442 2017 Bulnes 224694.9 2017 16102 21493 4829367278 1419 0.0660215 16102
16102051001 16102 265 2017 Bulnes 224694.9 2017 16102 21493 4829367278 949 0.0441539 16102
16102071001 16102 37 2017 Bulnes 224694.9 2017 16102 21493 4829367278 134 0.0062346 16102
16102991999 16102 13 2017 Bulnes 224694.9 2017 16102 21493 4829367278 51 0.0023729 16102
16103041001 16103 1101 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 3503 0.1133400 16103
16103041002 16103 1732 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 6143 0.1987576 16103
16103041003 16103 1224 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 4173 0.1350180 16103
16103041004 16103 1279 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 4071 0.1317177 16103
16103041005 16103 876 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 2991 0.0967742 16103
16103041006 16103 773 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 2508 0.0811467 16103
16103041007 16103 1130 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 3970 0.1284499 16103
16103991999 16103 18 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 50 0.0016178 16103
16104011001 16104 1308 2017 El Carmen 215566.5 2017 16104 12044 2596282563 4722 0.3920624 16104
16104041001 16104 20 2017 El Carmen 215566.5 2017 16104 12044 2596282563 133 0.0110428 16104
16104991999 16104 1 2017 El Carmen 215566.5 2017 16104 12044 2596282563 6 0.0004982 16104
16105011001 16105 1090 2017 Pemuco 262037.4 2017 16105 8448 2213691761 3963 0.4691051 16105
16105061001 16105 9 2017 Pemuco 262037.4 2017 16105 8448 2213691761 34 0.0040246 16105
16105091001 16105 40 2017 Pemuco 262037.4 2017 16105 8448 2213691761 164 0.0194129 16105
16105991999 16105 1 2017 Pemuco 262037.4 2017 16105 8448 2213691761 7 0.0008286 16105
16106011001 16106 728 2017 Pinto 175602.5 2017 16106 10827 1901248804 2222 0.2052277 16106
16106021001 16106 412 2017 Pinto 175602.5 2017 16106 10827 1901248804 1544 0.1426064 16106
16106051001 16106 298 2017 Pinto 175602.5 2017 16106 10827 1901248804 956 0.0882978 16106
16106051002 16106 203 2017 Pinto 175602.5 2017 16106 10827 1901248804 651 0.0601275 16106
16106991999 16106 29 2017 Pinto 175602.5 2017 16106 10827 1901248804 85 0.0078507 16106
16107011001 16107 1343 2017 Quillón 256072.4 2017 16107 17485 4477425886 4657 0.2663426 16107
16107011002 16107 1054 2017 Quillón 256072.4 2017 16107 17485 4477425886 3783 0.2163569 16107
16107011004 16107 473 2017 Quillón 256072.4 2017 16107 17485 4477425886 1550 0.0886474 16107
16107051001 16107 31 2017 Quillón 256072.4 2017 16107 17485 4477425886 118 0.0067486 16107
16107061001 16107 52 2017 Quillón 256072.4 2017 16107 17485 4477425886 171 0.0097798 16107
16107991999 16107 59 2017 Quillón 256072.4 2017 16107 17485 4477425886 144 0.0082356 16107
16108011001 16108 834 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 2932 0.1823496 16108
16108051001 16108 442 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 1492 0.0927918 16108
16108051002 16108 199 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 685 0.0426022 16108
16108061001 16108 363 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 1300 0.0808508 16108
16108061002 16108 108 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 370 0.0230114 16108
16108991999 16108 7 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 23 0.0014304 16108


