Expansión de la CASEN sobre el CENSO (Región 14)

De ingresos sobre una categoría de respuesta RURAL

DataIntelligence

Abstract

Expandiremos los ingresos promedios comunales obtenidos de la CASEN sobre la pregunta (P15): “Nivel del curso más alto aprobado” en la categoría de respuesta “Profesional” (12), que más alto correlaciona con los ingresos expandidos (obtenidos de la multiplicación del ingreso promedio y los habitantes), ambos a nivel comunal.

Haremos el análisis sobre la región 14.

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 de la Casen por zona”

Lo anterior para ir combinando a nivel nacional los mejores modelos para construir una tabla de valores predichos.

1 Variable CENSO

Necesitamos calcular las frecuencias a nivel censal de las respuestas correspondientes a la categoría: “Profesional (4 o más años)” del campo P15 a nivel rural del Censo de personas. Recordemos que ésta fué la más alta correlación en relación a los ingresos expandidos (ver punto 3.4 aquí).

1.1 Lectura y filtrado de la tabla censal de personas

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

tabla_con_clave <- readRDS("../censo_personas_con_clave_17")
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	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
15	152	15202	1	2	6	13225	39	1	2	4	2	22	1	98	998	2	98	998	1	98	998	9998	98	2	1	8	2	1	2	98	6	98	0	98	98	9998	998	998	998	9	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	43	1	1	1	2	26	1	98	998	2	98	998	1	98	998	9998	98	2	8	5	1	1	2	98	6	98	2	2	10	2013	998	998	998	8	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	43	1	2	2	1	24	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	43	1	3	13	2	71	1	98	998	2	98	998	1	98	998	9998	98	2	1	5	2	1	2	98	6	98	3	3	12	1974	998	998	998	1	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	43	1	4	5	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	43	1	5	5	2	3	1	98	998	1	98	998	1	98	998	9998	98	1	0	1	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	8	13910	5	1	1	1	1	44	1	98	998	2	98	998	3	98	998	2005	2	2	4	7	1	1	2	98	6	98	98	98	98	9998	998	998	604	12	2	15	152	15202	98	98	98	15202012008
15	152	15202	1	2	8	13910	5	1	2	2	2	42	1	98	998	2	98	998	1	98	998	9998	98	2	3	5	2	1	2	98	1	P	3	3	12	2006	998	998	998	3	2	15	152	15202	98	98	98	15202012008
15	152	15202	1	2	8	13910	5	1	3	5	2	10	1	98	998	2	98	998	1	98	998	9998	98	1	4	5	2	1	2	98	98	98	98	98	98	9998	998	998	998	4	2	15	152	15202	98	98	98	15202012008
15	152	15202	1	2	8	13910	7	1	1	1	2	70	1	98	998	2	98	998	1	98	998	9998	98	2	2	5	2	1	2	98	6	98	7	7	6	1994	998	998	998	2	2	15	152	15202	98	98	98	15202012008
15	152	15202	1	2	8	13910	7	1	2	5	1	44	1	98	998	2	98	998	1	98	998	9998	98	2	5	5	2	1	2	98	7	98	98	98	98	9998	998	998	998	5	2	15	152	15202	98	98	98	15202012008
15	152	15202	1	2	8	13910	8	1	1	1	1	58	1	98	998	2	98	998	3	98	998	2004	2	2	4	5	2	1	2	98	6	98	98	98	98	9998	998	998	604	4	2	15	152	15202	98	98	98	15202012008
15	152	15202	1	2	8	13910	8	1	2	2	2	59	1	98	998	2	98	998	3	98	998	2004	2	2	2	5	2	1	2	98	6	98	3	3	7	1999	998	998	604	2	2	15	152	15202	98	98	98	15202012008
15	152	15202	1	2	8	13910	19	1	1	1	1	58	99	99	999	99	99	999	99	99	999	9999	99	99	99	99	99	99	99	99	99	99	98	98	98	9998	999	999	999	99	99	15	152	15202	99	99	99	15202012008
15	152	15202	1	2	8	13910	21	1	1	1	1	53	1	98	998	2	98	998	1	98	998	9998	98	2	8	5	1	1	2	98	1	H	98	98	98	9998	998	998	998	8	2	15	152	15202	98	98	98	15202012008
15	152	15202	1	2	8	13910	21	1	2	2	2	46	1	98	998	2	98	998	1	98	998	9998	98	2	3	5	2	1	2	98	6	98	3	3	2	1990	998	998	998	3	2	15	152	15202	98	98	98	15202012008
15	152	15202	1	2	8	13910	22	1	1	1	2	73	1	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	6	98	6	5	3	1979	998	998	998	0	2	15	152	15202	98	98	98	15202012008
15	152	15202	1	2	8	13910	30	1	1	1	1	57	1	98	998	2	98	998	2	997	998	9998	98	2	3	5	2	1	2	98	6	98	98	98	98	9998	998	998	998	3	2	15	152	15202	98	98	997	15202012008
15	152	15202	1	2	12	8394	3	1	1	2	2	64	1	98	998	2	98	998	3	98	998	1974	4	3	98	98	98	1	2	98	1	A	12	10	99	9999	998	998	604	0	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	3	1	2	1	1	74	2	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	99	99	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	3	1	3	5	2	38	1	98	998	2	98	998	1	98	998	9998	98	2	8	5	1	1	2	98	2	A	0	98	98	9998	998	998	998	8	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	3	1	4	14	1	38	99	99	999	99	99	999	99	99	999	9999	99	99	99	99	99	99	99	99	8	98	98	98	98	9998	999	999	999	99	99	15	152	15202	99	99	99	15202012012
15	152	15202	1	2	12	8394	9	1	1	1	2	79	1	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	8	98	2	2	99	9999	998	998	998	0	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	19	1	1	1	1	46	99	99	999	99	99	999	99	99	999	9999	99	99	99	99	99	99	99	99	99	99	98	98	98	9998	999	999	999	99	99	15	152	15202	99	99	99	15202012012
15	152	15202	1	2	12	8394	20	1	1	1	2	58	1	98	998	2	98	998	1	98	998	9998	98	2	8	5	1	1	2	98	1	A	3	3	7	1982	998	998	998	8	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	21	1	1	1	2	45	1	98	998	6	98	998	2	997	998	9998	98	2	4	5	2	1	2	98	1	A	6	6	2	2007	998	68	998	4	2	15	152	15202	98	98	997	15202012012
15	152	15202	1	2	12	8394	21	1	2	5	2	10	1	98	998	6	98	998	2	3201	998	9998	98	1	4	5	2	1	2	98	98	98	98	98	98	9998	998	68	998	4	2	15	152	15202	98	98	3201	15202012012
15	152	15202	1	2	12	8394	24	1	1	1	1	67	1	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	8	98	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	24	1	2	2	2	53	1	98	998	2	98	998	3	98	998	9999	99	3	98	98	98	1	2	98	8	98	0	98	98	9998	998	998	604	0	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	27	1	1	1	1	48	1	98	998	2	98	998	1	98	998	9998	98	2	4	7	1	1	2	98	8	98	98	98	98	9998	998	998	998	12	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	31	1	1	1	1	49	1	98	998	4	98	998	3	98	998	2001	2	2	8	5	1	1	2	98	1	A	98	98	98	9998	998	604	604	8	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	42	1	1	1	1	46	1	98	998	2	98	998	3	98	998	1992	3	2	8	5	1	1	2	98	2	A	98	98	98	9998	998	998	604	8	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	42	1	2	2	2	24	1	98	998	6	98	998	5	98	998	2013	1	2	7	5	2	1	2	98	6	98	2	2	6	2016	998	68	68	7	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	42	1	3	6	2	2	1	98	998	1	98	998	1	98	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	98	15202012012
15	152	15202	1	2	12	8394	42	1	4	5	1	0	1	98	998	1	98	998	2	15101	998	9998	98	99	99	99	99	1	2	98	98	98	98	98	98	9998	998	998	998	99	2	15	152	15202	98	98	15101	15202012012
15	152	15202	1	2	12	8394	42	1	5	5	2	13	1	98	998	2	98	998	3	98	998	9999	99	1	7	5	2	1	2	98	98	98	98	98	98	9998	998	998	604	7	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	42	1	6	5	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	15202012012
15	152	15202	1	2	15	4094	2	1	1	1	1	41	1	98	998	2	98	998	1	98	998	9998	98	2	4	12	1	1	2	98	1	O	98	98	98	9998	998	998	998	16	2	15	152	15202	98	98	98	15202012015
15	152	15202	1	2	15	4094	8	1	1	17	1	70	2	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	15202012015
15	152	15202	1	2	15	4094	8	1	2	17	1	47	2	98	998	3	15101	998	2	8101	998	9998	98	2	4	8	1	1	2	98	1	Z	98	98	98	9998	998	998	998	12	2	15	152	15202	98	15101	8101	15202012015
15	152	15202	1	2	15	4094	8	1	3	17	1	19	2	98	998	3	15101	998	2	15101	998	9998	98	1	99	7	99	1	2	98	1	I	98	98	98	9998	998	998	998	99	2	15	152	15202	98	15101	15101	15202012015
15	152	15202	1	2	15	4094	8	1	4	17	1	43	2	98	998	3	4302	998	2	8101	998	9998	98	99	4	8	1	1	2	98	1	N	98	98	98	9998	998	998	998	12	2	15	152	15202	98	4302	8101	15202012015
15	152	15202	1	2	15	4094	8	1	5	17	2	35	2	98	998	6	98	998	5	98	998	2016	1	2	8	5	1	1	2	98	1	I	2	2	3	2007	998	68	68	8	2	15	152	15202	98	98	98	15202012015
15	152	15202	1	2	15	4094	8	1	6	17	1	36	3	13123	998	3	13123	998	2	12101	998	9998	98	2	5	12	1	2	98	98	1	J	98	98	98	9998	998	998	998	17	98	15	152	15202	13123	13123	12101	15202012015
15	152	15202	1	2	15	4094	8	1	7	17	2	25	2	98	998	3	15101	998	2	15101	998	9998	98	2	5	12	1	1	2	98	1	Q	1	1	12	2011	998	998	998	17	2	15	152	15202	98	15101	15101	15202012015
15	152	15202	1	2	15	4094	9	1	1	1	1	72	1	98	998	2	98	998	1	98	998	9998	98	2	1	5	2	1	2	98	1	G	98	98	98	9998	998	998	998	1	2	15	152	15202	98	98	98	15202012015
15	152	15202	1	2	15	4094	12	1	1	1	1	21	1	98	998	3	15101	998	2	15101	998	9998	98	2	4	8	1	1	2	98	1	N	98	98	98	9998	998	998	998	12	2	15	152	15202	98	15101	15101	15202012015
15	152	15202	1	2	15	4094	15	1	1	1	1	61	1	98	998	2	98	998	1	98	998	9998	98	2	3	7	2	1	2	98	4	98	98	98	98	9998	998	998	998	11	2	15	152	15202	98	98	98	15202012015
15	152	15202	1	2	15	4094	15	1	2	5	2	31	1	98	998	3	15101	998	1	98	998	9998	98	2	4	12	1	1	2	98	1	P	1	1	10	2007	998	998	998	16	2	15	152	15202	98	15101	98	15202012015
15	152	15202	1	2	15	4094	16	1	1	1	1	34	1	98	998	3	15101	998	1	98	998	9998	98	2	5	12	1	1	2	98	1	O	98	98	98	9998	998	998	998	17	2	15	152	15202	98	15101	98	15202012015

