Expansión de la CASEN sobre el CENSO (Región 13)

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 13.

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 == 13) 

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
13101011001	1	13101	211	2017
13101011002	1	13101	241	2017
13101011003	1	13101	141	2017
13101011004	1	13101	114	2017
13101011005	1	13101	96	2017
13101021001	1	13101	80	2017
13101021002	1	13101	29	2017
13101021003	1	13101	190	2017
13101021004	1	13101	391	2017
13101021005	1	13101	139	2017
13101021006	1	13101	326	2017
13101021007	1	13101	99	2017
13101021008	1	13101	71	2017
13101031001	1	13101	122	2017
13101031002	1	13101	265	2017
13101031003	1	13101	348	2017
13101031004	1	13101	359	2017
13101031005	1	13101	139	2017
13101031006	1	13101	174	2017
13101031007	1	13101	307	2017
13101041001	1	13101	300	2017
13101041002	1	13101	219	2017
13101041003	1	13101	266	2017
13101041004	1	13101	153	2017
13101041005	1	13101	219	2017
13101051001	1	13101	253	2017
13101051002	1	13101	370	2017
13101051003	1	13101	165	2017
13101061001	1	13101	417	2017
13101061002	1	13101	418	2017
13101061003	1	13101	227	2017
13101071001	1	13101	390	2017
13101071002	1	13101	230	2017
13101071003	1	13101	218	2017
13101081001	1	13101	234	2017
13101081002	1	13101	433	2017
13101081003	1	13101	125	2017
13101081004	1	13101	216	2017
13101091001	1	13101	186	2017
13101091002	1	13101	394	2017
13101091003	1	13101	373	2017
13101091004	1	13101	267	2017
13101101001	1	13101	44	2017
13101101002	1	13101	68	2017
13101101003	1	13101	156	2017
13101101004	1	13101	101	2017
13101101005	1	13101	52	2017
13101101006	1	13101	42	2017
13101101007	1	13101	105	2017
13101101008	1	13101	114	2017
13101101009	1	13101	33	2017
13101101010	1	13101	149	2017
13101111001	1	13101	56	2017
13101111002	1	13101	409	2017
13101111003	1	13101	84	2017
13101111004	1	13101	93	2017
13101111005	1	13101	85	2017
13101111006	1	13101	60	2017
13101111007	1	13101	105	2017
13101111008	1	13101	94	2017
13101111009	1	13101	140	2017
13101111010	1	13101	59	2017
13101111011	1	13101	68	2017
13101111012	1	13101	147	2017
13101111013	1	13101	85	2017
13101111014	1	13101	69	2017
13101111015	1	13101	187	2017
13101111016	1	13101	74	2017
13101111017	1	13101	113	2017
13101111018	1	13101	71	2017
13101111019	1	13101	152	2017
13101121001	1	13101	123	2017
13101121002	1	13101	152	2017
13101121003	1	13101	63	2017
13101121004	1	13101	375	2017
13101121005	1	13101	165	2017
13101121006	1	13101	213	2017
13101121007	1	13101	164	2017
13101121008	1	13101	276	2017
13101121009	1	13101	320	2017
13101131001	1	13101	114	2017
13101131002	1	13101	79	2017
13101131003	1	13101	143	2017
13101131004	1	13101	158	2017
13101131005	1	13101	102	2017
13101131006	1	13101	40	2017
13101131007	1	13101	202	2017
13101131008	1	13101	205	2017
13101131009	1	13101	173	2017
13101141001	1	13101	228	2017
13101141002	1	13101	101	2017
13101141003	1	13101	196	2017
13101151001	1	13101	497	2017
13101151002	1	13101	316	2017
13101151003	1	13101	210	2017
13101151004	1	13101	175	2017
13101161001	1	13101	190	2017
13101161002	1	13101	350	2017
13101161003	1	13101	292	2017
13101171001	1	13101	365	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
13101011001	211	2017	13101
13101011002	241	2017	13101
13101011003	141	2017	13101
13101011004	114	2017	13101
13101011005	96	2017	13101
13101021001	80	2017	13101
13101021002	29	2017	13101
13101021003	190	2017	13101
13101021004	391	2017	13101
13101021005	139	2017	13101
13101021006	326	2017	13101
13101021007	99	2017	13101
13101021008	71	2017	13101
13101031001	122	2017	13101
13101031002	265	2017	13101
13101031003	348	2017	13101
13101031004	359	2017	13101
13101031005	139	2017	13101
13101031006	174	2017	13101
13101031007	307	2017	13101
13101041001	300	2017	13101
13101041002	219	2017	13101
13101041003	266	2017	13101
13101041004	153	2017	13101
13101041005	219	2017	13101
13101051001	253	2017	13101
13101051002	370	2017	13101
13101051003	165	2017	13101
13101061001	417	2017	13101
13101061002	418	2017	13101
13101061003	227	2017	13101
13101071001	390	2017	13101
13101071002	230	2017	13101
13101071003	218	2017	13101
13101081001	234	2017	13101
13101081002	433	2017	13101
13101081003	125	2017	13101
13101081004	216	2017	13101
13101091001	186	2017	13101
13101091002	394	2017	13101
13101091003	373	2017	13101
13101091004	267	2017	13101
13101101001	44	2017	13101
13101101002	68	2017	13101
13101101003	156	2017	13101
13101101004	101	2017	13101
13101101005	52	2017	13101
13101101006	42	2017	13101
13101101007	105	2017	13101
13101101008	114	2017	13101
13101101009	33	2017	13101
13101101010	149	2017	13101
13101111001	56	2017	13101
13101111002	409	2017	13101
13101111003	84	2017	13101
13101111004	93	2017	13101
13101111005	85	2017	13101
13101111006	60	2017	13101
13101111007	105	2017	13101
13101111008	94	2017	13101
13101111009	140	2017	13101
13101111010	59	2017	13101
13101111011	68	2017	13101
13101111012	147	2017	13101
13101111013	85	2017	13101
13101111014	69	2017	13101
13101111015	187	2017	13101
13101111016	74	2017	13101
13101111017	113	2017	13101
13101111018	71	2017	13101
13101111019	152	2017	13101
13101121001	123	2017	13101
13101121002	152	2017	13101
13101121003	63	2017	13101
13101121004	375	2017	13101
13101121005	165	2017	13101
13101121006	213	2017	13101
13101121007	164	2017	13101
13101121008	276	2017	13101
13101121009	320	2017	13101
13101131001	114	2017	13101
13101131002	79	2017	13101
13101131003	143	2017	13101
13101131004	158	2017	13101
13101131005	102	2017	13101
13101131006	40	2017	13101
13101131007	202	2017	13101
13101131008	205	2017	13101
13101131009	173	2017	13101
13101141001	228	2017	13101
13101141002	101	2017	13101
13101141003	196	2017	13101
13101151001	497	2017	13101
13101151002	316	2017	13101
13101151003	210	2017	13101
13101151004	175	2017	13101
13101161001	190	2017	13101
13101161002	350	2017	13101
13101161003	292	2017	13101
13101171001	365	2017	13101

