date: 27-07-2021

1 Resumen

Iniciaremos expandiendo los ingresos promedios (multiplicación del ingreso promedio mensual comunal y los habitantes de la misma comuna) obtenidos de la CASEN 2017 sobre la categoría de respuesta: “ESCOLARIDAD” del CENSO de personas del 2017, que fue la categoría de respuesta que más alto correlacionó con los ingresos expandidos, ambos a nivel comunal y ambos a nivel RURAL.

Seguiremos con un análisis sobre todas las zonas Chile comenzando en éste artículo a nivel urbano. En un segundo artículo haremos la publicación a nivel rural.

Como una tercera parte, y ya construída nuestra tabla de trabajo, haremos el análisis por región. Ensayaremos diferentes modelos dentro del análisis de regresión cuya variable independiente será: “frecuencia de población que posee la variable Censal respecto a la zona” y la dependiente: “ingreso expandido por zona por proporción de población zonal respecto al total comunal (multipob)”. Lo anterior para elegir el que posea el mayor coeficiente de determinación y así construir una tabla de valores predichos (estimación del ingreso e ingreso estimado por zona).

1.1 Variable CENSO

Necesitamos calcular las frecuencias a nivel censal de las respuestas correspondientes a la categoría: ESCOLARIDAD del Censo de personas. Recordemos que ésta fué la más alta correlación en relación a los ingresos expandidos (ver aquí).

1.1.1 Lectura de la tabla censal de personas

Leemos la tabla Censo 2017 de personas que ya tiene integrada la clave zonal:

tabla_con_clave <-  readRDS("censo_personas_con_clave_17")

abc <- head(tabla_con_clave,50)
kbl(abc) %>%
  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	NHOGAR	PERSONAN	P07	P08	P09	P10	P10COMUNA	P10PAIS	P11	P11COMUNA	P11PAIS	P12	P12COMUNA	P12PAIS	P12A_LLEGADA	P12A_TRAMO	P13	P14	P15	P15A	P16	P16A	P16A_OTRO	P17	P18	P19	P20	P21M	P21A	P10PAIS_GRUPO	P11PAIS_GRUPO	P12PAIS_GRUPO	ESCOLARIDAD	P16A_GRUPO	REGION_15R	PROVINCIA_15R	COMUNA_15R	P10COMUNA_15R	P11COMUNA_15R	P12COMUNA_15R	clave
15	152	15202	1	2	6	13225	1	1	1	1	1	73	1	98	998	3	15101	998	1	98	998	9998	98	2	4	6	2	1	2	98	7	98	98	98	98	9998	998	998	998	4	2	15	152	15202	98	15101	98	15202012006
15	152	15202	1	2	6	13225	3	1	1	1	1	78	1	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	7	98	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	3	1	2	2	2	78	1	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	7	98	1	1	3	1965	998	998	998	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	3	1	3	5	2	52	1	98	998	2	98	998	1	98	998	9998	98	1	2	5	2	1	2	98	7	98	2	1	4	1995	998	998	998	2	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	3	1	4	11	1	44	1	98	998	2	98	998	1	98	998	9998	98	1	3	5	2	1	2	98	1	Z	98	98	98	9998	998	998	998	3	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	9	1	1	1	1	39	1	98	998	2	98	998	1	98	998	9998	98	2	8	5	1	1	2	98	8	98	98	98	98	9998	998	998	998	8	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	9	1	2	2	2	35	1	98	998	2	98	998	1	98	998	9998	98	2	6	5	2	1	2	98	1	Z	2	2	11	2004	998	998	998	6	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	9	1	3	5	1	13	1	98	998	2	98	998	1	98	998	9998	98	1	7	5	2	1	2	98	98	98	98	98	98	9998	998	998	998	7	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	9	1	4	5	1	12	1	98	998	2	98	998	1	98	998	9998	98	1	6	5	2	1	2	98	98	98	98	98	98	9998	998	998	998	6	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	10	1	1	1	2	65	1	98	998	2	98	998	1	98	998	9998	98	2	4	5	2	1	2	98	6	98	3	3	9	1992	998	998	998	4	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	13	1	1	1	1	50	1	98	998	2	98	998	1	98	998	9998	98	2	5	5	2	1	2	98	1	Z	98	98	98	9998	998	998	998	5	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	13	1	2	4	2	43	1	98	998	2	98	998	1	98	998	9998	98	2	6	5	2	1	2	98	6	98	2	2	3	2002	998	998	998	6	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	13	1	3	5	1	15	3	15201	998	2	98	998	1	98	998	9998	98	1	1	7	2	1	2	98	8	98	98	98	98	9998	998	998	998	9	2	15	152	15202	15201	98	98	15202012006
15	152	15202	1	2	6	13225	16	1	1	1	1	75	1	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	7	98	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	16	1	2	16	2	58	4	98	68	6	98	998	5	98	998	9999	1	3	98	98	98	1	2	98	7	98	4	4	99	9999	68	68	68	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	16	1	3	2	2	70	1	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	7	98	5	4	99	9999	998	998	998	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	17	1	1	1	2	43	2	98	998	2	98	998	1	98	998	9998	98	2	8	5	1	1	2	98	1	I	3	3	9	2008	998	998	998	8	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	17	1	2	4	1	55	2	98	998	2	98	998	1	98	998	9998	98	2	6	5	2	1	2	98	6	98	98	98	98	9998	998	998	998	6	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	17	1	3	5	2	13	2	98	998	2	98	998	2	15101	998	9998	98	1	7	5	2	1	2	98	98	98	98	98	98	9998	998	998	998	7	2	15	152	15202	98	98	15101	15202012006
15	152	15202	1	2	6	13225	17	1	4	5	1	8	2	98	998	2	98	998	2	15101	998	9998	98	1	2	5	2	1	2	98	98	98	98	98	98	9998	998	998	998	2	2	15	152	15202	98	98	15101	15202012006
15	152	15202	1	2	6	13225	17	1	5	15	2	29	2	98	998	4	98	998	3	98	998	2015	1	2	6	5	2	1	2	98	6	98	5	5	11	2014	998	604	604	6	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	17	1	6	15	1	4	2	98	998	1	98	998	5	98	998	2015	1	1	0	1	2	1	2	98	98	98	98	98	98	9998	998	998	68	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	17	1	7	15	2	2	2	98	998	1	98	998	3	98	998	2015	1	1	0	1	2	1	2	98	98	98	98	98	98	9998	998	998	604	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	17	1	8	15	1	16	2	98	998	6	98	998	1	98	998	9998	98	2	4	5	2	1	2	98	6	98	98	98	98	9998	998	68	998	4	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	18	1	1	1	2	74	1	98	998	2	98	998	1	98	998	9998	98	2	2	5	2	1	2	98	6	98	2	2	12	1976	998	998	998	2	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	19	1	1	1	1	68	1	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	7	98	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	20	1	1	1	1	74	1	98	998	3	15101	998	1	98	998	9998	98	2	2	5	2	1	2	98	1	Z	98	98	98	9998	998	998	998	2	2	15	152	15202	98	15101	98	15202012006
15	152	15202	1	2	6	13225	20	1	2	2	2	65	1	98	998	3	997	998	3	98	998	9999	2	2	2	5	2	1	2	98	6	98	2	2	9	1982	998	998	604	2	2	15	152	15202	98	997	98	15202012006
15	152	15202	1	2	6	13225	25	1	1	1	2	76	1	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	6	98	8	6	3	1981	998	998	998	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	25	1	2	5	2	36	1	98	998	2	98	998	1	98	998	9998	98	2	4	8	1	1	2	98	1	A	0	98	98	9998	998	998	998	12	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	28	1	1	1	2	31	1	98	998	2	98	998	5	98	998	2007	2	2	5	5	2	1	2	98	1	A	2	2	4	2008	998	998	68	5	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	28	1	2	4	1	35	1	98	998	2	98	998	5	98	998	2007	2	2	6	5	2	1	2	98	1	F	98	98	98	9998	998	998	68	6	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	28	1	3	5	1	11	1	98	998	2	98	998	5	98	998	2007	2	1	5	5	2	1	2	98	98	98	98	98	98	9998	998	998	68	5	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	28	1	4	5	1	8	1	98	998	2	98	998	1	98	998	9998	98	1	2	5	2	1	2	98	98	98	98	98	98	9998	998	998	998	2	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	28	1	5	15	2	74	1	98	998	2	98	998	1	98	998	9998	98	2	3	5	2	1	2	98	6	98	6	6	99	9999	998	998	998	3	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	33	1	1	1	1	41	1	98	998	2	98	998	1	98	998	9998	98	2	8	5	1	1	2	98	1	Z	98	98	98	9998	998	998	998	8	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	33	1	2	2	2	47	1	98	998	2	98	998	1	98	998	9998	98	2	8	5	1	1	2	98	1	A	2	1	4	1996	998	998	998	8	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	33	1	3	14	1	88	1	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	7	98	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	33	1	4	14	1	65	1	98	998	2	98	998	1	98	998	9998	98	2	2	5	2	1	2	98	7	98	98	98	98	9998	998	998	998	2	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	36	1	1	1	2	59	1	98	998	2	98	998	1	98	998	9998	98	2	2	5	2	1	2	98	6	98	8	8	2	1998	998	998	998	2	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	36	1	2	2	1	56	1	98	998	99	99	999	1	98	998	9998	98	2	2	5	2	1	2	98	6	98	98	98	98	9998	998	999	998	2	2	15	152	15202	98	99	98	15202012006
15	152	15202	1	2	6	13225	36	1	3	5	2	36	1	98	998	2	98	998	1	98	998	9998	98	2	8	5	1	1	2	98	6	98	2	2	7	2010	998	998	998	8	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	36	1	4	12	2	13	1	98	998	2	98	998	1	98	998	9998	98	1	7	5	2	1	2	98	98	98	98	98	98	9998	998	998	998	7	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	36	1	5	12	2	6	1	98	998	2	98	998	1	98	998	9998	98	1	0	3	1	1	2	98	98	98	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	36	1	6	5	1	24	1	98	998	3	15101	998	1	98	998	9998	98	2	4	7	1	1	2	98	1	Z	98	98	98	9998	998	998	998	12	2	15	152	15202	98	15101	98	15202012006
15	152	15202	1	2	6	13225	36	1	7	11	2	24	1	98	998	3	15101	998	1	98	998	9998	98	2	4	7	1	1	2	98	1	N	2	2	11	2015	998	998	998	12	2	15	152	15202	98	15101	98	15202012006
15	152	15202	1	2	6	13225	36	1	8	12	1	6	1	98	998	2	98	998	2	15101	998	9998	98	1	0	3	1	1	2	98	98	98	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	15101	15202012006
15	152	15202	1	2	6	13225	36	1	9	12	2	1	1	98	998	1	98	998	2	15101	998	9998	98	3	98	98	98	1	2	98	98	98	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	15101	15202012006
15	152	15202	1	2	6	13225	38	1	1	1	1	19	1	98	998	3	15101	998	2	15101	998	9998	98	1	1	8	2	1	2	98	1	A	98	98	98	9998	998	998	998	9	2	15	152	15202	98	15101	15101	15202012006
15	152	15202	1	2	6	13225	39	1	1	1	1	21	1	98	998	2	98	998	1	98	998	9998	98	2	1	7	2	1	2	98	1	F	98	98	98	9998	998	998	998	9	2	15	152	15202	98	98	98	15202012006

Cuantas personas hay en Chile?

length(tabla_con_clave$clave)

## [1] 17574003

Cuántas zonas hay en Chile?

length(unique(tabla_con_clave$clave))

## [1] 15500

1.1.2 Filtro a nivel rural:

tabla_con_clave_u <- filter(tabla_con_clave, tabla_con_clave$AREA ==2)

Cuantas personas hay en Chile rurales?

length(tabla_con_clave_u$clave)

## [1] 2149740

Cuantas zonas hay en el nivel rural?

length(unique(tabla_con_clave_u$clave))

