Expansión de la CASEN sobre el CENSO (Región 05)

De ingresos sobre una categoría de respuesta

DataIntelligence

Abstract

Expandiremos los ingresos promedios comunales obtenidos de la CASEN sobre la categoría de respuesta: “tejas o tejuelas de arcilla, metálicas, de cemento, de madera, asfálticas o plásticas” del campo P03B del CENSO de viviendas, que fue la categoría de respuesta 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 05.

Ensayaremos diferentes modelos dentro del análisis de regresión cuya variable independiente será: “frecuencia de población que posee la variable Censal respecto a la zona” y la dependiente: “ingreso expandido por zona”

Lo anterior para elegir el que posea el mayor coeficiente de determinación y así contruir una tabla de valores predichos.

1 Variable CENSO

Necesitamos calcular las frecuencias a nivel censal de las respuestas correspondientes a la categoría: “tejas o tejuelas de arcilla, metálicas, de cemento, de madera, asfálticas o plásticas” del campo P03B del Censo de viviendas. Recordemos que ésta fué la más alta correlación en relación a los ingresos expandidos (ver punto 1.2 aquí).

1.1 Lectura y filtrado de la tabla censal de viviendas

Leemos la tabla Casen 2017 de viviendas que ya tiene integrada la clave zonal:

tabla_con_clave <- readRDS("censo_viviendas_con_clave_17.rds")
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15202	1	2	6	13225	1	3	1	5	3	5	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	3	1	1	5	3	5	2	3	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	4	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	5	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	6	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	8	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	9	3	1	5	3	5	1	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	10	1	1	5	3	4	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	11	1	2	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	12	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	13	1	1	5	3	4	1	4	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	14	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	16	1	1	5	3	5	1	3	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	17	1	1	5	3	5	2	4	1	8	15	152	15202	15202012006
15	152	15202	1	2	6	13225	18	3	1	5	3	5	1	1	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	19	3	1	5	3	5	1	3	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	20	1	1	5	3	5	2	1	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	21	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	22	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	24	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	25	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	26	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	27	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	28	3	1	5	3	5	1	4	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	29	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	30	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	31	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	32	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	33	1	1	5	3	5	3	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	34	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	35	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	36	1	1	5	3	5	3	2	1	9	15	152	15202	15202012006
15	152	15202	1	2	6	13225	37	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	38	1	1	5	3	5	99	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	39	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	40	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	41	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	42	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	43	3	1	5	3	5	2	1	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	44	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	45	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	8	13910	1	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	3	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	4	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	5	3	1	5	3	5	2	3	1	3	15	152	15202	15202012008
15	152	15202	1	2	8	13910	6	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	7	1	1	5	99	5	2	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	8	3	1	5	3	5	3	3	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	9	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	10	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	11	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	12	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	13	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	14	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	16	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	18	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	20	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	21	1	1	5	3	5	1	4	1	2	15	152	15202	15202012008
15	152	15202	1	2	8	13910	22	3	1	5	3	5	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	8	13910	23	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	24	3	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	25	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	26	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	27	1	4	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	28	3	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	29	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	30	1	1	5	1	4	2	4	1	1	15	152	15202	15202012008
15	152	15202	1	2	12	8394	1	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	2	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	3	3	1	5	3	5	2	3	1	4	15	152	15202	15202012012
15	152	15202	1	2	12	8394	4	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	5	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	6	3	3	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	8	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	9	3	1	5	3	5	1	4	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	10	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	11	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	12	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	13	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	14	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	15	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	16	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	17	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	18	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	19	3	1	99	99	99	99	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	20	3	1	5	3	5	3	99	1	1	15	152	15202	15202012012
15	152	15202	1	2	12	8394	21	3	1	5	99	5	1	4	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	22	3	2	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012
15	152	15202	1	2	12	8394	24	3	1	5	3	5	1	2	1	2	15	152	15202	15202012012
15	152	15202	1	2	12	8394	25	3	4	98	98	98	98	98	0	0	15	152	15202	15202012012

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

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

Hagamos un subset con la 1:

tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$REGION == 5) 