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 406 2017 Chillán 275879.2 2017 16101 184739 50965643906 1080 0.0058461 16101 297949515
16101011002 16101 393 2017 Chillán 275879.2 2017 16101 184739 50965643906 1525 0.0082549 16101 420715750
16101011003 16101 813 2017 Chillán 275879.2 2017 16101 184739 50965643906 2051 0.0111021 16101 565828199
16101011004 16101 681 2017 Chillán 275879.2 2017 16101 184739 50965643906 1819 0.0098463 16101 501824229
16101021001 16101 407 2017 Chillán 275879.2 2017 16101 184739 50965643906 1345 0.0072805 16101 371057498
16101021002 16101 723 2017 Chillán 275879.2 2017 16101 184739 50965643906 1991 0.0107774 16101 549275448
16101021003 16101 699 2017 Chillán 275879.2 2017 16101 184739 50965643906 2007 0.0108640 16101 553689515
16101021004 16101 637 2017 Chillán 275879.2 2017 16101 184739 50965643906 1882 0.0101873 16101 519204617
16101031001 16101 1260 2017 Chillán 275879.2 2017 16101 184739 50965643906 3622 0.0196060 16101 999234391
16101031002 16101 756 2017 Chillán 275879.2 2017 16101 184739 50965643906 2516 0.0136192 16101 694112018
16101031003 16101 780 2017 Chillán 275879.2 2017 16101 184739 50965643906 2184 0.0118221 16101 602520130
16101031004 16101 589 2017 Chillán 275879.2 2017 16101 184739 50965643906 1866 0.0101007 16101 514790551
16101041001 16101 1072 2017 Chillán 275879.2 2017 16101 184739 50965643906 3315 0.0179442 16101 914539483
16101041002 16101 573 2017 Chillán 275879.2 2017 16101 184739 50965643906 1999 0.0108207 16101 551482482
16101041003 16101 1134 2017 Chillán 275879.2 2017 16101 184739 50965643906 4799 0.0259772 16101 1323944187
16101041004 16101 744 2017 Chillán 275879.2 2017 16101 184739 50965643906 2766 0.0149725 16101 763081813
16101051001 16101 414 2017 Chillán 275879.2 2017 16101 184739 50965643906 1466 0.0079355 16101 404438878
16101051002 16101 1556 2017 Chillán 275879.2 2017 16101 184739 50965643906 4764 0.0257877 16101 1314288415
16101051003 16101 677 2017 Chillán 275879.2 2017 16101 184739 50965643906 2374 0.0128506 16101 654937174
16101051004 16101 618 2017 Chillán 275879.2 2017 16101 184739 50965643906 2018 0.0109235 16101 556724186
16101051005 16101 1469 2017 Chillán 275879.2 2017 16101 184739 50965643906 4539 0.0245698 16101 1252215600
16101061001 16101 297 2017 Chillán 275879.2 2017 16101 184739 50965643906 941 0.0050937 16101 259602309
16101071001 16101 427 2017 Chillán 275879.2 2017 16101 184739 50965643906 1599 0.0086555 16101 441130809
16101071002 16101 289 2017 Chillán 275879.2 2017 16101 184739 50965643906 950 0.0051424 16101 262085221
16101081001 16101 377 2017 Chillán 275879.2 2017 16101 184739 50965643906 1276 0.0069070 16101 352021834
16101121001 16101 1328 2017 Chillán 275879.2 2017 16101 184739 50965643906 4876 0.0263940 16101 1345186884
16101121002 16101 49 2017 Chillán 275879.2 2017 16101 184739 50965643906 186 0.0010068 16101 51313528
16101131001 16101 1728 2017 Chillán 275879.2 2017 16101 184739 50965643906 5741 0.0310763 16101 1583822375
16101131002 16101 754 2017 Chillán 275879.2 2017 16101 184739 50965643906 2211 0.0119682 16101 609968868
16101131003 16101 763 2017 Chillán 275879.2 2017 16101 184739 50965643906 2135 0.0115568 16101 589002050
16101131004 16101 1217 2017 Chillán 275879.2 2017 16101 184739 50965643906 4141 0.0224154 16101 1142415686
16101141001 16101 1825 2017 Chillán 275879.2 2017 16101 184739 50965643906 5365 0.0290410 16101 1480091803
16101141002 16101 1756 2017 Chillán 275879.2 2017 16101 184739 50965643906 5814 0.0314714 16101 1603961555
16101141003 16101 984 2017 Chillán 275879.2 2017 16101 184739 50965643906 3016 0.0163257 16101 832051608
16101141004 16101 1206 2017 Chillán 275879.2 2017 16101 184739 50965643906 3759 0.0203476 16101 1037029839
16101151001 16101 949 2017 Chillán 275879.2 2017 16101 184739 50965643906 3362 0.0181986 16101 927505804
16101151002 16101 948 2017 Chillán 275879.2 2017 16101 184739 50965643906 3634 0.0196710 16101 1002544942
16101151003 16101 524 2017 Chillán 275879.2 2017 16101 184739 50965643906 1805 0.0097705 16101 497961921
16101151004 16101 1161 2017 Chillán 275879.2 2017 16101 184739 50965643906 3489 0.0188861 16101 962542460
16101151005 16101 1396 2017 Chillán 275879.2 2017 16101 184739 50965643906 4931 0.0266917 16101 1360360238
16101151006 16101 1113 2017 Chillán 275879.2 2017 16101 184739 50965643906 4103 0.0222097 16101 1131932277
16101151007 16101 643 2017 Chillán 275879.2 2017 16101 184739 50965643906 2402 0.0130021 16101 662661791
16101151008 16101 911 2017 Chillán 275879.2 2017 16101 184739 50965643906 3208 0.0173650 16101 885020411
16101151009 16101 1303 2017 Chillán 275879.2 2017 16101 184739 50965643906 4520 0.0244670 16101 1246973895
16101151010 16101 1294 2017 Chillán 275879.2 2017 16101 184739 50965643906 4906 0.0265564 16101 1353463259
16101151011 16101 737 2017 Chillán 275879.2 2017 16101 184739 50965643906 2718 0.0147126 16101 749839612
16101151012 16101 627 2017 Chillán 275879.2 2017 16101 184739 50965643906 2161 0.0116976 16101 596174909
16101151013 16101 1196 2017 Chillán 275879.2 2017 16101 184739 50965643906 3743 0.0202610 16101 1032615772
16101151014 16101 1093 2017 Chillán 275879.2 2017 16101 184739 50965643906 3883 0.0210188 16101 1071238857
16101151015 16101 70 2017 Chillán 275879.2 2017 16101 184739 50965643906 248 0.0013424 16101 68418037
16101161001 16101 637 2017 Chillán 275879.2 2017 16101 184739 50965643906 2140 0.0115839 16101 590381446
16101161002 16101 753 2017 Chillán 275879.2 2017 16101 184739 50965643906 2196 0.0118870 16101 605830680
16101161003 16101 1237 2017 Chillán 275879.2 2017 16101 184739 50965643906 3884 0.0210243 16101 1071514737
16101161004 16101 781 2017 Chillán 275879.2 2017 16101 184739 50965643906 2612 0.0141389 16101 720596419
16101161005 16101 950 2017 Chillán 275879.2 2017 16101 184739 50965643906 3326 0.0180038 16101 917574154
16101171001 16101 1162 2017 Chillán 275879.2 2017 16101 184739 50965643906 4382 0.0237200 16101 1208902568
16101171002 16101 1033 2017 Chillán 275879.2 2017 16101 184739 50965643906 2900 0.0156978 16101 800049623
16101171003 16101 678 2017 Chillán 275879.2 2017 16101 184739 50965643906 2262 0.0122443 16101 624038706
16101171004 16101 408 2017 Chillán 275879.2 2017 16101 184739 50965643906 1590 0.0086067 16101 438647897
16101991999 16101 59 2017 Chillán 275879.2 2017 16101 184739 50965643906 304 0.0016456 16101 83867271
16102011001 16102 702 2017 Bulnes 224694.9 2017 16102 21493 4829367278 2452 0.1140837 16102 550951871
16102011002 16102 1439 2017 Bulnes 224694.9 2017 16102 21493 4829367278 4765 0.2217001 16102 1070671152
16102011003 16102 994 2017 Bulnes 224694.9 2017 16102 21493 4829367278 3184 0.1481413 16102 715428531
16102021001 16102 151 2017 Bulnes 224694.9 2017 16102 21493 4829367278 537 0.0249849 16102 120661156
16102041001 16102 442 2017 Bulnes 224694.9 2017 16102 21493 4829367278 1419 0.0660215 16102 318842049
16102051001 16102 265 2017 Bulnes 224694.9 2017 16102 21493 4829367278 949 0.0441539 16102 213235451
16102071001 16102 37 2017 Bulnes 224694.9 2017 16102 21493 4829367278 134 0.0062346 16102 30109115
16102991999 16102 13 2017 Bulnes 224694.9 2017 16102 21493 4829367278 51 0.0023729 16102 11459439
16103041001 16103 1101 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 3503 0.1133400 16103 909300069
16103041002 16103 1732 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 6143 0.1987576 16103 1594584735
16103041003 16103 1224 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 4173 0.1350180 16103 1083217011
16103041004 16103 1279 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 4071 0.1317177 16103 1056740104
16103041005 16103 876 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 2991 0.0967742 16103 776396377
16103041006 16103 773 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 2508 0.0811467 16103 651020432
16103041007 16103 1130 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 3970 0.1284499 16103 1030522774
16103991999 16103 18 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 50 0.0016178 16103 12978876
16104011001 16104 1308 2017 El Carmen 215566.5 2017 16104 12044 2596282563 4722 0.3920624 16104 1017904871
16104041001 16104 20 2017 El Carmen 215566.5 2017 16104 12044 2596282563 133 0.0110428 16104 28670340
16104991999 16104 1 2017 El Carmen 215566.5 2017 16104 12044 2596282563 6 0.0004982 16104 1293399
16105011001 16105 1090 2017 Pemuco 262037.4 2017 16105 8448 2213691761 3963 0.4691051 16105 1038454125
16105061001 16105 9 2017 Pemuco 262037.4 2017 16105 8448 2213691761 34 0.0040246 16105 8909271
16105091001 16105 40 2017 Pemuco 262037.4 2017 16105 8448 2213691761 164 0.0194129 16105 42974130
16105991999 16105 1 2017 Pemuco 262037.4 2017 16105 8448 2213691761 7 0.0008286 16105 1834262
16106011001 16106 728 2017 Pinto 175602.5 2017 16106 10827 1901248804 2222 0.2052277 16106 390188865
16106021001 16106 412 2017 Pinto 175602.5 2017 16106 10827 1901248804 1544 0.1426064 16106 271130337
16106051001 16106 298 2017 Pinto 175602.5 2017 16106 10827 1901248804 956 0.0882978 16106 167876037
16106051002 16106 203 2017 Pinto 175602.5 2017 16106 10827 1901248804 651 0.0601275 16106 114317260
16106991999 16106 29 2017 Pinto 175602.5 2017 16106 10827 1901248804 85 0.0078507 16106 14926217
16107011001 16107 1343 2017 Quillón 256072.4 2017 16107 17485 4477425886 4657 0.2663426 16107 1192529159
16107011002 16107 1054 2017 Quillón 256072.4 2017 16107 17485 4477425886 3783 0.2163569 16107 968721883
16107011004 16107 473 2017 Quillón 256072.4 2017 16107 17485 4477425886 1550 0.0886474 16107 396912217
16107051001 16107 31 2017 Quillón 256072.4 2017 16107 17485 4477425886 118 0.0067486 16107 30216543
16107061001 16107 52 2017 Quillón 256072.4 2017 16107 17485 4477425886 171 0.0097798 16107 43788380
16107991999 16107 59 2017 Quillón 256072.4 2017 16107 17485 4477425886 144 0.0082356 16107 36874425
16108011001 16108 834 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 2932 0.1823496 16108 596167970
16108051001 16108 442 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 1492 0.0927918 16108 303370604
16108051002 16108 199 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 685 0.0426022 16108 139282080
16108061001 16108 363 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 1300 0.0808508 16108 264330955
16108061002 16108 108 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 370 0.0230114 16108 75232656
16108991999 16108 7 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 23 0.0014304 16108 4676625