## [1] 10331

Table 1.1: RURAL
ESCOLARIDAD	cat_r	Correlación	total	criterio
1_años	1_años	0.2767593	77.533637	21.4581556477961
2_años	2_años	0.2470193	82.750944	20.4410761048424
3_años	3_años	0.2362649	86.971252	20.5482559410038
4_años	4_años	0.2342442	88.607105	20.7557039968554
5_años	5_años	0.2571000	85.161165	21.8949337693051
6_años	6_años	0.2043421	92.449908	18.891404834352
7_años	7_años	0.2895047	82.499274	23.8839261134497
8_años	8_años	0.2608489	95.992643	25.0395797889307
9_años	9_años	0.3934692	80.873100	31.8210743546358
10_años	10_años	0.4110443	85.635466	35.1999687131626
11_años	11_años	0.4201018	76.710870	32.2263767237315
12_años	12_años	0.4255218	96.844449	41.2094267607503
13_años	13_años	0.4692992	47.246152	22.1725816849638
14_años	14_años	0.4923740	64.630723	31.8224876694169
15_años	15_años	0.4784635	64.137063	30.6872423603531
16_años	16_años	0.4633309	58.542251	27.1244364080257
17_años	17_años	0.4894226	69.364050	33.9483328702428
18_años	18_años	0.3755864	5.043074	1.89411023856027
19_años	19_años	0.4479310	23.598877	10.5706692354131
20_años	20_años	0.3853917	5.459297	2.1039675912991
21_años	21_años	0.4362933	8.140548	3.55166622218966

1.2 Criterio de seleccion de las respuestas a las preguntas:

No nos sirve de nada una alta correlación, si nuestra información abarca muy pocas zonas censales. se busca el óptimo simplemente obteniendo el mayor valor de la multiplicación entre ambos, que en nuestro caso fue la categoría 12 años cubriendo el 96.7% de zonas rurales.

tabla_con_clave_u <- filter(tabla_con_clave, tabla_con_clave$AREA ==2)
tabla_con_clave_f <- tabla_con_clave_u[,-c(1,2,4:40,42:48),drop=F]


codigos <- tabla_con_clave_f$COMUNA
rango <- seq(1:nrow(tabla_con_clave_f))
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(tabla_con_clave_f,cadena)
comuna_corr <- comuna_corr[,-c(1),drop=FALSE]
names(comuna_corr)[3] <- "código"

tabla_con_clave_f <- comuna_corr

claves_con_1 <- filter(tabla_con_clave_f, tabla_con_clave_f$ESCOLARIDAD == 12)

con4 <- xtabs(~ESCOLARIDAD+clave, data=claves_con_1)
con4 <- as.data.frame(con4)

trabajo_001 = merge( x = con4, y =claves_con_1, by = "clave", all.x = TRUE)
trabajo003 <- unique(trabajo_001)
trabajo003 <- trabajo003[,-c(2,4)]

df_2017_2 <- readRDS("Ingresos_expandidos_rural_17.rds")

comunas_censo_casen_666 = merge( x = trabajo003, y = df_2017_2, by = "código", all.x = TRUE)


tabla_de_prop_pob <- readRDS("tabla_de_prop_pob.rds")
names(tabla_de_prop_pob)[1]  <- "clave"


comunas_censo_casen_6666 = merge( x = comunas_censo_casen_666, y = tabla_de_prop_pob, by = "clave", all.x = TRUE)
comunas_censo_casen_6666$multipob <- comunas_censo_casen_6666$ingresos_expandidos*comunas_censo_casen_6666$p

write_xlsx(comunas_censo_casen_6666, "comunas_censo_casen_6666.xlsx")
kbl(head(comunas_censo_casen_6666,50)) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

clave	código.x	Freq.x	personas	comuna	promedio_i	año	ingresos_expandidos	Freq.y	p	código.y	multipob
10101032002	10101	27	245902	Puerto Montt	176237.4	2017	43337141298	129	0.0005246	10101	22734631
10101032011	10101	106	245902	Puerto Montt	176237.4	2017	43337141298	426	0.0017324	10101	75077153
10101032019	10101	174	245902	Puerto Montt	176237.4	2017	43337141298	829	0.0033713	10101	146100846
10101062003	10101	28	245902	Puerto Montt	176237.4	2017	43337141298	158	0.0006425	10101	27845517
10101062008	10101	83	245902	Puerto Montt	176237.4	2017	43337141298	581	0.0023627	10101	102393958
10101062013	10101	83	245902	Puerto Montt	176237.4	2017	43337141298	571	0.0023221	10101	100631584
10101062029	10101	15	245902	Puerto Montt	176237.4	2017	43337141298	47	0.0001911	10101	8283160
10101062039	10101	18	245902	Puerto Montt	176237.4	2017	43337141298	67	0.0002725	10101	11807909
10101072014	10101	204	245902	Puerto Montt	176237.4	2017	43337141298	997	0.0040545	10101	175708737
10101072021	10101	13	245902	Puerto Montt	176237.4	2017	43337141298	44	0.0001789	10101	7754448
10101072028	10101	22	245902	Puerto Montt	176237.4	2017	43337141298	145	0.0005897	10101	25554430
10101072029	10101	221	245902	Puerto Montt	176237.4	2017	43337141298	1051	0.0042741	10101	185225559
10101072036	10101	17	245902	Puerto Montt	176237.4	2017	43337141298	118	0.0004799	10101	20796019
10101072045	10101	19	245902	Puerto Montt	176237.4	2017	43337141298	113	0.0004595	10101	19914832
10101072050	10101	7	245902	Puerto Montt	176237.4	2017	43337141298	66	0.0002684	10101	11631672
10101082016	10101	18	245902	Puerto Montt	176237.4	2017	43337141298	121	0.0004921	10101	21324731
10101082017	10101	4	245902	Puerto Montt	176237.4	2017	43337141298	38	0.0001545	10101	6697023
10101082018	10101	102	245902	Puerto Montt	176237.4	2017	43337141298	623	0.0025335	10101	109795931
10101082030	10101	26	245902	Puerto Montt	176237.4	2017	43337141298	176	0.0007157	10101	31017791
10101082034	10101	11	245902	Puerto Montt	176237.4	2017	43337141298	66	0.0002684	10101	11631672
10101082042	10101	51	245902	Puerto Montt	176237.4	2017	43337141298	253	0.0010289	10101	44588075
10101082045	10101	23	245902	Puerto Montt	176237.4	2017	43337141298	123	0.0005002	10101	21677206
10101092004	10101	11	245902	Puerto Montt	176237.4	2017	43337141298	97	0.0003945	10101	17095033
10101092008	10101	126	245902	Puerto Montt	176237.4	2017	43337141298	752	0.0030581	10101	132530562
10101092037	10101	68	245902	Puerto Montt	176237.4	2017	43337141298	276	0.0011224	10101	48641536
10101092040	10101	101	245902	Puerto Montt	176237.4	2017	43337141298	509	0.0020699	10101	89704862
10101092041	10101	354	245902	Puerto Montt	176237.4	2017	43337141298	1683	0.0068442	10101	296607627
10101092044	10101	99	245902	Puerto Montt	176237.4	2017	43337141298	530	0.0021553	10101	93405848
10101102005	10101	24	245902	Puerto Montt	176237.4	2017	43337141298	147	0.0005978	10101	25906905
10101102007	10101	166	245902	Puerto Montt	176237.4	2017	43337141298	824	0.0033509	10101	145219658
10101102026	10101	54	245902	Puerto Montt	176237.4	2017	43337141298	245	0.0009963	10101	43178175
10101102035	10101	199	245902	Puerto Montt	176237.4	2017	43337141298	940	0.0038227	10101	165663202
10101102037	10101	24	245902	Puerto Montt	176237.4	2017	43337141298	164	0.0006669	10101	28902942
10101102051	10101	8	245902	Puerto Montt	176237.4	2017	43337141298	57	0.0002318	10101	10045535
10101102901	10101	1	245902	Puerto Montt	176237.4	2017	43337141298	16	0.0000651	10101	2819799
10101112025	10101	186	245902	Puerto Montt	176237.4	2017	43337141298	1078	0.0043839	10101	189983971
10101122024	10101	62	245902	Puerto Montt	176237.4	2017	43337141298	952	0.0038715	10101	167778052
10101132022	10101	121	245902	Puerto Montt	176237.4	2017	43337141298	703	0.0028589	10101	123894927
10101132023	10101	102	245902	Puerto Montt	176237.4	2017	43337141298	603	0.0024522	10101	106271182
10101132027	10101	29	245902	Puerto Montt	176237.4	2017	43337141298	105	0.0004270	10101	18504932
10101132049	10101	359	245902	Puerto Montt	176237.4	2017	43337141298	1883	0.0076575	10101	331855117
10101142009	10101	4	245902	Puerto Montt	176237.4	2017	43337141298	59	0.0002399	10101	10398010
10101142015	10101	14	245902	Puerto Montt	176237.4	2017	43337141298	124	0.0005043	10101	21853444
10101142027	10101	42	245902	Puerto Montt	176237.4	2017	43337141298	192	0.0007808	10101	33837590
10101142038	10101	6	245902	Puerto Montt	176237.4	2017	43337141298	53	0.0002155	10101	9340585
10101142046	10101	56	245902	Puerto Montt	176237.4	2017	43337141298	317	0.0012891	10101	55867271
10101142047	10101	32	245902	Puerto Montt	176237.4	2017	43337141298	263	0.0010695	10101	46350449
10101142049	10101	169	245902	Puerto Montt	176237.4	2017	43337141298	973	0.0039569	10101	171479038
10101152002	10101	118	245902	Puerto Montt	176237.4	2017	43337141298	554	0.0022529	10101	97635547
10101152006	10101	53	245902	Puerto Montt	176237.4	2017	43337141298	214	0.0008703	10101	37714814

Vemos que nuestra tabla de trabajo cubre mas del 90% de zonas rurales.

nrow(comunas_censo_casen_6666)

## [1] 10005

En total Chile posee 10,331 zonas rurales de las que estamos cubriendo 10005, cubriendo el 97% de las zonas rurales con una variable (12_años) de ESCOLARIDAD que difiere con la que mas alto correlaciono con ingresos expandidos en 0.066

x <- (10005 * 100) / 10331
x

## [1] 96.84445

1.3 Diagrama de dispersión loess

scatter.smooth(x=comunas_censo_casen_6666$Freq.x, y=comunas_censo_casen_6666$multipob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2)

2 Análisis de regresión

Aplicaremos un análisis de regresión donde:

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

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

2.1 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.

2.2 Modelo lineal

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

tabla_de_trabajo <- comunas_censo_casen_6666
linearMod <- lm( multipob~(Freq.x) , data=tabla_de_trabajo)
summary(linearMod)

## 
## Call:
## lm(formula = multipob ~ (Freq.x), data = tabla_de_trabajo)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -853492260   -6763317   -3646307    2208289  922748428 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  6344942     313835   20.22   <2e-16 ***
## Freq.x        900756       3494  257.79   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 27310000 on 9648 degrees of freedom
##   (355 observations deleted due to missingness)
## Multiple R-squared:  0.8732, Adjusted R-squared:  0.8732 
## F-statistic: 6.645e+04 on 1 and 9648 DF,  p-value: < 2.2e-16

2.3 Gráfica de la recta de regresión lineal

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

Si bien obtenemos nuestro modelo lineal da cuenta del 0.9168 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.