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)[2] <- "Tipo de techo" 
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$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
5101011001	1	5101	200	2017
5101011002	1	5101	183	2017
5101011003	1	5101	184	2017
5101011004	1	5101	49	2017
5101011005	1	5101	97	2017
5101011006	1	5101	193	2017
5101011007	1	5101	210	2017
5101021001	1	5101	5	2017
5101021002	1	5101	218	2017
5101021003	1	5101	140	2017
5101021004	1	5101	74	2017
5101031001	1	5101	86	2017
5101031002	1	5101	78	2017
5101031003	1	5101	81	2017
5101031004	1	5101	43	2017
5101031005	1	5101	29	2017
5101031006	1	5101	29	2017
5101031007	1	5101	84	2017
5101031008	1	5101	93	2017
5101031009	1	5101	29	2017
5101031010	1	5101	38	2017
5101031011	1	5101	104	2017
5101031012	1	5101	35	2017
5101041001	1	5101	10	2017
5101051001	1	5101	67	2017
5101051002	1	5101	49	2017
5101051003	1	5101	75	2017
5101051004	1	5101	65	2017
5101051005	1	5101	34	2017
5101051006	1	5101	53	2017
5101051007	1	5101	31	2017
5101061001	1	5101	52	2017
5101061002	1	5101	36	2017
5101061003	1	5101	83	2017
5101061004	1	5101	71	2017
5101061005	1	5101	102	2017
5101071001	1	5101	152	2017
5101081001	1	5101	68	2017
5101081002	1	5101	50	2017
5101081003	1	5101	23	2017
5101081004	1	5101	65	2017
5101081005	1	5101	70	2017
5101081006	1	5101	39	2017
5101081007	1	5101	63	2017
5101081008	1	5101	41	2017
5101081009	1	5101	57	2017
5101081010	1	5101	79	2017
5101081011	1	5101	25	2017
5101091001	1	5101	87	2017
5101091002	1	5101	97	2017
5101091003	1	5101	79	2017
5101091004	1	5101	37	2017
5101101001	1	5101	77	2017
5101101002	1	5101	115	2017
5101101003	1	5101	75	2017
5101101004	1	5101	47	2017
5101101005	1	5101	46	2017
5101101006	1	5101	87	2017
5101101007	1	5101	10	2017
5101101008	1	5101	38	2017
5101101009	1	5101	113	2017
5101111001	1	5101	142	2017
5101121001	1	5101	107	2017
5101121002	1	5101	79	2017
5101121003	1	5101	75	2017
5101131001	1	5101	49	2017
5101131002	1	5101	17	2017
5101131003	1	5101	20	2017
5101131004	1	5101	23	2017
5101131005	1	5101	24	2017
5101141001	1	5101	52	2017
5101141002	1	5101	27	2017
5101141003	1	5101	39	2017
5101141004	1	5101	9	2017
5101141005	1	5101	52	2017
5101141006	1	5101	53	2017
5101151001	1	5101	25	2017
5101151002	1	5101	35	2017
5101151003	1	5101	23	2017
5101151004	1	5101	20	2017
5101151005	1	5101	27	2017
5101151006	1	5101	28	2017
5101151007	1	5101	66	2017
5101161001	1	5101	17	2017
5101161002	1	5101	23	2017
5101161003	1	5101	8	2017
5101161004	1	5101	64	2017
5101161005	1	5101	13	2017
5101161006	1	5101	24	2017
5101161007	1	5101	24	2017
5101161008	1	5101	64	2017
5101161009	1	5101	24	2017
5101161010	1	5101	23	2017
5101161011	1	5101	40	2017
5101161012	1	5101	50	2017
5101171001	1	5101	79	2017
5101171002	1	5101	147	2017
5101171003	1	5101	114	2017
5101171004	1	5101	54	2017
5101171005	1	5101	60	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
5101011001	200	2017	05101
5101011002	183	2017	05101
5101011003	184	2017	05101
5101011004	49	2017	05101
5101011005	97	2017	05101
5101011006	193	2017	05101
5101011007	210	2017	05101
5101021001	5	2017	05101
5101021002	218	2017	05101
5101021003	140	2017	05101
5101021004	74	2017	05101
5101031001	86	2017	05101
5101031002	78	2017	05101
5101031003	81	2017	05101
5101031004	43	2017	05101
5101031005	29	2017	05101
5101031006	29	2017	05101
5101031007	84	2017	05101
5101031008	93	2017	05101
5101031009	29	2017	05101
5101031010	38	2017	05101
5101031011	104	2017	05101
5101031012	35	2017	05101
5101041001	10	2017	05101
5101051001	67	2017	05101
5101051002	49	2017	05101
5101051003	75	2017	05101
5101051004	65	2017	05101
5101051005	34	2017	05101
5101051006	53	2017	05101
5101051007	31	2017	05101
5101061001	52	2017	05101
5101061002	36	2017	05101
5101061003	83	2017	05101
5101061004	71	2017	05101
5101061005	102	2017	05101
5101071001	152	2017	05101
5101081001	68	2017	05101
5101081002	50	2017	05101
5101081003	23	2017	05101
5101081004	65	2017	05101
5101081005	70	2017	05101
5101081006	39	2017	05101
5101081007	63	2017	05101
5101081008	41	2017	05101
5101081009	57	2017	05101
5101081010	79	2017	05101
5101081011	25	2017	05101
5101091001	87	2017	05101
5101091002	97	2017	05101
5101091003	79	2017	05101
5101091004	37	2017	05101
5101101001	77	2017	05101
5101101002	115	2017	05101
5101101003	75	2017	05101
5101101004	47	2017	05101
5101101005	46	2017	05101
5101101006	87	2017	05101
5101101007	10	2017	05101
5101101008	38	2017	05101
5101101009	113	2017	05101
5101111001	142	2017	05101
5101121001	107	2017	05101
5101121002	79	2017	05101
5101121003	75	2017	05101
5101131001	49	2017	05101
5101131002	17	2017	05101
5101131003	20	2017	05101
5101131004	23	2017	05101
5101131005	24	2017	05101
5101141001	52	2017	05101
5101141002	27	2017	05101
5101141003	39	2017	05101
5101141004	9	2017	05101
5101141005	52	2017	05101
5101141006	53	2017	05101
5101151001	25	2017	05101
5101151002	35	2017	05101
5101151003	23	2017	05101
5101151004	20	2017	05101
5101151005	27	2017	05101
5101151006	28	2017	05101
5101151007	66	2017	05101
5101161001	17	2017	05101
5101161002	23	2017	05101
5101161003	8	2017	05101
5101161004	64	2017	05101
5101161005	13	2017	05101
5101161006	24	2017	05101
5101161007	24	2017	05101
5101161008	64	2017	05101
5101161009	24	2017	05101
5101161010	23	2017	05101
5101161011	40	2017	05101
5101161012	50	2017	05101
5101171001	79	2017	05101
5101171002	147	2017	05101
5101171003	114	2017	05101
5101171004	54	2017	05101
5101171005	60	2017	05101

2 Variable CASEN

2.1 Tabla de ingresos expandidos

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

h_y_m_2017_censo <- readRDS("ingresos_expandidos_17.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos
01101	Iquique	354820.7	2017	1101	191468	67936815240
01107	Alto Hospicio	301933.4	2017	1107	108375	32722034397
01401	Pozo Almonte	285981.8	2017	1401	15711	4493059532
01402	Camiña	262850.3	2017	1402	1250	328562901
01404	Huara	253968.5	2017	1404	2730	693334131
01405	Pica	313007.5	2017	1405	9296	2909717399
02101	Antofagasta	347580.2	2017	2101	361873	125779893517
02102	Mejillones	369770.7	2017	2102	13467	4979702302
02103	Sierra Gorda	403458.5	2017	2103	10186	4109628188
02104	Taltal	364539.1	2017	2104	13317	4854566842
02201	Calama	409671.3	2017	2201	165731	67895226712
02203	San Pedro de Atacama	426592.0	2017	2203	10996	4690805471
02301	Tocopilla	246615.3	2017	2301	25186	6211253937
02302	María Elena	466266.9	2017	2302	6457	3010685220
03101	Copiapó	330075.2	2017	3101	153937	50810778473
03102	Caldera	299314.8	2017	3102	17662	5286498241
03103	Tierra Amarilla	314643.9	2017	3103	14019	4410992711
03201	Chañaral	286389.3	2017	3201	12219	3499391196
03202	Diego de Almagro	336256.8	2017	3202	13925	4682376047
03301	Vallenar	304336.7	2017	3301	51917	15800246795
03302	Alto del Carmen	227130.4	2017	3302	5299	1203563833
03303	Freirina	253086.7	2017	3303	7041	1781983257
03304	Huasco	287406.6	2017	3304	10149	2916889629
04101	La Serena	270221.9	2017	4101	221054	59733627577
04102	Coquimbo	261852.6	2017	4102	227730	59631700074
04103	Andacollo	248209.3	2017	4103	11044	2741223967
04104	La Higuera	228356.8	2017	4104	4241	968461330
04105	Paiguano	205942.1	2017	4105	4497	926121774
04106	Vicuña	211431.9	2017	4106	27771	5871675449
04201	Illapel	238674.4	2017	4201	30848	7362627007
04202	Canela	207933.6	2017	4202	9093	1890740321
04203	Los Vilos	255200.4	2017	4203	21382	5456695139
04204	Salamanca	242879.5	2017	4204	29347	7127783272
04301	Ovalle	266522.9	2017	4301	111272	29656533187
04302	Combarbalá	210409.7	2017	4302	13322	2803077721
04303	Monte Patria	211907.9	2017	4303	30751	6516380780
04304	Punitaqui	194997.8	2017	4304	10956	2136395349
04305	Río Hurtado	182027.2	2017	4305	4278	778712384
05101	Valparaíso	298720.7	2017	5101	296655	88616992249
05102	Casablanca	312802.7	2017	5102	26867	8404070481
05103	Concón	318496.3	2017	5103	42152	13425257057
05105	Puchuncaví	288737.2	2017	5105	18546	5354920887
05107	Quintero	316659.1	2017	5107	31923	10108709691
05109	Viña del Mar	337006.1	2017	5109	334248	112643604611
05301	Los Andes	338182.5	2017	5301	66708	22559476922
05302	Calle Larga	245165.4	2017	5302	14832	3636293159
05303	Rinconada	281633.2	2017	5303	10207	2874630315
05304	San Esteban	220958.4	2017	5304	18855	4166170587
05401	La Ligua	229623.7	2017	5401	35390	8126381563
05402	Cabildo	249717.7	2017	5402	19388	4841527150