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la region 14 y con la zona = 2:

tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$REGION == 14) 
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA== 2) 

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE]

names(tabla_con_clave_f)[9] <- "Nivel del curso más alto aprobado"

# Ahora filtramos por Nivel del curso más alto aprobado = 11.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Nivel del curso más alto aprobado` == 12)

# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Nivel del curso más alto aprobado`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Veamos los primeros 100 registros:

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

	zona	unlist.c.	unlist.d.	Freq	anio
1	14101042005	12	14101	8	2017
2	14101042058	12	14101	8	2017
3	14101062011	12	14101	7	2017
4	14101062013	12	14101	13	2017
5	14101062021	12	14101	14	2017
6	14101062033	12	14101	33	2017
7	14101062047	12	14101	1	2017
8	14101072002	12	14101	84	2017
9	14101072033	12	14101	9	2017
10	14101072045	12	14101	60	2017
11	14101082002	12	14101	129	2017
12	14101082032	12	14101	4	2017
13	14101112005	12	14101	109	2017
14	14101112010	12	14101	48	2017
15	14101112017	12	14101	162	2017
16	14101112037	12	14101	43	2017
17	14101112046	12	14101	17	2017
18	14101112058	12	14101	16	2017
19	14101112901	12	14101	4	2017
20	14101122007	12	14101	86	2017
21	14101122009	12	14101	28	2017
22	14101122042	12	14101	48	2017
23	14101122058	12	14101	125	2017
24	14101132003	12	14101	27	2017
25	14101132050	12	14101	7	2017
26	14101132901	12	14101	6	2017
27	14101142014	12	14101	37	2017
28	14101142039	12	14101	1	2017
29	14101142043	12	14101	3	2017
30	14101142047	12	14101	1	2017
31	14101142059	12	14101	15	2017
32	14101152059	12	14101	2	2017
33	14101162018	12	14101	25	2017
34	14101162019	12	14101	27	2017
35	14101162034	12	14101	4	2017
36	14101162053	12	14101	32	2017
37	14101162061	12	14101	28	2017
38	14101162901	12	14101	3	2017
39	14101172001	12	14101	214	2017
40	14101172016	12	14101	54	2017
41	14101172027	12	14101	5	2017
42	14101172036	12	14101	8	2017
43	14101172055	12	14101	6	2017
44	14101172901	12	14101	9	2017
45	14101182004	12	14101	7	2017
46	14101182006	12	14101	6	2017
47	14101182015	12	14101	49	2017
48	14101182031	12	14101	1	2017
49	14101182040	12	14101	16	2017
50	14101182049	12	14101	2	2017
51	14101192031	12	14101	1	2017
52	14101192901	12	14101	2	2017
592	14102012011	12	14102	2	2017
593	14102012017	12	14102	7	2017
594	14102012018	12	14102	4	2017
595	14102022008	12	14102	22	2017
596	14102022010	12	14102	3	2017
597	14102032001	12	14102	2	2017
598	14102032005	12	14102	2	2017
599	14102032019	12	14102	1	2017
600	14102032901	12	14102	1	2017
601	14102042002	12	14102	19	2017
602	14102042006	12	14102	5	2017
603	14102042007	12	14102	1	2017
604	14102042011	12	14102	1	2017
605	14102042014	12	14102	1	2017
1145	14103012004	12	14103	25	2017
1146	14103012010	12	14103	3	2017
1147	14103012020	12	14103	2	2017
1148	14103012022	12	14103	9	2017
1149	14103012027	12	14103	3	2017
1150	14103012029	12	14103	1	2017
1151	14103012040	12	14103	4	2017
1152	14103022023	12	14103	13	2017
1153	14103022025	12	14103	6	2017
1154	14103022030	12	14103	9	2017
1155	14103022039	12	14103	9	2017
1156	14103022042	12	14103	5	2017
1157	14103022043	12	14103	3	2017
1158	14103022044	12	14103	7	2017
1159	14103022901	12	14103	1	2017
1160	14103032001	12	14103	1	2017
1161	14103032002	12	14103	4	2017
1162	14103032008	12	14103	2	2017
1163	14103032009	12	14103	6	2017
1164	14103032015	12	14103	2	2017
1165	14103032019	12	14103	2	2017
1166	14103032034	12	14103	1	2017
1167	14103032036	12	14103	1	2017
1168	14103032041	12	14103	2	2017
1169	14103032042	12	14103	20	2017
1170	14103032901	12	14103	12	2017
1171	14103042012	12	14103	5	2017
1172	14103042038	12	14103	2	2017
1173	14103052003	12	14103	10	2017
1174	14103052035	12	14103	1	2017
1175	14103052040	12	14103	12	2017
1176	14103062013	12	14103	2	2017
1177	14103062016	12	14103	5	2017
1178	14103062021	12	14103	27	2017

Agregamos un cero a los códigos comunales de cuatro dígitos:

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

	zona	Freq	anio	código
1	14101042005	8	2017	14101
2	14101042058	8	2017	14101
3	14101062011	7	2017	14101
4	14101062013	13	2017	14101
5	14101062021	14	2017	14101
6	14101062033	33	2017	14101
7	14101062047	1	2017	14101
8	14101072002	84	2017	14101
9	14101072033	9	2017	14101
10	14101072045	60	2017	14101
11	14101082002	129	2017	14101
12	14101082032	4	2017	14101
13	14101112005	109	2017	14101
14	14101112010	48	2017	14101
15	14101112017	162	2017	14101
16	14101112037	43	2017	14101
17	14101112046	17	2017	14101
18	14101112058	16	2017	14101
19	14101112901	4	2017	14101
20	14101122007	86	2017	14101
21	14101122009	28	2017	14101
22	14101122042	48	2017	14101
23	14101122058	125	2017	14101
24	14101132003	27	2017	14101
25	14101132050	7	2017	14101
26	14101132901	6	2017	14101
27	14101142014	37	2017	14101
28	14101142039	1	2017	14101
29	14101142043	3	2017	14101
30	14101142047	1	2017	14101
31	14101142059	15	2017	14101
32	14101152059	2	2017	14101
33	14101162018	25	2017	14101
34	14101162019	27	2017	14101
35	14101162034	4	2017	14101
36	14101162053	32	2017	14101
37	14101162061	28	2017	14101
38	14101162901	3	2017	14101
39	14101172001	214	2017	14101
40	14101172016	54	2017	14101
41	14101172027	5	2017	14101
42	14101172036	8	2017	14101
43	14101172055	6	2017	14101
44	14101172901	9	2017	14101
45	14101182004	7	2017	14101
46	14101182006	6	2017	14101
47	14101182015	49	2017	14101
48	14101182031	1	2017	14101
49	14101182040	16	2017	14101
50	14101182049	2	2017	14101
51	14101192031	1	2017	14101
52	14101192901	2	2017	14101
592	14102012011	2	2017	14102
593	14102012017	7	2017	14102
594	14102012018	4	2017	14102
595	14102022008	22	2017	14102
596	14102022010	3	2017	14102
597	14102032001	2	2017	14102
598	14102032005	2	2017	14102
599	14102032019	1	2017	14102
600	14102032901	1	2017	14102
601	14102042002	19	2017	14102
602	14102042006	5	2017	14102
603	14102042007	1	2017	14102
604	14102042011	1	2017	14102
605	14102042014	1	2017	14102
1145	14103012004	25	2017	14103
1146	14103012010	3	2017	14103
1147	14103012020	2	2017	14103
1148	14103012022	9	2017	14103
1149	14103012027	3	2017	14103
1150	14103012029	1	2017	14103
1151	14103012040	4	2017	14103
1152	14103022023	13	2017	14103
1153	14103022025	6	2017	14103
1154	14103022030	9	2017	14103
1155	14103022039	9	2017	14103
1156	14103022042	5	2017	14103
1157	14103022043	3	2017	14103
1158	14103022044	7	2017	14103
1159	14103022901	1	2017	14103
1160	14103032001	1	2017	14103
1161	14103032002	4	2017	14103
1162	14103032008	2	2017	14103
1163	14103032009	6	2017	14103
1164	14103032015	2	2017	14103
1165	14103032019	2	2017	14103
1166	14103032034	1	2017	14103
1167	14103032036	1	2017	14103
1168	14103032041	2	2017	14103
1169	14103032042	20	2017	14103
1170	14103032901	12	2017	14103
1171	14103042012	5	2017	14103
1172	14103042038	2	2017	14103
1173	14103052003	10	2017	14103
1174	14103052035	1	2017	14103
1175	14103052040	12	2017	14103
1176	14103062013	2	2017	14103
1177	14103062016	5	2017	14103
1178	14103062021	27	2017	14103

2 Variable CASEN

2.1 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("../corre_ing_exp-censo_casen/Ingresos_expandidos_rural_17.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	personas	Ingresos_expandidos
01101	Iquique	272529.7	2017	191468	52180713221
01401	Pozo Almonte	243272.4	2017	15711	3822052676
01402	Camiña	226831.0	2017	1250	283538750
01404	Huara	236599.7	2017	2730	645917134
01405	Pica	269198.0	2017	9296	2502464414
02103	Sierra Gorda	322997.9	2017	10186	3290056742
02104	Taltal	288653.8	2017	13317	3844002134
02201	Calama	238080.9	2017	165731	39457387800
02203	San Pedro de Atacama	271472.6	2017	10996	2985112297
02301	Tocopilla	166115.9	2017	25186	4183793832
03101	Copiapó	251396.0	2017	153937	38699138722
03103	Tierra Amarilla	287819.4	2017	14019	4034940816
03202	Diego de Almagro	326439.0	2017	13925	4545663075
03301	Vallenar	217644.6	2017	51917	11299454698
03302	Alto del Carmen	196109.9	2017	5299	1039186477
03303	Freirina	202463.8	2017	7041	1425547554
03304	Huasco	205839.6	2017	10149	2089066548
04101	La Serena	200287.4	2017	221054	44274327972
04102	Coquimbo	206027.8	2017	227730	46918711304
04103	Andacollo	217096.4	2017	11044	2397612293
04104	La Higuera	231674.2	2017	4241	982530309
04105	Paiguano	174868.5	2017	4497	786383423
04106	Vicuña	169077.1	2017	27771	4695441470
04201	Illapel	165639.6	2017	30848	5109649759
04202	Canela	171370.3	2017	9093	1558270441
04203	Los Vilos	173238.5	2017	21382	3704185607
04204	Salamanca	193602.0	2017	29347	5681637894
04301	Ovalle	230819.8	2017	111272	25683781418
04302	Combarbalá	172709.2	2017	13322	2300832587
04303	Monte Patria	189761.6	2017	30751	5835357638
04304	Punitaqui	165862.0	2017	10956	1817183694
04305	Río Hurtado	182027.2	2017	4278	778712384
05101	Valparaíso	251998.5	2017	296655	74756602991
05102	Casablanca	252317.7	2017	26867	6779018483
05105	Puchuncaví	231606.0	2017	18546	4295363979
05107	Quintero	285125.8	2017	31923	9102071069
05301	Los Andes	280548.0	2017	66708	18714795984
05302	Calle Larga	234044.6	2017	14832	3471349123
05303	Rinconada	246136.9	2017	10207	2512319225
05304	San Esteban	211907.3	2017	18855	3995512770
05401	La Ligua	172675.9	2017	35390	6111000517
05402	Cabildo	212985.0	2017	19388	4129354103
05404	Petorca	270139.8	2017	9826	2654393853
05405	Zapallar	235661.4	2017	7339	1729518700
05501	Quillota	212067.6	2017	90517	19195726144
05502	Calera	226906.2	2017	50554	11471016698
05503	Hijuelas	215402.0	2017	17988	3874650405
05504	La Cruz	243333.4	2017	22098	5377180726
05506	Nogales	219800.7	2017	22120	4861992055
05601	San Antonio	230261.5	2017	91350	21034388728