3 Modelos alternativos

### 8.1 Modelo cuadrático

linearMod <- lm( multipob~(Freq.x^2) , data=tabla_de_trabajo)
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)"
modelos1 <- cbind(modelo,dato,sintaxis)


### 8.2 Modelo cúbico

linearMod <- lm( multipob~(Freq.x^3) , data=tabla_de_trabajo)
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)"
modelos2 <- cbind(modelo,dato,sintaxis)

### 8.3 Modelo logarítmico

linearMod <- lm( multipob~log(Freq.x) , data=tabla_de_trabajo)
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)"
modelos3 <- cbind(modelo,dato,sintaxis)

### 8.5 Modelo con raíz cuadrada

linearMod <- lm( multipob~sqrt(Freq.x) , data=tabla_de_trabajo)
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)"
modelos5 <- cbind(modelo,dato,sintaxis)

### 8.6 Modelo raíz-raíz

linearMod <- lm( sqrt(multipob)~sqrt(Freq.x) , data=tabla_de_trabajo)
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)"
modelos6 <- cbind(modelo,dato,sintaxis)

### 8.7 Modelo log-raíz

linearMod <- lm( log(multipob)~sqrt(Freq.x) , data=tabla_de_trabajo)
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)"
modelos7 <- cbind(modelo,dato,sintaxis)

### 8.8 Modelo raíz-log

linearMod <- lm( sqrt(multipob)~log(Freq.x) , data=tabla_de_trabajo)
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)"
modelos8 <- cbind(modelo,dato,sintaxis)

### 8.9 Modelo log-log

linearMod <- lm( log(multipob)~log(Freq.x) , data=tabla_de_trabajo)
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)"
modelos9 <- cbind(modelo,dato,sintaxis)

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, decreasing = T ),]

h_y_m_comuna_corr_01 <<- tabla_de_trabajo

kbl(modelos_bind) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	modelo	dato	sintaxis
5	raíz-raíz	0.931469599155803	linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
8	log-log	0.885928812926616	linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
1	cuadrático	0.873209853833581	linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
2	cúbico	0.873209853833581	linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
6	log-raíz	0.787302082593954	linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
4	raíz cuadrada	0.775090090600853	linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
7	raíz-log	0.765335598062265	linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
3	logarítmico	0.45660973354207	linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)

4 Elección del modelo.

Elegimos el modelo log-log (8) pues tiene el más alto \(R^2\)

h_y_m_comuna_corr <- h_y_m_comuna_corr_01
metodo <- 5
switch (metodo,
        case = linearMod <- lm( multipob~(Freq.x^2) , data=h_y_m_comuna_corr),
        case = linearMod <- lm( multipob~(Freq.x^3) , data=h_y_m_comuna_corr),
        case = linearMod <- lm( multipob~log(Freq.x) , data=h_y_m_comuna_corr),
        case = linearMod <- lm( multipob~sqrt(Freq.x) , data=h_y_m_comuna_corr),
        case = linearMod <- lm( sqrt(multipob)~sqrt(Freq.x) , data=h_y_m_comuna_corr),
        case = linearMod <- lm( log(multipob)~sqrt(Freq.x) , data=h_y_m_comuna_corr),
        case = linearMod <- lm( sqrt(multipob)~log(Freq.x) , data=h_y_m_comuna_corr),
        case = linearMod <- lm( log(multipob)~log(Freq.x) , data=h_y_m_comuna_corr)
)
summary(linearMod)

## 
## Call:
## lm(formula = sqrt(multipob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12821.2   -575.0    -65.7    490.5  16381.0 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   485.321     16.989   28.57   <2e-16 ***
## sqrt(Freq.x)  953.222      2.632  362.15   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 999.4 on 9648 degrees of freedom
##   (355 observations deleted due to missingness)
## Multiple R-squared:  0.9315, Adjusted R-squared:  0.9315 
## F-statistic: 1.312e+05 on 1 and 9648 DF,  p-value: < 2.2e-16

4.1 Modelo raíz-raíz (raíz-raíz)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.9315).

4.1.1 Diagrama de dispersión sobre raíz-raíz

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

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

ggplot(tabla_de_trabajo, aes(x = sqrt(Freq.x) , y = sqrt(multipob))) + geom_point() + stat_smooth(method=lm , color="blue",  level = 0.9, fill="green", se=TRUE)

4.1.2 Análisis de residuos

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

4.1.3 Modelo sqrt-sqrt

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

linearMod <- lm( sqrt(multipob)~sqrt(Freq.x) , data=tabla_de_trabajo)
aa <- linearMod$coefficients[1]
bb <- linearMod$coefficients[2]

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

Esta nueva variable se llamará: est_ing

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

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

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

tabla_de_trabajo$ing_medio_zona <- tabla_de_trabajo$est_ing /(tabla_de_trabajo$personas  * tabla_de_trabajo$p)


write_xlsx(tabla_de_trabajo, "tabla_de_trabajo_escolaridad.xlsx")
write.dbf(tabla_de_trabajo, "tabla_de_trabajo_escolaridad.dbf")

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

clave	código.x	Freq.x	personas	comuna	promedio_i	año	ingresos_expandidos	Freq.y	p	código.y	multipob	est_ing	ing_medio_zona
10101032002	10101	27	245902	Puerto Montt	176237.4	2017	43337141298	129	0.0005246	10101	22734631.0	29576261	229273.34
10101032011	10101	106	245902	Puerto Montt	176237.4	2017	43337141298	426	0.0017324	10101	75077153.5	106076386	249005.60
10101032019	10101	174	245902	Puerto Montt	176237.4	2017	43337141298	829	0.0033713	10101	146100845.6	170542154	205720.33
10101062003	10101	28	245902	Puerto Montt	176237.4	2017	43337141298	158	0.0006425	10101	27845517.0	30573114	193500.72
10101062008	10101	83	245902	Puerto Montt	176237.4	2017	43337141298	581	0.0023627	10101	102393958.1	84081270	144718.19
10101062013	10101	83	245902	Puerto Montt	176237.4	2017	43337141298	571	0.0023221	10101	100631583.6	84081270	147252.66
10101062029	10101	15	245902	Puerto Montt	176237.4	2017	43337141298	47	0.0001911	10101	8283160.1	17448437	371243.33
10101062039	10101	18	245902	Puerto Montt	176237.4	2017	43337141298	67	0.0002725	10101	11807909.1	20516352	306214.21
10101072014	10101	204	245902	Puerto Montt	176237.4	2017	43337141298	997	0.0040545	10101	175708737.1	198811417	199409.65
10101072021	10101	13	245902	Puerto Montt	176237.4	2017	43337141298	44	0.0001789	10101	7754447.8	15383735	349630.35
10101072028	10101	22	245902	Puerto Montt	176237.4	2017	43337141298	145	0.0005897	10101	25554430.2	24565177	169415.01
10101072029	10101	221	245902	Puerto Montt	176237.4	2017	43337141298	1051	0.0042741	10101	185225559.4	214797765	204374.66
10101072036	10101	17	245902	Puerto Montt	176237.4	2017	43337141298	118	0.0004799	10101	20796019.0	19497122	165229.85
10101072045	10101	19	245902	Puerto Montt	176237.4	2017	43337141298	113	0.0004595	10101	19914831.8	21532550	190553.54
10101072050	10101	7	245902	Puerto Montt	176237.4	2017	43337141298	66	0.0002684	10101	11631671.7	9043903	137028.84
10101082016	10101	18	245902	Puerto Montt	176237.4	2017	43337141298	121	0.0004921	10101	21324731.4	20516352	169556.63
10101082017	10101	4	245902	Puerto Montt	176237.4	2017	43337141298	38	0.0001545	10101	6697023.1	5720536	150540.41
10101082018	10101	102	245902	Puerto Montt	176237.4	2017	43337141298	623	0.0025335	10101	109795931.0	102260397	164141.89
10101082030	10101	26	245902	Puerto Montt	176237.4	2017	43337141298	176	0.0007157	10101	31017791.1	28577758	162373.63
10101082034	10101	11	245902	Puerto Montt	176237.4	2017	43337141298	66	0.0002684	10101	11631671.7	13299147	201502.22
10101082042	10101	51	245902	Puerto Montt	176237.4	2017	43337141298	253	0.0010289	10101	44588074.7	53183261	210210.52
10101082045	10101	23	245902	Puerto Montt	176237.4	2017	43337141298	123	0.0005002	10101	21677206.3	25571343	207897.10
10101092004	10101	11	245902	Puerto Montt	176237.4	2017	43337141298	97	0.0003945	10101	17095032.6	13299147	137104.60
10101092008	10101	126	245902	Puerto Montt	176237.4	2017	43337141298	752	0.0030581	10101	132530562.0	125108880	166368.19
10101092037	10101	68	245902	Puerto Montt	176237.4	2017	43337141298	276	0.0011224	10101	48641536.1	69652185	252362.99
10101092040	10101	101	245902	Puerto Montt	176237.4	2017	43337141298	509	0.0020699	10101	89704861.8	101305846	199029.17
10101092041	10101	354	245902	Puerto Montt	176237.4	2017	43337141298	1683	0.0068442	10101	296607627.4	339299369	201603.90
10101092044	10101	99	245902	Puerto Montt	176237.4	2017	43337141298	530	0.0021553	10101	93405848.2	99396058	187539.73
10101102005	10101	24	245902	Puerto Montt	176237.4	2017	43337141298	147	0.0005978	10101	25906905.1	26575411	180785.11
10101102007	10101	166	245902	Puerto Montt	176237.4	2017	43337141298	824	0.0033509	10101	145219658.4	162989232	197802.47
10101102026	10101	54	245902	Puerto Montt	176237.4	2017	43337141298	245	0.0009963	10101	43178175.1	56100718	228982.52
10101102035	10101	199	245902	Puerto Montt	176237.4	2017	43337141298	940	0.0038227	10101	165663202.5	194105306	206495.01
10101102037	10101	24	245902	Puerto Montt	176237.4	2017	43337141298	164	0.0006669	10101	28902941.7	26575411	162045.19
10101102051	10101	8	245902	Puerto Montt	176237.4	2017	43337141298	57	0.0002318	10101	10045534.6	10121553	177571.11
10101102901	10101	1	245902	Puerto Montt	176237.4	2017	43337141298	16	0.0000651	10101	2819799.2	2069404	129337.77
10101112025	10101	186	245902	Puerto Montt	176237.4	2017	43337141298	1078	0.0043839	10101	189983970.5	181859570	168700.90
10101122024	10101	62	245902	Puerto Montt	176237.4	2017	43337141298	952	0.0038715	10101	167778051.9	63856019	67075.65
10101132022	10101	121	245902	Puerto Montt	176237.4	2017	43337141298	703	0.0028589	10101	123894927.0	120357570	171205.65
10101132023	10101	102	245902	Puerto Montt	176237.4	2017	43337141298	603	0.0024522	10101	106271182.0	102260397	169586.06
10101132027	10101	29	245902	Puerto Montt	176237.4	2017	43337141298	105	0.0004270	10101	18504932.2	31568405	300651.48
10101132049	10101	359	245902	Puerto Montt	176237.4	2017	43337141298	1883	0.0076575	10101	331855117.3	343965036	182668.63
10101142009	10101	4	245902	Puerto Montt	176237.4	2017	43337141298	59	0.0002399	10101	10398009.5	5720536	96958.23
10101142015	10101	14	245902	Puerto Montt	176237.4	2017	43337141298	124	0.0005043	10101	21853443.7	16418297	132405.62
10101142027	10101	42	245902	Puerto Montt	176237.4	2017	43337141298	192	0.0007808	10101	33837590.3	44394284	231220.23
10101142038	10101	6	245902	Puerto Montt	176237.4	2017	43337141298	53	0.0002155	10101	9340584.8	7953683	150069.50
10101142046	10101	56	245902	Puerto Montt	176237.4	2017	43337141298	317	0.0012891	10101	55867271.5	58042745	183100.14
10101142047	10101	32	245902	Puerto Montt	176237.4	2017	43337141298	263	0.0010695	10101	46350449.2	34545677	131352.39
10101142049	10101	169	245902	Puerto Montt	176237.4	2017	43337141298	973	0.0039569	10101	171479038.3	165822363	170423.81
10101152002	10101	118	245902	Puerto Montt	176237.4	2017	43337141298	554	0.0022529	10101	97635547.0	117504714	212102.37
10101152006	10101	53	245902	Puerto Montt	176237.4	2017	43337141298	214	0.0008703	10101	37714814.2	55128838	257611.39
10101152020	10101	154	245902	Puerto Montt	176237.4	2017	43337141298	617	0.0025091	10101	108738506.3	151646696	245780.71
10101152031	10101	147	245902	Puerto Montt	176237.4	2017	43337141298	642	0.0026108	10101	113144442.6	145022288	225891.41
10101152033	10101	145	245902	Puerto Montt	176237.4	2017	43337141298	700	0.0028467	10101	123366214.6	143128451	204469.22
10101152049	10101	21	245902	Puerto Montt	176237.4	2017	43337141298	96	0.0003904	10101	16918795.1	23556767	245382.99
10101152901	10101	5	245902	Puerto Montt	176237.4	2017	43337141298	46	0.0001871	10101	8106922.7	6847586	148860.57
10101162006	10101	71	245902	Puerto Montt	176237.4	2017	43337141298	233	0.0009475	10101	41063325.7	72544566	311350.07
10101162010	10101	96	245902	Puerto Montt	176237.4	2017	43337141298	351	0.0014274	10101	61859344.8	96529606	275013.12
10101162020	10101	45	245902	Puerto Montt	176237.4	2017	43337141298	205	0.0008337	10101	36128677.1	47330636	230881.15
10101162031	10101	183	245902	Puerto Montt	176237.4	2017	43337141298	762	0.0030988	10101	134292936.5	179031499	234949.47
10101162032	10101	2	245902	Puerto Montt	176237.4	2017	43337141298	24	0.0000976	10101	4229698.8	3361281	140053.39
10101162033	10101	35	245902	Puerto Montt	176237.4	2017	43337141298	168	0.0006832	10101	29607891.5	37511417	223282.25
10101172029	10101	153	245902	Puerto Montt	176237.4	2017	43337141298	854	0.0034729	10101	150506781.8	150700725	176464.55
10102012001	10102	16	33985	Calbuco	155443.9	2017	5282762017	80	0.0023540	10102	12435514.5	18474589	230932.36
10102012004	10102	59	33985	Calbuco	155443.9	2017	5282762017	283	0.0083272	10102	43990632.7	60951681	215376.96
10102012008	10102	103	33985	Calbuco	155443.9	2017	5282762017	697	0.0205090	10102	108344420.4	103214723	148084.25
10102012017	10102	24	33985	Calbuco	155443.9	2017	5282762017	268	0.0078858	10102	41658973.7	26575411	99161.98
10102012033	10102	309	33985	Calbuco	155443.9	2017	5282762017	1490	0.0438429	10102	231611458.2	297266896	199507.98
10102012037	10102	4	33985	Calbuco	155443.9	2017	5282762017	6	0.0001765	10102	932663.6	5720536	953422.62
10102022001	10102	31	33985	Calbuco	155443.9	2017	5282762017	164	0.0048257	10102	25492804.8	33554617	204601.32
10102022002	10102	48	33985	Calbuco	155443.9	2017	5282762017	308	0.0090628	10102	47876730.9	50260083	163182.09
10102022003	10102	32	33985	Calbuco	155443.9	2017	5282762017	184	0.0054142	10102	28601683.4	34545677	187748.25
10102022004	10102	14	33985	Calbuco	155443.9	2017	5282762017	74	0.0021774	10102	11502850.9	16418297	221868.88
10102022010	10102	39	33985	Calbuco	155443.9	2017	5282762017	294	0.0086509	10102	45700515.9	41450271	140987.32
10102022013	10102	47	33985	Calbuco	155443.9	2017	5282762017	450	0.0132411	10102	69949769.2	49284326	109520.73
10102032002	10102	6	33985	Calbuco	155443.9	2017	5282762017	54	0.0015889	10102	8393972.3	7953683	147290.43
10102032011	10102	31	33985	Calbuco	155443.9	2017	5282762017	163	0.0047962	10102	25337360.9	33554617	205856.54
10102032016	10102	18	33985	Calbuco	155443.9	2017	5282762017	95	0.0027954	10102	14767173.5	20516352	215961.60
10102032018	10102	68	33985	Calbuco	155443.9	2017	5282762017	470	0.0138296	10102	73058647.9	69652185	148196.14
10102032034	10102	87	33985	Calbuco	155443.9	2017	5282762017	695	0.0204502	10102	108033532.5	87916523	126498.59
10102032901	10102	13	33985	Calbuco	155443.9	2017	5282762017	89	0.0026188	10102	13834509.9	15383735	172850.96
10102042014	10102	18	33985	Calbuco	155443.9	2017	5282762017	87	0.0025600	10102	13523622.1	20516352	235820.14
10102042016	10102	7	33985	Calbuco	155443.9	2017	5282762017	48	0.0014124	10102	7461308.7	9043903	188414.65
10102042019	10102	72	33985	Calbuco	155443.9	2017	5282762017	336	0.0098867	10102	52229161.0	73507908	218773.54
10102042028	10102	27	33985	Calbuco	155443.9	2017	5282762017	171	0.0050316	10102	26580912.3	29576261	172960.59
10102042029	10102	24	33985	Calbuco	155443.9	2017	5282762017	183	0.0053847	10102	28446239.5	26575411	145220.82
10102042035	10102	55	33985	Calbuco	155443.9	2017	5282762017	271	0.0079741	10102	42125305.5	57072015	210597.84
10102042043	10102	23	33985	Calbuco	155443.9	2017	5282762017	86	0.0025305	10102	13368178.1	25571343	297341.19
10102042901	10102	25	33985	Calbuco	155443.9	2017	5282762017	127	0.0037369	10102	19741379.3	27577510	217145.75
10102052012	10102	67	33985	Calbuco	155443.9	2017	5282762017	302	0.0088863	10102	46944067.4	68687245	227441.21
10102052019	10102	61	33985	Calbuco	155443.9	2017	5282762017	332	0.0097690	10102	51607385.3	62888396	189422.88
10102052042	10102	95	33985	Calbuco	155443.9	2017	5282762017	410	0.0120641	10102	63732012.0	95573635	233106.43
10102052043	10102	52	33985	Calbuco	155443.9	2017	5282762017	273	0.0080330	10102	42436193.3	54156358	198374.94
10102062015	10102	151	33985	Calbuco	155443.9	2017	5282762017	1129	0.0332205	10102	175496198.8	148808415	131805.50
10102062019	10102	59	33985	Calbuco	155443.9	2017	5282762017	362	0.0106518	10102	56270703.3	60951681	168374.81
10102062027	10102	5	33985	Calbuco	155443.9	2017	5282762017	25	0.0007356	10102	3886098.3	6847586	273903.44
10102062038	10102	57	33985	Calbuco	155443.9	2017	5282762017	357	0.0105046	10102	55493483.6	59012924	165302.31
10102062039	10102	85	33985	Calbuco	155443.9	2017	5282762017	643	0.0189201	10102	99950448.1	85999487	133747.26
10102072022	10102	9	33985	Calbuco	155443.9	2017	5282762017	148	0.0043549	10102	23005701.9	11188931	75600.88
10102082022	10102	71	33985	Calbuco	155443.9	2017	5282762017	565	0.0166250	10102	87825821.4	72544566	128397.46
10102092022	10102	31	33985	Calbuco	155443.9	2017	5282762017	308	0.0090628	10102	47876730.9	33554617	108943.56

