date: 02-08-2021

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: “Trabajó por un pago o especie” del campo P17 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 URBANO.

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: “Trabajó por un pago o especie” del campo P17 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("../../../ds_correlaciones_censo_casen/corre_censo_casen_2017/censos_con_clave/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 urbano:

tabla_con_clave_u <- filter(tabla_con_clave, tabla_con_clave$AREA ==1)

Cuantas personas hay en Chile urbanas?

length(tabla_con_clave_u$clave)

## [1] 15424263

Cuantas zonas hay en el nivel urbano?

length(unique(tabla_con_clave_u$clave))

## [1] 5169

1.1.3 Cálculo de respuestas censales

Obtenemos las respuestas a la pregunta P17 por zona eliminando los campos innecesarios. Despleguemos los primeros 1000 registros:

tabla_con_clave_f <- tabla_con_clave_u[,-c(1,2,4:31,33:48),drop=F]
abc <- head(tabla_con_clave_f,1000)
kbl(abc) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

COMUNA	P17	clave
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	3	15201011001
15201	5	15201011001
15201	98	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	6	15201011001
15201	1	15201011001
15201	8	15201011001
15201	6	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	99	15201011001
15201	99	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	6	15201011001
15201	1	15201011001
15201	98	15201011001
15201	98	15201011001
15201	3	15201011001
15201	98	15201011001
15201	98	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	5	15201011001
15201	5	15201011001
15201	98	15201011001
15201	98	15201011001
15201	1	15201011001
15201	3	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	5	15201011001
15201	98	15201011001
15201	98	15201011001
15201	98	15201011001
15201	98	15201011001
15201	1	15201011001
15201	4	15201011001
15201	5	15201011001
15201	98	15201011001
15201	98	15201011001
15201	98	15201011001
15201	99	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	6	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	98	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	8	15201011001
15201	1	15201011001
15201	1	15201011001
15201	8	15201011001
15201	98	15201011001
15201	1	15201011001
15201	6	15201011001
15201	98	15201011001
15201	1	15201011001
15201	1	15201011001
15201	98	15201011001
15201	1	15201011001
15201	6	15201011001
15201	1	15201011001
15201	1	15201011001
15201	4	15201011001
15201	1	15201011001
15201	6	15201011001
15201	7	15201011001
15201	4	15201011001
15201	1	15201011001
15201	4	15201011001
15201	1	15201011001
15201	98	15201011001
15201	98	15201011001
15201	3	15201011001
15201	6	15201011001
15201	98	15201011001
15201	98	15201011001
15201	98	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	4	15201011001
15201	99	15201011001
15201	99	15201011001
15201	99	15201011001
15201	99	15201011001
15201	99	15201011001
15201	99	15201011001
15201	99	15201011001
15201	99	15201011001
15201	1	15201011001
15201	4	15201011001
15201	8	15201011001
15201	1	15201011001
15201	6	15201011001
15201	1	15201011001
15201	98	15201011001
15201	99	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	6	15201011001
15201	98	15201011001
15201	98	15201011001
15201	98	15201011001
15201	1	15201011001
15201	6	15201011001
15201	98	15201011001
15201	1	15201011001
15201	98	15201011001
15201	1	15201011001
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15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	5	15201011001
15201	6	15201011001
15201	6	15201011001
15201	1	15201011001
15201	4	15201011001
15201	2	15201011001
15201	3	15201011001
15201	5	15201011001
15201	8	15201011001
15201	1	15201011001
15201	6	15201011001
15201	1	15201011001
15201	6	15201011001
15201	8	15201011001
15201	3	15201011001
15201	8	15201011001
15201	5	15201011001
15201	1	15201011001
15201	8	15201011001
15201	6	15201011001
15201	5	15201011001
15201	1	15201011001
15201	6	15201011001
15201	6	15201011001
15201	5	15201011001
15201	8	15201011001
15201	6	15201011001
15201	5	15201011001
15201	6	15201011001
15201	6	15201011001
15201	8	15201011001
15201	6	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001
15201	1	15201011001

nrow(tabla_con_clave_f)

## [1] 15424263

Vemos que el número total de registros coincide con el total de personas urbanas.

Modifiquemos la tabla para poder trabajarla un poco mejor:

Agregamos un cero a los códigos comunales de cuatro dígitos.
Renombramos la columna clave por código.

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" 

abc <- head(comuna_corr,50)
kbl(abc) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

P17	clave	código
1	15201011001	15201
1	15201011001	15201
1	15201011001	15201
1	15201011001	15201
1	15201011001	15201
3	15201011001	15201
5	15201011001	15201
98	15201011001	15201
1	15201011001	15201
1	15201011001	15201
1	15201011001	15201
6	15201011001	15201
1	15201011001	15201
8	15201011001	15201
6	15201011001	15201
1	15201011001	15201
1	15201011001	15201
1	15201011001	15201
99	15201011001	15201
99	15201011001	15201
1	15201011001	15201
1	15201011001	15201
1	15201011001	15201
1	15201011001	15201
6	15201011001	15201
1	15201011001	15201
98	15201011001	15201
98	15201011001	15201
3	15201011001	15201
98	15201011001	15201
98	15201011001	15201
1	15201011001	15201
1	15201011001	15201
1	15201011001	15201
1	15201011001	15201
1	15201011001	15201
5	15201011001	15201
5	15201011001	15201
98	15201011001	15201
98	15201011001	15201
1	15201011001	15201
3	15201011001	15201
1	15201011001	15201
1	15201011001	15201
1	15201011001	15201
1	15201011001	15201
1	15201011001	15201
1	15201011001	15201
1	15201011001	15201
1	15201011001	15201

tabla_con_clave_f <- comuna_corr

Obtenemos la cuenta de las respuestas 1:

claves_con_1 <- filter(tabla_con_clave_f, tabla_con_clave_f$P17 == 1)
head(claves_con_1,10)

##    P17       clave código
## 1    1 15201011001  15201
## 2    1 15201011001  15201
## 3    1 15201011001  15201
## 4    1 15201011001  15201
## 5    1 15201011001  15201
## 6    1 15201011001  15201
## 7    1 15201011001  15201
## 8    1 15201011001  15201
## 9    1 15201011001  15201
## 10   1 15201011001  15201

1.1.3.1 Tabla de contingencia:

con4 <- xtabs(~P17+clave, data=claves_con_1)
con4 <- as.data.frame(con4)

abc <- head(con4,50)
kbl(abc) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

P17	clave	Freq
1	10101011001	320
1	10101011002	1309
1	10101021001	1828
1	10101021002	581
1	10101021003	1050
1	10101021004	1457
1	10101021005	1165
1	10101031001	2012
1	10101031002	2166
1	10101031003	1663
1	10101031004	1172
1	10101031005	2657
1	10101031006	1233
1	10101031007	1038
1	10101031008	1616
1	10101031009	1995
1	10101031010	1531
1	10101031011	1232
1	10101031012	837
1	10101031013	1642
1	10101031014	1030
1	10101031015	873
1	10101031016	1285
1	10101031017	1457
1	10101041001	1843
1	10101041002	895
1	10101041003	2559
1	10101051001	1148
1	10101051002	814
1	10101051003	1329
1	10101051004	1712
1	10101061001	3383
1	10101061002	1254
1	10101061003	1559
1	10101061004	1184
1	10101061005	1216
1	10101061006	1991
1	10101061007	340
1	10101061008	1008
1	10101061009	68
1	10101061010	610
1	10101071001	909
1	10101071002	1583
1	10101071003	1915
1	10101071004	1419
1	10101071005	1186
1	10101071006	1741
1	10101071007	987
1	10101071008	2104
1	10101071009	1509

nrow(con4)

## [1] 5165

No perdemos ni una zona a excepción de 4. Más adelante veremos porqué.

A la tabla de frecuencias por zona le añadimos el campo comunal:

trabajo_001 = merge( x = con4, y =claves_con_1, by = "clave", all.x = TRUE)
head(trabajo_001,10)

##          clave P17.x Freq P17.y código
## 1  10101011001     1  320     1  10101
## 2  10101011001     1  320     1  10101
## 3  10101011001     1  320     1  10101
## 4  10101011001     1  320     1  10101
## 5  10101011001     1  320     1  10101
## 6  10101011001     1  320     1  10101
## 7  10101011001     1  320     1  10101
## 8  10101011001     1  320     1  10101
## 9  10101011001     1  320     1  10101
## 10 10101011001     1  320     1  10101

Eliminamos los registros repetidos y renombramos COMUNA como código:

trabajo003 <- unique(trabajo_001)
trabajo003 <- trabajo003[,-c(2,4)]
# trabajo003$código <- as.numeric(trabajo003$código)
head(trabajo003,10)

##             clave Freq código
## 1     10101011001  320  10101
## 321   10101011002 1309  10101
## 1630  10101021001 1828  10101
## 3458  10101021002  581  10101
## 4039  10101021003 1050  10101
## 5089  10101021004 1457  10101
## 6546  10101021005 1165  10101
## 7711  10101031001 2012  10101
## 9723  10101031002 2166  10101
## 11889 10101031003 1663  10101

nrow(trabajo003)

## [1] 5165

Calculamos los ingresos expandidos a nivel urbano:

x <- import("../../../../archivos_grandes/Microdato_Censo2017-Personas.csv")
casen_2017 <- readRDS(file = "../../../../archivos_grandes/casen_2017_c.rds")

casen_2017_u <- filter(casen_2017, casen_2017$zona == "Urbano")
casen_2017_u <- casen_2017_u[!is.na(casen_2017_u$ytotcor),]
Q <- quantile(casen_2017_u$ytotcor, probs=c(.25, .75), na.rm = FALSE)
iqr <- IQR(casen_2017_u$ytotcor)
casen_2017_sin_o <- subset(casen_2017_u, casen_2017_u$ytotcor > 
                                 (Q[1] - 1.5*iqr) &
                                 casen_2017_u$ytotcor < (Q[2]+1.5*iqr))
casen_2017_sin_o <- data.frame(lapply(casen_2017_sin_o, as.character),
                               stringsAsFactors=FALSE)

b <-  as.numeric(casen_2017_sin_o$ytotcor)
a <- casen_2017_sin_o$comuna
promedios_grupales <-aggregate(b, by=list(a), FUN = mean , na.rm=TRUE )
names(promedios_grupales)[1] <- "comuna"
names(promedios_grupales)[2] <- "promedio_i"
promedios_grupales$año <- "2017"
codigos_comunales <- readRDS(file = "../../../../archivos_grandes/codigos_comunales_2011-2017.rds")
names(codigos_comunales)[1] <- "código"
names(codigos_comunales)[2] <- "comuna"
df_2017 = merge( promedios_grupales, codigos_comunales, 
                 by = "comuna", 
                 all.x = TRUE)

my_summary_data <- x %>%
    group_by(x$COMUNA) %>%
    summarise(Count = n()) 
names(my_summary_data)[1] <- "comuna"     
names(my_summary_data)[2] <- "personas"
# recogemos el campo Comuna:
codigos <- my_summary_data$comuna
# construimos una secuencia llamada rango del 1 al total de filas del 
# dataset:
rango <- seq(1:nrow(my_summary_data))
# Creamos un string que agrega un cero a todos los registros:
cadena <- paste("0",codigos[rango], sep = "")
# El string cadena tiene o 5 o 6 digitos, los cuales siempre deben ser 
# siempre 5 
# agregandole un cero al inicio de los que tienen 4.
# Para ello extraemos un substring de la cadena sobre todas las filas 
#(rangos) 
# comenzando desde el primero o el segundo y llegando siempre al 6.
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(my_summary_data,cadena)
names(comuna_corr)[3] <- "código"
comuna_corr <- comuna_corr[,-c(1),drop=FALSE]
df_2017_2 = merge( comuna_corr, df_2017, by = "código", all.x = TRUE)
df_2017_2$ingresos_expandidos <- df_2017_2$personas*df_2017_2$promedio_i
df_2017_2  <- na.omit(df_2017_2)

Veamos los primeros 10 registros:

head(df_2017_2,10)

##    código personas               comuna promedio_i  año ingresos_expandidos
## 1   01101   191468              Iquique   375676.9 2017         71930106513
## 2   01107   108375        Alto Hospicio   311571.7 2017         33766585496
## 3   01401    15711         Pozo Almonte   316138.5 2017          4966851883
## 7   01405     9296                 Pica   330061.1 2017          3068247619
## 8   02101   361873          Antofagasta   368221.4 2017        133249367039
## 9   02102    13467           Mejillones   369770.7 2017          4979702302
## 11  02104    13317               Taltal   383666.2 2017          5109282942
## 12  02201   165731               Calama   434325.1 2017         71981127235
## 14  02203    10996 San Pedro de Atacama   442861.0 2017          4869699464
## 15  02301    25186            Tocopilla   286187.2 2017          7207910819

