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$REGION == 14) 
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$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 14101011001 1 14101 1053 2017
2 14101021001 1 14101 1847 2017
3 14101031001 1 14101 564 2017
4 14101041001 1 14101 974 2017
5 14101041002 1 14101 356 2017
6 14101041003 1 14101 1076 2017
7 14101041004 1 14101 795 2017
8 14101041005 1 14101 1444 2017
9 14101051001 1 14101 1139 2017
10 14101051002 1 14101 852 2017
11 14101051003 1 14101 947 2017
12 14101051004 1 14101 337 2017
13 14101051005 1 14101 1241 2017
14 14101061001 1 14101 1318 2017
15 14101061002 1 14101 677 2017
16 14101061003 1 14101 1088 2017
17 14101061004 1 14101 1270 2017
18 14101061005 1 14101 728 2017
19 14101061006 1 14101 419 2017
20 14101061007 1 14101 13 2017
21 14101071001 1 14101 1291 2017
22 14101071002 1 14101 1147 2017
23 14101071003 1 14101 1444 2017
24 14101071004 1 14101 554 2017
25 14101071005 1 14101 1733 2017
26 14101071006 1 14101 1378 2017
27 14101081001 1 14101 1725 2017
28 14101081002 1 14101 900 2017
29 14101081003 1 14101 1423 2017
30 14101081004 1 14101 1276 2017
31 14101081005 1 14101 1286 2017
32 14101081006 1 14101 1400 2017
33 14101081007 1 14101 972 2017
34 14101081008 1 14101 1417 2017
35 14101081009 1 14101 1024 2017
36 14101081010 1 14101 1699 2017
37 14101081011 1 14101 781 2017
38 14101091001 1 14101 1081 2017
39 14101091002 1 14101 1524 2017
40 14101101001 1 14101 525 2017
41 14101101002 1 14101 911 2017
42 14101101003 1 14101 867 2017
43 14101161001 1 14101 536 2017
44 14101171001 1 14101 1349 2017
45 14101991999 1 14101 81 2017
145 14102011001 1 14102 1133 2017
146 14102991999 1 14102 2 2017
246 14103011001 1 14103 812 2017
247 14103011002 1 14103 670 2017
248 14103011003 1 14103 1000 2017
249 14103031001 1 14103 960 2017
250 14103991999 1 14103 8 2017
350 14104011001 1 14104 990 2017
351 14104051001 1 14104 1033 2017
352 14104051002 1 14104 807 2017
353 14104991999 1 14104 19 2017
453 14105011001 1 14105 1137 2017
454 14105991999 1 14105 2 2017
554 14106011001 1 14106 937 2017
555 14106011002 1 14106 843 2017
556 14106011003 1 14106 740 2017
557 14106991999 1 14106 81 2017
657 14107021001 1 14107 8 2017
658 14107031001 1 14107 835 2017
659 14107031002 1 14107 1217 2017
660 14107031003 1 14107 1057 2017
661 14107051001 1 14107 318 2017
662 14107991999 1 14107 51 2017
762 14108011001 1 14108 1334 2017
763 14108011002 1 14108 1330 2017
764 14108011003 1 14108 569 2017
765 14108051001 1 14108 469 2017
766 14108111001 1 14108 563 2017
767 14108991999 1 14108 242 2017
867 14201011001 1 14201 829 2017
868 14201011002 1 14201 469 2017
869 14201011003 1 14201 585 2017
870 14201011004 1 14201 837 2017
871 14201021001 1 14201 60 2017
872 14201091001 1 14201 447 2017
873 14201091002 1 14201 951 2017
874 14201091003 1 14201 862 2017
875 14201091004 1 14201 1113 2017
876 14201091005 1 14201 1434 2017
877 14201991999 1 14201 56 2017
977 14202011001 1 14202 1211 2017
978 14202011002 1 14202 901 2017
979 14202021001 1 14202 53 2017
980 14202041001 1 14202 262 2017
981 14202991999 1 14202 22 2017
1081 14203011001 1 14203 768 2017
1082 14203991999 1 14203 53 2017
1182 14204011001 1 14204 780 2017
1183 14204011002 1 14204 1201 2017
1184 14204011003 1 14204 1099 2017
1185 14204011004 1 14204 1009 2017
1186 14204011005 1 14204 697 2017
1187 14204071001 1 14204 116 2017
1188 14204991999 1 14204 98 2017
NA NA NA NA NA NA

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")
zona Freq anio código
1 14101011001 1053 2017 14101
2 14101021001 1847 2017 14101
3 14101031001 564 2017 14101
4 14101041001 974 2017 14101
5 14101041002 356 2017 14101
6 14101041003 1076 2017 14101
7 14101041004 795 2017 14101
8 14101041005 1444 2017 14101
9 14101051001 1139 2017 14101
10 14101051002 852 2017 14101
11 14101051003 947 2017 14101
12 14101051004 337 2017 14101
13 14101051005 1241 2017 14101
14 14101061001 1318 2017 14101
15 14101061002 677 2017 14101
16 14101061003 1088 2017 14101
17 14101061004 1270 2017 14101
18 14101061005 728 2017 14101
19 14101061006 419 2017 14101
20 14101061007 13 2017 14101
21 14101071001 1291 2017 14101
22 14101071002 1147 2017 14101
23 14101071003 1444 2017 14101
24 14101071004 554 2017 14101
25 14101071005 1733 2017 14101
26 14101071006 1378 2017 14101
27 14101081001 1725 2017 14101
28 14101081002 900 2017 14101
29 14101081003 1423 2017 14101
30 14101081004 1276 2017 14101
31 14101081005 1286 2017 14101
32 14101081006 1400 2017 14101
33 14101081007 972 2017 14101
34 14101081008 1417 2017 14101
35 14101081009 1024 2017 14101
36 14101081010 1699 2017 14101
37 14101081011 781 2017 14101
38 14101091001 1081 2017 14101
39 14101091002 1524 2017 14101
40 14101101001 525 2017 14101
41 14101101002 911 2017 14101
42 14101101003 867 2017 14101
43 14101161001 536 2017 14101
44 14101171001 1349 2017 14101
45 14101991999 81 2017 14101
145 14102011001 1133 2017 14102
146 14102991999 2 2017 14102
246 14103011001 812 2017 14103
247 14103011002 670 2017 14103
248 14103011003 1000 2017 14103
249 14103031001 960 2017 14103
250 14103991999 8 2017 14103
350 14104011001 990 2017 14104
351 14104051001 1033 2017 14104
352 14104051002 807 2017 14104
353 14104991999 19 2017 14104
453 14105011001 1137 2017 14105
454 14105991999 2 2017 14105
554 14106011001 937 2017 14106
555 14106011002 843 2017 14106
556 14106011003 740 2017 14106
557 14106991999 81 2017 14106
657 14107021001 8 2017 14107
658 14107031001 835 2017 14107
659 14107031002 1217 2017 14107
660 14107031003 1057 2017 14107
661 14107051001 318 2017 14107
662 14107991999 51 2017 14107
762 14108011001 1334 2017 14108
763 14108011002 1330 2017 14108
764 14108011003 569 2017 14108
765 14108051001 469 2017 14108
766 14108111001 563 2017 14108
767 14108991999 242 2017 14108
867 14201011001 829 2017 14201
868 14201011002 469 2017 14201
869 14201011003 585 2017 14201
870 14201011004 837 2017 14201
871 14201021001 60 2017 14201
872 14201091001 447 2017 14201
873 14201091002 951 2017 14201
874 14201091003 862 2017 14201
875 14201091004 1113 2017 14201
876 14201091005 1434 2017 14201
877 14201991999 56 2017 14201
977 14202011001 1211 2017 14202
978 14202011002 901 2017 14202
979 14202021001 53 2017 14202
980 14202041001 262 2017 14202
981 14202991999 22 2017 14202
1081 14203011001 768 2017 14203
1082 14203991999 53 2017 14203
1182 14204011001 780 2017 14204
1183 14204011002 1201 2017 14204
1184 14204011003 1099 2017 14204
1185 14204011004 1009 2017 14204
1186 14204011005 697 2017 14204
1187 14204071001 116 2017 14204
1188 14204991999 98 2017 14204
NA NA NA NA NA