6.1 Estadísticos

ingresos <- readRDS("Ingresos_expandidos_rural_17.rds")

kbl(ingresos) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	personas	comuna	promedio_i	año	ingresos_expandidos
1	01101	191468	Iquique	272529.7	2017	52180713221
3	01401	15711	Pozo Almonte	243272.4	2017	3822052676
4	01402	1250	Camiña	226831.0	2017	283538750
6	01404	2730	Huara	236599.7	2017	645917134
7	01405	9296	Pica	269198.0	2017	2502464414
10	02103	10186	Sierra Gorda	322997.9	2017	3290056742
11	02104	13317	Taltal	288653.8	2017	3844002134
12	02201	165731	Calama	238080.9	2017	39457387800
14	02203	10996	San Pedro de Atacama	271472.6	2017	2985112297
15	02301	25186	Tocopilla	166115.9	2017	4183793832
17	03101	153937	Copiapó	251396.0	2017	38699138722
19	03103	14019	Tierra Amarilla	287819.4	2017	4034940816
21	03202	13925	Diego de Almagro	326439.0	2017	4545663075
22	03301	51917	Vallenar	217644.6	2017	11299454698
23	03302	5299	Alto del Carmen	196109.9	2017	1039186477
24	03303	7041	Freirina	202463.8	2017	1425547554
25	03304	10149	Huasco	205839.6	2017	2089066548
26	04101	221054	La Serena	200287.4	2017	44274327972
27	04102	227730	Coquimbo	206027.8	2017	46918711304
28	04103	11044	Andacollo	217096.4	2017	2397612293
29	04104	4241	La Higuera	231674.2	2017	982530309
30	04105	4497	Paiguano	174868.5	2017	786383423
31	04106	27771	Vicuña	169077.1	2017	4695441470
32	04201	30848	Illapel	165639.6	2017	5109649759
33	04202	9093	Canela	171370.3	2017	1558270441
34	04203	21382	Los Vilos	173238.5	2017	3704185607
35	04204	29347	Salamanca	193602.0	2017	5681637894
36	04301	111272	Ovalle	230819.8	2017	25683781418
37	04302	13322	Combarbalá	172709.2	2017	2300832587
38	04303	30751	Monte Patria	189761.6	2017	5835357638
39	04304	10956	Punitaqui	165862.0	2017	1817183694
40	04305	4278	Río Hurtado	182027.2	2017	778712384
41	05101	296655	Valparaíso	251998.5	2017	74756602991
42	05102	26867	Casablanca	252317.7	2017	6779018483
45	05105	18546	Puchuncaví	231606.0	2017	4295363979
46	05107	31923	Quintero	285125.8	2017	9102071069
49	05301	66708	Los Andes	280548.0	2017	18714795984
50	05302	14832	Calle Larga	234044.6	2017	3471349123
51	05303	10207	Rinconada	246136.9	2017	2512319225
52	05304	18855	San Esteban	211907.3	2017	3995512770
53	05401	35390	La Ligua	172675.9	2017	6111000517
54	05402	19388	Cabildo	212985.0	2017	4129354103
56	05404	9826	Petorca	270139.8	2017	2654393853
57	05405	7339	Zapallar	235661.4	2017	1729518700
58	05501	90517	Quillota	212067.6	2017	19195726144
59	05502	50554	Calera	226906.2	2017	11471016698
60	05503	17988	Hijuelas	215402.0	2017	3874650405
61	05504	22098	La Cruz	243333.4	2017	5377180726
62	05506	22120	Nogales	219800.7	2017	4861992055
63	05601	91350	San Antonio	230261.5	2017	21034388728
64	05602	13817	Algarrobo	218057.0	2017	3012893845
65	05603	22738	Cartagena	246517.9	2017	5605324190
68	05606	10900	Santo Domingo	250404.5	2017	2729409577
69	05701	76844	San Felipe	240842.4	2017	18507290899
70	05702	13998	Catemu	204903.4	2017	2868237147
71	05703	24608	Llaillay	257020.9	2017	6324771348
72	05704	7273	Panquehue	210643.4	2017	1532009468
73	05705	16754	Putaendo	207222.5	2017	3471806107
74	05706	15241	Santa María	254903.6	2017	3884985562
75	05801	151708	Quilpué	296519.4	2017	44984360344
76	05802	46121	Limache	251682.2	2017	11607834893
77	05803	17516	Olmué	198292.3	2017	3473287749
78	05804	126548	Villa Alemana	249779.9	2017	31609146219
79	06101	241774	Rancagua	243717.4	2017	58924531866
80	06102	12988	Codegua	264737.7	2017	3438412620
81	06103	7359	Coinco	175814.1	2017	1293816308
82	06104	19597	Coltauco	254006.7	2017	4977769953
83	06105	20887	Doñihue	198486.5	2017	4145787348
84	06106	33437	Graneros	248394.9	2017	8305580885
85	06107	24640	Las Cabras	201772.1	2017	4971665251
86	06108	52505	Machalí	252049.6	2017	13233865906
87	06109	13407	Malloa	250691.2	2017	3361017589
88	06110	25343	Mostazal	264277.9	2017	6697593734
89	06111	13608	Olivar	256304.8	2017	3487795575
90	06112	14313	Peumo	230938.1	2017	3305417128
91	06113	19714	Pichidegua	217210.9	2017	4282095940
92	06114	13002	Quinta de Tilcoco	203672.8	2017	2648154389
93	06115	58825	Rengo	250531.0	2017	14737488444
94	06116	27968	Requínoa	244836.0	2017	6847572657
95	06117	46766	San Vicente	242866.0	2017	11357872282
96	06201	16394	Pichilemu	230362.3	2017	3776560181
97	06202	3041	La Estrella	211425.0	2017	642943494
98	06203	6294	Litueche	237979.9	2017	1497845780
99	06204	7308	Marchihue	237849.2	2017	1738201845
100	06205	6641	Navidad	165555.2	2017	1099452202
101	06206	6188	Paredones	194146.1	2017	1201375821
102	06301	73973	San Fernando	239724.5	2017	17733143348
103	06302	15037	Chépica	207192.9	2017	3115559148
104	06303	35399	Chimbarongo	227716.7	2017	8060942027
105	06304	6811	Lolol	210117.9	2017	1431112941
106	06305	17833	Nancagua	213675.0	2017	3810465416
107	06306	12482	Palmilla	230550.0	2017	2877725100
108	06307	11007	Peralillo	231695.3	2017	2550270534
109	06308	8738	Placilla	221358.7	2017	1934232402
110	06309	3421	Pumanque	239369.8	2017	818883984
111	06310	37855	Santa Cruz	224421.9	2017	8495489945
112	07101	220357	Talca	244658.0	2017	53912095394
113	07102	46068	Constitución	198314.7	2017	9135962663
114	07103	9448	Curepto	191743.1	2017	1811588746
115	07104	4142	Empedrado	172428.7	2017	714199777
116	07105	49721	Maule	195207.4	2017	9705908393
117	07106	8422	Pelarco	187124.6	2017	1575963241
118	07107	8245	Pencahue	220957.5	2017	1821794345
119	07108	13906	Río Claro	196539.7	2017	2733081178
120	07109	43269	San Clemente	179181.4	2017	7753001772
121	07110	9191	San Rafael	195848.9	2017	1800047360
122	07201	40441	Cauquenes	152604.5	2017	6171477801
123	07202	8928	Chanco	128982.1	2017	1151552040
124	07203	7571	Pelluhue	113986.7	2017	862993347
125	07301	149136	Curicó	265301.7	2017	39566034949
126	07302	9657	Hualañé	167967.3	2017	1622060226
127	07303	6653	Licantén	179919.7	2017	1197005482
128	07304	45976	Molina	227845.0	2017	10475401720
129	07305	10484	Rauco	196719.6	2017	2062408371
130	07306	15187	Romeral	218360.4	2017	3316239205
131	07307	18544	Sagrada Familia	204922.9	2017	3800089672
132	07308	28921	Teno	250368.7	2017	7240913928
133	07309	4322	Vichuquén	179935.1	2017	777679695
134	07401	93602	Linares	192783.1	2017	18044885598
135	07402	20765	Colbún	161250.1	2017	3348358419
136	07403	30534	Longaví	166612.7	2017	5087351933
137	07404	41637	Parral	183123.5	2017	7624714509
138	07405	19974	Retiro	146406.4	2017	2924321333
139	07406	45547	San Javier	170552.7	2017	7768163327
140	07407	16221	Villa Alegre	178486.5	2017	2895229121
141	07408	18081	Yerbas Buenas	203001.0	2017	3670461912
142	08101	223574	Concepción	197625.8	2017	44183983882
143	08102	116262	Coronel	217018.1	2017	25230952648
145	08104	10624	Florida	147425.2	2017	1566245750
146	08105	24333	Hualqui	202715.1	2017	4932666876
148	08107	47367	Penco	195212.7	2017	9246639961
150	08109	13749	Santa Juana	198449.1	2017	2728477197
151	08110	151749	Talcahuano	161731.1	2017	24542535584
152	08111	54946	Tomé	210053.2	2017	11541584520
154	08201	25522	Lebu	141842.6	2017	3620107931
155	08202	36257	Arauco	184849.0	2017	6702069405
156	08203	34537	Cañete	186912.3	2017	6455391501
157	08204	6031	Contulmo	131551.5	2017	793386801
158	08205	32288	Curanilahue	252073.4	2017	8138946477
159	08206	21035	Los Álamos	189429.4	2017	3984647129
160	08207	10417	Tirúa	144994.9	2017	1510411713
161	08301	202331	Los Ángeles	190810.3	2017	38606846296
162	08302	4073	Antuco	155662.3	2017	634012722
163	08303	28573	Cabrero	249163.0	2017	7119335384
164	08304	22389	Laja	174449.0	2017	3905739533
165	08305	29627	Mulchén	198258.1	2017	5873792045
166	08306	26315	Nacimiento	175829.2	2017	4626944798
167	08307	9737	Negrete	216999.7	2017	2112926492
168	08308	3988	Quilaco	167106.1	2017	666419314
169	08309	9587	Quilleco	222077.0	2017	2129051929
170	08310	3412	San Rosendo	165912.3	2017	566092732
171	08311	13773	Santa Bárbara	176010.5	2017	2424192819
172	08312	14134	Tucapel	155538.6	2017	2198382777
173	08313	21198	Yumbel	138515.0	2017	2936241535
174	08314	5923	Alto Biobío	130542.9	2017	773205492
175	09101	282415	Temuco	173314.1	2017	48946498862
176	09102	24533	Carahue	127924.5	2017	3138372109
177	09103	17526	Cunco	156882.5	2017	2749522512
178	09104	7489	Curarrehue	135420.9	2017	1014167156
179	09105	24606	Freire	197426.1	2017	4857867695
180	09106	11996	Galvarino	147518.2	2017	1769627798
181	09107	14414	Gorbea	140997.5	2017	2032338344
182	09108	38013	Lautaro	282496.1	2017	10738525406
183	09109	23612	Loncoche	160742.5	2017	3795451798
184	09110	6138	Melipeuco	164670.1	2017	1010744848
185	09111	32510	Nueva Imperial	158196.8	2017	5142978907
186	09112	76126	Padre Las Casas	169223.7	2017	12882320064
187	09113	6905	Perquenco	155106.7	2017	1071011969
188	09114	24837	Pitrufquén	205557.8	2017	5105439315
189	09115	28523	Pucón	187764.8	2017	5355614570
190	09116	12450	Saavedra	130775.6	2017	1628156299
191	09117	15045	Teodoro Schmidt	138894.2	2017	2089663239
192	09118	9722	Toltén	113791.8	2017	1106284328
193	09119	28151	Vilcún	135602.8	2017	3817354634
194	09120	55478	Villarrica	198745.4	2017	11026000004
195	09121	11611	Cholchol	115103.4	2017	1336465909
196	09201	53262	Angol	173377.3	2017	9234420713
197	09202	24598	Collipulli	182323.1	2017	4484784762
198	09203	17413	Curacautín	186604.9	2017	3249351008
199	09204	7733	Ercilla	136678.7	2017	1056936411
200	09205	10251	Lonquimay	138745.9	2017	1422283764
201	09206	7265	Los Sauces	142588.7	2017	1035906610
202	09207	9548	Lumaco	170538.2	2017	1628298886
203	09208	11779	Purén	133537.6	2017	1572938990
204	09209	10250	Renaico	218920.0	2017	2243930000
205	09210	18843	Traiguén	210526.3	2017	3966946195
206	09211	34182	Victoria	187662.8	2017	6414689393
207	10101	245902	Puerto Montt	176237.4	2017	43337141298
208	10102	33985	Calbuco	155443.9	2017	5282762017
210	10104	12261	Fresia	183977.2	2017	2255743895
211	10105	18428	Frutillar	174883.1	2017	3222744874
212	10106	17068	Los Muermos	192845.4	2017	3291484556
213	10107	17591	Llanquihue	149333.8	2017	2626930838
214	10108	14216	Maullín	137613.7	2017	1956316762
215	10109	44578	Puerto Varas	219839.1	2017	9799987895
216	10201	43807	Castro	183717.2	2017	8048100927
217	10202	38991	Ancud	161910.1	2017	6313036958
218	10203	14858	Chonchi	193642.9	2017	2877146807
219	10204	3829	Curaco de Vélez	177952.2	2017	681378864
220	10205	13762	Dalcahue	207717.6	2017	2858609503
221	10206	3921	Puqueldón	208274.8	2017	816645370
222	10207	5385	Queilén	151485.0	2017	815746659
223	10208	27192	Quellón	171685.5	2017	4668472212
224	10209	8352	Quemchi	122223.1	2017	1020807718
225	10210	8088	Quinchao	119852.6	2017	969367811
226	10301	161460	Osorno	196610.2	2017	31744688808
227	10302	8999	Puerto Octay	221980.9	2017	1997605810
228	10303	20369	Purranque	186719.5	2017	3803288945
229	10304	11667	Puyehue	176006.9	2017	2053472049
230	10305	14085	Río Negro	156568.1	2017	2205262341
231	10306	7512	San Juan de la Costa	152674.0	2017	1146887184
232	10307	10030	San Pablo	181411.6	2017	1819558805
237	11101	57818	Coyhaique	230013.0	2017	13298894369
239	11201	23959	Aysén	246614.5	2017	5908637554
240	11202	6517	Cisnes	262412.7	2017	1710143349
242	11301	3490	Cochrane	211652.6	2017	738667487
245	11401	4865	Chile Chico	188913.8	2017	919065674
246	11402	2666	Río Ibáñez	171315.6	2017	456727447
247	12101	131592	Punta Arenas	256903.3	2017	33806414442
253	12301	6801	Porvenir	381329.2	2017	2593419712
256	12401	21477	Natales	302167.3	2017	6489647004
291	13202	26521	Pirque	274675.6	2017	7284672878
292	13203	18189	San José de Maipo	344876.8	2017	6272964115
293	13301	146207	Colina	255373.7	2017	37337421744
294	13302	102034	Lampa	243425.7	2017	24837699582
295	13303	19312	Tiltil	264794.8	2017	5113717064
296	13401	301313	San Bernardo	251728.3	2017	75849003232
297	13402	96614	Buin	289884.0	2017	28006850165
298	13403	25392	Calera de Tango	298439.8	2017	7577982724
299	13404	72759	Paine	282280.9	2017	20538478428
300	13501	123627	Melipilla	199121.9	2017	24616837833
301	13502	6444	Alhué	242844.2	2017	1564887792
302	13503	32579	Curacaví	220990.2	2017	7199638514
303	13504	13590	María Pinto	198063.3	2017	2691680700
304	13505	9726	San Pedro	231429.7	2017	2250885401
305	13601	74237	Talagante	230734.4	2017	17129031774
306	13602	35923	El Monte	201444.7	2017	7236496479
307	13603	36219	Isla de Maipo	232595.7	2017	8424384020
308	13604	63250	Padre Hurtado	231845.6	2017	14664233522
309	13605	90201	Peñaflor	249848.3	2017	22536570306
310	14101	166080	Valdivia	211732.5	2017	35164529745
311	14102	5302	Corral	157428.1	2017	834683963
312	14103	16752	Lanco	184730.2	2017	3094599901
313	14104	19634	Los Lagos	190489.7	2017	3740075550
314	14105	7095	Máfil	180289.2	2017	1279152079
315	14106	21278	Mariquina	187045.1	2017	3979945072
316	14107	20188	Paillaco	163833.6	2017	3307473487
317	14108	34539	Panguipulli	180390.3	2017	6230498948
318	14201	38036	La Unión	201975.2	2017	7682327556
319	14202	14665	Futrono	193120.3	2017	2832109866
320	14203	9896	Lago Ranco	186595.7	2017	1846550611
321	14204	31372	Río Bueno	184360.5	2017	5783758517
322	15101	221364	Arica	250863.6	2017	55532177025
323	15102	1255	Camarones	222472.1	2017	279202446
324	15201	2765	Putre	194293.6	2017	537221762
326	16101	184739	Chillán	232041.6	2017	42867130063
327	16102	21493	Bulnes	167693.2	2017	3604229178
328	16103	30907	Chillán Viejo	179855.8	2017	5558803478
329	16104	12044	El Carmen	151144.7	2017	1820386198
330	16105	8448	Pemuco	151889.4	2017	1283161238
331	16106	10827	Pinto	153289.2	2017	1659661870
332	16107	17485	Quillón	133479.9	2017	2333895558
333	16108	16079	San Ignacio	174538.8	2017	2806409365
334	16109	17787	Yungay	194006.8	2017	3450799686
335	16201	11594	Quirihue	155446.9	2017	1802251665
336	16202	5012	Cobquecura	122513.3	2017	614036495
337	16203	15995	Coelemu	174050.2	2017	2783932983
338	16204	5213	Ninhue	161577.8	2017	842304828
339	16205	4862	Portezuelo	168595.2	2017	819710106
340	16206	5755	Ránquil	221951.3	2017	1277329463
341	16207	5401	Treguaco	178763.9	2017	965503625
342	16301	53024	San Carlos	175203.6	2017	9289995173
343	16302	26881	Coihueco	174853.6	2017	4700239750
344	16303	11152	Ñiquén	188830.1	2017	2105832760
345	16304	4308	San Fabián	158019.3	2017	680747063
346	16305	11603	San Nicolás	180675.3	2017	2096375354