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 loess

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 
## -245862635  -49156933    7250485   36917977  327909227 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -9436084   11068770  -0.852    0.395    
## Freq.x        886659      13656  64.928   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 81830000 on 152 degrees of freedom
## Multiple R-squared:  0.9652, Adjusted R-squared:  0.965 
## F-statistic:  4216 on 1 and 152 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

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
dato <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
latex <- "$$ '\'hat Y = '\'beta_0 + '\'beta_1  X^2  $$"
modelos1 <- cbind(modelo,dato,sintaxis,latex)
 
### 8.2 Modelo cúbico
 
linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
dato <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
latex <- "$$ '\'hat Y = '\'beta_0 + '\'beta_1  X^3  $$"
modelos2 <- cbind(modelo,dato,sintaxis,latex)
 
### 8.3 Modelo logarítmico
 
linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
dato <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
latex <- "$$ '\'hat Y = '\'beta_0 + '\'beta_1 ln X  $$"
modelos3 <- cbind(modelo,dato,sintaxis,latex)
 
### 8.5 Modelo con raíz cuadrada 
 
linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
dato <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
latex <- "$$ '\'hat Y = '\'beta_0 + '\'beta_1  '\'sqrt {X}  $$"
modelos5 <- cbind(modelo,dato,sintaxis,latex)
 
### 8.6 Modelo raíz-raíz
 
linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
dato <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
latex <- "$$ '\'hat Y = {'\'beta_0}^2 + 2  '\'beta_0  '\'beta_1 '\'sqrt{X}+  '\'beta_1^2 X  $$"
modelos6 <- cbind(modelo,dato,sintaxis,latex)
 
### 8.7 Modelo log-raíz
 
linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
dato <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
latex <- "$$ '\'hat Y = e^{'\'beta_0 + '\'beta_1 '\'sqrt{X}} $$"
modelos7 <- cbind(modelo,dato,sintaxis,latex)
 
### 8.8 Modelo raíz-log
 
linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
dato <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
latex <- "$$ '\'hat Y = {'\'beta_0}^2 + 2  '\'beta_0  '\'beta_1 '\'ln{X}+  '\'beta_1^2 ln^2X  $$"
modelos8 <- cbind(modelo,dato,sintaxis,latex)
 
### 8.9 Modelo log-log
 
linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
dato <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
latex <- "$$ '\'hat Y = e^{'\'beta_0+'\'beta_1  ln{X}} $$"
modelos9 <- cbind(modelo,dato,sintaxis,latex)
 
modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)

modelos_bind <<- modelos_bind[order(modelos_bind$dato ),]
h_y_m_comuna_corr_01 <<- h_y_m_comuna_corr_01