3 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)

comunas_con_ing_exp <-comunas_con_ing_exp[!(is.na(comunas_con_ing_exp$Ingresos_expandidos)),]

r3_100 <- comunas_con_ing_exp
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	zona	Freq	anio	comuna.x	promedio_i	año	personas	Ingresos_expandidos
14101	14101042005	8	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101042058	8	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101062011	7	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101062013	13	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101062021	14	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101062033	33	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101062047	1	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101072002	84	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101072033	9	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101072045	60	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101082002	129	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101082032	4	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101112005	109	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101112010	48	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101112017	162	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101112037	43	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101112046	17	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101112058	16	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101112901	4	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101122007	86	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101122009	28	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101122042	48	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101122058	125	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101132003	27	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101132050	7	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101132901	6	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101142014	37	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101142039	1	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101142043	3	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101142047	1	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101142059	15	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101152059	2	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101162018	25	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101162019	27	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101162034	4	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101162053	32	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101162061	28	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101162901	3	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101172001	214	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101172016	54	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101172027	5	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101172036	8	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101172055	6	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101172901	9	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101182004	7	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101182006	6	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101182015	49	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101182031	1	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101182040	16	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101182049	2	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101192031	1	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101192901	2	2017	Valdivia	211732.5	2017	166080	35164529745
14102	14102012011	2	2017	Corral	157428.1	2017	5302	834683963
14102	14102012017	7	2017	Corral	157428.1	2017	5302	834683963
14102	14102012018	4	2017	Corral	157428.1	2017	5302	834683963
14102	14102022008	22	2017	Corral	157428.1	2017	5302	834683963
14102	14102022010	3	2017	Corral	157428.1	2017	5302	834683963
14102	14102032001	2	2017	Corral	157428.1	2017	5302	834683963
14102	14102032005	2	2017	Corral	157428.1	2017	5302	834683963
14102	14102032019	1	2017	Corral	157428.1	2017	5302	834683963
14102	14102032901	1	2017	Corral	157428.1	2017	5302	834683963
14102	14102042002	19	2017	Corral	157428.1	2017	5302	834683963
14102	14102042006	5	2017	Corral	157428.1	2017	5302	834683963
14102	14102042007	1	2017	Corral	157428.1	2017	5302	834683963
14102	14102042011	1	2017	Corral	157428.1	2017	5302	834683963
14102	14102042014	1	2017	Corral	157428.1	2017	5302	834683963
14103	14103012004	25	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103012010	3	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103012020	2	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103012022	9	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103012027	3	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103012029	1	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103012040	4	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103022023	13	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103022025	6	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103022030	9	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103022039	9	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103022042	5	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103022043	3	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103022044	7	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103022901	1	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103032001	1	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103032002	4	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103032008	2	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103032009	6	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103032015	2	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103032019	2	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103032034	1	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103032036	1	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103032041	2	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103032042	20	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103032901	12	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103042012	5	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103042038	2	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103052003	10	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103052035	1	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103052040	12	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103062013	2	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103062016	5	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103062021	27	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103062037	6	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103072010	12	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103072020	13	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103072033	2	2017	Lanco	184730.2	2017	16752	3094599901
14104	14104012003	41	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104012010	6	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104012037	3	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104012042	3	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104012059	7	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104012901	8	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104022002	22	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104022005	6	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104022006	2	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104022018	18	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104022040	3	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104022050	18	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104022076	55	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104032012	5	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104032016	4	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104032047	4	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104032071	1	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104032901	2	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104042028	6	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104042039	1	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104042043	2	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104042052	10	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104042060	1	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104042068	11	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104042073	1	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104052008	2	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104052011	5	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104052013	22	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104052020	9	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104052022	7	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104052034	10	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104052036	5	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104052037	10	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104052041	19	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104052062	9	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104052064	7	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104062008	5	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104062022	10	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104062023	4	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104062037	4	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104072030	1	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104072031	1	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104072032	7	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104072051	2	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104072054	10	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104072063	3	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104072070	3	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104072074	1	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104072901	3	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104122001	5	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104122014	6	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104122026	27	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104122033	3	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104122035	5	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104122056	2	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104122058	7	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104122064	4	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104122065	3	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104122069	18	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104122073	4	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104122075	1	2017	Los Lagos	190489.7	2017	19634	3740075550
14104	14104122078	7	2017	Los Lagos	190489.7	2017	19634	3740075550
14105	14105012010	15	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105012013	12	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105012023	5	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105012901	11	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105022002	7	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105022019	11	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105022024	1	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105022031	5	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105022033	2	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105022035	3	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105022037	12	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105022901	2	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105032007	6	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105032010	16	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105032025	1	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105032029	5	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105032034	19	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105042009	1	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105042011	5	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105042012	1	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105042028	1	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105042901	1	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105052003	2	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105052013	3	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105052014	2	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105052016	9	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105052017	1	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105052022	6	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105052026	17	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105052036	3	2017	Máfil	180289.2	2017	7095	1279152079
14105	14105052901	3	2017	Máfil	180289.2	2017	7095	1279152079
14106	14106012005	2	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106012010	6	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106012017	1	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106012018	6	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106012019	3	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106012024	3	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106012049	15	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106012052	11	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106012053	31	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106012054	5	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106012057	3	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106012060	1	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106012069	1	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106012901	1	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106022901	1	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106032011	37	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106032020	7	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106032045	6	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106032049	3	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106032063	3	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106032069	1	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106042026	2	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106042038	2	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106042046	1	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106042059	5	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106052003	18	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106052023	27	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106052031	66	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106052039	73	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106052048	53	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106052059	8	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106052062	32	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106052064	19	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106062002	2	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106062009	2	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106062033	2	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106062035	2	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106062037	7	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106062061	1	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106062065	1	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106072037	49	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106072043	5	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106072044	8	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106072047	2	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106072070	3	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106072072	11	2017	Mariquina	187045.1	2017	21278	3979945072
14106	14106072901	2	2017	Mariquina	187045.1	2017	21278	3979945072
14107	14107012017	10	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107012021	2	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107012029	1	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107022019	4	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107022024	1	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107022028	5	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107022031	1	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107022036	1	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107022038	10	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107022039	31	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107022040	2	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107022047	2	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107022901	2	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107032001	2	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107032004	2	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107032011	15	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107032013	15	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107032014	5	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107032032	21	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107032033	2	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107032034	3	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107032035	2	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107032037	1	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107032039	18	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107032041	13	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107032043	4	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107032044	9	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107042002	19	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107042005	3	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107042007	4	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107042012	6	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107042016	3	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107042018	15	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107042025	11	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107042027	6	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107042041	1	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107042045	10	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107042046	11	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107042048	7	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107052003	3	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107052006	3	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107052008	13	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107052009	2	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107052010	3	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107052017	23	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107052020	4	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107052023	4	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107052030	8	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107052042	5	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107052044	2	2017	Paillaco	163833.6	2017	20188	3307473487
14107	14107052901	2	2017	Paillaco	163833.6	2017	20188	3307473487
14108	14108012006	24	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108012039	2	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108012040	18	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108012043	16	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108012046	58	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108012049	46	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108012054	60	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108012061	3	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108012069	2	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108012901	4	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108022007	3	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108022018	11	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108022025	19	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108022038	6	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108022047	24	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108022050	19	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108022060	24	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108032001	4	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108032002	17	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108032014	8	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108032017	1	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108032024	49	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108032026	10	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108032031	2	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108032057	11	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108032072	1	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108032901	1	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108042016	2	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108042041	22	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108052011	36	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108052012	4	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108052015	1	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108052034	2	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108052055	45	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108052071	8	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108062003	7	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108062032	42	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108062036	7	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108062064	3	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108062065	9	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108062069	2	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108062070	1	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108062074	2	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108072005	1	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108072008	60	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108072045	1	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108072901	1	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108082004	7	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108082009	18	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108082044	2	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108082051	5	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108082057	2	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108082073	13	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108082901	1	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108092019	6	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108092069	1	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108102013	3	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108102038	3	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108102067	1	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108102901	1	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108112030	11	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108112033	10	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108112042	61	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108112056	11	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108112058	3	2017	Panguipulli	180390.3	2017	34539	6230498948
14108	14108122052	8	2017	Panguipulli	180390.3	2017	34539	6230498948
14201	14201012005	4	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201012024	23	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201012042	1	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201012045	30	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201022009	1	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201022010	16	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201022014	8	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201022020	3	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201022054	11	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201032003	2	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201032036	7	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201032041	1	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201032047	1	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201032048	2	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201032055	1	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201032901	4	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201042012	1	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201042018	1	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201042028	3	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201052004	3	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201052005	12	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201052010	2	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201052022	3	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201052032	2	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201052050	2	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201052052	2	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201062016	6	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201062017	5	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201062030	4	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201062038	2	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201062042	7	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201072002	13	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201072007	33	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201072031	7	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201072042	8	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201072044	14	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201072053	13	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201082011	7	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201082029	23	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201082031	4	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201082040	38	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201082049	3	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201092006	15	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201092008	3	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201092013	1	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201092021	43	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201092023	3	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201092026	7	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201092034	9	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201092037	92	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201092042	3	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201092044	7	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201092046	36	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201092051	16	2017	La Unión	201975.2	2017	38036	7682327556
14201	14201092901	1	2017	La Unión	201975.2	2017	38036	7682327556
14202	14202012006	20	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202012010	3	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202012012	21	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202012019	11	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202012020	3	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202012024	3	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202012032	4	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202012039	6	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202012040	10	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202012042	1	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202012901	1	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202022009	4	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202022013	11	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202022017	1	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202022023	1	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202022038	2	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202022045	21	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202032001	4	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202032002	6	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202032005	6	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202032008	1	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202032027	92	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202032031	5	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202032046	5	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202042007	2	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202042009	2	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202042013	5	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202042028	8	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202042032	1	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202042037	2	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202042047	4	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202042901	4	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202052035	11	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202052042	13	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202062003	7	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202062004	1	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202062011	15	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202062016	1	2017	Futrono	193120.3	2017	14665	2832109866
14202	14202062034	9	2017	Futrono	193120.3	2017	14665	2832109866
14203	14203012008	2	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203012012	5	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203012028	8	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203012039	15	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203012040	14	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203022002	8	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203022003	1	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203022005	2	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203022006	9	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203022010	1	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203022014	8	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203022015	34	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203022024	2	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203022026	8	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203022029	4	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203022036	11	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203022037	26	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203032001	4	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203032008	5	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203032011	6	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203032013	29	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203032019	2	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203032030	11	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203032032	1	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203032033	10	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203032034	8	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203042023	4	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203042031	13	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203042035	1	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203042901	2	2017	Lago Ranco	186595.7	2017	9896	1846550611
14203	14203052038	4	2017	Lago Ranco	186595.7	2017	9896	1846550611
14204	14204012016	4	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204012039	20	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204022004	38	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204022006	12	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204022012	1	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204022033	6	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204022038	14	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204022048	5	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204032013	4	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204032015	38	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204032023	5	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204032035	5	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204032048	13	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204032050	1	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204052028	7	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204062001	9	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204062009	7	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204062010	9	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204062032	45	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204062037	4	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204062042	11	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204062049	6	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204062901	3	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204072008	6	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204072011	12	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204072014	16	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204072017	13	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204072024	4	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204072026	6	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204072029	13	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204072036	5	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204072040	7	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204072046	15	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204072047	6	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204082006	27	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204082020	11	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204082048	5	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204082050	12	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204092050	21	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204102002	1	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204102003	5	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204102007	5	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204102019	3	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204102022	20	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204102025	6	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204102045	5	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204112005	18	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204112018	6	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204112021	12	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204112027	4	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204112030	13	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204112031	2	2017	Río Bueno	184360.5	2017	31372	5783758517
14204	14204112901	2	2017	Río Bueno	184360.5	2017	31372	5783758517