Promedio

t_de_c <- tabla_de_trabajo %>%
  group_by(código.y) %>%
  summarize(mean = mean(ing_medio_zona, na.rm = TRUE))

names(t_de_c)[1] <- "código"

estadisticos_finales <- merge( x = ingresos, y = t_de_c, by = "código", all.x = TRUE)
#estadisticos_finales

Desviación standard

t_de_c_2 <- tabla_de_trabajo %>%
  group_by(código.y) %>%
  summarize(sd = sd(ing_medio_zona, na.rm = TRUE))

names(t_de_c_2)[1] <- "código"

estadisticos_finales <- merge( x = estadisticos_finales, y = t_de_c_2, by = "código", all.x = TRUE)
#estadisticos_finales

Mínimo

t_de_c_3 <- tabla_de_trabajo %>%
  group_by(código.y) %>%
  summarize(min = min(ing_medio_zona, na.rm = TRUE))

names(t_de_c_3)[1] <- "código"

estadisticos_finales <- merge( x = estadisticos_finales, y = t_de_c_3, by = "código", all.x = TRUE)
#estadisticos_finales

Máximo

t_de_c_4 <- tabla_de_trabajo %>%
  group_by(código.y) %>%
  summarize(max = max(ing_medio_zona, na.rm = TRUE))

names(t_de_c_4)[1] <- "código"

estadisticos_finales <- merge( x = estadisticos_finales, y = t_de_c_4, by = "código", all.x = TRUE)
#estadisticos_finales

Mediana

t_de_c_5 <- tabla_de_trabajo %>%
  group_by(código.y) %>%
  summarize(mediana = median(ing_medio_zona, na.rm = TRUE))

names(t_de_c_5)[1] <- "código"

estadisticos_finales <- merge( x = estadisticos_finales, y = t_de_c_5, by = "código", all.x = TRUE)