kbl(modelos_bind) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")
modelo dato sintaxis latex
3 logarítmico 0.626949268098588 linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01) \[ ''hat Y = ''beta_0 + ''beta_1 ln X \]
7 raíz-log 0.830889228009096 linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01) \[ ''hat Y = {''beta_0}^2 + 2 ''beta_0 ''beta_1 ''ln{X}+ ''beta_1^2 ln^2X \]
6 log-raíz 0.848542729042225 linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01) \[ ''hat Y = e^{''beta_0 + ''beta_1 ''sqrt{X}} \]
4 raíz cuadrada 0.89651976912865 linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01) \[ ''hat Y = ''beta_0 + ''beta_1 ''sqrt {X} \]
1 cuadrático 0.964970168505122 linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01) \[ ''hat Y = ''beta_0 + ''beta_1 X^2 \]
2 cúbico 0.964970168505122 linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01) \[ ''hat Y = ''beta_0 + ''beta_1 X^3 \]
5 raíz-raíz 0.980842117040379 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01) \[ ''hat Y = {''beta_0}^2 + 2 ''beta_0 ''beta_1 ''sqrt{X}+ ''beta_1^2 X \]
8 log-log 0.987414039008157 linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01) \[ ''hat Y = e^{''beta_0+''beta_1 ln{X}} \] 
h_y_m_comuna_corr <- h_y_m_comuna_corr_01
metodo <- 8


switch (metodo,
        case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr),
        case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr),
        case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr),
        case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr),
        case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr),
        case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr),
        case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr),
        case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr)
)
summary(linearMod)
## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.65285 -0.10828  0.00185  0.11227  0.88586 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 13.536296   0.055962   241.9   <2e-16 ***
## log(Freq.x)  1.019106   0.009301   109.6   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1987 on 152 degrees of freedom
## Multiple R-squared:  0.9875, Adjusted R-squared:  0.9874 
## F-statistic: 1.2e+04 on 1 and 152 DF,  p-value: < 2.2e-16
aa <- linearMod$coefficients[1]
aa
## (Intercept) 
##     13.5363
bb <- linearMod$coefficients[2]
bb
## log(Freq.x) 
##    1.019106

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.9874).

9.1 Diagrama de dispersión sobre log-log

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

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

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr)
summary(linearMod) 
## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.65285 -0.10828  0.00185  0.11227  0.88586 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 13.536296   0.055962   241.9   <2e-16 ***
## log(Freq.x)  1.019106   0.009301   109.6   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1987 on 152 degrees of freedom
## Multiple R-squared:  0.9875, Adjusted R-squared:  0.9874 
## F-statistic: 1.2e+04 on 1 and 152 DF,  p-value: < 2.2e-16
ggplot(h_y_m_comuna_corr, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

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

9.4 Ecuación del modelo


\[ \hat Y = e^{13.5363 +1.019106 \cdot ln{X}} \]