4 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional" 

Veamos los 100 primeros registros:

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

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

5 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

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

código	zona	Freq	anio	comuna.x	promedio_i	año	personas	Ingresos_expandidos
14101	14101042005	8	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101042058	8	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101062011	7	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101062013	13	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101062021	14	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101062033	33	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101062047	1	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101072002	84	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101072033	9	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101072045	60	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101082002	129	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101082032	4	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101112005	109	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101112010	48	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101112017	162	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101112037	43	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101112046	17	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101112058	16	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101112901	4	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101122007	86	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101122009	28	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101122042	48	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101122058	125	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101132003	27	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101132050	7	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101132901	6	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101142014	37	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101142039	1	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101142043	3	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101142047	1	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101142059	15	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101152059	2	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101162018	25	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101162019	27	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101162034	4	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101162053	32	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101162061	28	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101162901	3	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101172001	214	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101172016	54	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101172027	5	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101172036	8	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101172055	6	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101172901	9	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101182004	7	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101182006	6	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101182015	49	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101182031	1	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101182040	16	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101182049	2	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101192031	1	2017	Valdivia	211732.5	2017	166080	35164529745
14101	14101192901	2	2017	Valdivia	211732.5	2017	166080	35164529745
14102	14102012011	2	2017	Corral	157428.1	2017	5302	834683963
14102	14102012017	7	2017	Corral	157428.1	2017	5302	834683963
14102	14102012018	4	2017	Corral	157428.1	2017	5302	834683963
14102	14102022008	22	2017	Corral	157428.1	2017	5302	834683963
14102	14102022010	3	2017	Corral	157428.1	2017	5302	834683963
14102	14102032001	2	2017	Corral	157428.1	2017	5302	834683963
14102	14102032005	2	2017	Corral	157428.1	2017	5302	834683963
14102	14102032019	1	2017	Corral	157428.1	2017	5302	834683963
14102	14102032901	1	2017	Corral	157428.1	2017	5302	834683963
14102	14102042002	19	2017	Corral	157428.1	2017	5302	834683963
14102	14102042006	5	2017	Corral	157428.1	2017	5302	834683963
14102	14102042007	1	2017	Corral	157428.1	2017	5302	834683963
14102	14102042011	1	2017	Corral	157428.1	2017	5302	834683963
14102	14102042014	1	2017	Corral	157428.1	2017	5302	834683963
14103	14103012004	25	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103012010	3	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103012020	2	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103012022	9	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103012027	3	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103012029	1	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103012040	4	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103022023	13	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103022025	6	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103022030	9	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103022039	9	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103022042	5	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103022043	3	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103022044	7	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103022901	1	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103032001	1	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103032002	4	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103032008	2	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103032009	6	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103032015	2	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103032019	2	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103032034	1	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103032036	1	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103032041	2	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103032042	20	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103032901	12	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103042012	5	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103042038	2	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103052003	10	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103052035	1	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103052040	12	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103062013	2	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103062016	5	2017	Lanco	184730.2	2017	16752	3094599901
14103	14103062021	27	2017	Lanco	184730.2	2017	16752	3094599901

6 Ingreso promedio expandido por zona (multi_pob)

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

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

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

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	personas	Ingresos_expandidos	Freq.y	p_poblacional	código.y
14101042005	14101	8	2017	Valdivia	211732.5	2017	166080	35164529745	60	0.0003613	14101
14101042058	14101	8	2017	Valdivia	211732.5	2017	166080	35164529745	40	0.0002408	14101
14101062011	14101	7	2017	Valdivia	211732.5	2017	166080	35164529745	101	0.0006081	14101
14101062013	14101	13	2017	Valdivia	211732.5	2017	166080	35164529745	88	0.0005299	14101
14101062021	14101	14	2017	Valdivia	211732.5	2017	166080	35164529745	296	0.0017823	14101
14101062033	14101	33	2017	Valdivia	211732.5	2017	166080	35164529745	1052	0.0063343	14101
14101062047	14101	1	2017	Valdivia	211732.5	2017	166080	35164529745	16	0.0000963	14101
14101072002	14101	84	2017	Valdivia	211732.5	2017	166080	35164529745	294	0.0017702	14101
14101072033	14101	9	2017	Valdivia	211732.5	2017	166080	35164529745	49	0.0002950	14101
14101072045	14101	60	2017	Valdivia	211732.5	2017	166080	35164529745	287	0.0017281	14101
14101082002	14101	129	2017	Valdivia	211732.5	2017	166080	35164529745	429	0.0025831	14101
14101082032	14101	4	2017	Valdivia	211732.5	2017	166080	35164529745	22	0.0001325	14101
14101112005	14101	109	2017	Valdivia	211732.5	2017	166080	35164529745	380	0.0022881	14101
14101112010	14101	48	2017	Valdivia	211732.5	2017	166080	35164529745	176	0.0010597	14101
14101112017	14101	162	2017	Valdivia	211732.5	2017	166080	35164529745	1083	0.0065210	14101
14101112037	14101	43	2017	Valdivia	211732.5	2017	166080	35164529745	535	0.0032213	14101
14101112046	14101	17	2017	Valdivia	211732.5	2017	166080	35164529745	103	0.0006202	14101
14101112058	14101	16	2017	Valdivia	211732.5	2017	166080	35164529745	59	0.0003553	14101
14101112901	14101	4	2017	Valdivia	211732.5	2017	166080	35164529745	17	0.0001024	14101
14101122007	14101	86	2017	Valdivia	211732.5	2017	166080	35164529745	297	0.0017883	14101
14101122009	14101	28	2017	Valdivia	211732.5	2017	166080	35164529745	266	0.0016016	14101
14101122042	14101	48	2017	Valdivia	211732.5	2017	166080	35164529745	233	0.0014029	14101
14101122058	14101	125	2017	Valdivia	211732.5	2017	166080	35164529745	509	0.0030648	14101
14101132003	14101	27	2017	Valdivia	211732.5	2017	166080	35164529745	290	0.0017461	14101
14101132050	14101	7	2017	Valdivia	211732.5	2017	166080	35164529745	66	0.0003974	14101
14101132901	14101	6	2017	Valdivia	211732.5	2017	166080	35164529745	250	0.0015053	14101
14101142014	14101	37	2017	Valdivia	211732.5	2017	166080	35164529745	325	0.0019569	14101
14101142039	14101	1	2017	Valdivia	211732.5	2017	166080	35164529745	58	0.0003492	14101
14101142043	14101	3	2017	Valdivia	211732.5	2017	166080	35164529745	44	0.0002649	14101
14101142047	14101	1	2017	Valdivia	211732.5	2017	166080	35164529745	77	0.0004636	14101
14101142059	14101	15	2017	Valdivia	211732.5	2017	166080	35164529745	96	0.0005780	14101
14101152059	14101	2	2017	Valdivia	211732.5	2017	166080	35164529745	82	0.0004937	14101
14101162018	14101	25	2017	Valdivia	211732.5	2017	166080	35164529745	97	0.0005841	14101
14101162019	14101	27	2017	Valdivia	211732.5	2017	166080	35164529745	64	0.0003854	14101
14101162034	14101	4	2017	Valdivia	211732.5	2017	166080	35164529745	42	0.0002529	14101
14101162053	14101	32	2017	Valdivia	211732.5	2017	166080	35164529745	217	0.0013066	14101
14101162061	14101	28	2017	Valdivia	211732.5	2017	166080	35164529745	94	0.0005660	14101
14101162901	14101	3	2017	Valdivia	211732.5	2017	166080	35164529745	23	0.0001385	14101
14101172001	14101	214	2017	Valdivia	211732.5	2017	166080	35164529745	1348	0.0081166	14101
14101172016	14101	54	2017	Valdivia	211732.5	2017	166080	35164529745	148	0.0008911	14101
14101172027	14101	5	2017	Valdivia	211732.5	2017	166080	35164529745	103	0.0006202	14101
14101172036	14101	8	2017	Valdivia	211732.5	2017	166080	35164529745	110	0.0006623	14101
14101172055	14101	6	2017	Valdivia	211732.5	2017	166080	35164529745	88	0.0005299	14101
14101172901	14101	9	2017	Valdivia	211732.5	2017	166080	35164529745	39	0.0002348	14101
14101182004	14101	7	2017	Valdivia	211732.5	2017	166080	35164529745	188	0.0011320	14101
14101182006	14101	6	2017	Valdivia	211732.5	2017	166080	35164529745	67	0.0004034	14101
14101182015	14101	49	2017	Valdivia	211732.5	2017	166080	35164529745	455	0.0027396	14101
14101182031	14101	1	2017	Valdivia	211732.5	2017	166080	35164529745	38	0.0002288	14101
14101182040	14101	16	2017	Valdivia	211732.5	2017	166080	35164529745	323	0.0019448	14101
14101182049	14101	2	2017	Valdivia	211732.5	2017	166080	35164529745	32	0.0001927	14101
14101192031	14101	1	2017	Valdivia	211732.5	2017	166080	35164529745	22	0.0001325	14101
14101192901	14101	2	2017	Valdivia	211732.5	2017	166080	35164529745	28	0.0001686	14101
14102012011	14102	2	2017	Corral	157428.1	2017	5302	834683963	22	0.0041494	14102
14102012017	14102	7	2017	Corral	157428.1	2017	5302	834683963	61	0.0115051	14102
14102012018	14102	4	2017	Corral	157428.1	2017	5302	834683963	80	0.0150886	14102
14102022008	14102	22	2017	Corral	157428.1	2017	5302	834683963	507	0.0956243	14102
14102022010	14102	3	2017	Corral	157428.1	2017	5302	834683963	41	0.0077329	14102
14102032001	14102	2	2017	Corral	157428.1	2017	5302	834683963	68	0.0128253	14102
14102032005	14102	2	2017	Corral	157428.1	2017	5302	834683963	64	0.0120709	14102
14102032019	14102	1	2017	Corral	157428.1	2017	5302	834683963	62	0.0116937	14102
14102032901	14102	1	2017	Corral	157428.1	2017	5302	834683963	31	0.0058469	14102
14102042002	14102	19	2017	Corral	157428.1	2017	5302	834683963	325	0.0612976	14102
14102042006	14102	5	2017	Corral	157428.1	2017	5302	834683963	257	0.0484723	14102
14102042007	14102	1	2017	Corral	157428.1	2017	5302	834683963	115	0.0216899	14102
14102042011	14102	1	2017	Corral	157428.1	2017	5302	834683963	34	0.0064127	14102
14102042014	14102	1	2017	Corral	157428.1	2017	5302	834683963	48	0.0090532	14102
14103012004	14103	25	2017	Lanco	184730.2	2017	16752	3094599901	197	0.0117598	14103
14103012010	14103	3	2017	Lanco	184730.2	2017	16752	3094599901	39	0.0023281	14103
14103012020	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	39	0.0023281	14103
14103012022	14103	9	2017	Lanco	184730.2	2017	16752	3094599901	93	0.0055516	14103
14103012027	14103	3	2017	Lanco	184730.2	2017	16752	3094599901	105	0.0062679	14103
14103012029	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	16	0.0009551	14103
14103012040	14103	4	2017	Lanco	184730.2	2017	16752	3094599901	106	0.0063276	14103
14103022023	14103	13	2017	Lanco	184730.2	2017	16752	3094599901	224	0.0133715	14103
14103022025	14103	6	2017	Lanco	184730.2	2017	16752	3094599901	69	0.0041189	14103
14103022030	14103	9	2017	Lanco	184730.2	2017	16752	3094599901	199	0.0118792	14103
14103022039	14103	9	2017	Lanco	184730.2	2017	16752	3094599901	379	0.0226242	14103
14103022042	14103	5	2017	Lanco	184730.2	2017	16752	3094599901	39	0.0023281	14103
14103022043	14103	3	2017	Lanco	184730.2	2017	16752	3094599901	21	0.0012536	14103
14103022044	14103	7	2017	Lanco	184730.2	2017	16752	3094599901	96	0.0057307	14103
14103022901	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	21	0.0012536	14103
14103032001	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	100	0.0059694	14103
14103032002	14103	4	2017	Lanco	184730.2	2017	16752	3094599901	273	0.0162966	14103
14103032008	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	119	0.0071036	14103
14103032009	14103	6	2017	Lanco	184730.2	2017	16752	3094599901	93	0.0055516	14103
14103032015	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	96	0.0057307	14103
14103032019	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	55	0.0032832	14103
14103032034	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	59	0.0035220	14103
14103032036	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	21	0.0012536	14103
14103032041	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	159	0.0094914	14103
14103032042	14103	20	2017	Lanco	184730.2	2017	16752	3094599901	304	0.0181471	14103
14103032901	14103	12	2017	Lanco	184730.2	2017	16752	3094599901	179	0.0106853	14103
14103042012	14103	5	2017	Lanco	184730.2	2017	16752	3094599901	75	0.0044771	14103
14103042038	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	45	0.0026862	14103
14103052003	14103	10	2017	Lanco	184730.2	2017	16752	3094599901	250	0.0149236	14103
14103052035	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	173	0.0103271	14103
14103052040	14103	12	2017	Lanco	184730.2	2017	16752	3094599901	268	0.0159981	14103
14103062013	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	65	0.0038801	14103
14103062016	14103	5	2017	Lanco	184730.2	2017	16752	3094599901	155	0.0092526	14103
14103062021	14103	27	2017	Lanco	184730.2	2017	16752	3094599901	226	0.0134909	14103