kbl(estadisticos_finales) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	personas	comuna	promedio_i	año	ingresos_expandidos	mean	sd	min	max	mediana
01101	191468	Iquique	272529.7	2017	52180713221	339953.5	113569.64	183139.71	570498.5	316356.4
01401	15711	Pozo Almonte	243272.4	2017	3822052676	360876.6	181186.34	184028.24	855948.3	308088.5
01402	1250	Camiña	226831.0	2017	283538750	251212.6	165760.10	130012.18	715067.0	210701.7
01404	2730	Huara	236599.7	2017	645917134	253653.5	148547.48	59125.84	674770.2	229468.6
01405	9296	Pica	269198.0	2017	2502464414	501614.6	397458.49	243907.01	1521329.2	356104.5
02103	10186	Sierra Gorda	322997.9	2017	3290056742	469880.7	123604.30	301333.10	623158.4	475506.6
02104	13317	Taltal	288653.8	2017	3844002134	387771.1	153556.27	168343.63	843462.8	360965.2
02201	165731	Calama	238080.9	2017	39457387800	351733.9	224247.11	129275.09	1118893.1	284298.4
02203	10996	San Pedro de Atacama	271472.6	2017	2985112297	505101.6	358148.79	212847.94	1130487.9	308967.3
02301	25186	Tocopilla	166115.9	2017	4183793832	299805.8	169408.45	90845.44	753658.6	258560.1
03101	153937	Copiapó	251396.0	2017	38699138722	284981.0	137118.28	107034.52	715067.0	261395.9
03103	14019	Tierra Amarilla	287819.4	2017	4034940816	313280.0	119535.62	26875.38	500398.8	322369.3
03202	13925	Diego de Almagro	326439.0	2017	4545663075	287595.6	116096.06	206940.43	489113.3	243472.2
03301	51917	Vallenar	217644.6	2017	11299454698	293744.9	152594.98	124491.90	1004878.2	246201.4
03302	5299	Alto del Carmen	196109.9	2017	1039186477	346958.5	316318.80	79592.47	2069404.3	279951.8
03303	7041	Freirina	202463.8	2017	1425547554	229223.2	59916.24	138651.42	359436.4	234123.5
03304	10149	Huasco	205839.6	2017	2089066548	230695.5	76337.72	108916.01	384887.3	218206.7
04101	221054	La Serena	200287.4	2017	44274327972	227120.9	89528.39	73340.20	695684.9	220500.2
04102	227730	Coquimbo	206027.8	2017	46918711304	244543.2	66432.17	55102.97	381764.9	248876.7
04103	11044	Andacollo	217096.4	2017	2397612293	222565.2	112411.21	98543.06	480183.1	172105.4
04104	4241	La Higuera	231674.2	2017	982530309	223603.3	106033.49	63388.72	475994.9	213828.0
04105	4497	Paiguano	174868.5	2017	786383423	269202.1	92586.98	170640.03	517351.1	238798.9
04106	27771	Vicuña	169077.1	2017	4695441470	281699.8	163567.41	93802.55	920141.2	232715.2
04201	30848	Illapel	165639.6	2017	5109649759	194462.6	64279.88	101421.95	476711.3	193232.4
04202	9093	Canela	171370.3	2017	1558270441	181661.9	84144.51	43093.35	453618.8	184569.5
04203	21382	Los Vilos	173238.5	2017	3704185607	246155.5	123061.95	73645.22	578223.8	218330.4
04204	29347	Salamanca	193602.0	2017	5681637894	226713.0	71793.01	135305.44	443304.9	208336.1
04301	111272	Ovalle	230819.8	2017	25683781418	233596.7	120731.48	57483.45	1130487.9	217222.9
04302	13322	Combarbalá	172709.2	2017	2300832587	174494.9	65533.13	74430.28	396609.8	161497.3
04303	30751	Monte Patria	189761.6	2017	5835357638	223275.7	111786.41	51735.11	822173.0	201140.7
04304	10956	Punitaqui	165862.0	2017	1817183694	182698.4	84606.31	56021.36	381369.0	175465.5
04305	4278	Río Hurtado	182027.2	2017	778712384	217568.1	118306.38	60864.83	732558.8	199553.3
05101	296655	Valparaíso	251998.5	2017	74756602991	232483.7	35347.16	188716.06	273903.4	233657.6
05102	26867	Casablanca	252317.7	2017	6779018483	248552.7	75429.01	101421.95	421731.4	233485.7
05105	18546	Puchuncaví	231606.0	2017	4295363979	266809.7	87214.79	114966.90	437418.1	248718.9
05107	31923	Quintero	285125.8	2017	9102071069	210321.0	76565.47	103751.30	408609.7	205665.3
05301	66708	Los Andes	280548.0	2017	18714795984	299402.6	108037.12	179718.20	611821.8	280310.2
05302	14832	Calle Larga	234044.6	2017	3471349123	230982.9	82951.22	76644.60	376829.3	243249.6
05303	10207	Rinconada	246136.9	2017	2512319225	200840.5	66979.24	108954.57	360399.3	193953.3
05304	18855	San Esteban	211907.3	2017	3995512770	260084.5	86013.87	137253.94	512791.2	240375.2
05401	35390	La Ligua	172675.9	2017	6111000517	240505.4	60747.27	163421.16	410457.4	229427.2
05402	19388	Cabildo	212985.0	2017	4129354103	220969.0	68099.74	103470.21	420160.2	213036.3
05404	9826	Petorca	270139.8	2017	2654393853	240314.9	81916.98	103751.30	456505.7	239930.3
05405	7339	Zapallar	235661.4	2017	1729518700	241340.5	62848.88	174031.76	345558.7	224009.5
05501	90517	Quillota	212067.6	2017	19195726144	212795.2	35593.36	160217.96	290249.3	216095.7
05502	50554	Calera	226906.2	2017	11471016698	285865.6	68310.69	246074.51	387743.0	254822.5
05503	17988	Hijuelas	215402.0	2017	3874650405	247693.0	81200.45	142624.62	440041.2	249201.4
05504	22098	La Cruz	243333.4	2017	5377180726	237935.2	44851.22	209200.66	289617.1	214988.0
05506	22120	Nogales	219800.7	2017	4861992055	241051.4	35919.97	188267.64	283312.0	243972.1
05601	91350	San Antonio	230261.5	2017	21034388728	283021.5	122693.81	102152.42	715067.0	262534.2
05602	13817	Algarrobo	218057.0	2017	3012893845	240354.8	50112.82	147225.41	336311.1	242009.1
05603	22738	Cartagena	246517.9	2017	5605324190	259835.8	79652.09	102771.63	420982.0	242499.9
05606	10900	Santo Domingo	250404.5	2017	2729409577	238951.3	95040.08	86225.18	456505.7	223835.7
05701	76844	San Felipe	240842.4	2017	18507290899	254914.1	73628.45	180464.57	456398.8	238441.3
05702	13998	Catemu	204903.4	2017	2868237147	214550.1	44660.00	140701.58	312989.4	215251.2
05703	24608	Llaillay	257020.9	2017	6324771348	238706.0	27344.26	204365.17	297721.1	229146.6
05704	7273	Panquehue	210643.4	2017	1532009468	197197.6	64573.58	71516.63	276030.4	222952.7
05705	16754	Putaendo	207222.5	2017	3471806107	215121.4	54481.55	133201.55	351076.0	215141.3
05706	15241	Santa María	254903.6	2017	3884985562	252670.1	53974.51	155954.58	363509.1	248694.1
05801	151708	Quilpué	296519.4	2017	44984360344	243296.5	81480.29	119572.21	414908.0	226950.3
05802	46121	Limache	251682.2	2017	11607834893	203691.4	48398.19	84518.29	285316.1	201129.8
05803	17516	Olmué	198292.3	2017	3473287749	221511.1	52463.91	142624.62	341529.3	215485.1
05804	126548	Villa Alemana	249779.9	2017	31609146219	274113.5	88098.60	132916.98	402799.2	259527.0
06101	241774	Rancagua	243717.4	2017	58924531866	236177.8	113430.86	133201.55	695684.9	209853.8
06102	12988	Codegua	264737.7	2017	3438412620	235511.2	30545.45	194416.15	331019.1	231209.5
06103	7359	Coinco	175814.1	2017	1293816308	193510.3	24956.32	164398.11	223067.4	203990.1
06104	19597	Coltauco	254006.7	2017	4977769953	235813.8	44388.80	177281.75	325999.1	220075.3
06105	20887	Doñihue	198486.5	2017	4145787348	225555.2	23881.88	206179.15	271771.5	215171.7
06106	33437	Graneros	248394.9	2017	8305580885	257330.7	83108.93	177572.15	481978.7	238855.6
06107	24640	Las Cabras	201772.1	2017	4971665251	214975.6	46020.76	152889.60	342379.3	210558.0
06108	52505	Machalí	252049.6	2017	13233865906	324591.7	250571.17	134808.19	904390.3	222510.5
06109	13407	Malloa	250691.2	2017	3361017589	199899.7	43934.03	146142.67	360399.3	194714.9
06110	25343	Mostazal	264277.9	2017	6697593734	237897.9	44594.14	180811.99	380332.3	228706.5
06111	13608	Olivar	256304.8	2017	3487795575	199405.4	25076.44	166286.42	235887.2	196970.9
06112	14313	Peumo	230938.1	2017	3305417128	227342.3	47435.62	176685.40	324369.4	211687.1
06113	19714	Pichidegua	217210.9	2017	4282095940	209270.9	44384.51	141250.23	316266.3	208888.8
06114	13002	Quinta de Tilcoco	203672.8	2017	2648154389	213178.5	59346.98	103726.99	373143.1	202514.8
06115	58825	Rengo	250531.0	2017	14737488444	217213.7	62784.46	138651.42	526737.4	214596.0
06116	27968	Requínoa	244836.0	2017	6847572657	206790.3	38843.13	108428.43	278357.0	209180.9
06117	46766	San Vicente	242866.0	2017	11357872282	214373.5	51729.12	103470.21	397900.5	213781.1
06201	16394	Pichilemu	230362.3	2017	3776560181	200177.8	59959.32	87483.62	305196.4	209201.6
06202	3041	La Estrella	211425.0	2017	642943494	231730.5	74378.10	105040.04	440041.2	217332.7
06203	6294	Litueche	237979.9	2017	1497845780	232927.6	96173.71	48125.68	456505.7	202543.5
06204	7308	Marchihue	237849.2	2017	1738201845	264484.0	95433.94	149556.45	476711.3	235488.2
06205	6641	Navidad	165555.2	2017	1099452202	212534.2	63281.93	68980.14	344900.7	211894.5
06206	6188	Paredones	194146.1	2017	1201375821	191554.8	88633.22	77304.54	414908.0	160281.2
06301	73973	San Fernando	239724.5	2017	17733143348	225425.5	80656.15	120352.61	452195.2	211103.4
06302	15037	Chépica	207192.9	2017	3115559148	212337.7	61045.29	89718.96	414087.5	206940.4
06303	35399	Chimbarongo	227716.7	2017	8060942027	218961.1	59903.81	124209.99	380332.3	207173.1
06304	6811	Lolol	210117.9	2017	1431112941	200294.6	68557.51	112042.71	421731.4	198498.4
06305	17833	Nancagua	213675.0	2017	3810465416	225224.9	69095.84	152512.70	478309.2	214990.1
06306	12482	Palmilla	230550.0	2017	2877725100	239339.2	73306.74	134266.39	570498.5	231992.7