Guardemos como rds:

saveRDS(df_2017_2, "Ingresos_expandidos_urbano_17.rds")

De cuántas comunas disponemos del valor del ingreso promedio?

nrow(df_2017_2)

## [1] 312

#df_2017_2$código <- as.numeric(df_2017_2$código)
head(df_2017_2,10)

##    código personas               comuna promedio_i  año ingresos_expandidos
## 1   01101   191468              Iquique   375676.9 2017         71930106513
## 2   01107   108375        Alto Hospicio   311571.7 2017         33766585496
## 3   01401    15711         Pozo Almonte   316138.5 2017          4966851883
## 7   01405     9296                 Pica   330061.1 2017          3068247619
## 8   02101   361873          Antofagasta   368221.4 2017        133249367039
## 9   02102    13467           Mejillones   369770.7 2017          4979702302
## 11  02104    13317               Taltal   383666.2 2017          5109282942
## 12  02201   165731               Calama   434325.1 2017         71981127235
## 14  02203    10996 San Pedro de Atacama   442861.0 2017          4869699464
## 15  02301    25186            Tocopilla   286187.2 2017          7207910819

Unimos nuestra tabla de frecuencias por zona con la de ingresos expandidos:

comunas_censo_casen_666 = merge( x = trabajo003, y = df_2017_2, by = "código", all.x = TRUE)
head(comunas_censo_casen_666,10)

##    código      clave Freq personas  comuna promedio_i  año ingresos_expandidos
## 1   01101 1101011001 1255   191468 Iquique   375676.9 2017         71930106513
## 2   01101 1101011002  621   191468 Iquique   375676.9 2017         71930106513
## 3   01101 1101041001  713   191468 Iquique   375676.9 2017         71930106513
## 4   01101 1101041002 1084   191468 Iquique   375676.9 2017         71930106513
## 5   01101 1101041003 1691   191468 Iquique   375676.9 2017         71930106513
## 6   01101 1101021005 1927   191468 Iquique   375676.9 2017         71930106513
## 7   01101 1101041005 1840   191468 Iquique   375676.9 2017         71930106513
## 8   01101 1101041006 1102   191468 Iquique   375676.9 2017         71930106513
## 9   01101 1101051001 1515   191468 Iquique   375676.9 2017         71930106513
## 10  01101 1101041004 2403   191468 Iquique   375676.9 2017         71930106513

Cuantas zonas tenemos?

nrow(comunas_censo_casen_666)

## [1] 5165

tabla_de_prop_pob <- readRDS("../../../../archivos_grandes/tabla_de_prop_pob.rds")
names(tabla_de_prop_pob)[1]  <- "clave"
head(tabla_de_prop_pob,10)

##         clave Freq            p código
## 1  1101011001 2491 0.0130100069  01101
## 2  1101011002 1475 0.0077036372  01101
## 3  1101021001 1003 0.0052384733  01101
## 4  1101021002   54 0.0002820315  01101
## 5  1101021003 2895 0.0151200201  01101
## 6  1101021004 2398 0.0125242860  01101
## 7  1101021005 4525 0.0236331920  01101
## 8  1101031001 2725 0.0142321432  01101
## 9  1101031002 3554 0.0185618485  01101
## 10 1101031003 5246 0.0273988343  01101

nrow(tabla_de_prop_pob)

## [1] 15500

comunas_censo_casen_6666 = merge( x = comunas_censo_casen_666, y = tabla_de_prop_pob, by = "clave", all.x = TRUE)

nrow(comunas_censo_casen_6666)

## [1] 5165

head(comunas_censo_casen_6666,10)

##          clave código.x Freq.x personas       comuna promedio_i  año
## 1  10101011001    10101    320   245902 Puerto Montt   304409.6 2017
## 2  10101011002    10101   1309   245902 Puerto Montt   304409.6 2017
## 3  10101021001    10101   1828   245902 Puerto Montt   304409.6 2017
## 4  10101021002    10101    581   245902 Puerto Montt   304409.6 2017
## 5  10101021003    10101   1050   245902 Puerto Montt   304409.6 2017
## 6  10101021004    10101   1457   245902 Puerto Montt   304409.6 2017
## 7  10101021005    10101   1165   245902 Puerto Montt   304409.6 2017
## 8  10101031001    10101   2012   245902 Puerto Montt   304409.6 2017
## 9  10101031002    10101   2166   245902 Puerto Montt   304409.6 2017
## 10 10101031003    10101   1663   245902 Puerto Montt   304409.6 2017
##    ingresos_expandidos Freq.y           p código.y
## 1          74854925754    584 0.002374930    10101
## 2          74854925754   2941 0.011960049    10101
## 3          74854925754   3953 0.016075510    10101
## 4          74854925754   1107 0.004501793    10101
## 5          74854925754   2294 0.009328920    10101
## 6          74854925754   3391 0.013790046    10101
## 7          74854925754   2564 0.010426918    10101
## 8          74854925754   4530 0.018421973    10101
## 9          74854925754   4740 0.019275972    10101
## 10         74854925754   4107 0.016701776    10101

comunas_censo_casen_6666$multipob <- comunas_censo_casen_6666$ingresos_expandidos*comunas_censo_casen_6666$p

head(comunas_censo_casen_6666,10)

##          clave código.x Freq.x personas       comuna promedio_i  año
## 1  10101011001    10101    320   245902 Puerto Montt   304409.6 2017
## 2  10101011002    10101   1309   245902 Puerto Montt   304409.6 2017
## 3  10101021001    10101   1828   245902 Puerto Montt   304409.6 2017
## 4  10101021002    10101    581   245902 Puerto Montt   304409.6 2017
## 5  10101021003    10101   1050   245902 Puerto Montt   304409.6 2017
## 6  10101021004    10101   1457   245902 Puerto Montt   304409.6 2017
## 7  10101021005    10101   1165   245902 Puerto Montt   304409.6 2017
## 8  10101031001    10101   2012   245902 Puerto Montt   304409.6 2017
## 9  10101031002    10101   2166   245902 Puerto Montt   304409.6 2017
## 10 10101031003    10101   1663   245902 Puerto Montt   304409.6 2017
##    ingresos_expandidos Freq.y           p código.y   multipob
## 1          74854925754    584 0.002374930    10101  177775198
## 2          74854925754   2941 0.011960049    10101  895268589
## 3          74854925754   3953 0.016075510    10101 1203331089
## 4          74854925754   1107 0.004501793    10101  336981411
## 5          74854925754   2294 0.009328920    10101  698315588
## 6          74854925754   3391 0.013790046    10101 1032252903
## 7          74854925754   2564 0.010426918    10101  780506176
## 8          74854925754   4530 0.018421973    10101 1378975420
## 9          74854925754   4740 0.019275972    10101 1442901433
## 10         74854925754   4107 0.016701776    10101 1250210165

saveRDS(comunas_censo_casen_6666, "tabla_de_trabajo_2017_urbana.rds")
write_xlsx(comunas_censo_casen_6666, "tabla_de_trabajo_2017_urbana.xlsx")

2 Desaparecen 4 zonas

Porque no tienen categoría de respuesta 1 a la pregunta P17

tabla_original <-
readRDS("../../../ds_correlaciones_censo_casen/corre_censo_casen_2017/censos_con_clave/censo_personas_con_clave_17") 
tabla_de_trabajo <- readRDS("tabla_de_trabajo_2017_urbana.rds")

tabla_con_clave_u <- filter(tabla_original , tabla_original $AREA ==1)
unicos_001 <- unique(tabla_con_clave_u$clave)
length(unicos_001)

## [1] 5169

unicos_002 <- tabla_de_trabajo$clave
length(unicos_002)

## [1] 5165

Identifiquemos los que fueron excluídos:

ddd <- setdiff(unicos_001 ,unicos_002)
ttt <- unique(ddd)
ttt

## [1] "9113991999"  "16303991999" "7303991999"  "6204991999"

#9113991999

tabla_original_1 <- filter(tabla_original, tabla_original$clave == "9113991999")
tabla_original_1

##   REGION PROVINCIA COMUNA DC AREA ZC_LOC ID_ZONA_LOC NVIV NHOGAR PERSONAN P07
## 1      9        91   9113 99    1    999         232    1      1        1   1
## 2      9        91   9113 99    1    999         232    1      1        2   5
## 3      9        91   9113 99    1    999         232    1      1        3   5
##   P08 P09 P10 P10COMUNA P10PAIS P11 P11COMUNA P11PAIS P12 P12COMUNA P12PAIS
## 1   2  39   1        98     998   2        98     998   1        98     998
## 2   1  12   1        98     998   2        98     998   1        98     998
## 3   2   7   1        98     998   2        98     998   1        98     998
##   P12A_LLEGADA P12A_TRAMO P13 P14 P15 P15A P16 P16A P16A_OTRO P17 P18 P19 P20
## 1         9998         98   2   4   7    1   2   98        98   6  98   2   2
## 2         9998         98   1   6   5    2   2   98        98  98  98  98  98
## 3         9998         98   1   1   5    2   2   98        98  98  98  98  98
##   P21M P21A P10PAIS_GRUPO P11PAIS_GRUPO P12PAIS_GRUPO ESCOLARIDAD P16A_GRUPO
## 1   10 2009           998           998           998          12         98
## 2   98 9998           998           998           998           6         98
## 3   98 9998           998           998           998           1         98
##   REGION_15R PROVINCIA_15R COMUNA_15R P10COMUNA_15R P11COMUNA_15R P12COMUNA_15R
## 1          9            91       9113            98            98            98
## 2          9            91       9113            98            98            98
## 3          9            91       9113            98            98            98
##        clave
## 1 9113991999
## 2 9113991999
## 3 9113991999

Hay solamente 4 zonas que no poseen el valor 1 de respuesta a la pregunta P17.

2.1 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)

3 Análisis de regresión

Aplicaremos un análisis de regresión donde:

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

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

3.1 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.

3.2 Modelo lineal

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

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 
## -850547865  -78144039   -6002220   58511371 2933901395 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -3897469    4656177  -0.837    0.403    
## Freq.x        753444       3165 238.083   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 162300000 on 5143 degrees of freedom
##   (20 observations deleted due to missingness)
## Multiple R-squared:  0.9168, Adjusted R-squared:  0.9168 
## F-statistic: 5.668e+04 on 1 and 5143 DF,  p-value: < 2.2e-16

3.3 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.

4 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
8	log-log	0.982633689397265	linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
5	raíz-raíz	0.956016832106656	linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
1	cuadrático	0.916799197009415	linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
2	cúbico	0.916799197009415	linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
4	raíz cuadrada	0.858216862956833	linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
6	log-raíz	0.825833185026114	linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
7	raíz-log	0.804952865180797	linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
3	logarítmico	0.585652425658201	linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)

5 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 <- 8
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 = log(multipob) ~ log(Freq.x), data = h_y_m_comuna_corr)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.99023 -0.09645 -0.00222  0.09395  1.53885 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 13.270794   0.013259  1000.9   <2e-16 ***
## log(Freq.x)  1.033996   0.001917   539.5   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1618 on 5143 degrees of freedom
##   (20 observations deleted due to missingness)
## Multiple R-squared:  0.9826, Adjusted R-squared:  0.9826 
## F-statistic: 2.911e+05 on 1 and 5143 DF,  p-value: < 2.2e-16

5.1 Modelo log-log (log-log)

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

5.1.1 Diagrama de dispersión sobre log-log

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

head(tabla_de_trabajo,10)

##          clave código.x Freq.x personas       comuna promedio_i  año
## 1  10101011001    10101    320   245902 Puerto Montt   304409.6 2017
## 2  10101011002    10101   1309   245902 Puerto Montt   304409.6 2017
## 3  10101021001    10101   1828   245902 Puerto Montt   304409.6 2017
## 4  10101021002    10101    581   245902 Puerto Montt   304409.6 2017
## 5  10101021003    10101   1050   245902 Puerto Montt   304409.6 2017
## 6  10101021004    10101   1457   245902 Puerto Montt   304409.6 2017
## 7  10101021005    10101   1165   245902 Puerto Montt   304409.6 2017
## 8  10101031001    10101   2012   245902 Puerto Montt   304409.6 2017
## 9  10101031002    10101   2166   245902 Puerto Montt   304409.6 2017
## 10 10101031003    10101   1663   245902 Puerto Montt   304409.6 2017
##    ingresos_expandidos Freq.y           p código.y   multipob
## 1          74854925754    584 0.002374930    10101  177775198
## 2          74854925754   2941 0.011960049    10101  895268589
## 3          74854925754   3953 0.016075510    10101 1203331089
## 4          74854925754   1107 0.004501793    10101  336981411
## 5          74854925754   2294 0.009328920    10101  698315588
## 6          74854925754   3391 0.013790046    10101 1032252903
## 7          74854925754   2564 0.010426918    10101  780506176
## 8          74854925754   4530 0.018421973    10101 1378975420
## 9          74854925754   4740 0.019275972    10101 1442901433
## 10         74854925754   4107 0.016701776    10101 1250210165