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

Esta nueva variable se llamará: est_ing

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

r3_100 <- h_y_m_comuna_corr[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 406 2017 Chillán 275879.2 2017 16101 184739 50965643906 1080 0.0058461 16101 297949515 344430994.7
16101011002 16101 393 2017 Chillán 275879.2 2017 16101 184739 50965643906 1525 0.0082549 16101 420715750 333195181.9
16101011003 16101 813 2017 Chillán 275879.2 2017 16101 184739 50965643906 2051 0.0111021 16101 565828199 698921394.6
16101011004 16101 681 2017 Chillán 275879.2 2017 16101 184739 50965643906 1819 0.0098463 16101 501824229 583465046.6
16101021001 16101 407 2017 Chillán 275879.2 2017 16101 184739 50965643906 1345 0.0072805 16101 371057498 345295575.6
16101021002 16101 723 2017 Chillán 275879.2 2017 16101 184739 50965643906 1991 0.0107774 16101 549275448 620158369.0
16101021003 16101 699 2017 Chillán 275879.2 2017 16101 184739 50965643906 2007 0.0108640 16101 553689515 599185612.3
16101021004 16101 637 2017 Chillán 275879.2 2017 16101 184739 50965643906 1882 0.0101873 16101 519204617 545070844.9
16101031001 16101 1260 2017 Chillán 275879.2 2017 16101 184739 50965643906 3622 0.0196060 16101 999234391 1092304604.4
16101031002 16101 756 2017 Chillán 275879.2 2017 16101 184739 50965643906 2516 0.0136192 16101 694112018 649017552.2
16101031003 16101 780 2017 Chillán 275879.2 2017 16101 184739 50965643906 2184 0.0118221 16101 602520130 670021235.1
16101031004 16101 589 2017 Chillán 275879.2 2017 16101 184739 50965643906 1866 0.0101007 16101 514790551 503244179.8
16101041001 16101 1072 2017 Chillán 275879.2 2017 16101 184739 50965643906 3315 0.0179442 16101 914539483 926461227.0
16101041002 16101 573 2017 Chillán 275879.2 2017 16101 184739 50965643906 1999 0.0108207 16101 551482482 489316173.8
16101041003 16101 1134 2017 Chillán 275879.2 2017 16101 184739 50965643906 4799 0.0259772 16101 1323944187 981097220.9
16101041004 16101 744 2017 Chillán 275879.2 2017 16101 184739 50965643906 2766 0.0149725 16101 763081813 638520462.3
16101051001 16101 414 2017 Chillán 275879.2 2017 16101 184739 50965643906 1466 0.0079355 16101 404438878 351348772.7
16101051002 16101 1556 2017 Chillán 275879.2 2017 16101 184739 50965643906 4764 0.0257877 16101 1314288415 1354358503.2
16101051003 16101 677 2017 Chillán 275879.2 2017 16101 184739 50965643906 2374 0.0128506 16101 654937174 579972658.2
16101051004 16101 618 2017 Chillán 275879.2 2017 16101 184739 50965643906 2018 0.0109235 16101 556724186 528506992.3
16101051005 16101 1469 2017 Chillán 275879.2 2017 16101 184739 50965643906 4539 0.0245698 16101 1252215600 1277228006.2
16101061001 16101 297 2017 Chillán 275879.2 2017 16101 184739 50965643906 941 0.0050937 16101 259602309 250460169.7
16101071001 16101 427 2017 Chillán 275879.2 2017 16101 184739 50965643906 1599 0.0086555 16101 441130809 362595589.4
16101071002 16101 289 2017 Chillán 275879.2 2017 16101 184739 50965643906 950 0.0051424 16101 262085221 243586658.1
16101081001 16101 377 2017 Chillán 275879.2 2017 16101 184739 50965643906 1276 0.0069070 16101 352021834 319376260.9
16101121001 16101 1328 2017 Chillán 275879.2 2017 16101 184739 50965643906 4876 0.0263940 16101 1345186884 1152411092.4
16101121002 16101 49 2017 Chillán 275879.2 2017 16101 184739 50965643906 186 0.0010068 16101 51313528 39923347.4
16101131001 16101 1728 2017 Chillán 275879.2 2017 16101 184739 50965643906 5741 0.0310763 16101 1583822375 1507084993.1
16101131002 16101 754 2017 Chillán 275879.2 2017 16101 184739 50965643906 2211 0.0119682 16101 609968868 647267814.7
16101131003 16101 763 2017 Chillán 275879.2 2017 16101 184739 50965643906 2135 0.0115568 16101 589002050 655142328.1
16101131004 16101 1217 2017 Chillán 275879.2 2017 16101 184739 50965643906 4141 0.0224154 16101 1142415686 1054327864.1
16101141001 16101 1825 2017 Chillán 275879.2 2017 16101 184739 50965643906 5365 0.0290410 16101 1480091803 1593345823.3
16101141002 16101 1756 2017 Chillán 275879.2 2017 16101 184739 50965643906 5814 0.0314714 16101 1603961555 1531975751.5
16101141003 16101 984 2017 Chillán 275879.2 2017 16101 184739 50965643906 3016 0.0163257 16101 832051608 849017879.3
16101141004 16101 1206 2017 Chillán 275879.2 2017 16101 184739 50965643906 3759 0.0203476 16101 1037029839 1044616965.8
16101151001 16101 949 2017 Chillán 275879.2 2017 16101 184739 50965643906 3362 0.0181986 16101 927505804 818252684.6
16101151002 16101 948 2017 Chillán 275879.2 2017 16101 184739 50965643906 3634 0.0196710 16101 1002544942 817373993.8
16101151003 16101 524 2017 Chillán 275879.2 2017 16101 184739 50965643906 1805 0.0097705 16101 497961921 446708781.2
16101151004 16101 1161 2017 Chillán 275879.2 2017 16101 184739 50965643906 3489 0.0188861 16101 962542460 1004908349.9
16101151005 16101 1396 2017 Chillán 275879.2 2017 16101 184739 50965643906 4931 0.0266917 16101 1360360238 1212576437.0
16101151006 16101 1113 2017 Chillán 275879.2 2017 16101 184739 50965643906 4103 0.0222097 16101 1131932277 962584928.3
16101151007 16101 643 2017 Chillán 275879.2 2017 16101 184739 50965643906 2402 0.0130021 16101 662661791 550303510.2
16101151008 16101 911 2017 Chillán 275879.2 2017 16101 184739 50965643906 3208 0.0173650 16101 885020411 784875041.2
16101151009 16101 1303 2017 Chillán 275879.2 2017 16101 184739 50965643906 4520 0.0244670 16101 1246973895 1130306119.1
16101151010 16101 1294 2017 Chillán 275879.2 2017 16101 184739 50965643906 4906 0.0265564 16101 1353463259 1122350304.1
16101151011 16101 737 2017 Chillán 275879.2 2017 16101 184739 50965643906 2718 0.0147126 16101 749839612 632398650.2
16101151012 16101 627 2017 Chillán 275879.2 2017 16101 184739 50965643906 2161 0.0116976 16101 596174909 536351833.0
16101151013 16101 1196 2017 Chillán 275879.2 2017 16101 184739 50965643906 3743 0.0202610 16101 1032615772 1035790344.5