06307	11007	Peralillo	231695.3	2017	2550270534	246972.8	42613.74	155233.73	307607.9	237242.5
06308	8738	Placilla	221358.7	2017	1934232402	218702.6	55796.71	114966.90	338076.0	205054.6
06309	3421	Pumanque	239369.8	2017	818883984	220277.5	71837.55	68980.14	373475.7	226259.7
06310	37855	Santa Cruz	224421.9	2017	8495489945	223913.7	62601.33	79623.09	404862.1	212273.7
07101	220357	Talca	244658.0	2017	53912095394	205829.8	72534.55	92534.95	420160.2	196818.1
07102	46068	Constitución	198314.7	2017	9135962663	190947.9	108501.23	60022.88	672256.3	182861.3
07103	9448	Curepto	191743.1	2017	1811588746	156610.7	72671.42	55102.97	456398.8	139587.5
07104	4142	Empedrado	172428.7	2017	714199777	233257.8	99665.70	94063.83	480183.1	208767.0
07105	49721	Maule	195207.4	2017	9705908393	228759.7	69776.59	120722.77	456398.8	212357.3
07106	8422	Pelarco	187124.6	2017	1575963241	215792.3	64404.75	124501.57	420160.2	203653.9
07107	8245	Pencahue	220957.5	2017	1821794345	218436.9	90462.96	71516.63	568120.2	197722.4
07108	13906	Río Claro	196539.7	2017	2733081178	214556.2	78627.77	124501.57	520048.7	193886.3
07109	43269	San Clemente	179181.4	2017	7753001772	197860.3	60881.22	76644.60	452544.5	191330.0
07110	9191	San Rafael	195848.9	2017	1800047360	221031.7	47828.83	108833.91	297977.2	215058.6
07201	40441	Cauquenes	152604.5	2017	6171477801	184219.8	102327.66	39045.36	684758.6	163443.9
07202	8928	Chanco	128982.1	2017	1151552040	159033.7	72087.14	64281.52	427974.1	142237.9
07203	7571	Pelluhue	113986.7	2017	862993347	207491.2	81711.28	51101.39	381369.0	180199.6
07301	149136	Curicó	265301.7	2017	39566034949	201468.7	56685.10	59125.84	360399.3	203348.9
07302	9657	Hualañé	167967.3	2017	1622060226	163114.0	55576.94	56345.53	278442.5	162195.9
07303	6653	Licantén	179919.7	2017	1197005482	172942.3	55234.79	57483.45	257457.3	173710.9
07304	45976	Molina	227845.0	2017	10475401720	226126.7	63296.27	86225.18	456505.7	222059.0
07305	10484	Rauco	196719.6	2017	2062408371	219800.7	134233.84	59544.23	635615.1	205561.9
07306	15187	Romeral	218360.4	2017	3316239205	243841.1	174451.68	99475.11	1034702.1	212355.2
07307	18544	Sagrada Familia	204922.9	2017	3800089672	205202.4	53526.96	114099.69	304265.8	195712.8
07308	28921	Teno	250368.7	2017	7240913928	220526.5	62304.46	89974.10	506077.7	211462.9
07309	4322	Vichuquén	179935.1	2017	777679695	164168.4	53402.34	76392.76	285249.2	149257.2
07401	93602	Linares	192783.1	2017	18044885598	187009.2	71417.50	36535.67	414908.0	183909.7
07402	20765	Colbún	161250.1	2017	3348358419	201001.9	59903.52	43653.01	357533.5	198502.9
07403	30534	Longaví	166612.7	2017	5087351933	189760.3	60338.36	47224.73	371250.9	186352.4
07404	41637	Parral	183123.5	2017	7624714509	196214.1	77170.33	53069.62	476711.3	179160.7
07405	19974	Retiro	146406.4	2017	2924321333	195510.3	60505.69	51735.11	409835.1	193337.2
07406	45547	San Javier	170552.7	2017	7768163327	187190.3	77565.36	73907.29	572053.6	173178.1
07407	16221	Villa Alegre	178486.5	2017	2895229121	245808.9	61641.68	130012.18	381369.0	232685.6
07408	18081	Yerbas Buenas	203001.0	2017	3670461912	214799.5	45324.87	158276.02	376829.3	203974.2
08101	223574	Concepción	197625.8	2017	44183983882	235711.1	90897.02	114126.43	510694.6	210760.4
08102	116262	Coronel	217018.1	2017	25230952648	179735.7	58975.93	88259.01	258560.1	192366.1
08104	10624	Florida	147425.2	2017	1566245750	211913.4	97944.96	68980.14	562308.5	194861.5
08105	24333	Hualqui	202715.1	2017	4932666876	208327.3	108691.51	31354.61	502439.1	183546.5
08107	47367	Penco	195212.7	2017	9246639961	191583.0	70591.53	95198.98	326075.5	177861.5
08109	13749	Santa Juana	198449.1	2017	2728477197	168959.3	65207.09	82776.17	380332.3	157378.9
08110	151749	Talcahuano	161731.1	2017	24542535584	314620.2	217658.92	168251.05	564743.9	210865.7
08111	54946	Tomé	210053.2	2017	11541584520	185685.6	77960.59	44987.05	460070.6	174509.5
08201	25522	Lebu	141842.6	2017	3620107931	165770.0	60194.44	68597.58	301080.8	157378.9
08202	36257	Arauco	184849.0	2017	6702069405	174205.3	66310.15	67980.20	380421.4	173798.6
08203	34537	Cañete	186912.3	2017	6455391501	176231.3	69210.65	69037.43	489113.3	159083.7
08204	6031	Contulmo	131551.5	2017	793386801	168611.0	61709.02	77355.72	317807.5	168991.5
08205	32288	Curanilahue	252073.4	2017	8138946477	259355.4	159900.25	107436.03	835035.3	228423.4
08206	21035	Los Álamos	189429.4	2017	3984647129	190212.0	102415.53	98543.06	513189.3	166564.4
08207	10417	Tirúa	144994.9	2017	1510411713	167977.8	75454.19	50985.15	380332.3	147848.5
08301	202331	Los Ángeles	190810.3	2017	38606846296	214679.8	58087.14	110928.64	595630.3	205080.5
08302	4073	Antuco	155662.3	2017	634012722	358595.3	260769.75	140912.20	1034702.1	274423.0
08303	28573	Cabrero	249163.0	2017	7119335384	237473.0	71838.37	129280.05	520048.7	219986.9
08304	22389	Laja	174449.0	2017	3905739533	182947.5	58676.00	68980.14	381369.0	185309.2
08305	29627	Mulchén	198258.1	2017	5873792045	173288.6	80744.61	39796.24	380332.3	151204.0
08306	26315	Nacimiento	175829.2	2017	4626944798	174817.2	72938.94	52967.92	361756.1	152785.5
08307	9737	Negrete	216999.7	2017	2112926492	234603.3	85147.87	139167.03	502439.1	238414.6
08308	3988	Quilaco	167106.1	2017	666419314	305018.2	300907.59	115270.77	1521329.2	215875.2
08309	9587	Quilleco	222077.0	2017	2129051929	229197.4	120218.74	57953.13	532713.3	221961.0
08310	3412	San Rosendo	165912.3	2017	566092732	225293.1	117371.04	89974.10	441871.3	188516.2
08311	13773	Santa Bárbara	176010.5	2017	2424192819	190014.9	61698.15	98260.00	397684.2	183900.4
08312	14134	Tucapel	155538.6	2017	2198382777	220161.6	54613.01	143638.83	339058.5	208676.3
08313	21198	Yumbel	138515.0	2017	2936241535	217036.8	92435.58	56021.36	622507.8	199868.5
08314	5923	Alto Biobío	130542.9	2017	773205492	160689.9	79921.44	72277.97	336157.3	139072.4
09101	282415	Temuco	173314.1	2017	48946498862	192179.5	52989.35	95083.08	356919.5	188414.7
09102	24533	Carahue	127924.5	2017	3138372109	157525.2	55861.38	76536.00	318147.3	147464.2
09103	17526	Cunco	156882.5	2017	2749522512	240391.4	102416.83	118964.37	684758.6	218782.9
09104	7489	Curarrehue	135420.9	2017	1014167156	150236.3	37173.78	88967.80	217069.2	140372.4
09105	24606	Freire	197426.1	2017	4857867695	178727.5	50833.81	83507.15	297721.1	178403.1
09106	11996	Galvarino	147518.2	2017	1769627798	185567.2	70788.68	91950.19	408609.7	167340.5
09107	14414	Gorbea	140997.5	2017	2032338344	243989.2	83714.13	53693.97	418614.9	239614.8
09108	38013	Lautaro	282496.1	2017	10738525406	206642.3	80172.77	105551.72	530245.6	186288.7
09109	23612	Loncoche	160742.5	2017	3795451798	205457.8	126875.02	62520.38	1034702.1	187436.2
09110	6138	Melipeuco	164670.1	2017	1010744848	219711.0	78969.09	101558.35	420160.2	216665.0
09111	32510	Nueva Imperial	158196.8	2017	5142978907	189457.0	81045.13	62245.95	520048.7	175023.8
09112	76126	Padre Las Casas	169223.7	2017	12882320064	206522.7	68398.06	44029.88	489113.3	200619.0
09113	6905	Perquenco	155106.7	2017	1071011969	217786.4	76800.57	140602.99	426189.0	195877.4
09114	24837	Pitrufquén	205557.8	2017	5105439315	189618.8	65286.93	86112.98	397684.2	181560.6
09115	28523	Pucón	187764.8	2017	5355614570	215458.8	71521.29	59515.59	369420.7	209228.8
09116	12450	Saavedra	130775.6	2017	1628156299	186864.5	80306.28	61114.21	456398.8	158872.7
09117	15045	Teodoro Schmidt	138894.2	2017	2089663239	180043.6	65546.78	43056.49	478141.0	174432.2
09118	9722	Toltén	113791.8	2017	1106284328	184932.4	66760.57	60255.18	408609.7	173349.6
09119	28151	Vilcún	135602.8	2017	3817354634	221673.3	83415.78	60864.83	532713.3	205890.2
09120	55478	Villarrica	198745.4	2017	11026000004	222515.9	62114.35	107267.15	404862.1	210740.8
09121	11611	Cholchol	115103.4	2017	1336465909	185489.9	57150.64	60864.83	373475.7	180199.6
09201	53262	Angol	173377.3	2017	9234420713	169914.0	77768.69	55102.97	420160.2	147562.7
09202	24598	Collipulli	182323.1	2017	4484784762	205030.6	108602.87	92266.71	489113.3	166316.2
09203	17413	Curacautín	186604.9	2017	3249351008	237012.7	113134.61	83499.48	531994.3	207454.0
09204	7733	Ercilla	136678.7	2017	1056936411	210079.0	113557.94	90370.18	535149.2	193978.7
09205	10251	Lonquimay	138745.9	2017	1422283764	168076.0	82603.24	42232.74	410457.4	163052.9
09206	7265	Los Sauces	142588.7	2017	1035906610	172642.8	68199.82	64881.75	337385.1	172450.4
09207	9548	Lumaco	170538.2	2017	1628298886	154657.7	69391.75	39796.24	393600.1	149051.6
09208	11779	Purén	133537.6	2017	1572938990	168001.9	67821.85	61114.21	351076.0	157941.1
09209	10250	Renaico	218920.0	2017	2243930000	217386.4	59273.72	106993.53	344900.7	220197.5
09210	18843	Traiguén	210526.3	2017	3966946195	182873.9	90237.90	53061.65	572053.6	174010.0
09211	34182	Victoria	187662.8	2017	6414689393	215528.8	109608.16	54458.01	611821.8	189966.2