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
1 14101 14101011001 1053 2017 Valdivia 308754.5 2017 14101 166080 51277944139
2 14101 14101021001 1847 2017 Valdivia 308754.5 2017 14101 166080 51277944139
3 14101 14101031001 564 2017 Valdivia 308754.5 2017 14101 166080 51277944139
4 14101 14101041001 974 2017 Valdivia 308754.5 2017 14101 166080 51277944139
5 14101 14101041002 356 2017 Valdivia 308754.5 2017 14101 166080 51277944139
6 14101 14101041003 1076 2017 Valdivia 308754.5 2017 14101 166080 51277944139
7 14101 14101041004 795 2017 Valdivia 308754.5 2017 14101 166080 51277944139
8 14101 14101041005 1444 2017 Valdivia 308754.5 2017 14101 166080 51277944139
9 14101 14101051001 1139 2017 Valdivia 308754.5 2017 14101 166080 51277944139
10 14101 14101051002 852 2017 Valdivia 308754.5 2017 14101 166080 51277944139
11 14101 14101051003 947 2017 Valdivia 308754.5 2017 14101 166080 51277944139
12 14101 14101051004 337 2017 Valdivia 308754.5 2017 14101 166080 51277944139
13 14101 14101051005 1241 2017 Valdivia 308754.5 2017 14101 166080 51277944139
14 14101 14101061001 1318 2017 Valdivia 308754.5 2017 14101 166080 51277944139
15 14101 14101061002 677 2017 Valdivia 308754.5 2017 14101 166080 51277944139
16 14101 14101061003 1088 2017 Valdivia 308754.5 2017 14101 166080 51277944139
17 14101 14101061004 1270 2017 Valdivia 308754.5 2017 14101 166080 51277944139
18 14101 14101061005 728 2017 Valdivia 308754.5 2017 14101 166080 51277944139
19 14101 14101061006 419 2017 Valdivia 308754.5 2017 14101 166080 51277944139
20 14101 14101061007 13 2017 Valdivia 308754.5 2017 14101 166080 51277944139
21 14101 14101071001 1291 2017 Valdivia 308754.5 2017 14101 166080 51277944139
22 14101 14101071002 1147 2017 Valdivia 308754.5 2017 14101 166080 51277944139
23 14101 14101071003 1444 2017 Valdivia 308754.5 2017 14101 166080 51277944139
24 14101 14101071004 554 2017 Valdivia 308754.5 2017 14101 166080 51277944139
25 14101 14101071005 1733 2017 Valdivia 308754.5 2017 14101 166080 51277944139
26 14101 14101071006 1378 2017 Valdivia 308754.5 2017 14101 166080 51277944139
27 14101 14101081001 1725 2017 Valdivia 308754.5 2017 14101 166080 51277944139
28 14101 14101081002 900 2017 Valdivia 308754.5 2017 14101 166080 51277944139
29 14101 14101081003 1423 2017 Valdivia 308754.5 2017 14101 166080 51277944139
30 14101 14101081004 1276 2017 Valdivia 308754.5 2017 14101 166080 51277944139
31 14101 14101081005 1286 2017 Valdivia 308754.5 2017 14101 166080 51277944139
32 14101 14101081006 1400 2017 Valdivia 308754.5 2017 14101 166080 51277944139
33 14101 14101081007 972 2017 Valdivia 308754.5 2017 14101 166080 51277944139
34 14101 14101081008 1417 2017 Valdivia 308754.5 2017 14101 166080 51277944139
35 14101 14101081009 1024 2017 Valdivia 308754.5 2017 14101 166080 51277944139
36 14101 14101081010 1699 2017 Valdivia 308754.5 2017 14101 166080 51277944139
37 14101 14101081011 781 2017 Valdivia 308754.5 2017 14101 166080 51277944139
38 14101 14101091001 1081 2017 Valdivia 308754.5 2017 14101 166080 51277944139
39 14101 14101091002 1524 2017 Valdivia 308754.5 2017 14101 166080 51277944139
40 14101 14101101001 525 2017 Valdivia 308754.5 2017 14101 166080 51277944139
41 14101 14101101002 911 2017 Valdivia 308754.5 2017 14101 166080 51277944139
42 14101 14101101003 867 2017 Valdivia 308754.5 2017 14101 166080 51277944139
43 14101 14101161001 536 2017 Valdivia 308754.5 2017 14101 166080 51277944139
44 14101 14101171001 1349 2017 Valdivia 308754.5 2017 14101 166080 51277944139
45 14101 14101991999 81 2017 Valdivia 308754.5 2017 14101 166080 51277944139
46 14102 14102011001 1133 2017 Corral 222523.9 2017 14102 5302 1179821617
47 14102 14102991999 2 2017 Corral 222523.9 2017 14102 5302 1179821617
48 14103 14103011001 812 2017 Lanco 267286.0 2017 14103 16752 4477574931
49 14103 14103011002 670 2017 Lanco 267286.0 2017 14103 16752 4477574931
50 14103 14103011003 1000 2017 Lanco 267286.0 2017 14103 16752 4477574931
51 14103 14103031001 960 2017 Lanco 267286.0 2017 14103 16752 4477574931
52 14103 14103991999 8 2017 Lanco 267286.0 2017 14103 16752 4477574931
53 14104 14104011001 990 2017 Los Lagos 211843.1 2017 14104 19634 4159328181
54 14104 14104051001 1033 2017 Los Lagos 211843.1 2017 14104 19634 4159328181
55 14104 14104051002 807 2017 Los Lagos 211843.1 2017 14104 19634 4159328181
56 14104 14104991999 19 2017 Los Lagos 211843.1 2017 14104 19634 4159328181
57 14105 14105011001 1137 2017 Máfil 315022.1 2017 14105 7095 2235081533
58 14105 14105991999 2 2017 Máfil 315022.1 2017 14105 7095 2235081533
59 14106 14106011001 937 2017 Mariquina 251064.3 2017 14106 21278 5342147079
60 14106 14106011002 843 2017 Mariquina 251064.3 2017 14106 21278 5342147079
61 14106 14106011003 740 2017 Mariquina 251064.3 2017 14106 21278 5342147079
62 14106 14106991999 81 2017 Mariquina 251064.3 2017 14106 21278 5342147079
63 14107 14107021001 8 2017 Paillaco 223306.5 2017 14107 20188 4508111622
64 14107 14107031001 835 2017 Paillaco 223306.5 2017 14107 20188 4508111622
65 14107 14107031002 1217 2017 Paillaco 223306.5 2017 14107 20188 4508111622
66 14107 14107031003 1057 2017 Paillaco 223306.5 2017 14107 20188 4508111622
67 14107 14107051001 318 2017 Paillaco 223306.5 2017 14107 20188 4508111622
68 14107 14107991999 51 2017 Paillaco 223306.5 2017 14107 20188 4508111622
69 14108 14108011001 1334 2017 Panguipulli 287752.5 2017 14108 34539 9938682028
70 14108 14108011002 1330 2017 Panguipulli 287752.5 2017 14108 34539 9938682028
71 14108 14108011003 569 2017 Panguipulli 287752.5 2017 14108 34539 9938682028
72 14108 14108051001 469 2017 Panguipulli 287752.5 2017 14108 34539 9938682028
73 14108 14108111001 563 2017 Panguipulli 287752.5 2017 14108 34539 9938682028
74 14108 14108991999 242 2017 Panguipulli 287752.5 2017 14108 34539 9938682028
75 14201 14201011001 829 2017 La Unión 247291.7 2017 14201 38036 9405987850
76 14201 14201011002 469 2017 La Unión 247291.7 2017 14201 38036 9405987850
77 14201 14201011003 585 2017 La Unión 247291.7 2017 14201 38036 9405987850
78 14201 14201011004 837 2017 La Unión 247291.7 2017 14201 38036 9405987850
79 14201 14201021001 60 2017 La Unión 247291.7 2017 14201 38036 9405987850
80 14201 14201091001 447 2017 La Unión 247291.7 2017 14201 38036 9405987850
81 14201 14201091002 951 2017 La Unión 247291.7 2017 14201 38036 9405987850
82 14201 14201091003 862 2017 La Unión 247291.7 2017 14201 38036 9405987850
83 14201 14201091004 1113 2017 La Unión 247291.7 2017 14201 38036 9405987850
84 14201 14201091005 1434 2017 La Unión 247291.7 2017 14201 38036 9405987850
85 14201 14201991999 56 2017 La Unión 247291.7 2017 14201 38036 9405987850
86 14202 14202011001 1211 2017 Futrono 247331.7 2017 14202 14665 3627119212
87 14202 14202011002 901 2017 Futrono 247331.7 2017 14202 14665 3627119212
88 14202 14202021001 53 2017 Futrono 247331.7 2017 14202 14665 3627119212
89 14202 14202041001 262 2017 Futrono 247331.7 2017 14202 14665 3627119212
90 14202 14202991999 22 2017 Futrono 247331.7 2017 14202 14665 3627119212
91 14203 14203011001 768 2017 Lago Ranco 247154.2 2017 14203 9896 2445838259
92 14203 14203991999 53 2017 Lago Ranco 247154.2 2017 14203 9896 2445838259
93 14204 14204011001 780 2017 Río Bueno 267934.4 2017 14204 31372 8405637271
94 14204 14204011002 1201 2017 Río Bueno 267934.4 2017 14204 31372 8405637271
95 14204 14204011003 1099 2017 Río Bueno 267934.4 2017 14204 31372 8405637271
96 14204 14204011004 1009 2017 Río Bueno 267934.4 2017 14204 31372 8405637271
97 14204 14204011005 697 2017 Río Bueno 267934.4 2017 14204 31372 8405637271
98 14204 14204071001 116 2017 Río Bueno 267934.4 2017 14204 31372 8405637271
99 14204 14204991999 98 2017 Río Bueno 267934.4 2017 14204 31372 8405637271
NA NA NA NA NA NA NA NA NA NA NA