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

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos
05101	5101011004	49	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101011005	97	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101021001	5	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101021002	218	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101011007	210	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101011003	184	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031006	29	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101011006	193	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101011002	183	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031009	29	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031010	38	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031007	84	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031008	93	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101041001	10	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101051001	67	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031011	104	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031012	35	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101051004	65	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101051005	34	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101051006	53	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101051007	31	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101061001	52	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101061002	36	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101061003	83	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101061004	71	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101061005	102	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101071001	152	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101081001	68	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101051002	49	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101051003	75	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101011001	200	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101081005	70	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101081006	39	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101081007	63	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101081008	41	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101081009	57	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101081010	79	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101081011	25	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101091001	87	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101021003	140	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101021004	74	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031001	86	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031002	78	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031003	81	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031004	43	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031005	29	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101101005	46	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101101006	87	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101101007	10	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101101008	38	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101101009	113	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101111001	142	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101121001	107	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101121002	79	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101121003	75	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101131001	49	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101131002	17	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101131003	20	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101131004	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101131005	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101141001	52	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101141002	27	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101141003	39	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101141004	9	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101141005	52	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101141006	53	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101151001	25	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101151002	35	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101081002	50	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101081003	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101081004	65	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101151006	28	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101151007	66	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161001	17	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161002	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161003	8	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161004	64	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161005	13	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161006	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161007	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161008	64	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161009	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161010	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161011	40	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161012	50	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101171001	79	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101171002	147	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101171003	114	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101171004	54	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101171005	60	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101171006	113	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101171007	133	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101171008	7	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101171009	72	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101171010	49	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101181001	170	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101181002	122	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101181003	98	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101181004	125	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101191001	117	2017	Valparaíso	298720.7	2017	5101	296655	88616992249

4 Proporción poblacional zonal respecto a la comunal

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

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

Veamos los 100 primeros registros:

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

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

5 Ingreso medio

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

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

código	zona	Freq	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos
05101	5101011004	49	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101011005	97	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101021001	5	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101021002	218	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101011007	210	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101011003	184	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031006	29	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101011006	193	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101011002	183	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031009	29	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031010	38	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031007	84	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031008	93	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101041001	10	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101051001	67	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031011	104	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031012	35	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101051004	65	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101051005	34	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101051006	53	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101051007	31	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101061001	52	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101061002	36	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101061003	83	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101061004	71	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101061005	102	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101071001	152	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101081001	68	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101051002	49	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101051003	75	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101011001	200	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101081005	70	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101081006	39	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101081007	63	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101081008	41	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101081009	57	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101081010	79	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101081011	25	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101091001	87	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101021003	140	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101021004	74	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031001	86	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031002	78	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031003	81	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031004	43	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101031005	29	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101101005	46	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101101006	87	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101101007	10	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101101008	38	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101101009	113	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101111001	142	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101121001	107	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101121002	79	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101121003	75	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101131001	49	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101131002	17	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101131003	20	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101131004	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101131005	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101141001	52	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101141002	27	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101141003	39	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101141004	9	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101141005	52	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101141006	53	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101151001	25	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101151002	35	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101081002	50	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101081003	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101081004	65	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101151006	28	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101151007	66	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161001	17	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161002	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161003	8	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161004	64	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161005	13	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161006	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161007	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161008	64	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161009	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161010	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161011	40	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101161012	50	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101171001	79	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101171002	147	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101171003	114	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101171004	54	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101171005	60	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101171006	113	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101171007	133	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101171008	7	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101171009	72	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101171010	49	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101181001	170	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101181002	122	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101181003	98	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101181004	125	2017	Valparaíso	298720.7	2017	5101	296655	88616992249
05101	5101191001	117	2017	Valparaíso	298720.7	2017	5101	296655	88616992249