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

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

5.1.2 Análisis de residuos

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

5.1.3 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

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

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

Esta nueva variable se llamará: est_ing

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

7 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_2.xlsx")
write.dbf(tabla_de_trabajo, "tabla_de_trabajo_2.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
10101011001	10101	320	245902	Puerto Montt	304409.6	2017	74854925754	584	0.0023749	10101	177775197.6	225812484	386665.2
10101011002	10101	1309	245902	Puerto Montt	304409.6	2017	74854925754	2941	0.0119600	10101	895268589.3	969027564	329489.1
10101021001	10101	1828	245902	Puerto Montt	304409.6	2017	74854925754	3953	0.0160755	10101	1203331089.2	1368684549	346239.5
10101021002	10101	581	245902	Puerto Montt	304409.6	2017	74854925754	1107	0.0045018	10101	336981410.5	418388755	377948.3
10101021003	10101	1050	245902	Puerto Montt	304409.6	2017	74854925754	2294	0.0093289	10101	698315587.8	771490596	336308.0
10101021004	10101	1457	245902	Puerto Montt	304409.6	2017	74854925754	3391	0.0137900	10101	1032252902.5	1082524004	319234.4
10101021005	10101	1165	245902	Puerto Montt	304409.6	2017	74854925754	2564	0.0104269	10101	780506175.8	859016955	335030.0
10101031001	10101	2012	245902	Puerto Montt	304409.6	2017	74854925754	4530	0.0184220	10101	1378975419.7	1511371232	333636.0
10101031002	10101	2166	245902	Puerto Montt	304409.6	2017	74854925754	4740	0.0192760	10101	1442901432.6	1631137384	344121.8
10101031003	10101	1663	245902	Puerto Montt	304409.6	2017	74854925754	4107	0.0167018	10101	1250210165.3	1241145581	302202.5
10101031004	10101	1172	245902	Puerto Montt	304409.6	2017	74854925754	2856	0.0116144	10101	869393774.6	864354445	302645.1
10101031005	10101	2657	245902	Puerto Montt	304409.6	2017	74854925754	5690	0.0231393	10101	1732090538.3	2014838451	354101.7
10101031006	10101	1233	245902	Puerto Montt	304409.6	2017	74854925754	2460	0.0100040	10101	748847578.9	910912070	370289.5
10101031007	10101	1038	245902	Puerto Montt	304409.6	2017	74854925754	2292	0.0093208	10101	697706768.7	762375593	332624.6
10101031008	10101	1616	245902	Puerto Montt	304409.6	2017	74854925754	3585	0.0145790	10101	1091308362.0	1204893190	336092.9
10101031009	10101	1995	245902	Puerto Montt	304409.6	2017	74854925754	4436	0.0180397	10101	1350360918.8	1498168966	337729.7
10101031010	10101	1531	245902	Puerto Montt	304409.6	2017	74854925754	3566	0.0145017	10101	1085524579.9	1139422061	319523.9
10101031011	10101	1232	245902	Puerto Montt	304409.6	2017	74854925754	2757	0.0112118	10101	839257225.7	910148187	330122.7
10101031012	10101	837	245902	Puerto Montt	304409.6	2017	74854925754	1849	0.0075193	10101	562853322.5	610266318	330052.1
10101031013	10101	1642	245902	Puerto Montt	304409.6	2017	74854925754	3945	0.0160430	10101	1200895812.6	1224943339	310505.3
10101031014	10101	1030	245902	Puerto Montt	304409.6	2017	74854925754	2265	0.0092110	10101	689487709.9	756300911	333907.7
10101031015	10101	873	245902	Puerto Montt	304409.6	2017	74854925754	1930	0.0078487	10101	587510498.9	637426237	330272.7
10101031016	10101	1285	245902	Puerto Montt	304409.6	2017	74854925754	3071	0.0124887	10101	934841835.3	950662583	309561.2
10101031017	10101	1457	245902	Puerto Montt	304409.6	2017	74854925754	3885	0.0157990	10101	1182631237.5	1082524004	278642.0
10101041001	10101	1843	245902	Puerto Montt	304409.6	2017	74854925754	4342	0.0176574	10101	1321746417.8	1380298976	317894.7
10101041002	10101	895	245902	Puerto Montt	304409.6	2017	74854925754	2169	0.0088206	10101	660264389.7	654042823	301541.2
10101041003	10101	2559	245902	Puerto Montt	304409.6	2017	74854925754	5202	0.0211548	10101	1583538660.8	1938046089	372557.9
10101051001	10101	1148	245902	Puerto Montt	304409.6	2017	74854925754	2463	0.0100162	10101	749760807.7	846059032	343507.5
10101051002	10101	814	245902	Puerto Montt	304409.6	2017	74854925754	1913	0.0077795	10101	582335536.0	592934824	309950.2
10101051003	10101	1329	245902	Puerto Montt	304409.6	2017	74854925754	3272	0.0133061	10101	996028161.9	984340471	300837.6
10101051004	10101	1712	245902	Puerto Montt	304409.6	2017	74854925754	3633	0.0147742	10101	1105920022.1	1278977722	352044.5
10101061001	10101	3383	245902	Puerto Montt	304409.6	2017	74854925754	6787	0.0276004	10101	2066027852.9	2586528264	381100.4
10101061002	10101	1254	245902	Puerto Montt	304409.6	2017	74854925754	2729	0.0110979	10101	830733757.3	926958434	339669.6
10101061003	10101	1559	245902	Puerto Montt	304409.6	2017	74854925754	3668	0.0149165	10101	1116574357.5	1160975700	316514.6
10101061004	10101	1184	245902	Puerto Montt	304409.6	2017	74854925754	2995	0.0121796	10101	911706706.9	873506946	291655.1
10101061005	10101	1216	245902	Puerto Montt	304409.6	2017	74854925754	2571	0.0104554	10101	782637042.9	897928952	349252.8
10101061006	10101	1991	245902	Puerto Montt	304409.6	2017	74854925754	4130	0.0167953	10101	1257211585.8	1495063105	362000.8
10101061007	10101	340	245902	Puerto Montt	304409.6	2017	74854925754	817	0.0033225	10101	248702630.9	240420763	294272.7
10101061008	10101	1008	245902	Puerto Montt	304409.6	2017	74854925754	2109	0.0085766	10101	641999814.6	739603843	350689.4
10101061009	10101	68	245902	Puerto Montt	304409.6	2017	74854925754	168	0.0006832	10101	51140810.3	45523920	270975.7
10101061010	10101	610	245902	Puerto Montt	304409.6	2017	74854925754	1543	0.0062749	10101	469703989.6	440000177	285158.9
10101071001	10101	909	245902	Puerto Montt	304409.6	2017	74854925754	2352	0.0095648	10101	715971343.8	664624269	282578.3
10101071002	10101	1583	245902	Puerto Montt	304409.6	2017	74854925754	3919	0.0159372	10101	1192981163.4	1179460734	300959.6
10101071003	10101	1915	245902	Puerto Montt	304409.6	2017	74854925754	4978	0.0202438	10101	1515350913.8	1436092528	288487.9
10101071004	10101	1419	245902	Puerto Montt	304409.6	2017	74854925754	3443	0.0140015	10101	1048082200.9	1053343936	305937.8
10101071005	10101	1186	245902	Puerto Montt	304409.6	2017	74854925754	2751	0.0111874	10101	837430768.2	875032670	318078.0
10101071006	10101	1741	245902	Puerto Montt	304409.6	2017	74854925754	4214	0.0171369	10101	1282781990.9	1301385588	308824.3
10101071007	10101	987	245902	Puerto Montt	304409.6	2017	74854925754	2345	0.0095363	10101	713840476.7	723677283	308604.4
10101071008	10101	2104	245902	Puerto Montt	304409.6	2017	74854925754	5480	0.0222853	10101	1668164525.4	1582883822	288847.4
10101071009	10101	1509	245902	Puerto Montt	304409.6	2017	74854925754	3549	0.0144326	10101	1080349616.9	1122496446	316285.3
10101071010	10101	1442	245902	Puerto Montt	304409.6	2017	74854925754	3521	0.0143187	10101	1071826148.6	1071002427	304175.6
10101071011	10101	1301	245902	Puerto Montt	304409.6	2017	74854925754	3094	0.0125822	10101	941843255.8	962904621	311216.7
10101071012	10101	1066	245902	Puerto Montt	304409.6	2017	74854925754	2621	0.0106587	10101	797857522.1	783649437	298988.7
10101071013	10101	41	245902	Puerto Montt	304409.6	2017	74854925754	84	0.0003416	10101	25570405.1	26980176	321192.6
10101071014	10101	350	245902	Puerto Montt	304409.6	2017	74854925754	875	0.0035583	10101	266358386.8	247735976	283126.8
10101131001	10101	282	245902	Puerto Montt	304409.6	2017	74854925754	604	0.0024563	10101	183863389.3	198143878	328052.8
10101151001	10101	1757	245902	Puerto Montt	304409.6	2017	74854925754	3973	0.0161568	10101	1209419280.9	1313753995	330670.5
10101151002	10101	2237	245902	Puerto Montt	304409.6	2017	74854925754	4655	0.0189303	10101	1417026617.9	1686453130	362288.5
10101151003	10101	248	245902	Puerto Montt	304409.6	2017	74854925754	592	0.0024075	10101	180210474.3	173494747	293065.5
10101151004	10101	143	245902	Puerto Montt	304409.6	2017	74854925754	325	0.0013217	10101	98933115.1	98184214	302105.3
10101151005	10101	179	245902	Puerto Montt	304409.6	2017	74854925754	384	0.0015616	10101	116893280.6	123843685	322509.6
10101161001	10101	342	245902	Puerto Montt	304409.6	2017	74854925754	739	0.0030053	10101	224958683.3	241883227	327311.5
10101161002	10101	2677	245902	Puerto Montt	304409.6	2017	74854925754	6507	0.0264618	10101	1980793169.2	2030522314	312052.0
10101161003	10101	1242	245902	Puerto Montt	304409.6	2017	74854925754	2841	0.0115534	10101	864827630.8	917787954	323051.0
10101161004	10101	491	245902	Puerto Montt	304409.6	2017	74854925754	1224	0.0049776	10101	372597332.0	351560784	287222.9
10101161005	10101	70	245902	Puerto Montt	304409.6	2017	74854925754	188	0.0007645	10101	57229002.0	46909063	249516.3
10101161006	10101	190	245902	Puerto Montt	304409.6	2017	74854925754	435	0.0017690	10101	132418169.4	131720981	302806.9
10101171001	10101	735	245902	Puerto Montt	304409.6	2017	74854925754	1747	0.0071045	10101	531803544.9	533534615	305400.5
10101171002	10101	1196	245902	Puerto Montt	304409.6	2017	74854925754	2902	0.0118014	10101	883396615.5	882662601	304156.7
10101171003	10101	1335	245902	Puerto Montt	304409.6	2017	74854925754	2873	0.0116835	10101	874568737.5	988935876	344217.2
10101171004	10101	2230	245902	Puerto Montt	304409.6	2017	74854925754	4707	0.0191418	10101	1432855916.3	1680996781	357127.0
10101171005	10101	1799	245902	Puerto Montt	304409.6	2017	74854925754	3782	0.0153801	10101	1151277050.2	1346239200	355959.6
10101171006	10101	1635	245902	Puerto Montt	304409.6	2017	74854925754	3515	0.0142943	10101	1069999691.0	1219544153	346954.2
10101181001	10101	1384	245902	Puerto Montt	304409.6	2017	74854925754	3155	0.0128303	10101	960412240.5	1026491036	325353.7
10101181002	10101	1039	245902	Puerto Montt	304409.6	2017	74854925754	2282	0.0092801	10101	694662672.8	763135040	334415.0
10101181003	10101	601	245902	Puerto Montt	304409.6	2017	74854925754	1312	0.0053355	10101	399385375.4	433289366	330251.0
10101181004	10101	645	245902	Puerto Montt	304409.6	2017	74854925754	1466	0.0059617	10101	446264451.5	466129355	317960.0
10101991999	10101	763	245902	Puerto Montt	304409.6	2017	74854925754	1400	0.0056933	10101	426173418.9	554564165	396117.3
10102051001	10102	1243	33985	Calbuco	280878.2	2017	9545646863	3082	0.0906871	10102	865666724.5	918552046	298037.7
10102051002	10102	1603	33985	Calbuco	280878.2	2017	9545646863	3879	0.1141386	10102	1089526678.9	1194872215	308036.1
10102141001	10102	1473	33985	Calbuco	280878.2	2017	9545646863	3356	0.0987494	10102	942627361.2	1094818130	326227.1
10102141002	10102	2318	33985	Calbuco	280878.2	2017	9545646863	5586	0.1643666	10102	1568985828.3	1749632662	313217.4
10102991999	10102	51	33985	Calbuco	280878.2	2017	9545646863	93	0.0027365	10102	26121676.0	33810647	363555.3
10104011001	10104	1160	12261	Fresia	223666.2	2017	2742371891	2769	0.2258380	10104	619331846.2	855205130	308849.8
10104011002	10104	1735	12261	Fresia	223666.2	2017	2742371891	4559	0.3718294	10104	1019694433.8	1296748430	284437.0
10104991999	10104	3	12261	Fresia	223666.2	2017	2742371891	3	0.0002447	10104	670998.8	1806234	602078.0
10105011001	10105	1420	18428	Frutillar	281543.9	2017	5188291726	3426	0.1859127	10105	964569538.4	1054111495	307679.9
10105011002	10105	1299	18428	Frutillar	281543.9	2017	5188291726	3126	0.1696332	10105	880106356.4	961374085	307541.3
10105011003	10105	1345	18428	Frutillar	281543.9	2017	5188291726	3037	0.1648036	10105	855048945.8	996596444	328151.6
10105011004	10105	1396	18428	Frutillar	281543.9	2017	5188291726	3287	0.1783699	10105	925434930.8	1035695173	315088.3
10105991999	10105	37	18428	Frutillar	281543.9	2017	5188291726	76	0.0041242	10105	21397339.4	24263141	319251.9
10106011001	10106	2144	17068	Los Muermos	233220.1	2017	3980600731	5180	0.3034919	10106	1208080137.5	1614009706	311584.9
10106011002	10106	1105	17068	Los Muermos	233220.1	2017	3980600731	2748	0.1610030	10106	640888845.2	813312435	295965.2
10106991999	10106	79	17068	Los Muermos	233220.1	2017	3980600731	178	0.0104289	10106	41513178.5	53158363	298642.5
10107011001	10107	1772	17591	Llanquihue	251391.8	2017	4422233283	4286	0.2436473	10107	1077465286.3	1325352856	309228.4
10107011002	10107	455	17591	Llanquihue	251391.8	2017	4422233283	1159	0.0658860	10107	291363104.7	324942166	280364.3
10107011003	10107	1362	17591	Llanquihue	251391.8	2017	4422233283	3146	0.1788415	10107	790878625.9	1009623846	320923.0
10107021001	10107	937	17591	Llanquihue	251391.8	2017	4422233283	2292	0.1302939	10107	576190022.4	685803705	299216.3
10107021002	10107	1283	17591	Llanquihue	251391.8	2017	4422233283	3221	0.1831050	10107	809733011.4	949132691	294670.2
10107991999	10107	58	17591	Llanquihue	251391.8	2017	4422233283	118	0.0067080	10107	29664233.3	38619820	327286.6