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
1 14101 14101011001 1053 2017 Valdivia 308754.5 2017 14101 166080 51277944139
2 14101 14101021001 1847 2017 Valdivia 308754.5 2017 14101 166080 51277944139
3 14101 14101031001 564 2017 Valdivia 308754.5 2017 14101 166080 51277944139
4 14101 14101041001 974 2017 Valdivia 308754.5 2017 14101 166080 51277944139
5 14101 14101041002 356 2017 Valdivia 308754.5 2017 14101 166080 51277944139
6 14101 14101041003 1076 2017 Valdivia 308754.5 2017 14101 166080 51277944139
7 14101 14101041004 795 2017 Valdivia 308754.5 2017 14101 166080 51277944139
8 14101 14101041005 1444 2017 Valdivia 308754.5 2017 14101 166080 51277944139
9 14101 14101051001 1139 2017 Valdivia 308754.5 2017 14101 166080 51277944139
10 14101 14101051002 852 2017 Valdivia 308754.5 2017 14101 166080 51277944139
11 14101 14101051003 947 2017 Valdivia 308754.5 2017 14101 166080 51277944139
12 14101 14101051004 337 2017 Valdivia 308754.5 2017 14101 166080 51277944139
13 14101 14101051005 1241 2017 Valdivia 308754.5 2017 14101 166080 51277944139
14 14101 14101061001 1318 2017 Valdivia 308754.5 2017 14101 166080 51277944139
15 14101 14101061002 677 2017 Valdivia 308754.5 2017 14101 166080 51277944139
16 14101 14101061003 1088 2017 Valdivia 308754.5 2017 14101 166080 51277944139
17 14101 14101061004 1270 2017 Valdivia 308754.5 2017 14101 166080 51277944139
18 14101 14101061005 728 2017 Valdivia 308754.5 2017 14101 166080 51277944139
19 14101 14101061006 419 2017 Valdivia 308754.5 2017 14101 166080 51277944139
20 14101 14101061007 13 2017 Valdivia 308754.5 2017 14101 166080 51277944139
21 14101 14101071001 1291 2017 Valdivia 308754.5 2017 14101 166080 51277944139
22 14101 14101071002 1147 2017 Valdivia 308754.5 2017 14101 166080 51277944139
23 14101 14101071003 1444 2017 Valdivia 308754.5 2017 14101 166080 51277944139
24 14101 14101071004 554 2017 Valdivia 308754.5 2017 14101 166080 51277944139
25 14101 14101071005 1733 2017 Valdivia 308754.5 2017 14101 166080 51277944139
26 14101 14101071006 1378 2017 Valdivia 308754.5 2017 14101 166080 51277944139
27 14101 14101081001 1725 2017 Valdivia 308754.5 2017 14101 166080 51277944139
28 14101 14101081002 900 2017 Valdivia 308754.5 2017 14101 166080 51277944139
29 14101 14101081003 1423 2017 Valdivia 308754.5 2017 14101 166080 51277944139
30 14101 14101081004 1276 2017 Valdivia 308754.5 2017 14101 166080 51277944139
31 14101 14101081005 1286 2017 Valdivia 308754.5 2017 14101 166080 51277944139
32 14101 14101081006 1400 2017 Valdivia 308754.5 2017 14101 166080 51277944139
33 14101 14101081007 972 2017 Valdivia 308754.5 2017 14101 166080 51277944139
34 14101 14101081008 1417 2017 Valdivia 308754.5 2017 14101 166080 51277944139
35 14101 14101081009 1024 2017 Valdivia 308754.5 2017 14101 166080 51277944139
36 14101 14101081010 1699 2017 Valdivia 308754.5 2017 14101 166080 51277944139
37 14101 14101081011 781 2017 Valdivia 308754.5 2017 14101 166080 51277944139
38 14101 14101091001 1081 2017 Valdivia 308754.5 2017 14101 166080 51277944139
39 14101 14101091002 1524 2017 Valdivia 308754.5 2017 14101 166080 51277944139
40 14101 14101101001 525 2017 Valdivia 308754.5 2017 14101 166080 51277944139
41 14101 14101101002 911 2017 Valdivia 308754.5 2017 14101 166080 51277944139
42 14101 14101101003 867 2017 Valdivia 308754.5 2017 14101 166080 51277944139
43 14101 14101161001 536 2017 Valdivia 308754.5 2017 14101 166080 51277944139
44 14101 14101171001 1349 2017 Valdivia 308754.5 2017 14101 166080 51277944139
45 14101 14101991999 81 2017 Valdivia 308754.5 2017 14101 166080 51277944139
46 14102 14102011001 1133 2017 Corral 222523.9 2017 14102 5302 1179821617
47 14102 14102991999 2 2017 Corral 222523.9 2017 14102 5302 1179821617
48 14103 14103011001 812 2017 Lanco 267286.0 2017 14103 16752 4477574931
49 14103 14103011002 670 2017 Lanco 267286.0 2017 14103 16752 4477574931
50 14103 14103011003 1000 2017 Lanco 267286.0 2017 14103 16752 4477574931
51 14103 14103031001 960 2017 Lanco 267286.0 2017 14103 16752 4477574931
52 14103 14103991999 8 2017 Lanco 267286.0 2017 14103 16752 4477574931
53 14104 14104011001 990 2017 Los Lagos 211843.1 2017 14104 19634 4159328181
54 14104 14104051001 1033 2017 Los Lagos 211843.1 2017 14104 19634 4159328181
55 14104 14104051002 807 2017 Los Lagos 211843.1 2017 14104 19634 4159328181
56 14104 14104991999 19 2017 Los Lagos 211843.1 2017 14104 19634 4159328181
57 14105 14105011001 1137 2017 Máfil 315022.1 2017 14105 7095 2235081533
58 14105 14105991999 2 2017 Máfil 315022.1 2017 14105 7095 2235081533
59 14106 14106011001 937 2017 Mariquina 251064.3 2017 14106 21278 5342147079
60 14106 14106011002 843 2017 Mariquina 251064.3 2017 14106 21278 5342147079
61 14106 14106011003 740 2017 Mariquina 251064.3 2017 14106 21278 5342147079
62 14106 14106991999 81 2017 Mariquina 251064.3 2017 14106 21278 5342147079
63 14107 14107021001 8 2017 Paillaco 223306.5 2017 14107 20188 4508111622
64 14107 14107031001 835 2017 Paillaco 223306.5 2017 14107 20188 4508111622
65 14107 14107031002 1217 2017 Paillaco 223306.5 2017 14107 20188 4508111622
66 14107 14107031003 1057 2017 Paillaco 223306.5 2017 14107 20188 4508111622
67 14107 14107051001 318 2017 Paillaco 223306.5 2017 14107 20188 4508111622
68 14107 14107991999 51 2017 Paillaco 223306.5 2017 14107 20188 4508111622
69 14108 14108011001 1334 2017 Panguipulli 287752.5 2017 14108 34539 9938682028
70 14108 14108011002 1330 2017 Panguipulli 287752.5 2017 14108 34539 9938682028
71 14108 14108011003 569 2017 Panguipulli 287752.5 2017 14108 34539 9938682028
72 14108 14108051001 469 2017 Panguipulli 287752.5 2017 14108 34539 9938682028
73 14108 14108111001 563 2017 Panguipulli 287752.5 2017 14108 34539 9938682028
74 14108 14108991999 242 2017 Panguipulli 287752.5 2017 14108 34539 9938682028
75 14201 14201011001 829 2017 La Unión 247291.7 2017 14201 38036 9405987850
76 14201 14201011002 469 2017 La Unión 247291.7 2017 14201 38036 9405987850
77 14201 14201011003 585 2017 La Unión 247291.7 2017 14201 38036 9405987850
78 14201 14201011004 837 2017 La Unión 247291.7 2017 14201 38036 9405987850
79 14201 14201021001 60 2017 La Unión 247291.7 2017 14201 38036 9405987850
80 14201 14201091001 447 2017 La Unión 247291.7 2017 14201 38036 9405987850
81 14201 14201091002 951 2017 La Unión 247291.7 2017 14201 38036 9405987850
82 14201 14201091003 862 2017 La Unión 247291.7 2017 14201 38036 9405987850
83 14201 14201091004 1113 2017 La Unión 247291.7 2017 14201 38036 9405987850
84 14201 14201091005 1434 2017 La Unión 247291.7 2017 14201 38036 9405987850
85 14201 14201991999 56 2017 La Unión 247291.7 2017 14201 38036 9405987850
86 14202 14202011001 1211 2017 Futrono 247331.7 2017 14202 14665 3627119212
87 14202 14202011002 901 2017 Futrono 247331.7 2017 14202 14665 3627119212
88 14202 14202021001 53 2017 Futrono 247331.7 2017 14202 14665 3627119212
89 14202 14202041001 262 2017 Futrono 247331.7 2017 14202 14665 3627119212
90 14202 14202991999 22 2017 Futrono 247331.7 2017 14202 14665 3627119212
91 14203 14203011001 768 2017 Lago Ranco 247154.2 2017 14203 9896 2445838259
92 14203 14203991999 53 2017 Lago Ranco 247154.2 2017 14203 9896 2445838259
93 14204 14204011001 780 2017 Río Bueno 267934.4 2017 14204 31372 8405637271
94 14204 14204011002 1201 2017 Río Bueno 267934.4 2017 14204 31372 8405637271
95 14204 14204011003 1099 2017 Río Bueno 267934.4 2017 14204 31372 8405637271
96 14204 14204011004 1009 2017 Río Bueno 267934.4 2017 14204 31372 8405637271
97 14204 14204011005 697 2017 Río Bueno 267934.4 2017 14204 31372 8405637271
98 14204 14204071001 116 2017 Río Bueno 267934.4 2017 14204 31372 8405637271
99 14204 14204991999 98 2017 Río Bueno 267934.4 2017 14204 31372 8405637271
NA NA NA NA NA NA NA NA NA NA NA