6 Ingreso promedio expandido por zona (multi_pob)

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

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

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

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

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	Freq.y	p_poblacional	código.y
5101011001	05101	200	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3280	0.0110566	05101
5101011002	05101	183	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3761	0.0126780	05101
5101011003	05101	184	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2365	0.0079722	05101
5101011004	05101	49	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2690	0.0090678	05101
5101011005	05101	97	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2518	0.0084880	05101
5101011006	05101	193	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2848	0.0096004	05101
5101011007	05101	210	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3131	0.0105543	05101
5101021001	05101	5	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	273	0.0009203	05101
5101021002	05101	218	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1881	0.0063407	05101
5101021003	05101	140	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1696	0.0057171	05101
5101021004	05101	74	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1415	0.0047699	05101
5101031001	05101	86	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1402	0.0047260	05101
5101031002	05101	78	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1780	0.0060002	05101
5101031003	05101	81	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1071	0.0036103	05101
5101031004	05101	43	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	739	0.0024911	05101
5101031005	05101	29	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	457	0.0015405	05101
5101031006	05101	29	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1051	0.0035428	05101
5101031007	05101	84	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2154	0.0072610	05101
5101031008	05101	93	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2337	0.0078778	05101
5101031009	05101	29	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1059	0.0035698	05101
5101031010	05101	38	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1678	0.0056564	05101
5101031011	05101	104	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2610	0.0087981	05101
5101031012	05101	35	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	659	0.0022214	05101
5101041001	05101	10	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	177	0.0005967	05101
5101051001	05101	67	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1872	0.0063104	05101
5101051002	05101	49	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1714	0.0057778	05101
5101051003	05101	75	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2345	0.0079048	05101
5101051004	05101	65	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3014	0.0101600	05101
5101051005	05101	34	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1688	0.0056901	05101
5101051006	05101	53	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3387	0.0114173	05101
5101051007	05101	31	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2233	0.0075273	05101
5101061001	05101	52	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1196	0.0040316	05101
5101061002	05101	36	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	878	0.0029597	05101
5101061003	05101	83	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1642	0.0055350	05101
5101061004	05101	71	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	862	0.0029057	05101
5101061005	05101	102	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1546	0.0052114	05101
5101071001	05101	152	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1265	0.0042642	05101
5101081001	05101	68	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	687	0.0023158	05101
5101081002	05101	50	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	634	0.0021372	05101
5101081003	05101	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	619	0.0020866	05101
5101081004	05101	65	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	620	0.0020900	05101
5101081005	05101	70	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	979	0.0033001	05101
5101081006	05101	39	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1435	0.0048373	05101
5101081007	05101	63	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1791	0.0060373	05101
5101081008	05101	41	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1274	0.0042946	05101
5101081009	05101	57	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1519	0.0051204	05101
5101081010	05101	79	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1320	0.0044496	05101
5101081011	05101	25	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1143	0.0038530	05101
5101091001	05101	87	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1689	0.0056935	05101
5101091002	05101	97	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1322	0.0044564	05101
5101091003	05101	79	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1275	0.0042979	05101
5101091004	05101	37	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	4201	0.0141612	05101
5101101001	05101	77	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1044	0.0035192	05101
5101101002	05101	115	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	988	0.0033305	05101
5101101003	05101	75	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	849	0.0028619	05101
5101101004	05101	47	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	717	0.0024169	05101
5101101005	05101	46	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	936	0.0031552	05101
5101101006	05101	87	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1453	0.0048979	05101
5101101007	05101	10	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	604	0.0020360	05101
5101101008	05101	38	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2358	0.0079486	05101
5101101009	05101	113	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3502	0.0118050	05101
5101111001	05101	142	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2120	0.0071463	05101
5101121001	05101	107	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1435	0.0048373	05101
5101121002	05101	79	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1941	0.0065430	05101
5101121003	05101	75	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1528	0.0051508	05101
5101131001	05101	49	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1170	0.0039440	05101
5101131002	05101	17	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	572	0.0019282	05101
5101131003	05101	20	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	638	0.0021506	05101
5101131004	05101	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	880	0.0029664	05101
5101131005	05101	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	664	0.0022383	05101
5101141001	05101	52	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1502	0.0050631	05101
5101141002	05101	27	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1122	0.0037822	05101
5101141003	05101	39	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1031	0.0034754	05101
5101141004	05101	9	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	638	0.0021506	05101
5101141005	05101	52	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	704	0.0023731	05101
5101141006	05101	53	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1265	0.0042642	05101
5101151001	05101	25	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	532	0.0017933	05101
5101151002	05101	35	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	455	0.0015338	05101
5101151003	05101	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	640	0.0021574	05101
5101151004	05101	20	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	543	0.0018304	05101
5101151005	05101	27	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	503	0.0016956	05101
5101151006	05101	28	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	774	0.0026091	05101
5101151007	05101	66	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1448	0.0048811	05101
5101161001	05101	17	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	625	0.0021068	05101
5101161002	05101	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	697	0.0023495	05101
5101161003	05101	8	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	496	0.0016720	05101
5101161004	05101	64	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	892	0.0030069	05101
5101161005	05101	13	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	518	0.0017461	05101
5101161006	05101	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1016	0.0034249	05101
5101161007	05101	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	950	0.0032024	05101
5101161008	05101	64	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1194	0.0040249	05101
5101161009	05101	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	856	0.0028855	05101
5101161010	05101	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1235	0.0041631	05101
5101161011	05101	40	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1840	0.0062025	05101
5101161012	05101	50	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2467	0.0083161	05101
5101171001	05101	79	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	932	0.0031417	05101
5101171002	05101	147	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1142	0.0038496	05101
5101171003	05101	114	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1447	0.0048777	05101
5101171004	05101	54	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1122	0.0037822	05101
5101171005	05101	60	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1452	0.0048946	05101