7.1 Estadísticos

ingresos <- readRDS("Ingresos_expandidos_urbano_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	375676.9	2017	71930106513
2	01107	108375	Alto Hospicio	311571.7	2017	33766585496
3	01401	15711	Pozo Almonte	316138.5	2017	4966851883
7	01405	9296	Pica	330061.1	2017	3068247619
8	02101	361873	Antofagasta	368221.4	2017	133249367039
9	02102	13467	Mejillones	369770.7	2017	4979702302
11	02104	13317	Taltal	383666.2	2017	5109282942
12	02201	165731	Calama	434325.1	2017	71981127235
14	02203	10996	San Pedro de Atacama	442861.0	2017	4869699464
15	02301	25186	Tocopilla	286187.2	2017	7207910819
16	02302	6457	María Elena	477748.0	2017	3084818966
17	03101	153937	Copiapó	343121.0	2017	52819016037
18	03102	17662	Caldera	318653.2	2017	5628052276
19	03103	14019	Tierra Amarilla	333194.9	2017	4671058718
20	03201	12219	Chañaral	286389.3	2017	3499391196
21	03202	13925	Diego de Almagro	351583.9	2017	4895805596
22	03301	51917	Vallenar	315981.5	2017	16404810756
24	03303	7041	Freirina	289049.9	2017	2035200054
25	03304	10149	Huasco	337414.8	2017	3424422750
26	04101	221054	La Serena	279340.1	2017	61749247282
27	04102	227730	Coquimbo	269078.6	2017	61277269093
28	04103	11044	Andacollo	258539.7	2017	2855312920
29	04104	4241	La Higuera	214257.0	2017	908664019
31	04106	27771	Vicuña	254177.0	2017	7058750373
32	04201	30848	Illapel	282139.3	2017	8703433491
33	04202	9093	Canela	233397.3	2017	2122281844
34	04203	21382	Los Vilos	285214.0	2017	6098444926
35	04204	29347	Salamanca	262056.9	2017	7690585032
36	04301	111272	Ovalle	280373.5	2017	31197719080
37	04302	13322	Combarbalá	234537.3	2017	3124505460
38	04303	30751	Monte Patria	225369.1	2017	6930326684
39	04304	10956	Punitaqui	212496.1	2017	2328107498
41	05101	296655	Valparaíso	306572.5	2017	90946261553
42	05102	26867	Casablanca	348088.6	2017	9352095757
43	05103	42152	Concón	333932.4	2017	14075920021
45	05105	18546	Puchuncaví	296035.5	2017	5490274928
46	05107	31923	Quintero	308224.7	2017	9839456903
47	05109	334248	Viña del Mar	354715.9	2017	118563074323
49	05301	66708	Los Andes	355446.2	2017	23711104774
50	05302	14832	Calle Larga	246387.3	2017	3654416747
51	05303	10207	Rinconada	279807.9	2017	2855998928
52	05304	18855	San Esteban	219571.6	2017	4140022481
53	05401	35390	La Ligua	259482.3	2017	9183080280
54	05402	19388	Cabildo	262745.9	2017	5094117762
55	05403	6356	Papudo	302317.1	2017	1921527704
56	05404	9826	Petorca	237510.8	2017	2333781007
57	05405	7339	Zapallar	294389.2	2017	2160521991
58	05501	90517	Quillota	288694.2	2017	26131733924
59	05502	50554	Calera	282823.6	2017	14297866792
60	05503	17988	Hijuelas	268449.7	2017	4828872604
61	05504	22098	La Cruz	335544.3	2017	7414857001
62	05506	22120	Nogales	259917.8	2017	5749381300
63	05601	91350	San Antonio	246603.6	2017	22527241144
64	05602	13817	Algarrobo	390710.4	2017	5398446270
65	05603	22738	Cartagena	244949.4	2017	5569658994
66	05604	15955	El Quisco	270498.2	2017	4315799297
67	05605	13286	El Tabo	287271.0	2017	3816682340
68	05606	10900	Santo Domingo	404470.9	2017	4408732520
69	05701	76844	San Felipe	302021.4	2017	23208536043
70	05702	13998	Catemu	233238.3	2017	3264869972
71	05703	24608	Llaillay	295663.4	2017	7275684301
72	05704	7273	Panquehue	328043.3	2017	2385858928
73	05705	16754	Putaendo	309628.4	2017	5187514898
74	05706	15241	Santa María	256403.4	2017	3907844674
75	05801	151708	Quilpué	344393.1	2017	52247193426
76	05802	46121	Limache	307380.7	2017	14176705125
77	05803	17516	Olmué	293997.6	2017	5149662271
78	05804	126548	Villa Alemana	361923.3	2017	45800670899
79	06101	241774	Rancagua	318384.5	2017	76977097284
80	06102	12988	Codegua	289405.7	2017	3758801352
81	06103	7359	Coinco	224485.0	2017	1651985453
82	06104	19597	Coltauco	278925.9	2017	5466110795
83	06105	20887	Doñihue	306532.0	2017	6402533884
84	06106	33437	Graneros	311834.8	2017	10426820415
85	06107	24640	Las Cabras	279810.6	2017	6894533314
86	06108	52505	Machalí	316199.2	2017	16602037093
87	06109	13407	Malloa	213596.6	2017	2863689033
88	06110	25343	Mostazal	291701.8	2017	7392597596
89	06111	13608	Olivar	297914.9	2017	4054025678
90	06112	14313	Peumo	248687.4	2017	3559462966
91	06113	19714	Pichidegua	234187.0	2017	4616762518
92	06114	13002	Quinta de Tilcoco	210835.7	2017	2741286093
93	06115	58825	Rengo	293650.2	2017	17273974762
94	06116	27968	Requínoa	288865.3	2017	8078983811
95	06117	46766	San Vicente	285655.7	2017	13358975033
96	06201	16394	Pichilemu	344227.1	2017	5643258336
97	06202	3041	La Estrella	293280.7	2017	891866686
98	06203	6294	Litueche	298955.7	2017	1881627117
99	06204	7308	Marchihue	336379.3	2017	2458260033
100	06205	6641	Navidad	236383.5	2017	1569822543
101	06206	6188	Paredones	238518.3	2017	1475951353
102	06301	73973	San Fernando	324998.7	2017	24041131495
103	06302	15037	Chépica	245508.7	2017	3691714537
104	06303	35399	Chimbarongo	260706.7	2017	9228754903
105	06304	6811	Lolol	236668.2	2017	1611947197
106	06305	17833	Nancagua	245992.6	2017	4386786331
107	06306	12482	Palmilla	246745.0	2017	3079870843
108	06307	11007	Peralillo	265630.7	2017	2923796850
109	06308	8738	Placilla	240573.8	2017	2102134220
111	06310	37855	Santa Cruz	300976.4	2017	11393463346
112	07101	220357	Talca	307377.4	2017	67732753814
113	07102	46068	Constitución	280736.9	2017	12932986800
114	07103	9448	Curepto	281855.5	2017	2662971120
115	07104	4142	Empedrado	209235.2	2017	866652110
116	07105	49721	Maule	245019.7	2017	12182624190
117	07106	8422	Pelarco	216777.5	2017	1825700105
118	07107	8245	Pencahue	233692.6	2017	1926795579
119	07108	13906	Río Claro	224864.2	2017	3126961590
120	07109	43269	San Clemente	247003.5	2017	10687595452
121	07110	9191	San Rafael	249688.5	2017	2294886656
122	07201	40441	Cauquenes	235303.7	2017	9515918892
123	07202	8928	Chanco	250327.3	2017	2234922252
124	07203	7571	Pelluhue	202735.2	2017	1534908448
125	07301	149136	Curicó	282406.9	2017	42117028333
126	07302	9657	Hualañé	303280.6	2017	2928781043
127	07303	6653	Licantén	261799.2	2017	1741750148
128	07304	45976	Molina	261223.2	2017	12009998195
129	07305	10484	Rauco	271406.8	2017	2845428741
130	07306	15187	Romeral	269017.0	2017	4085560646
131	07307	18544	Sagrada Familia	248654.3	2017	4611045339
132	07308	28921	Teno	262087.1	2017	7579820261
133	07309	4322	Vichuquén	218281.8	2017	943414066
134	07401	93602	Linares	270205.2	2017	25291751487
135	07402	20765	Colbún	200983.0	2017	4173410967
136	07403	30534	Longaví	216067.2	2017	6597394825
137	07404	41637	Parral	266374.6	2017	11091040324
138	07405	19974	Retiro	225715.0	2017	4508431050
139	07406	45547	San Javier	278559.1	2017	12687530322
140	07407	16221	Villa Alegre	262111.0	2017	4251702731
141	07408	18081	Yerbas Buenas	244050.7	2017	4412680158
142	08101	223574	Concepción	323059.6	2017	72227728923
143	08102	116262	Coronel	277633.4	2017	32278209118
144	08103	85938	Chiguayante	298370.0	2017	25641323296
145	08104	10624	Florida	232450.3	2017	2469551785
146	08105	24333	Hualqui	232273.3	2017	5651905803
147	08106	43535	Lota	283449.0	2017	12339953990
148	08107	47367	Penco	265193.8	2017	12561435651
149	08108	131808	San Pedro de la Paz	274394.0	2017	36167321662
150	08109	13749	Santa Juana	260550.2	2017	3582304723
151	08110	151749	Talcahuano	320279.6	2017	48602104064
152	08111	54946	Tomé	275421.3	2017	15133299927
153	08112	91773	Hualpén	287452.1	2017	26380344663
154	08201	25522	Lebu	256023.5	2017	6534231082
155	08202	36257	Arauco	316263.6	2017	11466769473
156	08203	34537	Cañete	241126.1	2017	8327773342
157	08204	6031	Contulmo	213011.2	2017	1284670805
158	08205	32288	Curanilahue	262911.9	2017	8488900056
159	08206	21035	Los Álamos	230097.8	2017	4840107033
160	08207	10417	Tirúa	221347.3	2017	2305775206
161	08301	202331	Los Ángeles	298724.4	2017	60441208918
162	08302	4073	Antuco	191980.2	2017	781935233
163	08303	28573	Cabrero	225166.5	2017	6433682620
164	08304	22389	Laja	224428.0	2017	5024717382
165	08305	29627	Mulchén	246376.9	2017	7299407611
166	08306	26315	Nacimiento	292529.0	2017	7697899431
167	08307	9737	Negrete	196781.4	2017	1916060576
168	08308	3988	Quilaco	196761.0	2017	784682868
169	08309	9587	Quilleco	201931.6	2017	1935917806
170	08310	3412	San Rosendo	206738.0	2017	705390056