6 Ingreso promedio expandido por zona (multi_pob)

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

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

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

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")
zona código.x Freq.x anio comuna.x promedio_i año comuna.y personas Ingresos_expandidos Freq.y p_poblacional código.y
14101011001 14101 1053 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3316 0.0199663 14101
14101021001 14101 1847 2017 Valdivia 308754.5 2017 14101 166080 51277944139 5505 0.0331467 14101
14101031001 14101 564 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1916 0.0115366 14101
14101041001 14101 974 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3347 0.0201529 14101
14101041002 14101 356 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1217 0.0073278 14101
14101041003 14101 1076 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3319 0.0199843 14101
14101041004 14101 795 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3079 0.0185393 14101
14101041005 14101 1444 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4543 0.0273543 14101
14101051001 14101 1139 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3908 0.0235308 14101
14101051002 14101 852 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2924 0.0176060 14101
14101051003 14101 947 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3046 0.0183406 14101
14101051004 14101 337 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1117 0.0067257 14101
14101051005 14101 1241 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3677 0.0221399 14101
14101061001 14101 1318 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4741 0.0285465 14101
14101061002 14101 677 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2213 0.0133249 14101
14101061003 14101 1088 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3643 0.0219352 14101
14101061004 14101 1270 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4455 0.0268244 14101
14101061005 14101 728 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2502 0.0150650 14101
14101061006 14101 419 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1422 0.0085621 14101
14101061007 14101 13 2017 Valdivia 308754.5 2017 14101 166080 51277944139 33 0.0001987 14101
14101071001 14101 1291 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4214 0.0253733 14101
14101071002 14101 1147 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3859 0.0232358 14101
14101071003 14101 1444 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4649 0.0279925 14101
14101071004 14101 554 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1808 0.0108863 14101
14101071005 14101 1733 2017 Valdivia 308754.5 2017 14101 166080 51277944139 6057 0.0364704 14101
14101071006 14101 1378 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4766 0.0286970 14101
14101081001 14101 1725 2017 Valdivia 308754.5 2017 14101 166080 51277944139 5118 0.0308165 14101
14101081002 14101 900 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3425 0.0206226 14101
14101081003 14101 1423 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4434 0.0266980 14101
14101081004 14101 1276 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4941 0.0297507 14101
14101081005 14101 1286 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3782 0.0227722 14101
14101081006 14101 1400 2017 Valdivia 308754.5 2017 14101 166080 51277944139 5317 0.0320147 14101
14101081007 14101 972 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3719 0.0223928 14101
14101081008 14101 1417 2017 Valdivia 308754.5 2017 14101 166080 51277944139 5423 0.0326529 14101
14101081009 14101 1024 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3389 0.0204058 14101
14101081010 14101 1699 2017 Valdivia 308754.5 2017 14101 166080 51277944139 5849 0.0352180 14101
14101081011 14101 781 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2888 0.0173892 14101
14101091001 14101 1081 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3024 0.0182081 14101
14101091002 14101 1524 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4672 0.0281310 14101
14101101001 14101 525 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1538 0.0092606 14101
14101101002 14101 911 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2989 0.0179974 14101
14101101003 14101 867 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2463 0.0148302 14101
14101161001 14101 536 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1801 0.0108442 14101
14101171001 14101 1349 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3989 0.0240185 14101
14101991999 14101 81 2017 Valdivia 308754.5 2017 14101 166080 51277944139 679 0.0040884 14101
14102011001 14102 1133 2017 Corral 222523.9 2017 14102 5302 1179821617 3469 0.6542814 14102
14102991999 14102 2 2017 Corral 222523.9 2017 14102 5302 1179821617 12 0.0022633 14102
14103011001 14103 812 2017 Lanco 267286.0 2017 14103 16752 4477574931 2488 0.1485196 14103
14103011002 14103 670 2017 Lanco 267286.0 2017 14103 16752 4477574931 2058 0.1228510 14103
14103011003 14103 1000 2017 Lanco 267286.0 2017 14103 16752 4477574931 3975 0.2372851 14103
14103031001 14103 960 2017 Lanco 267286.0 2017 14103 16752 4477574931 3061 0.1827245 14103
14103991999 14103 8 2017 Lanco 267286.0 2017 14103 16752 4477574931 25 0.0014924 14103
14104011001 14104 990 2017 Los Lagos 211843.1 2017 14104 19634 4159328181 3294 0.1677702 14104
14104051001 14104 1033 2017 Los Lagos 211843.1 2017 14104 19634 4159328181 3514 0.1789752 14104
14104051002 14104 807 2017 Los Lagos 211843.1 2017 14104 19634 4159328181 2938 0.1496384 14104
14104991999 14104 19 2017 Los Lagos 211843.1 2017 14104 19634 4159328181 52 0.0026485 14104
14105011001 14105 1137 2017 Máfil 315022.1 2017 14105 7095 2235081533 4239 0.5974630 14105
14105991999 14105 2 2017 Máfil 315022.1 2017 14105 7095 2235081533 4 0.0005638 14105
14106011001 14106 937 2017 Mariquina 251064.3 2017 14106 21278 5342147079 3463 0.1627503 14106
14106011002 14106 843 2017 Mariquina 251064.3 2017 14106 21278 5342147079 3400 0.1597895 14106
14106011003 14106 740 2017 Mariquina 251064.3 2017 14106 21278 5342147079 2904 0.1364790 14106
14106991999 14106 81 2017 Mariquina 251064.3 2017 14106 21278 5342147079 192 0.0090234 14106
14107021001 14107 8 2017 Paillaco 223306.5 2017 14107 20188 4508111622 21 0.0010402 14107
14107031001 14107 835 2017 Paillaco 223306.5 2017 14107 20188 4508111622 2834 0.1403804 14107
14107031002 14107 1217 2017 Paillaco 223306.5 2017 14107 20188 4508111622 4222 0.2091341 14107
14107031003 14107 1057 2017 Paillaco 223306.5 2017 14107 20188 4508111622 4219 0.2089855 14107
14107051001 14107 318 2017 Paillaco 223306.5 2017 14107 20188 4508111622 1001 0.0495839 14107
14107991999 14107 51 2017 Paillaco 223306.5 2017 14107 20188 4508111622 155 0.0076778 14107
14108011001 14108 1334 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 5054 0.1463273 14108
14108011002 14108 1330 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 4341 0.1256840 14108
14108011003 14108 569 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 1696 0.0491039 14108
14108051001 14108 469 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 1477 0.0427633 14108
14108111001 14108 563 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 1955 0.0566027 14108
14108991999 14108 242 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 750 0.0217146 14108
14201011001 14201 829 2017 La Unión 247291.7 2017 14201 38036 9405987850 2522 0.0663056 14201
14201011002 14201 469 2017 La Unión 247291.7 2017 14201 38036 9405987850 1283 0.0337312 14201
14201011003 14201 585 2017 La Unión 247291.7 2017 14201 38036 9405987850 1697 0.0446156 14201
14201011004 14201 837 2017 La Unión 247291.7 2017 14201 38036 9405987850 3020 0.0793985 14201
14201021001 14201 60 2017 La Unión 247291.7 2017 14201 38036 9405987850 177 0.0046535 14201
14201091001 14201 447 2017 La Unión 247291.7 2017 14201 38036 9405987850 1445 0.0379903 14201
14201091002 14201 951 2017 La Unión 247291.7 2017 14201 38036 9405987850 3737 0.0982490 14201
14201091003 14201 862 2017 La Unión 247291.7 2017 14201 38036 9405987850 2778 0.0730361 14201
14201091004 14201 1113 2017 La Unión 247291.7 2017 14201 38036 9405987850 4130 0.1085813 14201
14201091005 14201 1434 2017 La Unión 247291.7 2017 14201 38036 9405987850 5728 0.1505942 14201
14201991999 14201 56 2017 La Unión 247291.7 2017 14201 38036 9405987850 145 0.0038122 14201
14202011001 14202 1211 2017 Futrono 247331.7 2017 14202 14665 3627119212 4140 0.2823048 14202
14202011002 14202 901 2017 Futrono 247331.7 2017 14202 14665 3627119212 2955 0.2015002 14202
14202021001 14202 53 2017 Futrono 247331.7 2017 14202 14665 3627119212 160 0.0109103 14202
14202041001 14202 262 2017 Futrono 247331.7 2017 14202 14665 3627119212 844 0.0575520 14202
14202991999 14202 22 2017 Futrono 247331.7 2017 14202 14665 3627119212 84 0.0057279 14202
14203011001 14203 768 2017 Lago Ranco 247154.2 2017 14203 9896 2445838259 2146 0.2168553 14203
14203991999 14203 53 2017 Lago Ranco 247154.2 2017 14203 9896 2445838259 123 0.0124293 14203
14204011001 14204 780 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 2356 0.0750988 14204
14204011002 14204 1201 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 4161 0.1326342 14204
14204011003 14204 1099 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 3509 0.1118513 14204
14204011004 14204 1009 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 3988 0.1271197 14204
14204011005 14204 697 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 2512 0.0800714 14204
14204071001 14204 116 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 383 0.0122083 14204
14204991999 14204 98 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 234 0.0074589 14204


Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")
zona código.x Freq.x anio comuna.x promedio_i año comuna.y personas Ingresos_expandidos Freq.y p_poblacional código.y multi_pob
14101011001 14101 1053 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3316 0.0199663 14101 1023829858
14101021001 14101 1847 2017 Valdivia 308754.5 2017 14101 166080 51277944139 5505 0.0331467 14101 1699693416
14101031001 14101 564 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1916 0.0115366 14101 591573585
14101041001 14101 974 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3347 0.0201529 14101 1033401247
14101041002 14101 356 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1217 0.0073278 14101 375754203
14101041003 14101 1076 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3319 0.0199843 14101 1024756121
14101041004 14101 795 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3079 0.0185393 14101 950655046
14101041005 14101 1444 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4543 0.0273543 14101 1402671605
14101051001 14101 1139 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3908 0.0235308 14101 1206612510
14101051002 14101 852 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2924 0.0176060 14101 902798101
14101051003 14101 947 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3046 0.0183406 14101 940466148
14101051004 14101 337 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1117 0.0067257 14101 344878755
14101051005 14101 1241 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3677 0.0221399 14101 1135290225
14101061001 14101 1318 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4741 0.0285465 14101 1463804993
14101061002 14101 677 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2213 0.0133249 14101 683273666
14101061003 14101 1088 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3643 0.0219352 14101 1124792573
14101061004 14101 1270 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4455 0.0268244 14101 1375501211
14101061005 14101 728 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2502 0.0150650 14101 772503710
14101061006 14101 419 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1422 0.0085621 14101 439048871
14101061007 14101 13 2017 Valdivia 308754.5 2017 14101 166080 51277944139 33 0.0001987 14101 10188898
14101071001 14101 1291 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4214 0.0253733 14101 1301091381
14101071002 14101 1147 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3859 0.0232358 14101 1191483541
14101071003 14101 1444 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4649 0.0279925 14101 1435399580
14101071004 14101 554 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1808 0.0108863 14101 558228101
14101071005 14101 1733 2017 Valdivia 308754.5 2017 14101 166080 51277944139 6057 0.0364704 14101 1870125889
14101071006 14101 1378 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4766 0.0286970 14101 1471523855
14101081001 14101 1725 2017 Valdivia 308754.5 2017 14101 166080 51277944139 5118 0.0308165 14101 1580205432
14101081002 14101 900 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3425 0.0206226 14101 1057484096
14101081003 14101 1423 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4434 0.0266980 14101 1369017367
14101081004 14101 1276 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4941 0.0297507 14101 1525555889
14101081005 14101 1286 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3782 0.0227722 14101 1167709446
14101081006 14101 1400 2017 Valdivia 308754.5 2017 14101 166080 51277944139 5317 0.0320147 14101 1641647573
14101081007 14101 972 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3719 0.0223928 14101 1148257913
14101081008 14101 1417 2017 Valdivia 308754.5 2017 14101 166080 51277944139 5423 0.0326529 14101 1674375548
14101081009 14101 1024 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3389 0.0204058 14101 1046368935
14101081010 14101 1699 2017 Valdivia 308754.5 2017 14101 166080 51277944139 5849 0.0352180 14101 1805904957
14101081011 14101 781 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2888 0.0173892 14101 891682940
14101091001 14101 1081 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3024 0.0182081 14101 933673549
14101091002 14101 1524 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4672 0.0281310 14101 1442500933
14101101001 14101 525 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1538 0.0092606 14101 474864391
14101101002 14101 911 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2989 0.0179974 14101 922867143
14101101003 14101 867 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2463 0.0148302 14101 760462286
14101161001 14101 536 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1801 0.0108442 14101 556066820
14101171001 14101 1349 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3989 0.0240185 14101 1231621623
14101991999 14101 81 2017 Valdivia 308754.5 2017 14101 166080 51277944139 679 0.0040884 14101 209644292
14102011001 14102 1133 2017 Corral 222523.9 2017 14102 5302 1179821617 3469 0.6542814 14102 771935343
14102991999 14102 2 2017 Corral 222523.9 2017 14102 5302 1179821617 12 0.0022633 14102 2670287
14103011001 14103 812 2017 Lanco 267286.0 2017 14103 16752 4477574931 2488 0.1485196 14103 665007547
14103011002 14103 670 2017 Lanco 267286.0 2017 14103 16752 4477574931 2058 0.1228510 14103 550074571
14103011003 14103 1000 2017 Lanco 267286.0 2017 14103 16752 4477574931 3975 0.2372851 14103 1062461817
14103031001 14103 960 2017 Lanco 267286.0 2017 14103 16752 4477574931 3061 0.1827245 14103 818162420
14103991999 14103 8 2017 Lanco 267286.0 2017 14103 16752 4477574931 25 0.0014924 14103 6682150
14104011001 14104 990 2017 Los Lagos 211843.1 2017 14104 19634 4159328181 3294 0.1677702 14104 697811298
14104051001 14104 1033 2017 Los Lagos 211843.1 2017 14104 19634 4159328181 3514 0.1789752 14104 744416789
14104051002 14104 807 2017 Los Lagos 211843.1 2017 14104 19634 4159328181 2938 0.1496384 14104 622395141
14104991999 14104 19 2017 Los Lagos 211843.1 2017 14104 19634 4159328181 52 0.0026485 14104 11015843
14105011001 14105 1137 2017 Máfil 315022.1 2017 14105 7095 2235081533 4239 0.5974630 14105 1335378523
14105991999 14105 2 2017 Máfil 315022.1 2017 14105 7095 2235081533 4 0.0005638 14105 1260088
14106011001 14106 937 2017 Mariquina 251064.3 2017 14106 21278 5342147079 3463 0.1627503 14106 869435818
14106011002 14106 843 2017 Mariquina 251064.3 2017 14106 21278 5342147079 3400 0.1597895 14106 853618764
14106011003 14106 740 2017 Mariquina 251064.3 2017 14106 21278 5342147079 2904 0.1364790 14106 729090851
14106991999 14106 81 2017 Mariquina 251064.3 2017 14106 21278 5342147079 192 0.0090234 14106 48204354
14107021001 14107 8 2017 Paillaco 223306.5 2017 14107 20188 4508111622 21 0.0010402 14107 4689437
14107031001 14107 835 2017 Paillaco 223306.5 2017 14107 20188 4508111622 2834 0.1403804 14107 632850621
14107031002 14107 1217 2017 Paillaco 223306.5 2017 14107 20188 4508111622 4222 0.2091341 14107 942800043
14107031003 14107 1057 2017 Paillaco 223306.5 2017 14107 20188 4508111622 4219 0.2089855 14107 942130124
14107051001 14107 318 2017 Paillaco 223306.5 2017 14107 20188 4508111622 1001 0.0495839 14107 223529807
14107991999 14107 51 2017 Paillaco 223306.5 2017 14107 20188 4508111622 155 0.0076778 14107 34612508
14108011001 14108 1334 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 5054 0.1463273 14108 1454300905
14108011002 14108 1330 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 4341 0.1256840 14108 1249133405
14108011003 14108 569 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 1696 0.0491039 14108 488028163
14108051001 14108 469 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 1477 0.0427633 14108 425010375
14108111001 14108 563 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 1955 0.0566027 14108 562556049
14108991999 14108 242 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 750 0.0217146 14108 215814341
14201011001 14201 829 2017 La Unión 247291.7 2017 14201 38036 9405987850 2522 0.0663056 14201 623669717
14201011002 14201 469 2017 La Unión 247291.7 2017 14201 38036 9405987850 1283 0.0337312 14201 317275276
14201011003 14201 585 2017 La Unión 247291.7 2017 14201 38036 9405987850 1697 0.0446156 14201 419654048
14201011004 14201 837 2017 La Unión 247291.7 2017 14201 38036 9405987850 3020 0.0793985 14201 746820993
14201021001 14201 60 2017 La Unión 247291.7 2017 14201 38036 9405987850 177 0.0046535 14201 43770634
14201091001 14201 447 2017 La Unión 247291.7 2017 14201 38036 9405987850 1445 0.0379903 14201 357336535
14201091002 14201 951 2017 La Unión 247291.7 2017 14201 38036 9405987850 3737 0.0982490 14201 924129156
14201091003 14201 862 2017 La Unión 247291.7 2017 14201 38036 9405987850 2778 0.0730361 14201 686976397
14201091004 14201 1113 2017 La Unión 247291.7 2017 14201 38036 9405987850 4130 0.1085813 14201 1021314802
14201091005 14201 1434 2017 La Unión 247291.7 2017 14201 38036 9405987850 5728 0.1505942 14201 1416486970
14201991999 14201 56 2017 La Unión 247291.7 2017 14201 38036 9405987850 145 0.0038122 14201 35857299
14202011001 14202 1211 2017 Futrono 247331.7 2017 14202 14665 3627119212 4140 0.2823048 14202 1023953190
14202011002 14202 901 2017 Futrono 247331.7 2017 14202 14665 3627119212 2955 0.2015002 14202 730865140
14202021001 14202 53 2017 Futrono 247331.7 2017 14202 14665 3627119212 160 0.0109103 14202 39573070
14202041001 14202 262 2017 Futrono 247331.7 2017 14202 14665 3627119212 844 0.0575520 14202 208747945
14202991999 14202 22 2017 Futrono 247331.7 2017 14202 14665 3627119212 84 0.0057279 14202 20775862
14203011001 14203 768 2017 Lago Ranco 247154.2 2017 14203 9896 2445838259 2146 0.2168553 14203 530392977
14203991999 14203 53 2017 Lago Ranco 247154.2 2017 14203 9896 2445838259 123 0.0124293 14203 30399970
14204011001 14204 780 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 2356 0.0750988 14204 631253392
14204011002 14204 1201 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 4161 0.1326342 14204 1114874942
14204011003 14204 1099 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 3509 0.1118513 14204 940181728
14204011004 14204 1009 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 3988 0.1271197 14204 1068522295
14204011005 14204 697 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 2512 0.0800714 14204 673051155
14204071001 14204 116 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 383 0.0122083 14204 102618866
14204991999 14204 98 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 234 0.0074589 14204 62696644