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	personas	Ingresos_expandidos	Freq.y	p_poblacional	código.y	multi_pob
14101042005	14101	8	2017	Valdivia	211732.5	2017	166080	35164529745	60	0.0003613	14101	12703949
14101042058	14101	8	2017	Valdivia	211732.5	2017	166080	35164529745	40	0.0002408	14101	8469299
14101062011	14101	7	2017	Valdivia	211732.5	2017	166080	35164529745	101	0.0006081	14101	21384980
14101062013	14101	13	2017	Valdivia	211732.5	2017	166080	35164529745	88	0.0005299	14101	18632458
14101062021	14101	14	2017	Valdivia	211732.5	2017	166080	35164529745	296	0.0017823	14101	62672813
14101062033	14101	33	2017	Valdivia	211732.5	2017	166080	35164529745	1052	0.0063343	14101	222742566
14101062047	14101	1	2017	Valdivia	211732.5	2017	166080	35164529745	16	0.0000963	14101	3387720
14101072002	14101	84	2017	Valdivia	211732.5	2017	166080	35164529745	294	0.0017702	14101	62249348
14101072033	14101	9	2017	Valdivia	211732.5	2017	166080	35164529745	49	0.0002950	14101	10374891
14101072045	14101	60	2017	Valdivia	211732.5	2017	166080	35164529745	287	0.0017281	14101	60767221
14101082002	14101	129	2017	Valdivia	211732.5	2017	166080	35164529745	429	0.0025831	14101	90833233
14101082032	14101	4	2017	Valdivia	211732.5	2017	166080	35164529745	22	0.0001325	14101	4658114
14101112005	14101	109	2017	Valdivia	211732.5	2017	166080	35164529745	380	0.0022881	14101	80458341
14101112010	14101	48	2017	Valdivia	211732.5	2017	166080	35164529745	176	0.0010597	14101	37264916
14101112017	14101	162	2017	Valdivia	211732.5	2017	166080	35164529745	1083	0.0065210	14101	229306272
14101112037	14101	43	2017	Valdivia	211732.5	2017	166080	35164529745	535	0.0032213	14101	113276875
14101112046	14101	17	2017	Valdivia	211732.5	2017	166080	35164529745	103	0.0006202	14101	21808445
14101112058	14101	16	2017	Valdivia	211732.5	2017	166080	35164529745	59	0.0003553	14101	12492216
14101112901	14101	4	2017	Valdivia	211732.5	2017	166080	35164529745	17	0.0001024	14101	3599452
14101122007	14101	86	2017	Valdivia	211732.5	2017	166080	35164529745	297	0.0017883	14101	62884546
14101122009	14101	28	2017	Valdivia	211732.5	2017	166080	35164529745	266	0.0016016	14101	56320839
14101122042	14101	48	2017	Valdivia	211732.5	2017	166080	35164529745	233	0.0014029	14101	49333667
14101122058	14101	125	2017	Valdivia	211732.5	2017	166080	35164529745	509	0.0030648	14101	107771831
14101132003	14101	27	2017	Valdivia	211732.5	2017	166080	35164529745	290	0.0017461	14101	61402418
14101132050	14101	7	2017	Valdivia	211732.5	2017	166080	35164529745	66	0.0003974	14101	13974343
14101132901	14101	6	2017	Valdivia	211732.5	2017	166080	35164529745	250	0.0015053	14101	52933119
14101142014	14101	37	2017	Valdivia	211732.5	2017	166080	35164529745	325	0.0019569	14101	68813055
14101142039	14101	1	2017	Valdivia	211732.5	2017	166080	35164529745	58	0.0003492	14101	12280484
14101142043	14101	3	2017	Valdivia	211732.5	2017	166080	35164529745	44	0.0002649	14101	9316229
14101142047	14101	1	2017	Valdivia	211732.5	2017	166080	35164529745	77	0.0004636	14101	16303401
14101142059	14101	15	2017	Valdivia	211732.5	2017	166080	35164529745	96	0.0005780	14101	20326318
14101152059	14101	2	2017	Valdivia	211732.5	2017	166080	35164529745	82	0.0004937	14101	17362063
14101162018	14101	25	2017	Valdivia	211732.5	2017	166080	35164529745	97	0.0005841	14101	20538050
14101162019	14101	27	2017	Valdivia	211732.5	2017	166080	35164529745	64	0.0003854	14101	13550879
14101162034	14101	4	2017	Valdivia	211732.5	2017	166080	35164529745	42	0.0002529	14101	8892764
14101162053	14101	32	2017	Valdivia	211732.5	2017	166080	35164529745	217	0.0013066	14101	45945947
14101162061	14101	28	2017	Valdivia	211732.5	2017	166080	35164529745	94	0.0005660	14101	19902853
14101162901	14101	3	2017	Valdivia	211732.5	2017	166080	35164529745	23	0.0001385	14101	4869847
14101172001	14101	214	2017	Valdivia	211732.5	2017	166080	35164529745	1348	0.0081166	14101	285415379
14101172016	14101	54	2017	Valdivia	211732.5	2017	166080	35164529745	148	0.0008911	14101	31336407
14101172027	14101	5	2017	Valdivia	211732.5	2017	166080	35164529745	103	0.0006202	14101	21808445
14101172036	14101	8	2017	Valdivia	211732.5	2017	166080	35164529745	110	0.0006623	14101	23290572
14101172055	14101	6	2017	Valdivia	211732.5	2017	166080	35164529745	88	0.0005299	14101	18632458
14101172901	14101	9	2017	Valdivia	211732.5	2017	166080	35164529745	39	0.0002348	14101	8257567
14101182004	14101	7	2017	Valdivia	211732.5	2017	166080	35164529745	188	0.0011320	14101	39805706
14101182006	14101	6	2017	Valdivia	211732.5	2017	166080	35164529745	67	0.0004034	14101	14186076
14101182015	14101	49	2017	Valdivia	211732.5	2017	166080	35164529745	455	0.0027396	14101	96338277
14101182031	14101	1	2017	Valdivia	211732.5	2017	166080	35164529745	38	0.0002288	14101	8045834
14101182040	14101	16	2017	Valdivia	211732.5	2017	166080	35164529745	323	0.0019448	14101	68389590
14101182049	14101	2	2017	Valdivia	211732.5	2017	166080	35164529745	32	0.0001927	14101	6775439
14101192031	14101	1	2017	Valdivia	211732.5	2017	166080	35164529745	22	0.0001325	14101	4658114
14101192901	14101	2	2017	Valdivia	211732.5	2017	166080	35164529745	28	0.0001686	14101	5928509
14102012011	14102	2	2017	Corral	157428.1	2017	5302	834683963	22	0.0041494	14102	3463419
14102012017	14102	7	2017	Corral	157428.1	2017	5302	834683963	61	0.0115051	14102	9603116
14102012018	14102	4	2017	Corral	157428.1	2017	5302	834683963	80	0.0150886	14102	12594251
14102022008	14102	22	2017	Corral	157428.1	2017	5302	834683963	507	0.0956243	14102	79816064
14102022010	14102	3	2017	Corral	157428.1	2017	5302	834683963	41	0.0077329	14102	6454553
14102032001	14102	2	2017	Corral	157428.1	2017	5302	834683963	68	0.0128253	14102	10705113
14102032005	14102	2	2017	Corral	157428.1	2017	5302	834683963	64	0.0120709	14102	10075401
14102032019	14102	1	2017	Corral	157428.1	2017	5302	834683963	62	0.0116937	14102	9760544
14102032901	14102	1	2017	Corral	157428.1	2017	5302	834683963	31	0.0058469	14102	4880272
14102042002	14102	19	2017	Corral	157428.1	2017	5302	834683963	325	0.0612976	14102	51164143
14102042006	14102	5	2017	Corral	157428.1	2017	5302	834683963	257	0.0484723	14102	40459030
14102042007	14102	1	2017	Corral	157428.1	2017	5302	834683963	115	0.0216899	14102	18104235
14102042011	14102	1	2017	Corral	157428.1	2017	5302	834683963	34	0.0064127	14102	5352557
14102042014	14102	1	2017	Corral	157428.1	2017	5302	834683963	48	0.0090532	14102	7556550
14103012004	14103	25	2017	Lanco	184730.2	2017	16752	3094599901	197	0.0117598	14103	36391845
14103012010	14103	3	2017	Lanco	184730.2	2017	16752	3094599901	39	0.0023281	14103	7204477
14103012020	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	39	0.0023281	14103	7204477
14103012022	14103	9	2017	Lanco	184730.2	2017	16752	3094599901	93	0.0055516	14103	17179906
14103012027	14103	3	2017	Lanco	184730.2	2017	16752	3094599901	105	0.0062679	14103	19396668
14103012029	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	16	0.0009551	14103	2955683
14103012040	14103	4	2017	Lanco	184730.2	2017	16752	3094599901	106	0.0063276	14103	19581399
14103022023	14103	13	2017	Lanco	184730.2	2017	16752	3094599901	224	0.0133715	14103	41379559
14103022025	14103	6	2017	Lanco	184730.2	2017	16752	3094599901	69	0.0041189	14103	12746382
14103022030	14103	9	2017	Lanco	184730.2	2017	16752	3094599901	199	0.0118792	14103	36761305
14103022039	14103	9	2017	Lanco	184730.2	2017	16752	3094599901	379	0.0226242	14103	70012737
14103022042	14103	5	2017	Lanco	184730.2	2017	16752	3094599901	39	0.0023281	14103	7204477
14103022043	14103	3	2017	Lanco	184730.2	2017	16752	3094599901	21	0.0012536	14103	3879334
14103022044	14103	7	2017	Lanco	184730.2	2017	16752	3094599901	96	0.0057307	14103	17734097
14103022901	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	21	0.0012536	14103	3879334
14103032001	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	100	0.0059694	14103	18473018
14103032002	14103	4	2017	Lanco	184730.2	2017	16752	3094599901	273	0.0162966	14103	50431338
14103032008	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	119	0.0071036	14103	21982891
14103032009	14103	6	2017	Lanco	184730.2	2017	16752	3094599901	93	0.0055516	14103	17179906
14103032015	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	96	0.0057307	14103	17734097
14103032019	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	55	0.0032832	14103	10160160
14103032034	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	59	0.0035220	14103	10899080
14103032036	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	21	0.0012536	14103	3879334
14103032041	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	159	0.0094914	14103	29372098
14103032042	14103	20	2017	Lanco	184730.2	2017	16752	3094599901	304	0.0181471	14103	56157973
14103032901	14103	12	2017	Lanco	184730.2	2017	16752	3094599901	179	0.0106853	14103	33066701
14103042012	14103	5	2017	Lanco	184730.2	2017	16752	3094599901	75	0.0044771	14103	13854763
14103042038	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	45	0.0026862	14103	8312858
14103052003	14103	10	2017	Lanco	184730.2	2017	16752	3094599901	250	0.0149236	14103	46182544
14103052035	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	173	0.0103271	14103	31958320
14103052040	14103	12	2017	Lanco	184730.2	2017	16752	3094599901	268	0.0159981	14103	49507687
14103062013	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	65	0.0038801	14103	12007461
14103062016	14103	5	2017	Lanco	184730.2	2017	16752	3094599901	155	0.0092526	14103	28633177
14103062021	14103	27	2017	Lanco	184730.2	2017	16752	3094599901	226	0.0134909	14103	41749020