171	08311	13773	Santa Bárbara	250849.5	2017	3454949584
172	08312	14134	Tucapel	214733.9	2017	3035048397
173	08313	21198	Yumbel	221417.8	2017	4693613938
174	08314	5923	Alto Biobío	251792.7	2017	1491367928
175	09101	282415	Temuco	294512.7	2017	83174794799
176	09102	24533	Carahue	237416.7	2017	5824543339
177	09103	17526	Cunco	247099.1	2017	4330659433
178	09104	7489	Curarrehue	204180.7	2017	1529109215
179	09105	24606	Freire	305541.7	2017	7518158340
180	09106	11996	Galvarino	244269.6	2017	2930258102
181	09107	14414	Gorbea	254627.9	2017	3670206245
182	09108	38013	Lautaro	296417.7	2017	11267725602
183	09109	23612	Loncoche	213841.9	2017	5049235445
184	09110	6138	Melipeuco	211980.8	2017	1301137941
185	09111	32510	Nueva Imperial	242015.9	2017	7867935676
186	09112	76126	Padre Las Casas	278372.7	2017	21191399108
187	09113	6905	Perquenco	260596.6	2017	1799419624
188	09114	24837	Pitrufquén	249811.8	2017	6204576082
189	09115	28523	Pucón	260967.9	2017	7443587942
190	09116	12450	Saavedra	229431.4	2017	2856420491
191	09117	15045	Teodoro Schmidt	224680.1	2017	3380311968
192	09118	9722	Toltén	219705.4	2017	2135976054
193	09119	28151	Vilcún	172649.0	2017	4860243131
194	09120	55478	Villarrica	246047.7	2017	13650235814
195	09121	11611	Cholchol	253226.0	2017	2940207311
196	09201	53262	Angol	268414.6	2017	14296297282
197	09202	24598	Collipulli	247911.8	2017	6098134776
198	09203	17413	Curacautín	234574.3	2017	4084643011
199	09204	7733	Ercilla	268071.3	2017	2072995481
200	09205	10251	Lonquimay	250560.5	2017	2568496128
201	09206	7265	Los Sauces	212950.7	2017	1547086780
202	09207	9548	Lumaco	282522.6	2017	2697526159
203	09208	11779	Purén	234139.4	2017	2757928013
204	09209	10250	Renaico	266020.9	2017	2726714090
205	09210	18843	Traiguén	258102.0	2017	4863416659
206	09211	34182	Victoria	225959.9	2017	7723760970
207	10101	245902	Puerto Montt	304409.6	2017	74854925754
208	10102	33985	Calbuco	280878.2	2017	9545646863
210	10104	12261	Fresia	223666.2	2017	2742371891
211	10105	18428	Frutillar	281543.9	2017	5188291726
212	10106	17068	Los Muermos	233220.1	2017	3980600731
213	10107	17591	Llanquihue	251391.8	2017	4422233283
214	10108	14216	Maullín	269082.7	2017	3825279050
215	10109	44578	Puerto Varas	312276.5	2017	13920663786
216	10201	43807	Castro	349368.8	2017	15304799118
217	10202	38991	Ancud	229798.1	2017	8960055930
218	10203	14858	Chonchi	239577.2	2017	3559637517
220	10205	13762	Dalcahue	325358.2	2017	4477578923
222	10207	5385	Queilén	183406.6	2017	987644627
223	10208	27192	Quellón	241965.8	2017	6579532876
224	10209	8352	Quemchi	308051.3	2017	2572844097
225	10210	8088	Quinchao	370474.4	2017	2996397098
226	10301	161460	Osorno	271587.3	2017	43850482486
227	10302	8999	Puerto Octay	262148.9	2017	2359078294
228	10303	20369	Purranque	302471.6	2017	6161043438
229	10304	11667	Puyehue	230541.3	2017	2689725003
230	10305	14085	Río Negro	276657.1	2017	3896715111
231	10306	7512	San Juan de la Costa	222910.5	2017	1674503801
232	10307	10030	San Pablo	195712.4	2017	1962995435
237	11101	57818	Coyhaique	327100.3	2017	18912283227
239	11201	23959	Aysén	307831.4	2017	7375332218
240	11202	6517	Cisnes	251971.0	2017	1642095149
242	11301	3490	Cochrane	350724.8	2017	1224029692
245	11401	4865	Chile Chico	333445.3	2017	1622211456
247	12101	131592	Punta Arenas	391758.4	2017	51552266922
253	12301	6801	Porvenir	446255.2	2017	3034981682
256	12401	21477	Natales	336808.6	2017	7233637635
258	13101	404495	Santiago	450851.7	2017	182367246208
259	13102	80832	Cerrillos	276766.5	2017	22371586546
260	13103	132622	Cerro Navia	270634.1	2017	35892031153
261	13104	126955	Conchalí	310325.3	2017	39397353402
262	13105	162505	El Bosque	281653.9	2017	45770170398
263	13106	147041	Estación Central	340680.2	2017	50093952387
264	13107	98671	Huechuraba	315250.1	2017	31106038806
265	13108	100281	Independencia	376152.6	2017	37720956327
266	13109	90119	La Cisterna	367262.4	2017	33097323323
267	13110	366916	La Florida	349483.5	2017	128231071590
268	13111	116571	La Granja	306768.3	2017	35760286668
269	13112	177335	La Pintana	232647.0	2017	41256447003
270	13113	92787	La Reina	434408.5	2017	40307459856
271	13114	294838	Las Condes	456515.7	2017	134598169599
272	13115	105833	Lo Barnechea	349308.7	2017	36968385127
273	13116	98804	Lo Espejo	264154.1	2017	26099479542
274	13117	96249	Lo Prado	305431.2	2017	29397444939
275	13118	116534	Macul	345701.4	2017	40285970358
276	13119	521627	Maipú	358559.2	2017	187034167391
277	13120	208237	Ñuñoa	426460.1	2017	88804766896
278	13121	101174	Pedro Aguirre Cerda	316863.2	2017	32058321741
279	13122	241599	Peñalolén	321570.6	2017	77691132095
280	13123	142079	Providencia	516122.3	2017	73330144381
281	13124	230293	Pudahuel	320572.7	2017	73825647438
282	13125	210410	Quilicura	383485.8	2017	80689241762
283	13126	110026	Quinta Normal	311731.1	2017	34298531093
284	13127	157851	Recoleta	344997.0	2017	54458123369
285	13128	147151	Renca	294000.5	2017	43262464632
286	13129	94492	San Joaquín	336046.8	2017	31753732439
287	13130	107954	San Miguel	351632.1	2017	37960091353
288	13131	82900	San Ramón	281439.6	2017	23331343432
289	13132	85384	Vitacura	496933.1	2017	42430139879
290	13201	568106	Puente Alto	328342.7	2017	186533464474
291	13202	26521	Pirque	332454.5	2017	8817024774
292	13203	18189	San José de Maipo	381218.4	2017	6933981276
293	13301	146207	Colina	300609.0	2017	43951136523
294	13302	102034	Lampa	372624.0	2017	38020316317
295	13303	19312	Tiltil	327523.1	2017	6325126322
296	13401	301313	San Bernardo	286991.8	2017	86474375157
297	13402	96614	Buin	314979.3	2017	30431412042
298	13403	25392	Calera de Tango	307306.5	2017	7803125477
299	13404	72759	Paine	330137.7	2017	24020488982
300	13501	123627	Melipilla	291641.9	2017	36054817558
301	13502	6444	Alhué	349434.5	2017	2251756129
302	13503	32579	Curacaví	269095.1	2017	8766848005
303	13504	13590	María Pinto	253962.5	2017	3451350898
305	13601	74237	Talagante	394670.6	2017	29299162746
306	13602	35923	El Monte	297691.7	2017	10693979408
307	13603	36219	Isla de Maipo	229284.1	2017	8304441408
308	13604	63250	Padre Hurtado	277563.2	2017	17555873230
309	13605	90201	Peñaflor	351564.7	2017	31711490484
310	14101	166080	Valdivia	308754.5	2017	51277944139
311	14102	5302	Corral	222523.9	2017	1179821617
312	14103	16752	Lanco	267286.0	2017	4477574931
313	14104	19634	Los Lagos	211843.1	2017	4159328181
314	14105	7095	Máfil	315022.1	2017	2235081533
315	14106	21278	Mariquina	251064.3	2017	5342147079
316	14107	20188	Paillaco	223306.5	2017	4508111622
317	14108	34539	Panguipulli	287752.5	2017	9938682028
318	14201	38036	La Unión	247291.7	2017	9405987850
319	14202	14665	Futrono	247331.7	2017	3627119212
320	14203	9896	Lago Ranco	247154.2	2017	2445838259
321	14204	31372	Río Bueno	267934.4	2017	8405637271
322	15101	221364	Arica	310013.3	2017	68625788545
324	15201	2765	Putre	283661.5	2017	784324030
326	16101	184739	Chillán	275879.2	2017	50965643906
327	16102	21493	Bulnes	224694.9	2017	4829367278
328	16103	30907	Chillán Viejo	259577.5	2017	8022762560
329	16104	12044	El Carmen	215566.5	2017	2596282563
330	16105	8448	Pemuco	262037.4	2017	2213691761
331	16106	10827	Pinto	175602.5	2017	1901248804
332	16107	17485	Quillón	256072.4	2017	4477425886
333	16108	16079	San Ignacio	203331.5	2017	3269367252
334	16109	17787	Yungay	258601.1	2017	4599738091
335	16201	11594	Quirihue	252923.7	2017	2932397811
336	16202	5012	Cobquecura	259487.2	2017	1300549630
337	16203	15995	Coelemu	296882.1	2017	4748629723
338	16204	5213	Ninhue	301493.8	2017	1571687052
339	16205	4862	Portezuelo	196869.9	2017	957181342
340	16206	5755	Ránquil	286762.9	2017	1650320432
341	16207	5401	Treguaco	218702.4	2017	1181211462
342	16301	53024	San Carlos	252551.6	2017	13391296803
343	16302	26881	Coihueco	213580.4	2017	5741254097
344	16303	11152	Ñiquén	236681.5	2017	2639471976
345	16304	4308	San Fabián	259592.5	2017	1118324609
346	16305	11603	San Nicolás	266207.1	2017	3088800683

7.1.1 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)

7.1.2 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)

7.1.3 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)

7.1.4 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)