16101151014 16101 1093 2017 Chillán 275879.2 2017 16101 184739 50965643906 3883 0.0210188 16101 1071238857 944960375.2
16101151015 16101 70 2017 Chillán 275879.2 2017 16101 184739 50965643906 248 0.0013424 16101 68418037 57423335.7
16101161001 16101 637 2017 Chillán 275879.2 2017 16101 184739 50965643906 2140 0.0115839 16101 590381446 545070844.9
16101161002 16101 753 2017 Chillán 275879.2 2017 16101 184739 50965643906 2196 0.0118870 16101 605830680 646392979.3
16101161003 16101 1237 2017 Chillán 275879.2 2017 16101 184739 50965643906 3884 0.0210243 16101 1071514737 1071988329.2
16101161004 16101 781 2017 Chillán 275879.2 2017 16101 184739 50965643906 2612 0.0141389 16101 720596419 670896659.2
16101161005 16101 950 2017 Chillán 275879.2 2017 16101 184739 50965643906 3326 0.0180038 16101 917574154 819131393.1
16101171001 16101 1162 2017 Chillán 275879.2 2017 16101 184739 50965643906 4382 0.0237200 16101 1208902568 1005790448.3
16101171002 16101 1033 2017 Chillán 275879.2 2017 16101 184739 50965643906 2900 0.0156978 16101 800049623 892124135.5
16101171003 16101 678 2017 Chillán 275879.2 2017 16101 184739 50965643906 2262 0.0122443 16101 624038706 580845718.4
16101171004 16101 408 2017 Chillán 275879.2 2017 16101 184739 50965643906 1590 0.0086067 16101 438647897 346160197.0
16101991999 16101 59 2017 Chillán 275879.2 2017 16101 184739 50965643906 304 0.0016456 16101 83867271 48241840.3
16102011001 16102 702 2017 Bulnes 224694.9 2017 16102 21493 4829367278 2452 0.1140837 16102 550951871 601806464.1
16102011002 16102 1439 2017 Bulnes 224694.9 2017 16102 21493 4829367278 4765 0.2217001 16102 1070671152 1250651259.9
16102011003 16102 994 2017 Bulnes 224694.9 2017 16102 21493 4829367278 3184 0.1481413 16102 715428531 857811808.8
16102021001 16102 151 2017 Bulnes 224694.9 2017 16102 21493 4829367278 537 0.0249849 16102 120661156 125703193.0
16102041001 16102 442 2017 Bulnes 224694.9 2017 16102 21493 4829367278 1419 0.0660215 16102 318842049 375580805.9
16102051001 16102 265 2017 Bulnes 224694.9 2017 16102 21493 4829367278 949 0.0441539 16102 213235451 222988343.7
16102071001 16102 37 2017 Bulnes 224694.9 2017 16102 21493 4829367278 134 0.0062346 16102 30109115 29984844.9
16102991999 16102 13 2017 Bulnes 224694.9 2017 16102 21493 4829367278 51 0.0023729 16102 11459439 10326770.3
16103041001 16103 1101 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 3503 0.1133400 16103 909300069 952009463.3
16103041002 16103 1732 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 6143 0.1987576 16103 1594584735 1510640346.8
16103041003 16103 1224 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 4173 0.1350180 16103 1083217011 1060508400.7
16103041004 16103 1279 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 4071 0.1317177 16103 1056740104 1109092965.2
16103041005 16103 876 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 2991 0.0967742 16103 776396377 754155979.4
16103041006 16103 773 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 2508 0.0811467 16103 651020432 663893868.2
16103041007 16103 1130 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 3970 0.1284499 16103 1030522774 977570561.2
16103991999 16103 18 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 50 0.0016178 16103 12978876 14387782.4
16104011001 16104 1308 2017 El Carmen 215566.5 2017 16104 12044 2596282563 4722 0.3920624 16104 1017904871 1134726470.4
16104041001 16104 20 2017 El Carmen 215566.5 2017 16104 12044 2596282563 133 0.0110428 16104 28670340 16018637.7
16104991999 16104 1 2017 El Carmen 215566.5 2017 16104 12044 2596282563 6 0.0004982 16104 1293399 756377.3
16105011001 16105 1090 2017 Pemuco 262037.4 2017 16105 8448 2213691761 3963 0.4691051 16105 1038454125 942317220.9
16105061001 16105 9 2017 Pemuco 262037.4 2017 16105 8448 2213691761 34 0.0040246 16105 8909271 7099250.2
16105091001 16105 40 2017 Pemuco 262037.4 2017 16105 8448 2213691761 164 0.0194129 16105 42974130 32464368.5
16105991999 16105 1 2017 Pemuco 262037.4 2017 16105 8448 2213691761 7 0.0008286 16105 1834262 756377.3
16106011001 16106 728 2017 Pinto 175602.5 2017 16106 10827 1901248804 2222 0.2052277 16106 390188865 624529382.7
16106021001 16106 412 2017 Pinto 175602.5 2017 16106 10827 1901248804 1544 0.1426064 16106 271130337 349619086.8
16106051001 16106 298 2017 Pinto 175602.5 2017 16106 10827 1901248804 956 0.0882978 16106 167876037 251319609.4
16106051002 16106 203 2017 Pinto 175602.5 2017 16106 10827 1901248804 651 0.0601275 16106 114317260 169949873.5
16106991999 16106 29 2017 Pinto 175602.5 2017 16106 10827 1901248804 85 0.0078507 16106 14926217 23392499.4
16107011001 16107 1343 2017 Quillón 256072.4 2017 16107 17485 4477425886 4657 0.2663426 16107 1192529159 1165677902.8
16107011002 16107 1054 2017 Quillón 256072.4 2017 16107 17485 4477425886 3783 0.2163569 16107 968721883 910610319.2
16107011004 16107 473 2017 Quillón 256072.4 2017 16107 17485 4477425886 1550 0.0886474 16107 396912217 402443308.6
16107051001 16107 31 2017 Quillón 256072.4 2017 16107 17485 4477425886 118 0.0067486 16107 30216543 25037657.5
16107061001 16107 52 2017 Quillón 256072.4 2017 16107 17485 4477425886 171 0.0097798 16107 43788380 42415762.4
16107991999 16107 59 2017 Quillón 256072.4 2017 16107 17485 4477425886 144 0.0082356 16107 36874425 48241840.3
16108011001 16108 834 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 2932 0.1823496 16108 596167970 717324137.8
16108051001 16108 442 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 1492 0.0927918 16108 303370604 375580805.9
16108051002 16108 199 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 685 0.0426022 16108 139282080 166537773.6
16108061001 16108 363 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 1300 0.0808508 16108 264330955 307293879.4
16108061002 16108 108 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 370 0.0230114 16108 75232656 89333062.8
16108991999 16108 7 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 23 0.0014304 16108 4676625 5495190.2