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

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

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	comuna.y	personas	Ingresos_expandidos	Freq.y	p_poblacional	código.y	multi_pob
5101011001	05101	200	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3280	0.0110566	05101	979803929
5101011002	05101	183	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3761	0.0126780	05101	1123488591
5101011003	05101	184	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2365	0.0079722	05101	706474479
5101011004	05101	49	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2690	0.0090678	05101	803558710
5101011005	05101	97	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2518	0.0084880	05101	752178748
5101011006	05101	193	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2848	0.0096004	05101	850756582
5101011007	05101	210	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3131	0.0105543	05101	935294543
5101021001	05101	5	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	273	0.0009203	05101	81550754
5101021002	05101	218	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1881	0.0063407	05101	561893656
5101021003	05101	140	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1696	0.0057171	05101	506630324
5101021004	05101	74	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1415	0.0047699	05101	422689805
5101031001	05101	86	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1402	0.0047260	05101	418806436
5101031002	05101	78	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1780	0.0060002	05101	531722864
5101031003	05101	81	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1071	0.0036103	05101	319929880
5101031004	05101	43	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	739	0.0024911	05101	220754605
5101031005	05101	29	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	457	0.0015405	05101	136515365
5101031006	05101	29	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1051	0.0035428	05101	313955466
5101031007	05101	84	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2154	0.0072610	05101	643444410
5101031008	05101	93	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2337	0.0078778	05101	698110299
5101031009	05101	29	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1059	0.0035698	05101	316345232
5101031010	05101	38	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1678	0.0056564	05101	501253352
5101031011	05101	104	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2610	0.0087981	05101	779661053
5101031012	05101	35	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	659	0.0022214	05101	196856948
5101041001	05101	10	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	177	0.0005967	05101	52873566
5101051001	05101	67	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1872	0.0063104	05101	559205169
5101051002	05101	49	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1714	0.0057778	05101	512007297
5101051003	05101	75	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2345	0.0079048	05101	700500065
5101051004	05101	65	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3014	0.0101600	05101	900344220
5101051005	05101	34	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1688	0.0056901	05101	504240559
5101051006	05101	53	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3387	0.0114173	05101	1011767045
5101051007	05101	31	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2233	0.0075273	05101	667043346
5101061001	05101	52	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1196	0.0040316	05101	357269969
5101061002	05101	36	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	878	0.0029597	05101	262276783
5101061003	05101	83	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1642	0.0055350	05101	490499406
5101061004	05101	71	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	862	0.0029057	05101	257497252
5101061005	05101	102	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1546	0.0052114	05101	461822218
5101071001	05101	152	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1265	0.0042642	05101	377881698
5101081001	05101	68	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	687	0.0023158	05101	205221128
5101081002	05101	50	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	634	0.0021372	05101	189388930
5101081003	05101	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	619	0.0020866	05101	184908120
5101081004	05101	65	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	620	0.0020900	05101	185206840
5101081005	05101	70	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	979	0.0033001	05101	292447575
5101081006	05101	39	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1435	0.0048373	05101	428664219
5101081007	05101	63	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1791	0.0060373	05101	535008792
5101081008	05101	41	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1274	0.0042946	05101	380570185
5101081009	05101	57	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1519	0.0051204	05101	453756759
5101081010	05101	79	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1320	0.0044496	05101	394311337
5101081011	05101	25	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1143	0.0038530	05101	341437772
5101091001	05101	87	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1689	0.0056935	05101	504539279
5101091002	05101	97	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1322	0.0044564	05101	394908779
5101091003	05101	79	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1275	0.0042979	05101	380868905
5101091004	05101	37	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	4201	0.0141612	05101	1254925703
5101101001	05101	77	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1044	0.0035192	05101	311864421
5101101002	05101	115	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	988	0.0033305	05101	295136062
5101101003	05101	75	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	849	0.0028619	05101	253613883
5101101004	05101	47	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	717	0.0024169	05101	214182749
5101101005	05101	46	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	936	0.0031552	05101	279602585
5101101006	05101	87	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1453	0.0048979	05101	434041192
5101101007	05101	10	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	604	0.0020360	05101	180427309
5101101008	05101	38	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2358	0.0079486	05101	704383434
5101101009	05101	113	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3502	0.0118050	05101	1046119927
5101111001	05101	142	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2120	0.0071463	05101	633287905
5101121001	05101	107	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1435	0.0048373	05101	428664219
5101121002	05101	79	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1941	0.0065430	05101	579816898
5101121003	05101	75	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1528	0.0051508	05101	456445245
5101131001	05101	49	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1170	0.0039440	05101	349503231
5101131002	05101	17	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	572	0.0019282	05101	170868246
5101131003	05101	20	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	638	0.0021506	05101	190583813
5101131004	05101	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	880	0.0029664	05101	262874225
5101131005	05101	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	664	0.0022383	05101	198350551
5101141001	05101	52	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1502	0.0050631	05101	448678507
5101141002	05101	27	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1122	0.0037822	05101	335164637
5101141003	05101	39	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1031	0.0034754	05101	307981052
5101141004	05101	9	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	638	0.0021506	05101	190583813
5101141005	05101	52	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	704	0.0023731	05101	210299380
5101141006	05101	53	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1265	0.0042642	05101	377881698
5101151001	05101	25	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	532	0.0017933	05101	158919418
5101151002	05101	35	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	455	0.0015338	05101	135917923
5101151003	05101	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	640	0.0021574	05101	191181254
5101151004	05101	20	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	543	0.0018304	05101	162205346
5101151005	05101	27	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	503	0.0016956	05101	150256517
5101151006	05101	28	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	774	0.0026091	05101	231209830
5101151007	05101	66	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1448	0.0048811	05101	432547588
5101161001	05101	17	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	625	0.0021068	05101	186700444
5101161002	05101	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	697	0.0023495	05101	208208335
5101161003	05101	8	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	496	0.0016720	05101	148165472
5101161004	05101	64	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	892	0.0030069	05101	266458873
5101161005	05101	13	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	518	0.0017461	05101	154737328
5101161006	05101	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1016	0.0034249	05101	303500241
5101161007	05101	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	950	0.0032024	05101	283784675
5101161008	05101	64	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1194	0.0040249	05101	356672528
5101161009	05101	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	856	0.0028855	05101	255704928
5101161010	05101	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1235	0.0041631	05101	368920077
5101161011	05101	40	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1840	0.0062025	05101	549646107
5101161012	05101	50	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2467	0.0083161	05101	736943992
5101171001	05101	79	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	932	0.0031417	05101	278407702
5101171002	05101	147	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1142	0.0038496	05101	341139051
5101171003	05101	114	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1447	0.0048777	05101	432248867
5101171004	05101	54	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1122	0.0037822	05101	335164637
5101171005	05101	60	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1452	0.0048946	05101	433742471