7.1.5 Mediana

t_de_c_5 <- tabla_de_trabajo %>%
  group_by(código.y) %>%
  summarize(median = 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	median
01101	191468	Iquique	375676.9	2017	71930106513	336932.5	30379.201	287239.14	404791.9	330193.5
01107	108375	Alto Hospicio	311571.7	2017	33766585496	287355.9	13224.968	268365.43	325896.6	284302.9
01401	15711	Pozo Almonte	316138.5	2017	4966851883	404223.4	217202.815	260413.30	654078.0	298178.9
01405	9296	Pica	330061.1	2017	3068247619	305865.5	19913.032	291784.88	319946.2	305865.5
02101	361873	Antofagasta	368221.4	2017	133249367039	316433.3	35863.939	263943.86	459458.1	306122.7
02102	13467	Mejillones	369770.7	2017	4979702302	439311.2	72542.396	377530.10	519188.7	421214.7
02104	13317	Taltal	383666.2	2017	5109282942	324585.5	39014.970	289770.48	379173.0	314699.3
02201	165731	Calama	434325.1	2017	71981127235	312175.4	47419.462	244061.27	553019.5	304505.4
02203	10996	San Pedro de Atacama	442861.0	2017	4869699464	377684.1	25754.958	359472.63	395895.6	377684.1
02301	25186	Tocopilla	286187.2	2017	7207910819	282459.8	67548.694	231307.27	476934.1	255506.8
02302	6457	María Elena	477748.0	2017	3084818966	364409.8	23849.594	344634.52	390895.8	357699.0
03101	153937	Copiapó	343121.0	2017	52819016037	294190.3	32048.528	246016.17	396376.8	287619.5
03102	17662	Caldera	318653.2	2017	5628052276	333036.6	38246.203	297260.93	396426.7	324567.0
03103	14019	Tierra Amarilla	333194.9	2017	4671058718	278189.4	92142.116	226469.17	415905.0	235191.7
03201	12219	Chañaral	286389.3	2017	3499391196	256025.0	67923.871	118767.14	324874.6	270558.5
03202	13925	Diego de Almagro	351583.9	2017	4895805596	315058.6	54526.666	250393.26	401415.5	303361.4
03301	51917	Vallenar	315981.5	2017	16404810756	278400.9	39223.506	234014.36	343600.5	259078.4
03303	7041	Freirina	289049.9	2017	2035200054	390884.2	234843.652	241720.41	661588.9	269343.2
03304	10149	Huasco	337414.8	2017	3424422750	311636.7	40859.835	272211.41	379345.8	298448.7
04101	221054	La Serena	279340.1	2017	61749247282	282492.2	23765.052	240651.67	386556.6	280354.4
04102	227730	Coquimbo	269078.6	2017	61277269093	286925.6	20722.703	246807.05	338325.4	286053.3
04103	11044	Andacollo	258539.7	2017	2855312920	256551.7	4914.570	252752.69	262102.1	254800.4
04104	4241	La Higuera	214257.0	2017	908664019	231254.3	16320.050	219714.33	242794.4	231254.3
04106	27771	Vicuña	254177.0	2017	7058750373	287777.1	43439.033	257521.05	382890.6	267819.2
04201	30848	Illapel	282139.3	2017	8703433491	287112.2	33893.073	257095.70	354536.2	274326.4
04202	9093	Canela	233397.3	2017	2122281844	234142.4	76111.371	180323.54	287961.3	234142.4
04203	21382	Los Vilos	285214.0	2017	6098444926	338040.5	46966.082	297974.03	438023.5	335335.6
04204	29347	Salamanca	262056.9	2017	7690585032	304302.8	41409.818	242849.26	369610.5	297046.3
04301	111272	Ovalle	280373.5	2017	31197719080	284137.0	50989.002	60175.86	365477.2	289736.0
04302	13322	Combarbalá	234537.3	2017	3124505460	297031.7	21921.280	273496.12	316867.9	300731.2
04303	30751	Monte Patria	225369.1	2017	6930326684	261347.9	25270.346	217361.36	285252.9	272774.5
04304	10956	Punitaqui	212496.1	2017	2328107498	258995.4	30208.768	237724.16	293573.1	245689.1
05101	296655	Valparaíso	306572.5	2017	90946261553	302170.4	29821.003	223199.50	454453.1	299214.3
05102	26867	Casablanca	348088.6	2017	9352095757	316917.2	37785.292	255893.67	386685.6	317050.8
05103	42152	Concón	333932.4	2017	14075920021	319879.6	19339.641	291740.62	352321.0	318512.7
05105	18546	Puchuncaví	296035.5	2017	5490274928	288714.5	39644.175	193093.04	346074.8	290227.1
05107	31923	Quintero	308224.7	2017	9839456903	286112.8	56974.301	215054.40	415282.0	286169.9
05109	334248	Viña del Mar	354715.9	2017	118563074323	309752.7	18252.407	246149.66	350475.0	310744.3
05301	66708	Los Andes	355446.2	2017	23711104774	304312.8	37640.068	251789.51	429650.3	297699.8
05302	14832	Calle Larga	246387.3	2017	3654416747	294230.3	27888.188	256819.97	351556.8	292327.0
05303	10207	Rinconada	279807.9	2017	2855998928	293129.1	31863.591	238687.95	328933.7	303490.3
05304	18855	San Esteban	219571.6	2017	4140022481	288947.8	13727.706	270468.84	306305.7	285014.7
05401	35390	La Ligua	259482.3	2017	9183080280	297433.4	43734.939	176938.32	331647.7	301720.1
05402	19388	Cabildo	262745.9	2017	5094117762	261878.3	18676.693	235561.61	277375.5	272934.0
05403	6356	Papudo	302317.1	2017	1921527704	317023.7	14650.982	301038.98	333094.3	324556.0
05404	9826	Petorca	237510.8	2017	2333781007	284712.6	19680.635	261620.74	307904.3	284662.7
05405	7339	Zapallar	294389.2	2017	2160521991	298253.3	72726.232	169667.34	372978.5	316239.6
05501	90517	Quillota	288694.2	2017	26131733924	305119.2	40928.375	249307.49	486283.7	298560.2
05502	50554	Calera	282823.6	2017	14297866792	283973.5	47275.393	196272.23	445860.2	274964.1
05503	17988	Hijuelas	268449.7	2017	4828872604	279500.9	48213.452	193335.31	303046.0	300817.4
05504	22098	La Cruz	335544.3	2017	7414857001	296860.8	20533.257	267715.43	319231.5	303582.6
05506	22120	Nogales	259917.8	2017	5749381300	258843.6	14645.694	239429.03	279399.2	262879.2
05601	91350	San Antonio	246603.6	2017	22527241144	311419.1	27907.777	281031.53	399154.5	307300.9
05602	13817	Algarrobo	390710.4	2017	5398446270	281941.1	32275.299	233374.16	337725.1	285617.5
05603	22738	Cartagena	244949.4	2017	5569658994	265487.4	27402.143	213297.44	305831.0	263935.1
05604	15955	El Quisco	270498.2	2017	4315799297	267425.0	25467.544	217506.89	306418.1	265788.3
05605	13286	El Tabo	287271.0	2017	3816682340	259243.4	31856.527	218161.27	314666.9	259479.4
05606	10900	Santo Domingo	404470.9	2017	4408732520	290626.6	41616.420	215082.33	341685.7	297339.8
05701	76844	San Felipe	302021.4	2017	23208536043	308061.5	34133.274	231134.24	402113.0	310686.5
05702	13998	Catemu	233238.3	2017	3264869972	310834.7	30287.536	291104.21	345707.3	295692.6
05703	24608	Llaillay	295663.4	2017	7275684301	295956.0	28310.994	235338.01	324628.9	298428.4
05704	7273	Panquehue	328043.3	2017	2385858928	302700.9	19562.426	283022.01	322144.7	302936.0
05705	16754	Putaendo	309628.4	2017	5187514898	313331.4	43810.674	275807.13	361475.9	302711.2
05706	15241	Santa María	256403.4	2017	3907844674	306559.3	28421.152	258033.41	345899.7	311837.8
05801	151708	Quilpué	344393.1	2017	52247193426	298298.2	15249.379	266063.91	342110.7	296250.4
05802	46121	Limache	307380.7	2017	14176705125	294288.0	13121.522	276725.39	326649.6	292740.7
05803	17516	Olmué	293997.6	2017	5149662271	290245.3	14171.915	275867.51	313625.7	287083.0
05804	126548	Villa Alemana	361923.3	2017	45800670899	296526.2	19609.725	265056.98	373987.3	298298.3
06101	241774	Rancagua	318384.5	2017	76977097284	302505.4	29222.661	230727.50	406352.3	301332.2
06102	12988	Codegua	289405.7	2017	3758801352	311046.2	25781.799	283822.85	334113.2	313124.5
06103	7359	Coinco	224485.0	2017	1651985453	292125.0	52176.270	252617.84	368960.3	273460.9
06104	19597	Coltauco	278925.9	2017	5466110795	274798.2	26559.579	212445.07	303606.9	280331.6
06105	20887	Doñihue	306532.0	2017	6402533884	271845.8	58023.565	156650.00	308908.9	292993.3
06106	33437	Graneros	311834.8	2017	10426820415	304668.8	14883.148	278913.20	325275.2	309697.3
06107	24640	Las Cabras	279810.6	2017	6894533314	298699.7	18073.359	279212.37	319548.1	303554.9
06108	52505	Machalí	316199.2	2017	16602037093	304567.5	23841.121	262517.44	363900.1	302208.4
06109	13407	Malloa	213596.6	2017	2863689033	259140.0	76554.414	145001.48	306744.3	292407.2
06110	25343	Mostazal	291701.8	2017	7392597596	298661.2	11969.901	277518.52	312387.7	299063.8
06111	13608	Olivar	297914.9	2017	4054025678	297071.2	34660.679	243198.09	328487.8	309688.0
06112	14313	Peumo	248687.4	2017	3559462966	308154.5	19584.660	269962.31	330122.4	310233.3
06113	19714	Pichidegua	234187.0	2017	4616762518	310757.2	61025.584	240137.15	389205.1	306843.2
06114	13002	Quinta de Tilcoco	210835.7	2017	2741286093	315599.1	15572.896	294126.65	329766.0	319251.9
06115	58825	Rengo	293650.2	2017	17273974762	298321.6	34594.690	199197.56	334776.3	304436.9
06116	27968	Requínoa	288865.3	2017	8078983811	318634.5	21560.582	285249.50	357518.7	319399.3
06117	46766	San Vicente	285655.7	2017	13358975033	298777.1	24280.672	262261.97	342153.5	296366.8
06201	16394	Pichilemu	344227.1	2017	5643258336	303759.3	34211.726	223166.43	333618.2	313136.8
06202	3041	La Estrella	293280.7	2017	891866686	236400.4	129257.528	145001.48	327799.2	236400.4
06203	6294	Litueche	298955.7	2017	1881627117	319277.5	16913.720	300830.21	334055.1	322947.2
06204	7308	Marchihue	336379.3	2017	2458260033	313662.5	11317.603	305659.79	321665.3	313662.5
06205	6641	Navidad	236383.5	2017	1569822543	NA	NA	NA	NA	NA
06206	6188	Paredones	238518.3	2017	1475951353	439597.5	198567.556	299188.99	580005.9	439597.5
06301	73973	San Fernando	324998.7	2017	24041131495	310093.9	19446.932	259200.72	339240.2	310731.8
06302	15037	Chépica	245508.7	2017	3691714537	270656.8	20386.617	246573.61	296155.3	269949.2
06303	35399	Chimbarongo	260706.7	2017	9228754903	296635.3	35327.054	203947.27	340681.3	305670.1
06304	6811	Lolol	236668.2	2017	1611947197	307398.5	11823.738	299037.87	315759.2	307398.5
06305	17833	Nancagua	245992.6	2017	4386786331	278648.0	54436.525	197945.23	317207.3	299719.8
06306	12482	Palmilla	246745.0	2017	3079870843	319520.0	39939.360	287619.11	364313.2	306627.8
06307	11007	Peralillo	265630.7	2017	2923796850	288996.9	14793.731	276770.36	309491.9	284862.6
06308	8738	Placilla	240573.8	2017	2102134220	319928.5	26713.853	301038.98	338818.1	319928.5
06310	37855	Santa Cruz	300976.4	2017	11393463346	326091.3	21246.123	288548.29	355406.5	327814.1
07101	220357	Talca	307377.4	2017	67732753814	296681.1	29808.239	185058.02	376683.5	295195.6
07102	46068	Constitución	280736.9	2017	12932986800	311162.9	28491.828	257524.87	359735.7	304458.3
07103	9448	Curepto	281855.5	2017	2662971120	282559.7	20798.632	267852.84	297266.5	282559.7
07104	4142	Empedrado	209235.2	2017	866652110	315700.4	85134.346	226360.87	395890.5	324850.0