7 Análisis de regresión

Aplicaremos un análisis de regresión donde:

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

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

7.1 Diagrama de dispersión loess

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2) 

7.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

7.3 Modelo lineal

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

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -116522337  -16321431   -8484117    7790775  161025317 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 20166036    1536562   13.12   <2e-16 ***
## Freq.x       1451082      68706   21.12   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 30730000 on 537 degrees of freedom
## Multiple R-squared:  0.4537, Adjusted R-squared:  0.4527 
## F-statistic: 446.1 on 1 and 537 DF,  p-value: < 2.2e-16

7.4 Gráfica de la recta de regresión lineal

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

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

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

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x^2), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -116522337  -16321431   -8484117    7790775  161025317 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 20166036    1536562   13.12   <2e-16 ***
## Freq.x       1451082      68706   21.12   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 30730000 on 537 degrees of freedom
## Multiple R-squared:  0.4537, Adjusted R-squared:  0.4527 
## F-statistic: 446.1 on 1 and 537 DF,  p-value: < 2.2e-16

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x^3), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -116522337  -16321431   -8484117    7790775  161025317 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 20166036    1536562   13.12   <2e-16 ***
## Freq.x       1451082      68706   21.12   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 30730000 on 537 degrees of freedom
## Multiple R-squared:  0.4537, Adjusted R-squared:  0.4527 
## F-statistic: 446.1 on 1 and 537 DF,  p-value: < 2.2e-16

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -70411679 -19469669  -4576325  12366614 160379534 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -4384730    2339723  -1.874   0.0615 .  
## log(Freq.x) 24118739    1135369  21.243   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 30650000 on 537 degrees of freedom
## Multiple R-squared:  0.4566, Adjusted R-squared:  0.4556 
## F-statistic: 451.3 on 1 and 537 DF,  p-value: < 2.2e-16

8.4 Modelo exponencial

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

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

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

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = multi_pob ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -93038246 -13801080  -4409347   8676024 146850668 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -8415276    2195691  -3.833 0.000142 ***
## sqrt(Freq.x) 16107834     651400  24.728  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 28430000 on 537 degrees of freedom
## Multiple R-squared:  0.5324, Adjusted R-squared:  0.5316 
## F-statistic: 611.5 on 1 and 537 DF,  p-value: < 2.2e-16

8.6 Modelo raíz-raíz

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

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = sqrt(multi_pob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -5988  -1298   -297   1113   6647 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   2233.02     148.86   15.00   <2e-16 ***
## sqrt(Freq.x)  1112.56      44.16   25.19   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1927 on 537 degrees of freedom
## Multiple R-squared:  0.5417, Adjusted R-squared:  0.5408 
## F-statistic: 634.6 on 1 and 537 DF,  p-value: < 2.2e-16

8.7 Modelo log-raíz

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

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.27433 -0.47825  0.04621  0.57614  1.91617 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  15.83567    0.06021  263.03   <2e-16 ***
## sqrt(Freq.x)  0.37944    0.01786   21.24   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7795 on 537 degrees of freedom
## Multiple R-squared:  0.4566, Adjusted R-squared:  0.4556 
## F-statistic: 451.3 on 1 and 537 DF,  p-value: < 2.2e-16

8.8 Modelo raíz-log

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

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = sqrt(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -4741  -1326   -286   1083   6571 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2288.04     147.31   15.53   <2e-16 ***
## log(Freq.x)  1797.15      71.48   25.14   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1929 on 537 degrees of freedom
## Multiple R-squared:  0.5407, Adjusted R-squared:  0.5398 
## F-statistic: 632.1 on 1 and 537 DF,  p-value: < 2.2e-16

8.9 Modelo log-log

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

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.27932 -0.49409 -0.00345  0.50315  1.85153 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 15.76996    0.05519  285.72   <2e-16 ***
## log(Freq.x)  0.66256    0.02678   24.74   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7229 on 537 degrees of freedom
## Multiple R-squared:  0.5326, Adjusted R-squared:  0.5317 
## F-statistic:   612 on 1 and 537 DF,  p-value: < 2.2e-16

9 Modelo elegido: raíz-raíz (sqrt-sqrt)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.5408).

9.1 Diagrama de dispersión sobre sqrt-sqrt

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

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

9.2 Modelo sqrt-sqrt

Observemos nuevamente el resultado sobre sqrt-sqrt.

linearMod <- lm(sqrt( multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod) 

## 
## Call:
## lm(formula = sqrt(multi_pob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -5988  -1298   -297   1113   6647 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   2233.02     148.86   15.00   <2e-16 ***
## sqrt(Freq.x)  1112.56      44.16   25.19   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1927 on 537 degrees of freedom
## Multiple R-squared:  0.5417, Adjusted R-squared:  0.5408 
## F-statistic: 634.6 on 1 and 537 DF,  p-value: < 2.2e-16

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

9.3 Análisis de residuos

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

9.4 Ecuación del modelo

\[ \hat Y = {2233.02}^2 + 2 \cdot 2233.02 \cdot 1112.56 \sqrt{X}+ 1112.56^2 \cdot X \]

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

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- 
(2233.02)^2 + 2 * 2233.02 * 1112.56 * sqrt(h_y_m_comuna_corr_01$Freq.x)+  1112.56^2 * (h_y_m_comuna_corr_01$Freq.x)