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

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 
## -1.964e+09 -2.126e+08 -1.081e+08  1.424e+08  1.516e+09 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 224951349   11316040   19.88   <2e-16 ***
## Freq.x        2282427      60741   37.58   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 319100000 on 1166 degrees of freedom
##   (10 observations deleted due to missingness)
## Multiple R-squared:  0.5477, Adjusted R-squared:  0.5473 
## F-statistic:  1412 on 1 and 1166 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 
## -1.964e+09 -2.126e+08 -1.081e+08  1.424e+08  1.516e+09 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 224951349   11316040   19.88   <2e-16 ***
## Freq.x        2282427      60741   37.58   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 319100000 on 1166 degrees of freedom
##   (10 observations deleted due to missingness)
## Multiple R-squared:  0.5477, Adjusted R-squared:  0.5473 
## F-statistic:  1412 on 1 and 1166 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 
## -1.964e+09 -2.126e+08 -1.081e+08  1.424e+08  1.516e+09 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 224951349   11316040   19.88   <2e-16 ***
## Freq.x        2282427      60741   37.58   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 319100000 on 1166 degrees of freedom
##   (10 observations deleted due to missingness)
## Multiple R-squared:  0.5477, Adjusted R-squared:  0.5473 
## F-statistic:  1412 on 1 and 1166 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 
## -598747383 -221856049  -35746986  184122556 1282820917 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -325716939   19884864  -16.38   <2e-16 ***
## log(Freq.x)  219762321    4993551   44.01   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 290900000 on 1166 degrees of freedom
##   (10 observations deleted due to missingness)
## Multiple R-squared:  0.6242, Adjusted R-squared:  0.6239 
## F-statistic:  1937 on 1 and 1166 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 
## -924495683 -134907468  -27844439   69831571 1388411078 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -77931926   12545422  -6.212 7.27e-10 ***
## sqrt(Freq.x)  65757473    1222793  53.776  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 254300000 on 1166 degrees of freedom
##   (10 observations deleted due to missingness)
## Multiple R-squared:  0.7127, Adjusted R-squared:  0.7124 
## F-statistic:  2892 on 1 and 1166 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 
## -26990.5  -4208.2   -961.6   3454.5  23261.7 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    4892.8      294.4   16.62   <2e-16 ***
## sqrt(Freq.x)   1616.5       28.7   56.32   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5969 on 1166 degrees of freedom
##   (10 observations deleted due to missingness)
## Multiple R-squared:  0.7312, Adjusted R-squared:  0.731 
## F-statistic:  3172 on 1 and 1166 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 
## -4.3367 -0.6386  0.1446  0.7291  2.3431 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  17.308133   0.050219  344.65   <2e-16 ***
## sqrt(Freq.x)  0.213575   0.004895   43.63   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.018 on 1166 degrees of freedom
##   (10 observations deleted due to missingness)
## Multiple R-squared:  0.6202, Adjusted R-squared:  0.6198 
## F-statistic:  1904 on 1 and 1166 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 
## -13566  -4065    -79   4018  20188 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2890.50     388.19  -7.446 1.87e-13 ***
## log(Freq.x)  5872.41      97.48  60.239  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5678 on 1166 degrees of freedom
##   (10 observations deleted due to missingness)
## Multiple R-squared:  0.7568, Adjusted R-squared:  0.7566 
## F-statistic:  3629 on 1 and 1166 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 
## -2.71149 -0.47661 -0.00491  0.48204  2.55066 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 15.94843    0.05010  318.35   <2e-16 ***
## log(Freq.x)  0.86796    0.01258   68.99   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7328 on 1166 degrees of freedom
##   (10 observations deleted due to missingness)
## Multiple R-squared:  0.8032, Adjusted R-squared:  0.8031 
## F-statistic:  4760 on 1 and 1166 DF,  p-value: < 2.2e-16

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.8031).

9.1 Diagrama de dispersión sobre log-log

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

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

9.2 Modelo log-log

Observemos nuevamente el resultado sobre log-log.

linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_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 
## -2.71149 -0.47661 -0.00491  0.48204  2.55066 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 15.94843    0.05010  318.35   <2e-16 ***
## log(Freq.x)  0.86796    0.01258   68.99   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7328 on 1166 degrees of freedom
##   (10 observations deleted due to missingness)
## Multiple R-squared:  0.8032, Adjusted R-squared:  0.8031 
## F-statistic:  4760 on 1 and 1166 DF,  p-value: < 2.2e-16

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

9.3 Análisis de residuos

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

9.4 Ecuación del modelo

\[ \hat Y = e^{(15.94843+0.86796 \cdot lnX)}\]