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

r3_100 <- h_y_m_comuna_corr[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 406 2017 Chillán 275879.2 2017 16101 184739 50965643906 1080 0.0058461 16101 297949515 344430994.7 318917.6
16101011002 16101 393 2017 Chillán 275879.2 2017 16101 184739 50965643906 1525 0.0082549 16101 420715750 333195181.9 218488.6
16101011003 16101 813 2017 Chillán 275879.2 2017 16101 184739 50965643906 2051 0.0111021 16101 565828199 698921394.6 340771.0
16101011004 16101 681 2017 Chillán 275879.2 2017 16101 184739 50965643906 1819 0.0098463 16101 501824229 583465046.6 320761.4
16101021001 16101 407 2017 Chillán 275879.2 2017 16101 184739 50965643906 1345 0.0072805 16101 371057498 345295575.6 256725.3
16101021002 16101 723 2017 Chillán 275879.2 2017 16101 184739 50965643906 1991 0.0107774 16101 549275448 620158369.0 311480.8
16101021003 16101 699 2017 Chillán 275879.2 2017 16101 184739 50965643906 2007 0.0108640 16101 553689515 599185612.3 298547.9
16101021004 16101 637 2017 Chillán 275879.2 2017 16101 184739 50965643906 1882 0.0101873 16101 519204617 545070844.9 289623.2
16101031001 16101 1260 2017 Chillán 275879.2 2017 16101 184739 50965643906 3622 0.0196060 16101 999234391 1092304604.4 301575.0
16101031002 16101 756 2017 Chillán 275879.2 2017 16101 184739 50965643906 2516 0.0136192 16101 694112018 649017552.2 257956.1
16101031003 16101 780 2017 Chillán 275879.2 2017 16101 184739 50965643906 2184 0.0118221 16101 602520130 670021235.1 306786.3
16101031004 16101 589 2017 Chillán 275879.2 2017 16101 184739 50965643906 1866 0.0101007 16101 514790551 503244179.8 269691.4
16101041001 16101 1072 2017 Chillán 275879.2 2017 16101 184739 50965643906 3315 0.0179442 16101 914539483 926461227.0 279475.5
16101041002 16101 573 2017 Chillán 275879.2 2017 16101 184739 50965643906 1999 0.0108207 16101 551482482 489316173.8 244780.5
16101041003 16101 1134 2017 Chillán 275879.2 2017 16101 184739 50965643906 4799 0.0259772 16101 1323944187 981097220.9 204437.8
16101041004 16101 744 2017 Chillán 275879.2 2017 16101 184739 50965643906 2766 0.0149725 16101 763081813 638520462.3 230846.2
16101051001 16101 414 2017 Chillán 275879.2 2017 16101 184739 50965643906 1466 0.0079355 16101 404438878 351348772.7 239664.9
16101051002 16101 1556 2017 Chillán 275879.2 2017 16101 184739 50965643906 4764 0.0257877 16101 1314288415 1354358503.2 284290.2
16101051003 16101 677 2017 Chillán 275879.2 2017 16101 184739 50965643906 2374 0.0128506 16101 654937174 579972658.2 244301.9
16101051004 16101 618 2017 Chillán 275879.2 2017 16101 184739 50965643906 2018 0.0109235 16101 556724186 528506992.3 261896.4
16101051005 16101 1469 2017 Chillán 275879.2 2017 16101 184739 50965643906 4539 0.0245698 16101 1252215600 1277228006.2 281389.7
16101061001 16101 297 2017 Chillán 275879.2 2017 16101 184739 50965643906 941 0.0050937 16101 259602309 250460169.7 266163.8
16101071001 16101 427 2017 Chillán 275879.2 2017 16101 184739 50965643906 1599 0.0086555 16101 441130809 362595589.4 226764.0
16101071002 16101 289 2017 Chillán 275879.2 2017 16101 184739 50965643906 950 0.0051424 16101 262085221 243586658.1 256407.0
16101081001 16101 377 2017 Chillán 275879.2 2017 16101 184739 50965643906 1276 0.0069070 16101 352021834 319376260.9 250294.9
16101121001 16101 1328 2017 Chillán 275879.2 2017 16101 184739 50965643906 4876 0.0263940 16101 1345186884 1152411092.4 236343.5
16101121002 16101 49 2017 Chillán 275879.2 2017 16101 184739 50965643906 186 0.0010068 16101 51313528 39923347.4 214641.7
16101131001 16101 1728 2017 Chillán 275879.2 2017 16101 184739 50965643906 5741 0.0310763 16101 1583822375 1507084993.1 262512.6
16101131002 16101 754 2017 Chillán 275879.2 2017 16101 184739 50965643906 2211 0.0119682 16101 609968868 647267814.7 292748.9
16101131003 16101 763 2017 Chillán 275879.2 2017 16101 184739 50965643906 2135 0.0115568 16101 589002050 655142328.1 306858.2
16101131004 16101 1217 2017 Chillán 275879.2 2017 16101 184739 50965643906 4141 0.0224154 16101 1142415686 1054327864.1 254607.1
16101141001 16101 1825 2017 Chillán 275879.2 2017 16101 184739 50965643906 5365 0.0290410 16101 1480091803 1593345823.3 296989.0
16101141002 16101 1756 2017 Chillán 275879.2 2017 16101 184739 50965643906 5814 0.0314714 16101 1603961555 1531975751.5 263497.7
16101141003 16101 984 2017 Chillán 275879.2 2017 16101 184739 50965643906 3016 0.0163257 16101 832051608 849017879.3 281504.6
16101141004 16101 1206 2017 Chillán 275879.2 2017 16101 184739 50965643906 3759 0.0203476 16101 1037029839 1044616965.8 277897.6
16101151001 16101 949 2017 Chillán 275879.2 2017 16101 184739 50965643906 3362 0.0181986 16101 927505804 818252684.6 243382.7
16101151002 16101 948 2017 Chillán 275879.2 2017 16101 184739 50965643906 3634 0.0196710 16101 1002544942 817373993.8 224924.0
16101151003 16101 524 2017 Chillán 275879.2 2017 16101 184739 50965643906 1805 0.0097705 16101 497961921 446708781.2 247484.1
16101151004 16101 1161 2017 Chillán 275879.2 2017 16101 184739 50965643906 3489 0.0188861 16101 962542460 1004908349.9 288021.9
16101151005 16101 1396 2017 Chillán 275879.2 2017 16101 184739 50965643906 4931 0.0266917 16101 1360360238 1212576437.0 245908.8
16101151006 16101 1113 2017 Chillán 275879.2 2017 16101 184739 50965643906 4103 0.0222097 16101 1131932277 962584928.3 234605.1
16101151007 16101 643 2017 Chillán 275879.2 2017 16101 184739 50965643906 2402 0.0130021 16101 662661791 550303510.2 229102.2
16101151008 16101 911 2017 Chillán 275879.2 2017 16101 184739 50965643906 3208 0.0173650 16101 885020411 784875041.2 244661.8
16101151009 16101 1303 2017 Chillán 275879.2 2017 16101 184739 50965643906 4520 0.0244670 16101 1246973895 1130306119.1 250067.7
16101151010 16101 1294 2017 Chillán 275879.2 2017 16101 184739 50965643906 4906 0.0265564 16101 1353463259 1122350304.1 228771.0
16101151011 16101 737 2017 Chillán 275879.2 2017 16101 184739 50965643906 2718 0.0147126 16101 749839612 632398650.2 232670.6
16101151012 16101 627 2017 Chillán 275879.2 2017 16101 184739 50965643906 2161 0.0116976 16101 596174909 536351833.0 248196.1
16101151013 16101 1196 2017 Chillán 275879.2 2017 16101 184739 50965643906 3743 0.0202610 16101 1032615772 1035790344.5 276727.3
16101151014 16101 1093 2017 Chillán 275879.2 2017 16101 184739 50965643906 3883 0.0210188 16101 1071238857 944960375.2 243358.3