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

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	personas	Ingresos_expandidos	Freq.y	p_poblacional	código.y	multi_pob	est_ing
14101042005	14101	8	2017	Valdivia	211732.5	2017	166080	35164529745	60	0.0003613	14101	12703949	28942408
14101042058	14101	8	2017	Valdivia	211732.5	2017	166080	35164529745	40	0.0002408	14101	8469299	28942408
14101062011	14101	7	2017	Valdivia	211732.5	2017	166080	35164529745	101	0.0006081	14101	21384980	26796950
14101062013	14101	13	2017	Valdivia	211732.5	2017	166080	35164529745	88	0.0005299	14101	18632458	38992683
14101062021	14101	14	2017	Valdivia	211732.5	2017	166080	35164529745	296	0.0017823	14101	62672813	40906748
14101062033	14101	33	2017	Valdivia	211732.5	2017	166080	35164529745	1052	0.0063343	14101	222742566	74376664
14101062047	14101	1	2017	Valdivia	211732.5	2017	166080	35164529745	16	0.0000963	14101	3387720	11192906
14101072002	14101	84	2017	Valdivia	211732.5	2017	166080	35164529745	294	0.0017702	14101	62249348	154499949
14101072033	14101	9	2017	Valdivia	211732.5	2017	166080	35164529745	49	0.0002950	14101	10374891	31032698
14101072045	14101	60	2017	Valdivia	211732.5	2017	166080	35164529745	287	0.0017281	14101	60767221	117741438
14101082002	14101	129	2017	Valdivia	211732.5	2017	166080	35164529745	429	0.0025831	14101	90833233	221095266
14101082032	14101	4	2017	Valdivia	211732.5	2017	166080	35164529745	22	0.0001325	14101	4658114	19875012
14101112005	14101	109	2017	Valdivia	211732.5	2017	166080	35164529745	380	0.0022881	14101	80458341	191780604
14101112010	14101	48	2017	Valdivia	211732.5	2017	166080	35164529745	176	0.0010597	14101	37264916	98824709
14101112017	14101	162	2017	Valdivia	211732.5	2017	166080	35164529745	1083	0.0065210	14101	229306272	268750022
14101112037	14101	43	2017	Valdivia	211732.5	2017	166080	35164529745	535	0.0032213	14101	113276875	90793528
14101112046	14101	17	2017	Valdivia	211732.5	2017	166080	35164529745	103	0.0006202	14101	21808445	46515434
14101112058	14101	16	2017	Valdivia	211732.5	2017	166080	35164529745	59	0.0003553	14101	12492216	44665964
14101112901	14101	4	2017	Valdivia	211732.5	2017	166080	35164529745	17	0.0001024	14101	3599452	19875012
14101122007	14101	86	2017	Valdivia	211732.5	2017	166080	35164529745	297	0.0017883	14101	62884546	157514473
14101122009	14101	28	2017	Valdivia	211732.5	2017	166080	35164529745	266	0.0016016	14101	56320839	65936579
14101122042	14101	48	2017	Valdivia	211732.5	2017	166080	35164529745	233	0.0014029	14101	49333667	98824709
14101122058	14101	125	2017	Valdivia	211732.5	2017	166080	35164529745	509	0.0030648	14101	107771831	215262271
14101132003	14101	27	2017	Valdivia	211732.5	2017	166080	35164529745	290	0.0017461	14101	61402418	64225019
14101132050	14101	7	2017	Valdivia	211732.5	2017	166080	35164529745	66	0.0003974	14101	13974343	26796950
14101132901	14101	6	2017	Valdivia	211732.5	2017	166080	35164529745	250	0.0015053	14101	52933119	24583988
14101142014	14101	37	2017	Valdivia	211732.5	2017	166080	35164529745	325	0.0019569	14101	68813055	81008249
14101142039	14101	1	2017	Valdivia	211732.5	2017	166080	35164529745	58	0.0003492	14101	12280484	11192906
14101142043	14101	3	2017	Valdivia	211732.5	2017	166080	35164529745	44	0.0002649	14101	9316229	17305853
14101142047	14101	1	2017	Valdivia	211732.5	2017	166080	35164529745	77	0.0004636	14101	16303401	11192906
14101142059	14101	15	2017	Valdivia	211732.5	2017	166080	35164529745	96	0.0005780	14101	20326318	42797062
14101152059	14101	2	2017	Valdivia	211732.5	2017	166080	35164529745	82	0.0004937	14101	17362063	14488814
14101162018	14101	25	2017	Valdivia	211732.5	2017	166080	35164529745	97	0.0005841	14101	20538050	60774809
14101162019	14101	27	2017	Valdivia	211732.5	2017	166080	35164529745	64	0.0003854	14101	13550879	64225019
14101162034	14101	4	2017	Valdivia	211732.5	2017	166080	35164529745	42	0.0002529	14101	8892764	19875012
14101162053	14101	32	2017	Valdivia	211732.5	2017	166080	35164529745	217	0.0013066	14101	45945947	72703074
14101162061	14101	28	2017	Valdivia	211732.5	2017	166080	35164529745	94	0.0005660	14101	19902853	65936579
14101162901	14101	3	2017	Valdivia	211732.5	2017	166080	35164529745	23	0.0001385	14101	4869847	17305853
14101172001	14101	214	2017	Valdivia	211732.5	2017	166080	35164529745	1348	0.0081166	14101	285415379	342559748
14101172016	14101	54	2017	Valdivia	211732.5	2017	166080	35164529745	148	0.0008911	14101	31336407	108339639
14101172027	14101	5	2017	Valdivia	211732.5	2017	166080	35164529745	103	0.0006202	14101	21808445	22285762
14101172036	14101	8	2017	Valdivia	211732.5	2017	166080	35164529745	110	0.0006623	14101	23290572	28942408
14101172055	14101	6	2017	Valdivia	211732.5	2017	166080	35164529745	88	0.0005299	14101	18632458	24583988
14101172901	14101	9	2017	Valdivia	211732.5	2017	166080	35164529745	39	0.0002348	14101	8257567	31032698
14101182004	14101	7	2017	Valdivia	211732.5	2017	166080	35164529745	188	0.0011320	14101	39805706	26796950
14101182006	14101	6	2017	Valdivia	211732.5	2017	166080	35164529745	67	0.0004034	14101	14186076	24583988
14101182015	14101	49	2017	Valdivia	211732.5	2017	166080	35164529745	455	0.0027396	14101	96338277	100419238
14101182031	14101	1	2017	Valdivia	211732.5	2017	166080	35164529745	38	0.0002288	14101	8045834	11192906
14101182040	14101	16	2017	Valdivia	211732.5	2017	166080	35164529745	323	0.0019448	14101	68389590	44665964
14101182049	14101	2	2017	Valdivia	211732.5	2017	166080	35164529745	32	0.0001927	14101	6775439	14488814
14101192031	14101	1	2017	Valdivia	211732.5	2017	166080	35164529745	22	0.0001325	14101	4658114	11192906
14101192901	14101	2	2017	Valdivia	211732.5	2017	166080	35164529745	28	0.0001686	14101	5928509	14488814
14102012011	14102	2	2017	Corral	157428.1	2017	5302	834683963	22	0.0041494	14102	3463419	14488814
14102012017	14102	7	2017	Corral	157428.1	2017	5302	834683963	61	0.0115051	14102	9603116	26796950
14102012018	14102	4	2017	Corral	157428.1	2017	5302	834683963	80	0.0150886	14102	12594251	19875012
14102022008	14102	22	2017	Corral	157428.1	2017	5302	834683963	507	0.0956243	14102	79816064	55523197
14102022010	14102	3	2017	Corral	157428.1	2017	5302	834683963	41	0.0077329	14102	6454553	17305853
14102032001	14102	2	2017	Corral	157428.1	2017	5302	834683963	68	0.0128253	14102	10705113	14488814
14102032005	14102	2	2017	Corral	157428.1	2017	5302	834683963	64	0.0120709	14102	10075401	14488814
14102032019	14102	1	2017	Corral	157428.1	2017	5302	834683963	62	0.0116937	14102	9760544	11192906
14102032901	14102	1	2017	Corral	157428.1	2017	5302	834683963	31	0.0058469	14102	4880272	11192906
14102042002	14102	19	2017	Corral	157428.1	2017	5302	834683963	325	0.0612976	14102	51164143	50162608
14102042006	14102	5	2017	Corral	157428.1	2017	5302	834683963	257	0.0484723	14102	40459030	22285762
14102042007	14102	1	2017	Corral	157428.1	2017	5302	834683963	115	0.0216899	14102	18104235	11192906
14102042011	14102	1	2017	Corral	157428.1	2017	5302	834683963	34	0.0064127	14102	5352557	11192906
14102042014	14102	1	2017	Corral	157428.1	2017	5302	834683963	48	0.0090532	14102	7556550	11192906
14103012004	14103	25	2017	Lanco	184730.2	2017	16752	3094599901	197	0.0117598	14103	36391845	60774809
14103012010	14103	3	2017	Lanco	184730.2	2017	16752	3094599901	39	0.0023281	14103	7204477	17305853
14103012020	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	39	0.0023281	14103	7204477	14488814
14103012022	14103	9	2017	Lanco	184730.2	2017	16752	3094599901	93	0.0055516	14103	17179906	31032698
14103012027	14103	3	2017	Lanco	184730.2	2017	16752	3094599901	105	0.0062679	14103	19396668	17305853
14103012029	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	16	0.0009551	14103	2955683	11192906
14103012040	14103	4	2017	Lanco	184730.2	2017	16752	3094599901	106	0.0063276	14103	19581399	19875012
14103022023	14103	13	2017	Lanco	184730.2	2017	16752	3094599901	224	0.0133715	14103	41379559	38992683
14103022025	14103	6	2017	Lanco	184730.2	2017	16752	3094599901	69	0.0041189	14103	12746382	24583988
14103022030	14103	9	2017	Lanco	184730.2	2017	16752	3094599901	199	0.0118792	14103	36761305	31032698
14103022039	14103	9	2017	Lanco	184730.2	2017	16752	3094599901	379	0.0226242	14103	70012737	31032698
14103022042	14103	5	2017	Lanco	184730.2	2017	16752	3094599901	39	0.0023281	14103	7204477	22285762
14103022043	14103	3	2017	Lanco	184730.2	2017	16752	3094599901	21	0.0012536	14103	3879334	17305853
14103022044	14103	7	2017	Lanco	184730.2	2017	16752	3094599901	96	0.0057307	14103	17734097	26796950
14103022901	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	21	0.0012536	14103	3879334	11192906
14103032001	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	100	0.0059694	14103	18473018	11192906
14103032002	14103	4	2017	Lanco	184730.2	2017	16752	3094599901	273	0.0162966	14103	50431338	19875012
14103032008	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	119	0.0071036	14103	21982891	14488814
14103032009	14103	6	2017	Lanco	184730.2	2017	16752	3094599901	93	0.0055516	14103	17179906	24583988
14103032015	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	96	0.0057307	14103	17734097	14488814
14103032019	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	55	0.0032832	14103	10160160	14488814
14103032034	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	59	0.0035220	14103	10899080	11192906
14103032036	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	21	0.0012536	14103	3879334	11192906
14103032041	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	159	0.0094914	14103	29372098	14488814
14103032042	14103	20	2017	Lanco	184730.2	2017	16752	3094599901	304	0.0181471	14103	56157973	51963043
14103032901	14103	12	2017	Lanco	184730.2	2017	16752	3094599901	179	0.0106853	14103	33066701	37052067
14103042012	14103	5	2017	Lanco	184730.2	2017	16752	3094599901	75	0.0044771	14103	13854763	22285762
14103042038	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	45	0.0026862	14103	8312858	14488814
14103052003	14103	10	2017	Lanco	184730.2	2017	16752	3094599901	250	0.0149236	14103	46182544	33076803
14103052035	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	173	0.0103271	14103	31958320	11192906
14103052040	14103	12	2017	Lanco	184730.2	2017	16752	3094599901	268	0.0159981	14103	49507687	37052067
14103062013	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	65	0.0038801	14103	12007461	14488814
14103062016	14103	5	2017	Lanco	184730.2	2017	16752	3094599901	155	0.0092526	14103	28633177	22285762
14103062021	14103	27	2017	Lanco	184730.2	2017	16752	3094599901	226	0.0134909	14103	41749020	64225019