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$est_ing <- exp(15.94843+0.86796 *log(h_y_m_comuna_corr_01$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	comuna.y	personas	Ingresos_expandidos	Freq.y	p_poblacional	código.y	multi_pob	est_ing
5101011001	05101	200	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3280	0.0110566	05101	979803929	838527170
5101011002	05101	183	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3761	0.0126780	05101	1123488591	776304658
5101011003	05101	184	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2365	0.0079722	05101	706474479	779985307
5101011004	05101	49	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2690	0.0090678	05101	803558710	247364705
5101011005	05101	97	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2518	0.0084880	05101	752178748	447459300
5101011006	05101	193	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2848	0.0096004	05101	850756582	812994235
5101011007	05101	210	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3131	0.0105543	05101	935294543	874799655
5101021001	05101	5	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	273	0.0009203	05101	81550754	34118753
5101021002	05101	218	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1881	0.0063407	05101	561893656	903653307
5101021003	05101	140	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1696	0.0057171	05101	506630324	615273817
5101021004	05101	74	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1415	0.0047699	05101	422689805	353780187
5101031001	05101	86	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1402	0.0047260	05101	418806436	403071805
5101031002	05101	78	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1780	0.0060002	05101	531722864	370320348
5101031003	05101	81	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1071	0.0036103	05101	319929880	382651836
5101031004	05101	43	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	739	0.0024911	05101	220754605	220851536
5101031005	05101	29	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	457	0.0015405	05101	136515365	156898254
5101031006	05101	29	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1051	0.0035428	05101	313955466	156898254
5101031007	05101	84	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2154	0.0072610	05101	643444410	394923151
5101031008	05101	93	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2337	0.0078778	05101	698110299	431399467
5101031009	05101	29	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1059	0.0035698	05101	316345232	156898254
5101031010	05101	38	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1678	0.0056564	05101	501253352	198382845
5101031011	05101	104	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2610	0.0087981	05101	779661053	475356461
5101031012	05101	35	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	659	0.0022214	05101	196856948	184715970
5101041001	05101	10	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	177	0.0005967	05101	52873566	62269466
5101051001	05101	67	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1872	0.0063104	05101	559205169	324545080
5101051002	05101	49	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1714	0.0057778	05101	512007297	247364705
5101051003	05101	75	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2345	0.0079048	05101	700500065	357926059
5101051004	05101	65	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3014	0.0101600	05101	900344220	316119598
5101051005	05101	34	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1688	0.0056901	05101	504240559	180126489
5101051006	05101	53	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3387	0.0114173	05101	1011767045	264799785
5101051007	05101	31	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2233	0.0075273	05101	667043346	166248387
5101061001	05101	52	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1196	0.0040316	05101	357269969	260457823
5101061002	05101	36	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	878	0.0029597	05101	262276783	189288166
5101061003	05101	83	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1642	0.0055350	05101	490499406	390839244
5101061004	05101	71	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	862	0.0029057	05101	257497252	341297683
5101061005	05101	102	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1546	0.0052114	05101	461822218	467411883
5101071001	05101	152	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1265	0.0042642	05101	377881698	660797064
5101081001	05101	68	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	687	0.0023158	05101	205221128	328745321
5101081002	05101	50	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	634	0.0021372	05101	189388930	251740534
5101081003	05101	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	619	0.0020866	05101	184908120	128304071
5101081004	05101	65	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	620	0.0020900	05101	185206840	316119598
5101081005	05101	70	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	979	0.0033001	05101	292447575	337121491
5101081006	05101	39	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1435	0.0048373	05101	428664219	202906323
5101081007	05101	63	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1791	0.0060373	05101	535008792	307659810
5101081008	05101	41	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1274	0.0042946	05101	380570185	211907838
5101081009	05101	57	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1519	0.0051204	05101	453756759	282061807
5101081010	05101	79	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1320	0.0044496	05101	394311337	374437687
5101081011	05101	25	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1143	0.0038530	05101	341437772	137933944
5101091001	05101	87	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1689	0.0056935	05101	504539279	407136721
5101091002	05101	97	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1322	0.0044564	05101	394908779	447459300
5101091003	05101	79	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1275	0.0042979	05101	380868905	374437687
5101091004	05101	37	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	4201	0.0141612	05101	1254925703	193843620
5101101001	05101	77	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1044	0.0035192	05101	311864421	366196032
5101101002	05101	115	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	988	0.0033305	05101	295136062	518702620
5101101003	05101	75	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	849	0.0028619	05101	253613883	357926059
5101101004	05101	47	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	717	0.0024169	05101	214182749	238577345
5101101005	05101	46	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	936	0.0031552	05101	279602585	234165242
5101101006	05101	87	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1453	0.0048979	05101	434041192	407136721
5101101007	05101	10	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	604	0.0020360	05101	180427309	62269466
5101101008	05101	38	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2358	0.0079486	05101	704383434	198382845
5101101009	05101	113	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3502	0.0118050	05101	1046119927	510863777
5101111001	05101	142	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2120	0.0071463	05101	633287905	622895704
5101121001	05101	107	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1435	0.0048373	05101	428664219	487235684
5101121002	05101	79	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1941	0.0065430	05101	579816898	374437687
5101121003	05101	75	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1528	0.0051508	05101	456445245	357926059
5101131001	05101	49	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1170	0.0039440	05101	349503231	247364705
5101131002	05101	17	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	572	0.0019282	05101	170868246	98695100
5101131003	05101	20	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	638	0.0021506	05101	190583813	113646781
5101131004	05101	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	880	0.0029664	05101	262874225	128304071
5101131005	05101	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	664	0.0022383	05101	198350551	133132257
5101141001	05101	52	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1502	0.0050631	05101	448678507	260457823
5101141002	05101	27	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1122	0.0037822	05101	335164637	147462515
5101141003	05101	39	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1031	0.0034754	05101	307981052	202906323
5101141004	05101	9	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	638	0.0021506	05101	190583813	56827621
5101141005	05101	52	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	704	0.0023731	05101	210299380	260457823
5101141006	05101	53	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1265	0.0042642	05101	377881698	264799785
5101151001	05101	25	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	532	0.0017933	05101	158919418	137933944
5101151002	05101	35	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	455	0.0015338	05101	135917923	184715970
5101151003	05101	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	640	0.0021574	05101	191181254	128304071
5101151004	05101	20	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	543	0.0018304	05101	162205346	113646781
5101151005	05101	27	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	503	0.0016956	05101	150256517	147462515
5101151006	05101	28	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	774	0.0026091	05101	231209830	152191511
5101151007	05101	66	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1448	0.0048811	05101	432547588	320336553
5101161001	05101	17	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	625	0.0021068	05101	186700444	98695100
5101161002	05101	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	697	0.0023495	05101	208208335	128304071
5101161003	05101	8	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	496	0.0016720	05101	148165472	51305170
5101161004	05101	64	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	892	0.0030069	05101	266458873	311894068
5101161005	05101	13	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	518	0.0017461	05101	154737328	78193998
5101161006	05101	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1016	0.0034249	05101	303500241	133132257
5101161007	05101	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	950	0.0032024	05101	283784675	133132257
5101161008	05101	64	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1194	0.0040249	05101	356672528	311894068
5101161009	05101	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	856	0.0028855	05101	255704928	133132257
5101161010	05101	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1235	0.0041631	05101	368920077	128304071
5101161011	05101	40	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1840	0.0062025	05101	549646107	207414510
5101161012	05101	50	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2467	0.0083161	05101	736943992	251740534
5101171001	05101	79	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	932	0.0031417	05101	278407702	374437687
5101171002	05101	147	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1142	0.0038496	05101	341139051	641888948
5101171003	05101	114	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1447	0.0048777	05101	432248867	514785468
5101171004	05101	54	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1122	0.0037822	05101	335164637	269130942
5101171005	05101	60	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1452	0.0048946	05101	433742471	294903077