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 
## -331726228  -76444910   13836444   52452272  285490242 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -34220444   23869779  -1.434    0.155    
## Freq.x        1004309      25106  40.003   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 118600000 on 97 degrees of freedom
## Multiple R-squared:  0.9428, Adjusted R-squared:  0.9423 
## F-statistic:  1600 on 1 and 97 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 0.62611522923095 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 0.828997404603517 linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01) \[ ''hat Y = {''beta_0}^2 + 2 ''beta_0 ''beta_1 ''ln{X}+ ''beta_1^2 ln^2X \]
4 raíz cuadrada 0.867971896970868 linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01) \[ ''hat Y = ''beta_0 + ''beta_1 ''sqrt {X} \]
6 log-raíz 0.875170453358846 linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01) \[ ''hat Y = e^{''beta_0 + ''beta_1 ''sqrt{X}} \]
1 cuadrático 0.942259177965357 linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01) \[ ''hat Y = ''beta_0 + ''beta_1 X^2 \]
2 cúbico 0.942259177965357 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 0.966310400975436 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 0.982639246743163 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 
## -0.35921 -0.11892  0.00643  0.10975  1.12768 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 13.43240    0.08989  149.44   <2e-16 ***
## log(Freq.x)  1.04697    0.01406   74.48   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2032 on 97 degrees of freedom
## Multiple R-squared:  0.9828, Adjusted R-squared:  0.9826 
## F-statistic:  5548 on 1 and 97 DF,  p-value: < 2.2e-16
aa <- linearMod$coefficients[1]
aa
## (Intercept) 
##     13.4324
bb <- linearMod$coefficients[2]
bb
## log(Freq.x) 
##    1.046968

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.9826).

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 
## -0.35921 -0.11892  0.00643  0.10975  1.12768 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 13.43240    0.08989  149.44   <2e-16 ***
## log(Freq.x)  1.04697    0.01406   74.48   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2032 on 97 degrees of freedom
## Multiple R-squared:  0.9828, Adjusted R-squared:  0.9826 
## F-statistic:  5548 on 1 and 97 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


\[ \hat Y = e^{13.4324 +1.046968 \cdot ln{X}} \]


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
1 14101011001 14101 1053 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3316 0.0199663 14101 1023829858 995408931
2 14101021001 14101 1847 2017 Valdivia 308754.5 2017 14101 166080 51277944139 5505 0.0331467 14101 1699693416 1792677270
3 14101031001 14101 564 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1916 0.0115366 14101 591573585 517746137
4 14101041001 14101 974 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3347 0.0201529 14101 1033401247 917363238
5 14101041002 14101 356 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1217 0.0073278 14101 375754203 319817441
6 14101041003 14101 1076 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3319 0.0199843 14101 1024756121 1018183789
7 14101041004 14101 795 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3079 0.0185393 14101 950655046 741664157
8 14101041005 14101 1444 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4543 0.0273543 14101 1402671605 1385420218
9 14101051001 14101 1139 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3908 0.0235308 14101 1206612510 1080682910
10 14101051002 14101 852 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2924 0.0176060 14101 902798101 797429334
11 14101051003 14101 947 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3046 0.0183406 14101 940466148 890756337
12 14101051004 14101 337 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1117 0.0067257 14101 344878755 301969625
13 14101051005 14101 1241 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3677 0.0221399 14101 1135290225 1182213232
14 14101061001 14101 1318 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4741 0.0285465 14101 1463804993 1259120702
15 14101061002 14101 677 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2213 0.0133249 14101 683273666 626832436
16 14101061003 14101 1088 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3643 0.0219352 14101 1124792573 1030075435
17 14101061004 14101 1270 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4455 0.0268244 14101 1375501211 1211152801
18 14101061005 14101 728 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2502 0.0150650 14101 772503710 676356513
19 14101061006 14101 419 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1422 0.0085621 14101 439048871 379306102
20 14101061007 14101 13 2017 Valdivia 308754.5 2017 14101 166080 51277944139 33 0.0001987 14101 10188898 9997200
21 14101071001 14101 1291 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4214 0.0253733 14101 1301091381 1232128466
22 14101071002 14101 1147 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3859 0.0232358 14101 1191483541 1088631124
23 14101071003 14101 1444 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4649 0.0279925 14101 1435399580 1385420218
24 14101071004 14101 554 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1808 0.0108863 14101 558228101 508139102
25 14101071005 14101 1733 2017 Valdivia 308754.5 2017 14101 166080 51277944139 6057 0.0364704 14101 1870125889 1677004573
26 14101071006 14101 1378 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4766 0.0286970 14101 1471523855 1319195759
27 14101081001 14101 1725 2017 Valdivia 308754.5 2017 14101 166080 51277944139 5118 0.0308165 14101 1580205432 1668900338
28 14101081002 14101 900 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3425 0.0206226 14101 1057484096 844526152
29 14101081003 14101 1423 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4434 0.0266980 14101 1369017367 1364333058
30 14101081004 14101 1276 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4941 0.0297507 14101 1525555889 1217144198
31 14101081005 14101 1286 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3782 0.0227722 14101 1167709446 1227132797
32 14101081006 14101 1400 2017 Valdivia 308754.5 2017 14101 166080 51277944139 5317 0.0320147 14101 1641647573 1341254371
33 14101081007 14101 972 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3719 0.0223928 14101 1148257913 915391156
34 14101081008 14101 1417 2017 Valdivia 308754.5 2017 14101 166080 51277944139 5423 0.0326529 14101 1674375548 1358310830
35 14101081009 14101 1024 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3389 0.0204058 14101 1046368935 966726154
36 14101081010 14101 1699 2017 Valdivia 308754.5 2017 14101 166080 51277944139 5849 0.0352180 14101 1805904957 1642573806
37 14101081011 14101 781 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2888 0.0173892 14101 891682940 727995651
38 14101091001 14101 1081 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3024 0.0182081 14101 933673549 1023137888