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

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

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

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

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	personas	Ingresos_expandidos	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
14101042005	14101	8	2017	Valdivia	211732.5	2017	166080	35164529745	60	0.0003613	14101	12703949	28942408	482373.47
14101042058	14101	8	2017	Valdivia	211732.5	2017	166080	35164529745	40	0.0002408	14101	8469299	28942408	723560.20
14101062011	14101	7	2017	Valdivia	211732.5	2017	166080	35164529745	101	0.0006081	14101	21384980	26796950	265316.34
14101062013	14101	13	2017	Valdivia	211732.5	2017	166080	35164529745	88	0.0005299	14101	18632458	38992683	443098.67
14101062021	14101	14	2017	Valdivia	211732.5	2017	166080	35164529745	296	0.0017823	14101	62672813	40906748	138198.47
14101062033	14101	33	2017	Valdivia	211732.5	2017	166080	35164529745	1052	0.0063343	14101	222742566	74376664	70700.25
14101062047	14101	1	2017	Valdivia	211732.5	2017	166080	35164529745	16	0.0000963	14101	3387720	11192906	699556.60
14101072002	14101	84	2017	Valdivia	211732.5	2017	166080	35164529745	294	0.0017702	14101	62249348	154499949	525510.03
14101072033	14101	9	2017	Valdivia	211732.5	2017	166080	35164529745	49	0.0002950	14101	10374891	31032698	633320.38
14101072045	14101	60	2017	Valdivia	211732.5	2017	166080	35164529745	287	0.0017281	14101	60767221	117741438	410248.91
14101082002	14101	129	2017	Valdivia	211732.5	2017	166080	35164529745	429	0.0025831	14101	90833233	221095266	515373.58
14101082032	14101	4	2017	Valdivia	211732.5	2017	166080	35164529745	22	0.0001325	14101	4658114	19875012	903409.65
14101112005	14101	109	2017	Valdivia	211732.5	2017	166080	35164529745	380	0.0022881	14101	80458341	191780604	504685.80
14101112010	14101	48	2017	Valdivia	211732.5	2017	166080	35164529745	176	0.0010597	14101	37264916	98824709	561504.03
14101112017	14101	162	2017	Valdivia	211732.5	2017	166080	35164529745	1083	0.0065210	14101	229306272	268750022	248153.30
14101112037	14101	43	2017	Valdivia	211732.5	2017	166080	35164529745	535	0.0032213	14101	113276875	90793528	169707.53
14101112046	14101	17	2017	Valdivia	211732.5	2017	166080	35164529745	103	0.0006202	14101	21808445	46515434	451606.15
14101112058	14101	16	2017	Valdivia	211732.5	2017	166080	35164529745	59	0.0003553	14101	12492216	44665964	757050.24
14101112901	14101	4	2017	Valdivia	211732.5	2017	166080	35164529745	17	0.0001024	14101	3599452	19875012	1169118.37
14101122007	14101	86	2017	Valdivia	211732.5	2017	166080	35164529745	297	0.0017883	14101	62884546	157514473	530351.76
14101122009	14101	28	2017	Valdivia	211732.5	2017	166080	35164529745	266	0.0016016	14101	56320839	65936579	247881.87
14101122042	14101	48	2017	Valdivia	211732.5	2017	166080	35164529745	233	0.0014029	14101	49333667	98824709	424140.38
14101122058	14101	125	2017	Valdivia	211732.5	2017	166080	35164529745	509	0.0030648	14101	107771831	215262271	422912.12
14101132003	14101	27	2017	Valdivia	211732.5	2017	166080	35164529745	290	0.0017461	14101	61402418	64225019	221465.58
14101132050	14101	7	2017	Valdivia	211732.5	2017	166080	35164529745	66	0.0003974	14101	13974343	26796950	406014.40
14101132901	14101	6	2017	Valdivia	211732.5	2017	166080	35164529745	250	0.0015053	14101	52933119	24583988	98335.95
14101142014	14101	37	2017	Valdivia	211732.5	2017	166080	35164529745	325	0.0019569	14101	68813055	81008249	249256.15
14101142039	14101	1	2017	Valdivia	211732.5	2017	166080	35164529745	58	0.0003492	14101	12280484	11192906	192981.13
14101142043	14101	3	2017	Valdivia	211732.5	2017	166080	35164529745	44	0.0002649	14101	9316229	17305853	393314.85
14101142047	14101	1	2017	Valdivia	211732.5	2017	166080	35164529745	77	0.0004636	14101	16303401	11192906	145362.41
14101142059	14101	15	2017	Valdivia	211732.5	2017	166080	35164529745	96	0.0005780	14101	20326318	42797062	445802.73
14101152059	14101	2	2017	Valdivia	211732.5	2017	166080	35164529745	82	0.0004937	14101	17362063	14488814	176692.85
14101162018	14101	25	2017	Valdivia	211732.5	2017	166080	35164529745	97	0.0005841	14101	20538050	60774809	626544.43
14101162019	14101	27	2017	Valdivia	211732.5	2017	166080	35164529745	64	0.0003854	14101	13550879	64225019	1003515.92
14101162034	14101	4	2017	Valdivia	211732.5	2017	166080	35164529745	42	0.0002529	14101	8892764	19875012	473214.58
14101162053	14101	32	2017	Valdivia	211732.5	2017	166080	35164529745	217	0.0013066	14101	45945947	72703074	335037.21
14101162061	14101	28	2017	Valdivia	211732.5	2017	166080	35164529745	94	0.0005660	14101	19902853	65936579	701452.97
14101162901	14101	3	2017	Valdivia	211732.5	2017	166080	35164529745	23	0.0001385	14101	4869847	17305853	752428.41
14101172001	14101	214	2017	Valdivia	211732.5	2017	166080	35164529745	1348	0.0081166	14101	285415379	342559748	254124.44
14101172016	14101	54	2017	Valdivia	211732.5	2017	166080	35164529745	148	0.0008911	14101	31336407	108339639	732024.59
14101172027	14101	5	2017	Valdivia	211732.5	2017	166080	35164529745	103	0.0006202	14101	21808445	22285762	216366.62
14101172036	14101	8	2017	Valdivia	211732.5	2017	166080	35164529745	110	0.0006623	14101	23290572	28942408	263112.80
14101172055	14101	6	2017	Valdivia	211732.5	2017	166080	35164529745	88	0.0005299	14101	18632458	24583988	279363.50
14101172901	14101	9	2017	Valdivia	211732.5	2017	166080	35164529745	39	0.0002348	14101	8257567	31032698	795710.22
14101182004	14101	7	2017	Valdivia	211732.5	2017	166080	35164529745	188	0.0011320	14101	39805706	26796950	142536.97
14101182006	14101	6	2017	Valdivia	211732.5	2017	166080	35164529745	67	0.0004034	14101	14186076	24583988	366925.20
14101182015	14101	49	2017	Valdivia	211732.5	2017	166080	35164529745	455	0.0027396	14101	96338277	100419238	220701.62
14101182031	14101	1	2017	Valdivia	211732.5	2017	166080	35164529745	38	0.0002288	14101	8045834	11192906	294550.15
14101182040	14101	16	2017	Valdivia	211732.5	2017	166080	35164529745	323	0.0019448	14101	68389590	44665964	138284.72
14101182049	14101	2	2017	Valdivia	211732.5	2017	166080	35164529745	32	0.0001927	14101	6775439	14488814	452775.43
14101192031	14101	1	2017	Valdivia	211732.5	2017	166080	35164529745	22	0.0001325	14101	4658114	11192906	508768.43
14101192901	14101	2	2017	Valdivia	211732.5	2017	166080	35164529745	28	0.0001686	14101	5928509	14488814	517457.63
14102012011	14102	2	2017	Corral	157428.1	2017	5302	834683963	22	0.0041494	14102	3463419	14488814	658582.44
14102012017	14102	7	2017	Corral	157428.1	2017	5302	834683963	61	0.0115051	14102	9603116	26796950	439294.27
14102012018	14102	4	2017	Corral	157428.1	2017	5302	834683963	80	0.0150886	14102	12594251	19875012	248437.65
14102022008	14102	22	2017	Corral	157428.1	2017	5302	834683963	507	0.0956243	14102	79816064	55523197	109513.21
14102022010	14102	3	2017	Corral	157428.1	2017	5302	834683963	41	0.0077329	14102	6454553	17305853	422093.98
14102032001	14102	2	2017	Corral	157428.1	2017	5302	834683963	68	0.0128253	14102	10705113	14488814	213070.79
14102032005	14102	2	2017	Corral	157428.1	2017	5302	834683963	64	0.0120709	14102	10075401	14488814	226387.71
14102032019	14102	1	2017	Corral	157428.1	2017	5302	834683963	62	0.0116937	14102	9760544	11192906	180530.73
14102032901	14102	1	2017	Corral	157428.1	2017	5302	834683963	31	0.0058469	14102	4880272	11192906	361061.47
14102042002	14102	19	2017	Corral	157428.1	2017	5302	834683963	325	0.0612976	14102	51164143	50162608	154346.49
14102042006	14102	5	2017	Corral	157428.1	2017	5302	834683963	257	0.0484723	14102	40459030	22285762	86715.03
14102042007	14102	1	2017	Corral	157428.1	2017	5302	834683963	115	0.0216899	14102	18104235	11192906	97329.61
14102042011	14102	1	2017	Corral	157428.1	2017	5302	834683963	34	0.0064127	14102	5352557	11192906	329203.10
14102042014	14102	1	2017	Corral	157428.1	2017	5302	834683963	48	0.0090532	14102	7556550	11192906	233185.53
14103012004	14103	25	2017	Lanco	184730.2	2017	16752	3094599901	197	0.0117598	14103	36391845	60774809	308501.57
14103012010	14103	3	2017	Lanco	184730.2	2017	16752	3094599901	39	0.0023281	14103	7204477	17305853	443739.83
14103012020	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	39	0.0023281	14103	7204477	14488814	371508.04
14103012022	14103	9	2017	Lanco	184730.2	2017	16752	3094599901	93	0.0055516	14103	17179906	31032698	333684.93
14103012027	14103	3	2017	Lanco	184730.2	2017	16752	3094599901	105	0.0062679	14103	19396668	17305853	164817.65
14103012029	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	16	0.0009551	14103	2955683	11192906	699556.60
14103012040	14103	4	2017	Lanco	184730.2	2017	16752	3094599901	106	0.0063276	14103	19581399	19875012	187500.12
14103022023	14103	13	2017	Lanco	184730.2	2017	16752	3094599901	224	0.0133715	14103	41379559	38992683	174074.48
14103022025	14103	6	2017	Lanco	184730.2	2017	16752	3094599901	69	0.0041189	14103	12746382	24583988	356289.69
14103022030	14103	9	2017	Lanco	184730.2	2017	16752	3094599901	199	0.0118792	14103	36761305	31032698	155943.21
14103022039	14103	9	2017	Lanco	184730.2	2017	16752	3094599901	379	0.0226242	14103	70012737	31032698	81880.47
14103022042	14103	5	2017	Lanco	184730.2	2017	16752	3094599901	39	0.0023281	14103	7204477	22285762	571429.79
14103022043	14103	3	2017	Lanco	184730.2	2017	16752	3094599901	21	0.0012536	14103	3879334	17305853	824088.25
14103022044	14103	7	2017	Lanco	184730.2	2017	16752	3094599901	96	0.0057307	14103	17734097	26796950	279134.90
14103022901	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	21	0.0012536	14103	3879334	11192906	532995.50
14103032001	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	100	0.0059694	14103	18473018	11192906	111929.06
14103032002	14103	4	2017	Lanco	184730.2	2017	16752	3094599901	273	0.0162966	14103	50431338	19875012	72802.24
14103032008	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	119	0.0071036	14103	21982891	14488814	121754.74
14103032009	14103	6	2017	Lanco	184730.2	2017	16752	3094599901	93	0.0055516	14103	17179906	24583988	264343.96
14103032015	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	96	0.0057307	14103	17734097	14488814	150925.14
14103032019	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	55	0.0032832	14103	10160160	14488814	263432.98
14103032034	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	59	0.0035220	14103	10899080	11192906	189710.26
14103032036	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	21	0.0012536	14103	3879334	11192906	532995.50
14103032041	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	159	0.0094914	14103	29372098	14488814	91124.61
14103032042	14103	20	2017	Lanco	184730.2	2017	16752	3094599901	304	0.0181471	14103	56157973	51963043	170931.06
14103032901	14103	12	2017	Lanco	184730.2	2017	16752	3094599901	179	0.0106853	14103	33066701	37052067	206994.79
14103042012	14103	5	2017	Lanco	184730.2	2017	16752	3094599901	75	0.0044771	14103	13854763	22285762	297143.49
14103042038	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	45	0.0026862	14103	8312858	14488814	321973.64
14103052003	14103	10	2017	Lanco	184730.2	2017	16752	3094599901	250	0.0149236	14103	46182544	33076803	132307.21
14103052035	14103	1	2017	Lanco	184730.2	2017	16752	3094599901	173	0.0103271	14103	31958320	11192906	64698.88
14103052040	14103	12	2017	Lanco	184730.2	2017	16752	3094599901	268	0.0159981	14103	49507687	37052067	138253.98
14103062013	14103	2	2017	Lanco	184730.2	2017	16752	3094599901	65	0.0038801	14103	12007461	14488814	222904.83
14103062016	14103	5	2017	Lanco	184730.2	2017	16752	3094599901	155	0.0092526	14103	28633177	22285762	143779.11
14103062021	14103	27	2017	Lanco	184730.2	2017	16752	3094599901	226	0.0134909	14103	41749020	64225019	284181.50

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "P15/region_14_P15_r.rds")

12 Referencias

https://rpubs.com/osoramirez/316691

https://dataintelligencechile.shinyapps.io/casenfinal

Manual_de_usuario_Censo_2017_16R.pdf

http://www.censo2017.cl/microdatos/

Censo de Población y Vivienda

https://www.ine.cl/estadisticas/sociales/censos-de-poblacion-y-vivienda/poblacion-y-vivienda

http://r-statistics.co/Linear-Regression.html