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	comuna.y	personas	Ingresos_expandidos	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
5101011001	05101	200	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3280	0.0110566	05101	979803929	838527170	255648.53
5101011002	05101	183	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3761	0.0126780	05101	1123488591	776304658	206409.11
5101011003	05101	184	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2365	0.0079722	05101	706474479	779985307	329803.51
5101011004	05101	49	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2690	0.0090678	05101	803558710	247364705	91957.14
5101011005	05101	97	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2518	0.0084880	05101	752178748	447459300	177704.25
5101011006	05101	193	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2848	0.0096004	05101	850756582	812994235	285461.46
5101011007	05101	210	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3131	0.0105543	05101	935294543	874799655	279399.44
5101021001	05101	5	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	273	0.0009203	05101	81550754	34118753	124977.12
5101021002	05101	218	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1881	0.0063407	05101	561893656	903653307	480411.11
5101021003	05101	140	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1696	0.0057171	05101	506630324	615273817	362779.37
5101021004	05101	74	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1415	0.0047699	05101	422689805	353780187	250021.33
5101031001	05101	86	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1402	0.0047260	05101	418806436	403071805	287497.72
5101031002	05101	78	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1780	0.0060002	05101	531722864	370320348	208045.14
5101031003	05101	81	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1071	0.0036103	05101	319929880	382651836	357284.63
5101031004	05101	43	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	739	0.0024911	05101	220754605	220851536	298851.88
5101031005	05101	29	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	457	0.0015405	05101	136515365	156898254	343322.22
5101031006	05101	29	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1051	0.0035428	05101	313955466	156898254	149284.73
5101031007	05101	84	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2154	0.0072610	05101	643444410	394923151	183344.08
5101031008	05101	93	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2337	0.0078778	05101	698110299	431399467	184595.41
5101031009	05101	29	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1059	0.0035698	05101	316345232	156898254	148156.99
5101031010	05101	38	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1678	0.0056564	05101	501253352	198382845	118225.77
5101031011	05101	104	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2610	0.0087981	05101	779661053	475356461	182128.91
5101031012	05101	35	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	659	0.0022214	05101	196856948	184715970	280297.37
5101041001	05101	10	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	177	0.0005967	05101	52873566	62269466	351804.89
5101051001	05101	67	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1872	0.0063104	05101	559205169	324545080	173368.10
5101051002	05101	49	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1714	0.0057778	05101	512007297	247364705	144320.13
5101051003	05101	75	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2345	0.0079048	05101	700500065	357926059	152633.71
5101051004	05101	65	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3014	0.0101600	05101	900344220	316119598	104883.74
5101051005	05101	34	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1688	0.0056901	05101	504240559	180126489	106710.01
5101051006	05101	53	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3387	0.0114173	05101	1011767045	264799785	78181.22
5101051007	05101	31	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2233	0.0075273	05101	667043346	166248387	74450.69
5101061001	05101	52	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1196	0.0040316	05101	357269969	260457823	217774.10
5101061002	05101	36	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	878	0.0029597	05101	262276783	189288166	215590.17
5101061003	05101	83	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1642	0.0055350	05101	490499406	390839244	238026.34
5101061004	05101	71	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	862	0.0029057	05101	257497252	341297683	395936.99
5101061005	05101	102	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1546	0.0052114	05101	461822218	467411883	302336.28
5101071001	05101	152	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1265	0.0042642	05101	377881698	660797064	522369.22
5101081001	05101	68	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	687	0.0023158	05101	205221128	328745321	478523.03
5101081002	05101	50	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	634	0.0021372	05101	189388930	251740534	397067.09
5101081003	05101	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	619	0.0020866	05101	184908120	128304071	207276.37
5101081004	05101	65	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	620	0.0020900	05101	185206840	316119598	509870.32
5101081005	05101	70	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	979	0.0033001	05101	292447575	337121491	344352.90
5101081006	05101	39	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1435	0.0048373	05101	428664219	202906323	141398.13
5101081007	05101	63	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1791	0.0060373	05101	535008792	307659810	171781.02
5101081008	05101	41	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1274	0.0042946	05101	380570185	211907838	166332.68
5101081009	05101	57	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1519	0.0051204	05101	453756759	282061807	185689.14
5101081010	05101	79	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1320	0.0044496	05101	394311337	374437687	283664.91
5101081011	05101	25	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1143	0.0038530	05101	341437772	137933944	120677.12
5101091001	05101	87	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1689	0.0056935	05101	504539279	407136721	241051.94
5101091002	05101	97	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1322	0.0044564	05101	394908779	447459300	338471.48
5101091003	05101	79	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1275	0.0042979	05101	380868905	374437687	293676.62
5101091004	05101	37	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	4201	0.0141612	05101	1254925703	193843620	46142.26
5101101001	05101	77	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1044	0.0035192	05101	311864421	366196032	350762.48
5101101002	05101	115	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	988	0.0033305	05101	295136062	518702620	525002.65
5101101003	05101	75	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	849	0.0028619	05101	253613883	357926059	421585.46
5101101004	05101	47	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	717	0.0024169	05101	214182749	238577345	332743.86
5101101005	05101	46	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	936	0.0031552	05101	279602585	234165242	250176.54
5101101006	05101	87	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1453	0.0048979	05101	434041192	407136721	280204.21
5101101007	05101	10	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	604	0.0020360	05101	180427309	62269466	103095.14
5101101008	05101	38	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2358	0.0079486	05101	704383434	198382845	84131.83
5101101009	05101	113	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	3502	0.0118050	05101	1046119927	510863777	145877.72
5101111001	05101	142	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2120	0.0071463	05101	633287905	622895704	293818.73
5101121001	05101	107	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1435	0.0048373	05101	428664219	487235684	339537.06
5101121002	05101	79	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1941	0.0065430	05101	579816898	374437687	192909.68
5101121003	05101	75	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1528	0.0051508	05101	456445245	357926059	234244.80
5101131001	05101	49	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1170	0.0039440	05101	349503231	247364705	211422.83
5101131002	05101	17	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	572	0.0019282	05101	170868246	98695100	172543.88
5101131003	05101	20	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	638	0.0021506	05101	190583813	113646781	178129.75
5101131004	05101	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	880	0.0029664	05101	262874225	128304071	145800.08
5101131005	05101	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	664	0.0022383	05101	198350551	133132257	200500.39
5101141001	05101	52	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1502	0.0050631	05101	448678507	260457823	173407.34
5101141002	05101	27	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1122	0.0037822	05101	335164637	147462515	131428.27
5101141003	05101	39	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1031	0.0034754	05101	307981052	202906323	196805.36
5101141004	05101	9	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	638	0.0021506	05101	190583813	56827621	89071.51
5101141005	05101	52	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	704	0.0023731	05101	210299380	260457823	369968.50
5101141006	05101	53	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1265	0.0042642	05101	377881698	264799785	209327.89
5101151001	05101	25	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	532	0.0017933	05101	158919418	137933944	259274.33
5101151002	05101	35	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	455	0.0015338	05101	135917923	184715970	405969.16
5101151003	05101	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	640	0.0021574	05101	191181254	128304071	200475.11
5101151004	05101	20	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	543	0.0018304	05101	162205346	113646781	209294.26
5101151005	05101	27	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	503	0.0016956	05101	150256517	147462515	293166.03
5101151006	05101	28	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	774	0.0026091	05101	231209830	152191511	196629.86
5101151007	05101	66	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1448	0.0048811	05101	432547588	320336553	221226.90
5101161001	05101	17	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	625	0.0021068	05101	186700444	98695100	157912.16
5101161002	05101	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	697	0.0023495	05101	208208335	128304071	184080.45
5101161003	05101	8	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	496	0.0016720	05101	148165472	51305170	103437.84
5101161004	05101	64	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	892	0.0030069	05101	266458873	311894068	349657.03
5101161005	05101	13	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	518	0.0017461	05101	154737328	78193998	150953.66
5101161006	05101	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1016	0.0034249	05101	303500241	133132257	131035.69
5101161007	05101	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	950	0.0032024	05101	283784675	133132257	140139.22
5101161008	05101	64	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1194	0.0040249	05101	356672528	311894068	261217.81
5101161009	05101	24	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	856	0.0028855	05101	255704928	133132257	155528.34
5101161010	05101	23	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1235	0.0041631	05101	368920077	128304071	103889.94
5101161011	05101	40	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1840	0.0062025	05101	549646107	207414510	112725.28
5101161012	05101	50	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	2467	0.0083161	05101	736943992	251740534	102043.18
5101171001	05101	79	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	932	0.0031417	05101	278407702	374437687	401757.18
5101171002	05101	147	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1142	0.0038496	05101	341139051	641888948	562074.39
5101171003	05101	114	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1447	0.0048777	05101	432248867	514785468	355760.52
5101171004	05101	54	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1122	0.0037822	05101	335164637	269130942	239867.15
5101171005	05101	60	2017	Valparaíso	298720.7	2017	5101	296655	88616992249	1452	0.0048946	05101	433742471	294903077	203101.29

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_region_05.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