39 14101091002 14101 1524 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4672 0.0281310 14101 1442500933 1465882587
40 14101101001 14101 525 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1538 0.0092606 14101 474864391 480325259
41 14101101002 14101 911 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2989 0.0179974 14101 922867143 855336033
42 14101101003 14101 867 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2463 0.0148302 14101 760462286 812134025
43 14101161001 14101 536 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1801 0.0108442 14101 556066820 490867053
44 14101171001 14101 1349 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3989 0.0240185 14101 1231621623 1290143802
45 14101991999 14101 81 2017 Valdivia 308754.5 2017 14101 166080 51277944139 679 0.0040884 14101 209644292 67879439
46 14102011001 14102 1133 2017 Corral 222523.9 2017 14102 5302 1179821617 3469 0.6542814 14102 771935343 1074723470
47 14102991999 14102 2 2017 Corral 222523.9 2017 14102 5302 1179821617 12 0.0022633 14102 2670287 1408588
48 14103011001 14103 812 2017 Lanco 267286.0 2017 14103 16752 4477574931 2488 0.1485196 14103 665007547 758276816
49 14103011002 14103 670 2017 Lanco 267286.0 2017 14103 16752 4477574931 2058 0.1228510 14103 550074571 620048394
50 14103011003 14103 1000 2017 Lanco 267286.0 2017 14103 16752 4477574931 3975 0.2372851 14103 1062461817 943017476
51 14103031001 14103 960 2017 Lanco 267286.0 2017 14103 16752 4477574931 3061 0.1827245 14103 818162420 903562681
52 14103991999 14103 8 2017 Lanco 267286.0 2017 14103 16752 4477574931 25 0.0014924 14103 6682150 6013421
53 14104011001 14104 990 2017 Los Lagos 211843.1 2017 14104 19634 4159328181 3294 0.1677702 14104 697811298 933146708
54 14104051001 14104 1033 2017 Los Lagos 211843.1 2017 14104 19634 4159328181 3514 0.1789752 14104 744416789 975623673
55 14104051002 14104 807 2017 Los Lagos 211843.1 2017 14104 19634 4159328181 2938 0.1496384 14104 622395141 753389029
56 14104991999 14104 19 2017 Los Lagos 211843.1 2017 14104 19634 4159328181 52 0.0026485 14104 11015843 14874058
57 14105011001 14105 1137 2017 Máfil 315022.1 2017 14105 7095 2235081533 4239 0.5974630 14105 1335378523 1078696266
58 14105991999 14105 2 2017 Máfil 315022.1 2017 14105 7095 2235081533 4 0.0005638 14105 1260088 1408588
59 14106011001 14106 937 2017 Mariquina 251064.3 2017 14106 21278 5342147079 3463 0.1627503 14106 869435818 880910914
60 14106011002 14106 843 2017 Mariquina 251064.3 2017 14106 21278 5342147079 3400 0.1597895 14106 853618764 788612341
61 14106011003 14106 740 2017 Mariquina 251064.3 2017 14106 21278 5342147079 2904 0.1364790 14106 729090851 688033378
62 14106991999 14106 81 2017 Mariquina 251064.3 2017 14106 21278 5342147079 192 0.0090234 14106 48204354 67879439
63 14107021001 14107 8 2017 Paillaco 223306.5 2017 14107 20188 4508111622 21 0.0010402 14107 4689437 6013421
64 14107031001 14107 835 2017 Paillaco 223306.5 2017 14107 20188 4508111622 2834 0.1403804 14107 632850621 780778723
65 14107031002 14107 1217 2017 Paillaco 223306.5 2017 14107 20188 4508111622 4222 0.2091341 14107 942800043 1158287223
66 14107031003 14107 1057 2017 Paillaco 223306.5 2017 14107 20188 4508111622 4219 0.2089855 14107 942130124 999368111
67 14107051001 14107 318 2017 Paillaco 223306.5 2017 14107 20188 4508111622 1001 0.0495839 14107 223529807 284169033
68 14107991999 14107 51 2017 Paillaco 223306.5 2017 14107 20188 4508111622 155 0.0076778 14107 34612508 41820266
69 14108011001 14108 1334 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 5054 0.1463273 14108 1454300905 1275128395
70 14108011002 14108 1330 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 4341 0.1256840 14108 1249133405 1271125622
71 14108011003 14108 569 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 1696 0.0491039 14108 488028163 522552665
72 14108051001 14108 469 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 1477 0.0427633 14108 425010375 426823338
73 14108111001 14108 563 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 1955 0.0566027 14108 562556049 516785071
74 14108991999 14108 242 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 750 0.0217146 14108 215814341 213498099
75 14201011001 14201 829 2017 La Unión 247291.7 2017 14201 38036 9405987850 2522 0.0663056 14201 623669717 774905820
76 14201011002 14201 469 2017 La Unión 247291.7 2017 14201 38036 9405987850 1283 0.0337312 14201 317275276 426823338
77 14201011003 14201 585 2017 La Unión 247291.7 2017 14201 38036 9405987850 1697 0.0446156 14201 419654048 537946805
78 14201011004 14201 837 2017 La Unión 247291.7 2017 14201 38036 9405987850 3020 0.0793985 14201 746820993 782736798
79 14201021001 14201 60 2017 La Unión 247291.7 2017 14201 38036 9405987850 177 0.0046535 14201 43770634 49577307
80 14201091001 14201 447 2017 La Unión 247291.7 2017 14201 38036 9405987850 1445 0.0379903 14201 357336535 405884841
81 14201091002 14201 951 2017 La Unión 247291.7 2017 14201 38036 9405987850 3737 0.0982490 14201 924129156 894695876
82 14201091003 14201 862 2017 La Unión 247291.7 2017 14201 38036 9405987850 2778 0.0730361 14201 686976397 807231123
83 14201091004 14201 1113 2017 La Unión 247291.7 2017 14201 38036 9405987850 4130 0.1085813 14201 1021314802 1054869415
84 14201091005 14201 1434 2017 La Unión 247291.7 2017 14201 38036 9405987850 5728 0.1505942 14201 1416486970 1375376904
85 14201991999 14201 56 2017 La Unión 247291.7 2017 14201 38036 9405987850 145 0.0038122 14201 35857299 46122452
86 14202011001 14202 1211 2017 Futrono 247331.7 2017 14202 14665 3627119212 4140 0.2823048 14202 1023953190 1152309166
87 14202011002 14202 901 2017 Futrono 247331.7 2017 14202 14665 3627119212 2955 0.2015002 14202 730865140 845508613
88 14202021001 14202 53 2017 Futrono 247331.7 2017 14202 14665 3627119212 160 0.0109103 14202 39573070 43538867
89 14202041001 14202 262 2017 Futrono 247331.7 2017 14202 14665 3627119212 844 0.0575520 14202 208747945 232006248
90 14202991999 14202 22 2017 Futrono 247331.7 2017 14202 14665 3627119212 84 0.0057279 14202 20775862 17341592
91 14203011001 14203 768 2017 Lago Ranco 247154.2 2017 14203 9896 2445838259 2146 0.2168553 14203 530392977 715313764
92 14203991999 14203 53 2017 Lago Ranco 247154.2 2017 14203 9896 2445838259 123 0.0124293 14203 30399970 43538867
93 14204011001 14204 780 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 2356 0.0750988 14204 631253392 727019767
94 14204011002 14204 1201 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 4161 0.1326342 14204 1114874942 1142348831
95 14204011003 14204 1099 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 3509 0.1118513 14204 940181728 1040981525
96 14204011004 14204 1009 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 3988 0.1271197 14204 1068522295 951905132
97 14204011005 14204 697 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 2512 0.0800714 14204 673051155 646233462
98 14204071001 14204 116 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 383 0.0122083 14204 102618866 98863729
99 14204991999 14204 98 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 234 0.0074589 14204 62696644 82863925
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA


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


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


h_y_m_comuna_corr$ing_medio_zona <- h_y_m_comuna_corr$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
1 14101011001 14101 1053 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3316 0.0199663 14101 1023829858 995408931 300183.63
2 14101021001 14101 1847 2017 Valdivia 308754.5 2017 14101 166080 51277944139 5505 0.0331467 14101 1699693416 1792677270 325645.28
3 14101031001 14101 564 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1916 0.0115366 14101 591573585 517746137 270222.41
4 14101041001 14101 974 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3347 0.0201529 14101 1033401247 917363238 274085.22
5 14101041002 14101 356 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1217 0.0073278 14101 375754203 319817441 262791.65
6 14101041003 14101 1076 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3319 0.0199843 14101 1024756121 1018183789 306774.27
7 14101041004 14101 795 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3079 0.0185393 14101 950655046 741664157 240878.26
8 14101041005 14101 1444 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4543 0.0273543 14101 1402671605 1385420218 304957.12
9 14101051001 14101 1139 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3908 0.0235308 14101 1206612510 1080682910 276530.94
10 14101051002 14101 852 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2924 0.0176060 14101 902798101 797429334 272718.65
11 14101051003 14101 947 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3046 0.0183406 14101 940466148 890756337 292434.78
12 14101051004 14101 337 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1117 0.0067257 14101 344878755 301969625 270339.86
13 14101051005 14101 1241 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3677 0.0221399 14101 1135290225 1182213232 321515.70
14 14101061001 14101 1318 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4741 0.0285465 14101 1463804993 1259120702 265581.25
15 14101061002 14101 677 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2213 0.0133249 14101 683273666 626832436 283250.08
16 14101061003 14101 1088 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3643 0.0219352 14101 1124792573 1030075435 282754.72
17 14101061004 14101 1270 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4455 0.0268244 14101 1375501211 1211152801 271863.70
18 14101061005 14101 728 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2502 0.0150650 14101 772503710 676356513 270326.34
19 14101061006 14101 419 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1422 0.0085621 14101 439048871 379306102 266741.28
20 14101061007 14101 13 2017 Valdivia 308754.5 2017 14101 166080 51277944139 33 0.0001987 14101 10188898 9997200 302945.45
21 14101071001 14101 1291 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4214 0.0253733 14101 1301091381 1232128466 292389.29
22 14101071002 14101 1147 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3859 0.0232358 14101 1191483541 1088631124 282101.87
23 14101071003 14101 1444 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4649 0.0279925 14101 1435399580 1385420218 298003.92
24 14101071004 14101 554 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1808 0.0108863 14101 558228101 508139102 281050.39
25 14101071005 14101 1733 2017 Valdivia 308754.5 2017 14101 166080 51277944139 6057 0.0364704 14101 1870125889 1677004573 276870.49
26 14101071006 14101 1378 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4766 0.0286970 14101 1471523855 1319195759 276793.07
27 14101081001 14101 1725 2017 Valdivia 308754.5 2017 14101 166080 51277944139 5118 0.0308165 14101 1580205432 1668900338 326084.47
28 14101081002 14101 900 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3425 0.0206226 14101 1057484096 844526152 246576.98
29 14101081003 14101 1423 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4434 0.0266980 14101 1369017367 1364333058 307698.03
30 14101081004 14101 1276 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4941 0.0297507 14101 1525555889 1217144198 246335.60
31 14101081005 14101 1286 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3782 0.0227722 14101 1167709446 1227132797 324466.63
32 14101081006 14101 1400 2017 Valdivia 308754.5 2017 14101 166080 51277944139 5317 0.0320147 14101 1641647573 1341254371 252257.73
33 14101081007 14101 972 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3719 0.0223928 14101 1148257913 915391156 246139.06
34 14101081008 14101 1417 2017 Valdivia 308754.5 2017 14101 166080 51277944139 5423 0.0326529 14101 1674375548 1358310830 250472.22
35 14101081009 14101 1024 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3389 0.0204058 14101 1046368935 966726154 285254.10
36 14101081010 14101 1699 2017 Valdivia 308754.5 2017 14101 166080 51277944139 5849 0.0352180 14101 1805904957 1642573806 280829.85
37 14101081011 14101 781 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2888 0.0173892 14101 891682940 727995651 252076.06
38 14101091001 14101 1081 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3024 0.0182081 14101 933673549 1023137888 338339.25
39 14101091002 14101 1524 2017 Valdivia 308754.5 2017 14101 166080 51277944139 4672 0.0281310 14101 1442500933 1465882587 313759.12
40 14101101001 14101 525 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1538 0.0092606 14101 474864391 480325259 312305.11
41 14101101002 14101 911 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2989 0.0179974 14101 922867143 855336033 286161.27
42 14101101003 14101 867 2017 Valdivia 308754.5 2017 14101 166080 51277944139 2463 0.0148302 14101 760462286 812134025 329733.67
43 14101161001 14101 536 2017 Valdivia 308754.5 2017 14101 166080 51277944139 1801 0.0108442 14101 556066820 490867053 272552.50
44 14101171001 14101 1349 2017 Valdivia 308754.5 2017 14101 166080 51277944139 3989 0.0240185 14101 1231621623 1290143802 323425.37
45 14101991999 14101 81 2017 Valdivia 308754.5 2017 14101 166080 51277944139 679 0.0040884 14101 209644292 67879439 99969.72
46 14102011001 14102 1133 2017 Corral 222523.9 2017 14102 5302 1179821617 3469 0.6542814 14102 771935343 1074723470 309807.86
47 14102991999 14102 2 2017 Corral 222523.9 2017 14102 5302 1179821617 12 0.0022633 14102 2670287 1408588 117382.32
48 14103011001 14103 812 2017 Lanco 267286.0 2017 14103 16752 4477574931 2488 0.1485196 14103 665007547 758276816 304773.64
49 14103011002 14103 670 2017 Lanco 267286.0 2017 14103 16752 4477574931 2058 0.1228510 14103 550074571 620048394 301286.88
50 14103011003 14103 1000 2017 Lanco 267286.0 2017 14103 16752 4477574931 3975 0.2372851 14103 1062461817 943017476 237237.10
51 14103031001 14103 960 2017 Lanco 267286.0 2017 14103 16752 4477574931 3061 0.1827245 14103 818162420 903562681 295185.46
52 14103991999 14103 8 2017 Lanco 267286.0 2017 14103 16752 4477574931 25 0.0014924 14103 6682150 6013421 240536.83
53 14104011001 14104 990 2017 Los Lagos 211843.1 2017 14104 19634 4159328181 3294 0.1677702 14104 697811298 933146708 283286.80
54 14104051001 14104 1033 2017 Los Lagos 211843.1 2017 14104 19634 4159328181 3514 0.1789752 14104 744416789 975623673 277639.06
55 14104051002 14104 807 2017 Los Lagos 211843.1 2017 14104 19634 4159328181 2938 0.1496384 14104 622395141 753389029 256429.21
56 14104991999 14104 19 2017 Los Lagos 211843.1 2017 14104 19634 4159328181 52 0.0026485 14104 11015843 14874058 286039.57
57 14105011001 14105 1137 2017 Máfil 315022.1 2017 14105 7095 2235081533 4239 0.5974630 14105 1335378523 1078696266 254469.51
58 14105991999 14105 2 2017 Máfil 315022.1 2017 14105 7095 2235081533 4 0.0005638 14105 1260088 1408588 352146.95
59 14106011001 14106 937 2017 Mariquina 251064.3 2017 14106 21278 5342147079 3463 0.1627503 14106 869435818 880910914 254377.97
60 14106011002 14106 843 2017 Mariquina 251064.3 2017 14106 21278 5342147079 3400 0.1597895 14106 853618764 788612341 231944.81
61 14106011003 14106 740 2017 Mariquina 251064.3 2017 14106 21278 5342147079 2904 0.1364790 14106 729090851 688033378 236926.09
62 14106991999 14106 81 2017 Mariquina 251064.3 2017 14106 21278 5342147079 192 0.0090234 14106 48204354 67879439 353538.75
63 14107021001 14107 8 2017 Paillaco 223306.5 2017 14107 20188 4508111622 21 0.0010402 14107 4689437 6013421 286353.37
64 14107031001 14107 835 2017 Paillaco 223306.5 2017 14107 20188 4508111622 2834 0.1403804 14107 632850621 780778723 275504.14
65 14107031002 14107 1217 2017 Paillaco 223306.5 2017 14107 20188 4508111622 4222 0.2091341 14107 942800043 1158287223 274345.62
66 14107031003 14107 1057 2017 Paillaco 223306.5 2017 14107 20188 4508111622 4219 0.2089855 14107 942130124 999368111 236873.22
67 14107051001 14107 318 2017 Paillaco 223306.5 2017 14107 20188 4508111622 1001 0.0495839 14107 223529807 284169033 283885.15
68 14107991999 14107 51 2017 Paillaco 223306.5 2017 14107 20188 4508111622 155 0.0076778 14107 34612508 41820266 269808.17
69 14108011001 14108 1334 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 5054 0.1463273 14108 1454300905 1275128395 252300.83
70 14108011002 14108 1330 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 4341 0.1256840 14108 1249133405 1271125622 292818.62
71 14108011003 14108 569 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 1696 0.0491039 14108 488028163 522552665 308108.88
72 14108051001 14108 469 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 1477 0.0427633 14108 425010375 426823338 288979.92
73 14108111001 14108 563 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 1955 0.0566027 14108 562556049 516785071 264340.19
74 14108991999 14108 242 2017 Panguipulli 287752.5 2017 14108 34539 9938682028 750 0.0217146 14108 215814341 213498099 284664.13
75 14201011001 14201 829 2017 La Unión 247291.7 2017 14201 38036 9405987850 2522 0.0663056 14201 623669717 774905820 307258.45
76 14201011002 14201 469 2017 La Unión 247291.7 2017 14201 38036 9405987850 1283 0.0337312 14201 317275276 426823338 332676.02
77 14201011003 14201 585 2017 La Unión 247291.7 2017 14201 38036 9405987850 1697 0.0446156 14201 419654048 537946805 316998.71
78 14201011004 14201 837 2017 La Unión 247291.7 2017 14201 38036 9405987850 3020 0.0793985 14201 746820993 782736798 259184.37
79 14201021001 14201 60 2017 La Unión 247291.7 2017 14201 38036 9405987850 177 0.0046535 14201 43770634 49577307 280097.78
80 14201091001 14201 447 2017 La Unión 247291.7 2017 14201 38036 9405987850 1445 0.0379903 14201 357336535 405884841 280889.16
81 14201091002 14201 951 2017 La Unión 247291.7 2017 14201 38036 9405987850 3737 0.0982490 14201 924129156 894695876 239415.54
82 14201091003 14201 862 2017 La Unión 247291.7 2017 14201 38036 9405987850 2778 0.0730361 14201 686976397 807231123 290579.96
83 14201091004 14201 1113 2017 La Unión 247291.7 2017 14201 38036 9405987850 4130 0.1085813 14201 1021314802 1054869415 255416.32
84 14201091005 14201 1434 2017 La Unión 247291.7 2017 14201 38036 9405987850 5728 0.1505942 14201 1416486970 1375376904 240114.68
85 14201991999 14201 56 2017 La Unión 247291.7 2017 14201 38036 9405987850 145 0.0038122 14201 35857299 46122452 318085.88
86 14202011001 14202 1211 2017 Futrono 247331.7 2017 14202 14665 3627119212 4140 0.2823048 14202 1023953190 1152309166 278335.55
87 14202011002 14202 901 2017 Futrono 247331.7 2017 14202 14665 3627119212 2955 0.2015002 14202 730865140 845508613 286128.13
88 14202021001 14202 53 2017 Futrono 247331.7 2017 14202 14665 3627119212 160 0.0109103 14202 39573070 43538867 272117.92
89 14202041001 14202 262 2017 Futrono 247331.7 2017 14202 14665 3627119212 844 0.0575520 14202 208747945 232006248 274888.92
90 14202991999 14202 22 2017 Futrono 247331.7 2017 14202 14665 3627119212 84 0.0057279 14202 20775862 17341592 206447.53
91 14203011001 14203 768 2017 Lago Ranco 247154.2 2017 14203 9896 2445838259 2146 0.2168553 14203 530392977 715313764 333324.21
92 14203991999 14203 53 2017 Lago Ranco 247154.2 2017 14203 9896 2445838259 123 0.0124293 14203 30399970 43538867 353974.53
93 14204011001 14204 780 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 2356 0.0750988 14204 631253392 727019767 308582.24
94 14204011002 14204 1201 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 4161 0.1326342 14204 1114874942 1142348831 274537.09
95 14204011003 14204 1099 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 3509 0.1118513 14204 940181728 1040981525 296660.45
96 14204011004 14204 1009 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 3988 0.1271197 14204 1068522295 951905132 238692.36
97 14204011005 14204 697 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 2512 0.0800714 14204 673051155 646233462 257258.54
98 14204071001 14204 116 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 383 0.0122083 14204 102618866 98863729 258129.84
99 14204991999 14204 98 2017 Río Bueno 267934.4 2017 14204 31372 8405637271 234 0.0074589 14204 62696644 82863925 354119.34
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA


Guardamos:

saveRDS(h_y_m_comuna_corr, "P03C/region_14_P03C_u.rds")