07105	49721	Maule	245019.7	2017	12182624190	301160.4	26170.230	264664.99	348925.2	296372.3
07106	8422	Pelarco	216777.5	2017	1825700105	332375.8	40829.732	303504.82	361246.8	332375.8
07107	8245	Pencahue	233692.6	2017	1926795579	295650.8	50463.574	237534.28	328373.4	321044.6
07108	13906	Río Claro	224864.2	2017	3126961590	292710.2	41555.961	263325.69	322094.7	292710.2
07109	43269	San Clemente	247003.5	2017	10687595452	277295.5	64529.235	231214.90	445505.2	256072.2
07110	9191	San Rafael	249688.5	2017	2294886656	333009.1	70543.216	283127.48	382890.6	333009.1
07201	40441	Cauquenes	235303.7	2017	9515918892	273011.3	23874.033	240565.51	334929.9	270848.7
07202	8928	Chanco	250327.3	2017	2234922252	266348.0	35105.386	229502.69	299406.4	270135.0
07203	7571	Pelluhue	202735.2	2017	1534908448	261387.9	21799.268	239664.05	292407.1	252895.9
07301	149136	Curicó	282406.9	2017	42117028333	323388.3	22314.394	276793.86	363944.1	325424.3
07302	9657	Hualañé	303280.6	2017	2928781043	251434.9	56793.080	193335.31	306824.4	254144.9
07303	6653	Licantén	261799.2	2017	1741750148	318432.3	13919.583	308589.67	328274.9	318432.3
07304	45976	Molina	261223.2	2017	12009998195	303505.4	25884.602	235574.33	337106.4	311008.9
07305	10484	Rauco	271406.8	2017	2845428741	311255.5	31088.888	276438.36	336236.7	321091.3
07306	15187	Romeral	269017.0	2017	4085560646	305806.0	49584.771	248578.00	335957.4	332882.7
07307	18544	Sagrada Familia	248654.3	2017	4611045339	310147.7	22190.858	280594.84	342794.9	307258.6
07308	28921	Teno	262087.1	2017	7579820261	315374.3	40599.780	263294.74	370095.0	313983.9
07309	4322	Vichuquén	218281.8	2017	943414066	307408.4	44044.239	276264.45	338552.4	307408.4
07401	93602	Linares	270205.2	2017	25291751487	294027.2	30360.299	233518.83	356071.2	287440.6
07402	20765	Colbún	200983.0	2017	4173410967	260833.6	29441.815	238510.59	311657.1	254990.2
07403	30534	Longaví	216067.2	2017	6597394825	265474.8	30319.178	212443.15	289587.0	275304.9
07404	41637	Parral	266374.6	2017	11091040324	271569.3	32754.481	208329.98	321470.1	281437.3
07405	19974	Retiro	225715.0	2017	4508431050	260096.9	32629.221	216885.53	308130.4	260514.9
07406	45547	San Javier	278559.1	2017	12687530322	270224.2	21537.570	222956.86	308774.0	272893.0
07407	16221	Villa Alegre	262111.0	2017	4251702731	275395.3	20507.708	242334.41	295291.6	282334.0
07408	18081	Yerbas Buenas	244050.7	2017	4412680158	255841.5	17672.506	220424.31	276940.4	261476.9
08101	223574	Concepción	323059.6	2017	72227728923	304706.7	24280.608	237800.37	359079.5	301343.6
08102	116262	Coronel	277633.4	2017	32278209118	255142.1	21087.205	192736.11	303229.2	255764.7
08103	85938	Chiguayante	298370.0	2017	25641323296	298400.0	21465.971	268121.45	362236.9	295060.4
08104	10624	Florida	232450.3	2017	2469551785	282105.3	13842.886	266190.56	291353.2	288772.2
08105	24333	Hualqui	232273.3	2017	5651905803	268758.5	31410.240	197304.90	308130.4	278481.2
08106	43535	Lota	283449.0	2017	12339953990	261008.2	14826.101	247039.11	293702.7	256135.6
08107	47367	Penco	265193.8	2017	12561435651	282732.5	15762.852	250714.58	311213.5	279650.5
08108	131808	San Pedro de la Paz	274394.0	2017	36167321662	304044.5	23113.144	274447.10	363569.7	297209.0
08109	13749	Santa Juana	260550.2	2017	3582304723	233738.5	63083.114	139385.02	271556.7	262006.0
08110	151749	Talcahuano	320279.6	2017	48602104064	281151.4	26912.372	244725.41	365267.3	274214.3
08111	54946	Tomé	275421.3	2017	15133299927	274032.6	32604.680	224141.68	386779.3	271361.1
08112	91773	Hualpén	287452.1	2017	26380344663	282873.9	24856.704	240261.33	342543.8	276264.2
08201	25522	Lebu	256023.5	2017	6534231082	268978.9	52003.704	183718.60	387751.9	262402.5
08202	36257	Arauco	316263.6	2017	11466769473	302319.5	98678.055	221779.72	556966.8	278180.1
08203	34537	Cañete	241126.1	2017	8327773342	279581.0	38531.385	230653.53	359337.1	278059.0
08204	6031	Contulmo	213011.2	2017	1284670805	305825.5	NA	305825.51	305825.5	305825.5
08205	32288	Curanilahue	262911.9	2017	8488900056	269708.5	17442.456	238540.76	310201.5	271317.2
08206	21035	Los Álamos	230097.8	2017	4840107033	233242.2	46129.593	145863.10	273307.8	247561.0
08207	10417	Tirúa	221347.3	2017	2305775206	292778.6	27804.612	264664.99	320263.6	293407.2
08301	202331	Los Ángeles	298724.4	2017	60441208918	292288.4	34521.202	197111.74	347528.7	296461.7
08302	4073	Antuco	191980.2	2017	781935233	288752.2	105618.684	214068.46	363435.8	288752.2
08303	28573	Cabrero	225166.5	2017	6433682620	271740.7	42501.269	239047.90	361475.9	262429.1
08304	22389	Laja	224428.0	2017	5024717382	223170.1	16917.557	197744.63	248866.4	219748.7
08305	29627	Mulchén	246376.9	2017	7299407611	267441.5	24688.798	237209.22	319946.2	265765.5
08306	26315	Nacimiento	292529.0	2017	7697899431	249362.7	15612.947	228540.67	281547.7	248470.3
08307	9737	Negrete	196781.4	2017	1916060576	227345.8	26844.443	200692.65	254377.5	226967.3
08308	3988	Quilaco	196761.0	2017	784682868	229642.8	23115.771	213297.44	245988.1	229642.8
08309	9587	Quilleco	201931.6	2017	1935917806	214690.2	18954.032	187496.98	232574.8	223696.0
08310	3412	San Rosendo	206738.0	2017	705390056	242835.8	65661.181	196406.33	289265.3	242835.8
08311	13773	Santa Bárbara	250849.5	2017	3454949584	262803.9	6760.668	255125.24	267861.9	265424.6
08312	14134	Tucapel	214733.9	2017	3035048397	225121.6	35243.716	173712.92	254230.8	242007.5
08313	21198	Yumbel	221417.8	2017	4693613938	286828.1	66066.993	236904.48	383935.4	263236.2
08314	5923	Alto Biobío	251792.7	2017	1491367928	NA	NA	NA	NA	NA
09101	282415	Temuco	294512.7	2017	83174794799	308710.1	23826.178	256182.42	392002.1	305911.0
09102	24533	Carahue	237416.7	2017	5824543339	271817.4	45269.383	205353.31	326939.1	265185.8
09103	17526	Cunco	247099.1	2017	4330659433	246879.1	32647.802	196376.89	286226.0	252261.4
09104	7489	Curarrehue	204180.7	2017	1529109215	279439.1	14051.966	269502.85	289375.3	279439.1
09105	24606	Freire	305541.7	2017	7518158340	244377.9	36195.091	197331.75	282892.0	249473.3
09106	11996	Galvarino	244269.6	2017	2930258102	277023.4	3317.876	274677.35	279369.5	277023.4
09107	14414	Gorbea	254627.9	2017	3670206245	264719.8	21770.848	240729.64	301039.0	258426.6
09108	38013	Lautaro	296417.7	2017	11267725602	263508.8	16477.328	238296.28	286329.6	262106.3
09109	23612	Loncoche	213841.9	2017	5049235445	231962.4	52238.318	127730.02	268385.5	250823.0
09110	6138	Melipeuco	211980.8	2017	1301137941	240532.7	26372.155	221884.75	259180.6	240532.7
09111	32510	Nueva Imperial	242015.9	2017	7867935676	266922.6	9629.951	252000.13	279412.1	265894.3
09112	76126	Padre Las Casas	278372.7	2017	21191399108	296704.8	23112.436	249619.67	323243.2	304294.2
09113	6905	Perquenco	260596.6	2017	1799419624	276852.6	NA	276852.62	276852.6	276852.6
09114	24837	Pitrufquén	249811.8	2017	6204576082	291183.7	37712.428	268192.51	374994.0	277846.5
09115	28523	Pucón	260967.9	2017	7443587942	331611.6	15114.724	311504.77	356659.8	333006.6
09116	12450	Saavedra	229431.4	2017	2856420491	328228.1	39709.865	300148.97	356307.2	328228.1
09117	15045	Teodoro Schmidt	224680.1	2017	3380311968	253110.7	33071.156	217564.95	282969.8	258797.2
09118	9722	Toltén	219705.4	2017	2135976054	237150.7	81972.499	145001.48	301957.1	264493.6
09119	28151	Vilcún	172649.0	2017	4860243131	257646.6	55421.187	164932.88	324386.1	273609.4
09120	55478	Villarrica	246047.7	2017	13650235814	295286.4	22024.568	249715.94	336408.4	296086.8
09121	11611	Cholchol	253226.0	2017	2940207311	222869.7	40881.066	193962.40	251777.0	222869.7
09201	53262	Angol	268414.6	2017	14296297282	292721.6	33538.577	247142.56	382781.6	288640.5
09202	24598	Collipulli	247911.8	2017	6098134776	258173.4	38974.350	182540.44	317749.3	263913.0
09203	17413	Curacautín	234574.3	2017	4084643011	264813.7	28466.715	236680.12	325581.3	258773.1
09204	7733	Ercilla	268071.3	2017	2072995481	275932.3	26020.139	254056.67	304706.6	269033.6
09205	10251	Lonquimay	250560.5	2017	2568496128	340170.6	2697.560	338263.11	342078.0	340170.6
09206	7265	Los Sauces	212950.7	2017	1547086780	260090.1	17697.999	241220.44	276319.9	262730.1
09207	9548	Lumaco	282522.6	2017	2697526159	272360.7	11893.621	260738.15	284508.0	271835.8
09208	11779	Purén	234139.4	2017	2757928013	263556.4	16656.076	244648.34	283008.9	263284.2
09209	10250	Renaico	266020.9	2017	2726714090	268081.4	6138.245	261222.13	276142.0	267480.8
09210	18843	Traiguén	258102.0	2017	4863416659	268281.0	28770.246	232797.16	312085.3	262524.6
09211	34182	Victoria	225959.9	2017	7723760970	281172.8	21014.455	257461.71	329429.4	274785.3
10101	245902	Puerto Montt	304409.6	2017	74854925754	323364.6	28086.643	249516.29	396117.3	321851.1
10102	33985	Calbuco	280878.2	2017	9545646863	321814.7	25450.243	298037.65	363555.3	313217.4
10104	12261	Fresia	223666.2	2017	2742371891	398454.9	176764.671	284437.03	602078.0	308849.8
10105	18428	Frutillar	281543.9	2017	5188291726	315542.6	8642.835	307541.29	328151.6	315088.3
10106	17068	Los Muermos	233220.1	2017	3980600731	302064.2	8353.115	295965.22	311584.9	298642.5
10107	17591	Llanquihue	251391.8	2017	4422233283	305281.5	17405.265	280364.25	327286.6	304222.3
10108	14216	Maullín	269082.7	2017	3825279050	370115.0	101003.272	304184.47	486396.2	319764.5
10109	44578	Puerto Varas	312276.5	2017	13920663786	337050.7	39691.333	235338.01	375616.8	350660.4
10201	43807	Castro	349368.8	2017	15304799118	338599.5	16940.464	315366.44	372754.0	333221.7
10202	38991	Ancud	229798.1	2017	8960055930	305819.6	12074.189	288410.21	322404.1	306227.4
10203	14858	Chonchi	239577.2	2017	3559637517	305133.2	31157.332	271120.24	332294.4	311985.1
10205	13762	Dalcahue	325358.2	2017	4477578923	304228.8	27458.783	277898.24	332691.5	302096.6
10207	5385	Queilén	183406.6	2017	987644627	358690.7	68565.088	310207.88	407173.6	358690.7
10208	27192	Quellón	241965.8	2017	6579532876	342364.3	33297.418	305011.78	404593.8	336631.7
10209	8352	Quemchi	308051.3	2017	2572844097	299840.9	44066.592	248996.95	327004.0	323521.8
10210	8088	Quinchao	370474.4	2017	2996397098	252208.4	92111.925	187075.46	317341.4	252208.4