10101	245902	Puerto Montt	176237.4	2017	43337141298	197473.1	56043.23	67075.65	371243.3	192027.1
10102	33985	Calbuco	155443.9	2017	5282762017	182657.5	118466.41	75600.88	953422.6	170612.9
10104	12261	Fresia	183977.2	2017	2255743895	181292.2	77863.33	73907.29	440041.2	162737.8
10105	18428	Frutillar	174883.1	2017	3222744874	174292.4	45345.16	95677.31	286895.7	167014.3
10106	17068	Los Muermos	192845.4	2017	3291484556	157078.2	76053.84	45422.72	520048.7	144003.3
10107	17591	Llanquihue	149333.8	2017	2626930838	168757.9	64423.01	54214.22	381369.0	161413.8
10108	14216	Maullín	137613.7	2017	1956316762	189086.9	64407.05	87768.99	380332.3	176981.5
10109	44578	Puerto Varas	219839.1	2017	9799987895	206584.0	76873.99	78853.08	476711.3	188469.3
10201	43807	Castro	183717.2	2017	8048100927	173333.2	53071.56	52714.31	299575.3	171895.8
10202	38991	Ancud	161910.1	2017	6313036958	188868.7	79048.79	38322.30	526737.4	175380.0
10203	14858	Chonchi	193642.9	2017	2877146807	167785.7	65722.61	63420.40	381369.0	159841.9
10204	3829	Curaco de Vélez	177952.2	2017	681378864	208858.2	89448.70	107578.57	476711.3	176865.1
10205	13762	Dalcahue	207717.6	2017	2858609503	186524.8	59299.26	95105.36	336128.1	184230.3
10206	3921	Puqueldón	208274.8	2017	816645370	173632.0	49454.82	117833.71	325999.1	159529.2
10207	5385	Queilén	151485.0	2017	815746659	168544.5	115193.32	80030.51	689801.4	138579.3
10208	27192	Quellón	171685.5	2017	4668472212	165555.2	65965.54	48893.47	385825.2	161428.4
10209	8352	Quemchi	122223.1	2017	1020807718	152747.8	57849.69	40015.25	306713.7	150855.5
10210	8088	Quinchao	119852.6	2017	969367811	177598.0	102715.38	49156.58	440041.2	164995.9
10301	161460	Osorno	196610.2	2017	31744688808	207382.7	73230.81	55929.84	440067.5	197227.4
10302	8999	Puerto Octay	221980.9	2017	1997605810	211239.8	66399.41	122955.28	489113.3	198135.6
10303	20369	Purranque	186719.5	2017	3803288945	187588.0	59423.37	98193.62	336502.1	175146.2
10304	11667	Puyehue	176006.9	2017	2053472049	225363.5	86360.65	73907.29	684244.0	221399.2
10305	14085	Río Negro	156568.1	2017	2205262341	193961.7	57947.88	46617.38	355501.3	190044.4
10306	7512	San Juan de la Costa	152674.0	2017	1146887184	170936.0	81437.05	44987.05	570632.2	156976.9
10307	10030	San Pablo	181411.6	2017	1819558805	210759.2	89224.71	48893.47	568120.2	184533.4
11101	57818	Coyhaique	230013.0	2017	13298894369	192566.1	94976.28	48125.68	520048.7	171795.0
11201	23959	Aysén	246614.5	2017	5908637554	334920.4	188844.10	34490.07	874836.2	301080.8
11202	6517	Cisnes	262412.7	2017	1710143349	406630.7	165461.96	149428.61	738841.5	358028.8
11301	3490	Cochrane	211652.6	2017	738667487	212939.8	86545.33	89974.10	351076.0	201304.7
11401	4865	Chile Chico	188913.8	2017	919065674	214902.6	115487.63	89440.32	480183.1	191067.4
11402	2666	Río Ibáñez	171315.6	2017	456727447	208216.7	77860.29	76644.60	325999.1	202239.5
12101	131592	Punta Arenas	256903.3	2017	33806414442	284163.3	102902.75	129337.77	635615.1	272563.9
12301	6801	Porvenir	381329.2	2017	2593419712	227287.7	132627.53	33377.49	476711.3	198434.2
12401	21477	Natales	302167.3	2017	6489647004	354855.1	196933.65	86186.70	799209.3	271651.7
13202	26521	Pirque	274675.6	2017	7284672878	199119.2	48723.60	129429.40	318527.4	195157.2
13203	18189	San José de Maipo	344876.8	2017	6272964115	285581.4	111899.84	146142.67	572053.6	248124.8
13301	146207	Colina	255373.7	2017	37337421744	164112.6	66152.35	75432.24	311253.9	153485.6
13302	102034	Lampa	243425.7	2017	24837699582	214233.3	57533.27	82754.78	360399.3	213694.0
13303	19312	Tiltil	264794.8	2017	5113717064	228172.9	53071.29	164483.41	347635.6	211341.8
13401	301313	San Bernardo	251728.3	2017	75849003232	250483.1	88039.30	89974.10	460070.6	223957.1
13402	96614	Buin	289884.0	2017	28006850165	200234.0	51963.97	123750.05	306568.8	196650.5
13403	25392	Calera de Tango	298439.8	2017	7577982724	200869.5	45365.93	125722.44	305464.5	206170.3
13404	72759	Paine	282280.9	2017	20538478428	219347.8	41982.94	158094.23	351076.0	214237.1
13501	123627	Melipilla	199121.9	2017	24616837833	236950.5	74290.78	90845.44	635615.1	222727.0
13502	6444	Alhué	242844.2	2017	1564887792	287621.5	115997.86	198434.25	570632.2	234731.0
13503	32579	Curacaví	220990.2	2017	7199638514	234089.5	68700.74	129337.77	497105.2	221980.8
13504	13590	María Pinto	198063.3	2017	2691680700	251327.7	80360.91	155626.96	583224.2	234062.0
13505	9726	San Pedro	231429.7	2017	2250885401	240349.3	94173.98	62709.22	570498.5	218249.4
13601	74237	Talagante	230734.4	2017	17129031774	190341.0	42017.86	112061.05	280437.7	188402.9
13602	35923	El Monte	201444.7	2017	7236496479	216599.9	24035.59	170198.31	237288.2	223350.8
13603	36219	Isla de Maipo	232595.7	2017	8424384020	230501.4	47062.08	169663.89	325999.1	224391.3
13604	63250	Padre Hurtado	231845.6	2017	14664233522	206410.4	37350.59	138206.83	276670.0	206169.6
13605	90201	Peñaflor	249848.3	2017	22536570306	212279.7	53564.17	164434.61	337385.1	202477.3
14101	166080	Valdivia	211732.5	2017	35164529745	215231.9	81987.77	62709.22	497105.2	208838.1
14102	5302	Corral	157428.1	2017	834683963	176293.7	69774.41	50473.27	297692.7	168925.4
14103	16752	Lanco	184730.2	2017	3094599901	224325.2	61463.93	82981.59	341402.4	222769.4
14104	19634	Los Lagos	190489.7	2017	3740075550	235958.3	80319.36	91279.75	560213.6	232490.9
14105	7095	Máfil	180289.2	2017	1279152079	251663.6	95372.69	62863.03	602926.9	229364.9
14106	21278	Mariquina	187045.1	2017	3979945072	213736.1	86451.95	63420.40	486475.2	206259.8
14107	20188	Paillaco	163833.6	2017	3307473487	212936.6	71288.17	98267.51	502439.1	204304.8
14108	34539	Panguipulli	180390.3	2017	6230498948	202737.1	83020.41	68737.66	427974.1	186554.5
14201	38036	La Unión	201975.2	2017	7682327556	172209.8	64321.07	79592.47	414908.0	159500.4
14202	14665	Futrono	193120.3	2017	2832109866	224472.0	87682.03	79592.47	530245.6	203197.2
14203	9896	Lago Ranco	186595.7	2017	1846550611	167270.3	54064.74	89682.47	318147.3	167814.9
14204	31372	Río Bueno	184360.5	2017	5783758517	181340.9	63209.14	57607.35	452195.2	168223.0
15101	221364	Arica	250863.6	2017	55532177025	354756.1	169246.50	227500.96	817219.4	279433.6
15102	1255	Camarones	222472.1	2017	279202446	317728.1	112352.51	231592.91	489113.3	246422.9
15201	2765	Putre	194293.6	2017	537221762	392864.4	283886.06	171189.65	1140996.9	281369.3
16101	184739	Chillán	232041.6	2017	42867130063	222934.7	108093.70	147920.53	822173.0	201140.7
16102	21493	Bulnes	167693.2	2017	3604229178	211065.7	77544.66	75749.37	506077.7	207686.9
16103	30907	Chillán Viejo	179855.8	2017	5558803478	232844.8	65182.54	76644.60	342047.9	240209.9
16104	12044	El Carmen	151144.7	2017	1820386198	152159.4	52015.46	57635.39	314043.8	135612.5
16105	8448	Pemuco	151889.4	2017	1283161238	208912.1	89354.72	42232.74	480183.1	200617.0
16106	10827	Pinto	153289.2	2017	1659661870	195999.3	102525.38	87768.99	589120.1	177640.3
16107	17485	Quillón	133479.9	2017	2333895558	204120.1	86476.93	53693.97	415598.3	194635.6
16108	16079	San Ignacio	174538.8	2017	2806409365	199182.1	72336.49	105133.85	568120.2	197543.7
16109	17787	Yungay	194006.8	2017	3450799686	225714.7	111632.12	51735.11	695684.9	206475.7
16201	11594	Quirihue	155446.9	2017	1802251665	228160.7	126558.91	76084.29	928434.4	217332.7
16202	5012	Cobquecura	122513.3	2017	614036495	193636.3	92605.11	66754.98	441871.3	174288.6
16203	15995	Coelemu	174050.2	2017	2783932983	193315.9	74882.46	53295.85	351076.0	178352.6
16204	5213	Ninhue	161577.8	2017	842304828	173292.0	74143.70	84306.90	393213.2	144090.7
16205	4862	Portezuelo	168595.2	2017	819710106	189701.3	75678.81	71506.70	380421.4	172027.3
16206	5755	Ránquil	221951.3	2017	1277329463	207346.5	86035.64	50473.27	414908.0	193011.4
16207	5401	Treguaco	178763.9	2017	965503625	173468.9	79267.60	57953.13	414908.0	166050.8
16301	53024	San Carlos	175203.6	2017	9289995173	198025.9	86288.63	40576.55	520048.7	184965.9
16302	26881	Coihueco	174853.6	2017	4700239750	199215.6	61481.47	90099.82	356090.5	191944.7
16303	11152	Ñiquén	188830.1	2017	2105832760	185733.7	55845.46	54458.01	336128.1	194949.9
16304	4308	San Fabián	158019.3	2017	680747063	214696.4	77350.35	76644.60	381369.0	214964.4
16305	11603	San Nicolás	180675.3	2017	2096375354	207126.3	74502.87	79592.47	402606.3	194378.0

write_xlsx(estadisticos_finales, "estadisticos_finales_escolaridad.xlsx")
write.dbf(estadisticos_finales, "estadisticos_finales_escolaridad.dbf")

Expansión de la CASEN sobre el CENSO de PERSONAS (Nivel nacional rural para el 2017)

Y regresión lineal de ingresos medios por zona sobre frecuencias de respuesta a la pregunta: ¿Cuantos años de escolaridad tiene?, cuya correlación necesitó de su corrección por porcentaje de existencia por zona.

VE-CC-AJ