16101151015 16101 70 2017 Chillán 275879.2 2017 16101 184739 50965643906 248 0.0013424 16101 68418037 57423335.7 231545.7
16101161001 16101 637 2017 Chillán 275879.2 2017 16101 184739 50965643906 2140 0.0115839 16101 590381446 545070844.9 254706.0
16101161002 16101 753 2017 Chillán 275879.2 2017 16101 184739 50965643906 2196 0.0118870 16101 605830680 646392979.3 294350.2
16101161003 16101 1237 2017 Chillán 275879.2 2017 16101 184739 50965643906 3884 0.0210243 16101 1071514737 1071988329.2 276001.1
16101161004 16101 781 2017 Chillán 275879.2 2017 16101 184739 50965643906 2612 0.0141389 16101 720596419 670896659.2 256851.7
16101161005 16101 950 2017 Chillán 275879.2 2017 16101 184739 50965643906 3326 0.0180038 16101 917574154 819131393.1 246281.2
16101171001 16101 1162 2017 Chillán 275879.2 2017 16101 184739 50965643906 4382 0.0237200 16101 1208902568 1005790448.3 229527.7
16101171002 16101 1033 2017 Chillán 275879.2 2017 16101 184739 50965643906 2900 0.0156978 16101 800049623 892124135.5 307629.0
16101171003 16101 678 2017 Chillán 275879.2 2017 16101 184739 50965643906 2262 0.0122443 16101 624038706 580845718.4 256784.1
16101171004 16101 408 2017 Chillán 275879.2 2017 16101 184739 50965643906 1590 0.0086067 16101 438647897 346160197.0 217710.8
16101991999 16101 59 2017 Chillán 275879.2 2017 16101 184739 50965643906 304 0.0016456 16101 83867271 48241840.3 158690.3
16102011001 16102 702 2017 Bulnes 224694.9 2017 16102 21493 4829367278 2452 0.1140837 16102 550951871 601806464.1 245434.9
16102011002 16102 1439 2017 Bulnes 224694.9 2017 16102 21493 4829367278 4765 0.2217001 16102 1070671152 1250651259.9 262466.2
16102011003 16102 994 2017 Bulnes 224694.9 2017 16102 21493 4829367278 3184 0.1481413 16102 715428531 857811808.8 269413.3
16102021001 16102 151 2017 Bulnes 224694.9 2017 16102 21493 4829367278 537 0.0249849 16102 120661156 125703193.0 234084.2
16102041001 16102 442 2017 Bulnes 224694.9 2017 16102 21493 4829367278 1419 0.0660215 16102 318842049 375580805.9 264679.9
16102051001 16102 265 2017 Bulnes 224694.9 2017 16102 21493 4829367278 949 0.0441539 16102 213235451 222988343.7 234971.9
16102071001 16102 37 2017 Bulnes 224694.9 2017 16102 21493 4829367278 134 0.0062346 16102 30109115 29984844.9 223767.5
16102991999 16102 13 2017 Bulnes 224694.9 2017 16102 21493 4829367278 51 0.0023729 16102 11459439 10326770.3 202485.7
16103041001 16103 1101 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 3503 0.1133400 16103 909300069 952009463.3 271769.8
16103041002 16103 1732 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 6143 0.1987576 16103 1594584735 1510640346.8 245912.5
16103041003 16103 1224 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 4173 0.1350180 16103 1083217011 1060508400.7 254135.7
16103041004 16103 1279 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 4071 0.1317177 16103 1056740104 1109092965.2 272437.5
16103041005 16103 876 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 2991 0.0967742 16103 776396377 754155979.4 252141.8
16103041006 16103 773 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 2508 0.0811467 16103 651020432 663893868.2 264710.5
16103041007 16103 1130 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 3970 0.1284499 16103 1030522774 977570561.2 246239.4
16103991999 16103 18 2017 Chillán Viejo 259577.5 2017 16103 30907 8022762560 50 0.0016178 16103 12978876 14387782.4 287755.6
16104011001 16104 1308 2017 El Carmen 215566.5 2017 16104 12044 2596282563 4722 0.3920624 16104 1017904871 1134726470.4 240306.3
16104041001 16104 20 2017 El Carmen 215566.5 2017 16104 12044 2596282563 133 0.0110428 16104 28670340 16018637.7 120440.9
16104991999 16104 1 2017 El Carmen 215566.5 2017 16104 12044 2596282563 6 0.0004982 16104 1293399 756377.3 126062.9
16105011001 16105 1090 2017 Pemuco 262037.4 2017 16105 8448 2213691761 3963 0.4691051 16105 1038454125 942317220.9 237778.8
16105061001 16105 9 2017 Pemuco 262037.4 2017 16105 8448 2213691761 34 0.0040246 16105 8909271 7099250.2 208801.5
16105091001 16105 40 2017 Pemuco 262037.4 2017 16105 8448 2213691761 164 0.0194129 16105 42974130 32464368.5 197953.5
16105991999 16105 1 2017 Pemuco 262037.4 2017 16105 8448 2213691761 7 0.0008286 16105 1834262 756377.3 108053.9
16106011001 16106 728 2017 Pinto 175602.5 2017 16106 10827 1901248804 2222 0.2052277 16106 390188865 624529382.7 281066.3
16106021001 16106 412 2017 Pinto 175602.5 2017 16106 10827 1901248804 1544 0.1426064 16106 271130337 349619086.8 226437.2
16106051001 16106 298 2017 Pinto 175602.5 2017 16106 10827 1901248804 956 0.0882978 16106 167876037 251319609.4 262886.6
16106051002 16106 203 2017 Pinto 175602.5 2017 16106 10827 1901248804 651 0.0601275 16106 114317260 169949873.5 261059.7
16106991999 16106 29 2017 Pinto 175602.5 2017 16106 10827 1901248804 85 0.0078507 16106 14926217 23392499.4 275205.9
16107011001 16107 1343 2017 Quillón 256072.4 2017 16107 17485 4477425886 4657 0.2663426 16107 1192529159 1165677902.8 250306.6
16107011002 16107 1054 2017 Quillón 256072.4 2017 16107 17485 4477425886 3783 0.2163569 16107 968721883 910610319.2 240711.2
16107011004 16107 473 2017 Quillón 256072.4 2017 16107 17485 4477425886 1550 0.0886474 16107 396912217 402443308.6 259640.8
16107051001 16107 31 2017 Quillón 256072.4 2017 16107 17485 4477425886 118 0.0067486 16107 30216543 25037657.5 212183.5
16107061001 16107 52 2017 Quillón 256072.4 2017 16107 17485 4477425886 171 0.0097798 16107 43788380 42415762.4 248045.4
16107991999 16107 59 2017 Quillón 256072.4 2017 16107 17485 4477425886 144 0.0082356 16107 36874425 48241840.3 335012.8
16108011001 16108 834 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 2932 0.1823496 16108 596167970 717324137.8 244653.5
16108051001 16108 442 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 1492 0.0927918 16108 303370604 375580805.9 251729.8
16108051002 16108 199 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 685 0.0426022 16108 139282080 166537773.6 243120.8
16108061001 16108 363 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 1300 0.0808508 16108 264330955 307293879.4 236379.9
16108061002 16108 108 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 370 0.0230114 16108 75232656 89333062.8 241440.7
16108991999 16108 7 2017 San Ignacio 203331.5 2017 16108 16079 3269367252 23 0.0014304 16108 4676625 5495190.2 238921.3


Guardamos:

saveRDS(h_y_m_comuna_corr, "P03C/region_16_P03C_u.rds")