10301	161460	Osorno	271587.3	2017	43850482486	313715.1	28778.147	256448.63	384550.8	308858.3
10302	8999	Puerto Octay	262148.9	2017	2359078294	295721.6	1691.740	294525.37	296917.8	295721.6
10303	20369	Purranque	302471.6	2017	6161043438	293065.9	47462.524	251391.30	374994.0	278899.5
10304	11667	Puyehue	230541.3	2017	2689725003	252340.2	20938.767	237534.28	267146.2	252340.2
10305	14085	Río Negro	276657.1	2017	3896715111	241486.7	38071.388	197945.23	268504.9	258009.9
10306	7512	San Juan de la Costa	222910.5	2017	1674503801	270543.7	24552.991	253182.10	287905.3	270543.7
10307	10030	San Pablo	195712.4	2017	1962995435	338456.3	102530.029	265956.66	410956.0	338456.3
11101	57818	Coyhaique	327100.3	2017	18912283227	344164.9	31644.111	235625.01	382396.3	350366.5
11201	23959	Aysén	307831.4	2017	7375332218	330385.2	52412.484	285693.90	463767.6	315327.0
11202	6517	Cisnes	251971.0	2017	1642095149	350240.3	16273.355	331482.51	360583.6	358654.9
11301	3490	Cochrane	350724.8	2017	1224029692	446971.1	120164.252	362002.10	531940.0	446971.1
11401	4865	Chile Chico	333445.3	2017	1622211456	353728.5	21739.468	338356.42	369100.7	353728.5
12101	131592	Punta Arenas	391758.4	2017	51552266922	351715.6	29319.890	316522.57	454638.0	345815.2
12301	6801	Porvenir	446255.2	2017	3034981682	400454.3	46627.544	346616.30	427850.5	426896.2
12401	21477	Natales	336808.6	2017	7233637635	338499.5	16960.265	322327.29	378820.0	334401.1
13101	404495	Santiago	450851.7	2017	182367246208	449866.5	48044.908	186563.20	539749.7	457340.8
13102	80832	Cerrillos	276766.5	2017	22371586546	326585.5	33485.911	270387.80	445898.9	322334.0
13103	132622	Cerro Navia	270634.1	2017	35892031153	322225.9	15491.767	278407.85	350253.6	324465.0
13104	126955	Conchalí	310325.3	2017	39397353402	325736.1	21227.453	266948.14	377656.1	319640.7
13105	162505	El Bosque	281653.9	2017	45770170398	322706.8	30311.115	295869.56	473272.2	317115.1
13106	147041	Estación Central	340680.2	2017	50093952387	360896.4	48210.583	294110.94	486934.1	346694.5
13107	98671	Huechuraba	315250.1	2017	31106038806	326688.6	27135.610	293575.63	410986.3	326591.7
13108	100281	Independencia	376152.6	2017	37720956327	380517.1	47287.575	318221.11	461557.2	363354.1
13109	90119	La Cisterna	367262.4	2017	33097323323	341778.4	17404.565	306765.53	372871.2	341576.6
13110	366916	La Florida	349483.5	2017	128231071590	338536.6	22019.622	285825.98	419345.1	336157.8
13111	116571	La Granja	306768.3	2017	35760286668	325307.0	15902.594	293356.18	354980.9	322067.6
13112	177335	La Pintana	232647.0	2017	41256447003	310929.5	30350.628	274264.69	497993.9	307816.6
13113	92787	La Reina	434408.5	2017	40307459856	346490.1	17858.449	294344.48	370921.6	349123.2
13114	294838	Las Condes	456515.7	2017	134598169599	369219.5	34154.121	318516.24	465128.3	359172.4
13115	105833	Lo Barnechea	349308.7	2017	36968385127	338198.3	31429.650	274239.57	384041.1	333753.9
13116	98804	Lo Espejo	264154.1	2017	26099479542	317033.7	13429.289	277571.24	347440.3	318145.2
13117	96249	Lo Prado	305431.2	2017	29397444939	329136.6	18551.833	288457.90	371364.6	327782.4
13118	116534	Macul	345701.4	2017	40285970358	354762.8	28906.123	304728.16	428018.3	353438.6
13119	521627	Maipú	358559.2	2017	187034167391	338313.3	19739.727	278211.79	399801.5	337559.1
13120	208237	Ñuñoa	426460.1	2017	88804766896	382296.2	33494.320	321678.15	482868.2	381940.3
13121	101174	Pedro Aguirre Cerda	316863.2	2017	32058321741	324390.9	20962.540	302241.91	417524.2	320184.8
13122	241599	Peñalolén	321570.6	2017	77691132095	343077.6	10961.811	316772.22	370749.9	341958.5
13123	142079	Providencia	516122.3	2017	73330144381	404163.7	31229.699	312085.27	469965.8	405631.0
13124	230293	Pudahuel	320572.7	2017	73825647438	338317.7	23697.537	296685.70	443868.7	332280.2
13125	210410	Quilicura	383485.8	2017	80689241762	343614.8	15489.944	290963.05	387614.3	345575.2
13126	110026	Quinta Normal	311731.1	2017	34298531093	351328.6	27540.147	310842.91	414589.6	344132.6
13127	157851	Recoleta	344997.0	2017	54458123369	355525.8	42516.727	305234.41	466310.1	343686.2
13128	147151	Renca	294000.5	2017	43262464632	319351.3	21827.160	215843.76	346588.9	320042.2
13129	94492	San Joaquín	336046.8	2017	31753732439	333476.6	20043.038	290326.48	393890.5	329416.2
13130	107954	San Miguel	351632.1	2017	37960091353	373225.6	27993.488	333056.20	418872.6	368890.8
13131	82900	San Ramón	281439.6	2017	23331343432	317741.0	14379.749	291856.72	340498.4	317786.2
13132	85384	Vitacura	496933.1	2017	42430139879	348352.3	39278.438	200553.47	428169.9	343005.1
13201	568106	Puente Alto	328342.7	2017	186533464474	331967.8	23287.400	235096.48	391765.6	333630.7
13202	26521	Pirque	332454.5	2017	8817024774	319421.3	19097.655	287383.75	335058.2	322241.2
13203	18189	San José de Maipo	381218.4	2017	6933981276	341712.2	35893.783	301038.93	404396.9	338323.1
13301	146207	Colina	300609.0	2017	43951136523	328631.3	44844.048	229455.56	557074.4	325596.5
13302	102034	Lampa	372624.0	2017	38020316317	323021.2	21785.177	261243.52	356023.3	319512.8
13303	19312	Tiltil	327523.1	2017	6325126322	299915.5	35848.414	235625.01	338575.8	309964.8
13401	301313	San Bernardo	286991.8	2017	86474375157	315766.5	17429.906	258702.50	355232.6	315609.1
13402	96614	Buin	314979.3	2017	30431412042	324513.3	21418.931	281347.83	359271.9	326639.1
13403	25392	Calera de Tango	307306.5	2017	7803125477	314434.4	53650.922	227120.85	401900.0	322844.9
13404	72759	Paine	330137.7	2017	24020488982	304899.8	20133.016	250241.34	332860.5	310346.7
13501	123627	Melipilla	291641.9	2017	36054817558	309278.7	25404.201	265030.59	369025.7	302818.4
13502	6444	Alhué	349434.5	2017	2251756129	370601.5	26873.212	351599.25	389603.7	370601.5
13503	32579	Curacaví	269095.1	2017	8766848005	312443.8	24525.290	271852.77	353645.9	314881.7
13504	13590	María Pinto	253962.5	2017	3451350898	320531.4	85161.401	268060.08	511355.4	286529.9
13601	74237	Talagante	394670.6	2017	29299162746	323131.9	20250.441	287674.11	357121.5	323626.4
13602	35923	El Monte	297691.7	2017	10693979408	302912.3	10426.084	288437.51	317574.4	303760.4
13603	36219	Isla de Maipo	229284.1	2017	8304441408	310069.7	13965.619	290623.19	328947.2	314928.8
13604	63250	Padre Hurtado	277563.2	2017	17555873230	323746.2	12757.391	302505.03	348225.4	324311.9
13605	90201	Peñaflor	351564.7	2017	31711490484	312376.1	20152.546	274104.59	360451.0	313182.8
14101	166080	Valdivia	308754.5	2017	51277944139	311790.9	20956.836	249305.39	350028.1	308155.6
14102	5302	Corral	222523.9	2017	1179821617	399361.9	174417.656	276030.01	522693.8	399361.9
14103	16752	Lanco	267286.0	2017	4477574931	248623.2	57856.754	147944.17	289039.0	268007.0
14104	19634	Los Lagos	211843.1	2017	4159328181	271844.6	20518.954	246995.55	292332.9	274025.0
14105	7095	Máfil	315022.1	2017	2235081533	210894.0	93186.083	145001.48	276786.5	210894.0
14106	21278	Mariquina	251064.3	2017	5342147079	259513.9	33008.610	215505.01	288890.4	266830.0
14107	20188	Paillaco	223306.5	2017	4508111622	266112.6	31361.995	227044.90	302017.0	267311.8
14108	34539	Panguipulli	287752.5	2017	9938682028	287862.1	26250.737	254583.42	325654.3	283641.8
14201	38036	La Unión	247291.7	2017	9405987850	279750.5	26979.607	247373.38	331829.3	273467.2
14202	14665	Futrono	247331.7	2017	3627119212	259492.4	25393.802	228476.13	286755.0	264665.0
14203	9896	Lago Ranco	247154.2	2017	2445838259	314247.5	63095.994	269631.89	358863.1	314247.5
14204	31372	Río Bueno	267934.4	2017	8405637271	261457.9	39567.878	175272.12	286877.0	279645.8
15101	221364	Arica	310013.3	2017	68625788545	300491.6	32272.782	263247.39	428284.2	290446.1
15201	2765	Putre	283661.5	2017	784324030	522557.6	126349.015	433215.36	611899.8	522557.6
16101	184739	Chillán	275879.2	2017	50965643906	283378.1	27637.463	222319.63	351037.5	282598.4
16102	21493	Bulnes	224694.9	2017	4829367278	258513.5	18998.990	226784.25	282851.4	260395.6
16103	30907	Chillán Viejo	259577.5	2017	8022762560	281428.8	23519.323	243606.44	309425.4	282296.6
16104	12044	El Carmen	215566.5	2017	2596282563	182974.6	95206.507	96667.65	285099.4	167156.8
16105	8448	Pemuco	262037.4	2017	2213691761	181438.1	71309.826	82857.99	243166.8	199863.9
16106	10827	Pinto	175602.5	2017	1901248804	271411.0	27974.535	236331.16	301417.3	272930.1
16107	17485	Quillón	256072.4	2017	4477425886	247251.0	20687.393	217122.66	277124.0	248488.0
16108	16079	San Ignacio	203331.5	2017	3269367252	252640.5	25609.994	231322.35	300954.4	246470.5
16109	17787	Yungay	258601.1	2017	4599738091	254386.4	27699.622	215753.18	291767.6	255205.0
16201	11594	Quirihue	252923.7	2017	2932397811	258915.4	38454.372	192976.35	313262.7	263059.9
16202	5012	Cobquecura	259487.2	2017	1300549630	331339.9	42618.719	301203.92	361475.9	331339.9
16203	15995	Coelemu	296882.1	2017	4748629723	265510.5	41168.591	206202.67	321605.7	268033.2
16204	5213	Ninhue	301493.8	2017	1571687052	234292.3	25429.411	216310.95	252273.6	234292.3
16205	4862	Portezuelo	196869.9	2017	957181342	274960.2	21273.655	259917.47	290003.0	274960.2
16206	5755	Ránquil	286762.9	2017	1650320432	167473.5	119664.426	82857.99	252089.0	167473.5
16207	5401	Treguaco	218702.4	2017	1181211462	253494.6	NA	253494.65	253494.6	253494.6
16301	53024	San Carlos	252551.6	2017	13391296803	273382.9	17517.541	243470.90	297822.8	274743.2
16302	26881	Coihueco	213580.4	2017	5741254097	248093.5	27317.634	200692.65	270026.2	255380.5
16303	11152	Ñiquén	236681.5	2017	2639471976	221285.6	NA	221285.58	221285.6	221285.6
16304	4308	San Fabián	259592.5	2017	1118324609	255083.0	8170.730	249305.39	260860.5	255083.0
16305	11603	San Nicolás	266207.1	2017	3088800683	268453.6	8724.779	262284.24	274622.9	268453.6

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

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

Y regresión lineal de ingresos medios por zona sobre frecuencias de respuesta a la pregunta: P17 ¿Trabajó por un pago o especie?, cuya correlación (0.8717) resultó ser la más alta con los ingresos expandidos.

VE-CC-AJ