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
13101	13101011001	211	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101011002	241	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101011003	141	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101011004	114	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101011005	96	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101021001	80	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101021002	29	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101021003	190	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101021004	391	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101021005	139	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101021006	326	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101021007	99	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101021008	71	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101031001	122	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101031002	265	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101031003	348	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101031004	359	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101031005	139	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101031006	174	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101031007	307	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101041001	300	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101041002	219	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101041003	266	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101041004	153	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101041005	219	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101051001	253	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101051002	370	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101051003	165	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101061001	417	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101061002	418	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101061003	227	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101071001	390	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101071002	230	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101071003	218	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101081001	234	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101081002	433	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101081003	125	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101081004	216	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101091001	186	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101091002	394	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101091003	373	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101091004	267	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101101001	44	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101101002	68	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101101003	156	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101101004	101	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101101005	52	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101101006	42	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101101007	105	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101101008	114	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101101009	33	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101101010	149	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111001	56	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111002	409	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111003	84	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111004	93	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111005	85	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111006	60	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111007	105	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111008	94	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111009	140	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111010	59	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111011	68	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111012	147	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111013	85	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111014	69	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111015	187	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111016	74	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111017	113	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111018	71	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111019	152	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101121001	123	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101121002	152	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101121003	63	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101121004	375	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101121005	165	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101121006	213	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101121007	164	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101121008	276	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101121009	320	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101131001	114	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101131002	79	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101131003	143	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101131004	158	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101131005	102	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101131006	40	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101131007	202	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101131008	205	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101131009	173	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101141001	228	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101141002	101	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101141003	196	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101151001	497	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101151002	316	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101151003	210	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101151004	175	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101161001	190	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101161002	350	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101161003	292	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101171001	365	2017	Santiago	425637.2	2017	13101	404495	172168109577

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
13101	13101011001	211	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101011002	241	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101011003	141	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101011004	114	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101011005	96	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101021001	80	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101021002	29	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101021003	190	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101021004	391	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101021005	139	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101021006	326	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101021007	99	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101021008	71	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101031001	122	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101031002	265	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101031003	348	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101031004	359	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101031005	139	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101031006	174	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101031007	307	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101041001	300	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101041002	219	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101041003	266	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101041004	153	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101041005	219	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101051001	253	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101051002	370	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101051003	165	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101061001	417	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101061002	418	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101061003	227	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101071001	390	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101071002	230	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101071003	218	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101081001	234	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101081002	433	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101081003	125	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101081004	216	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101091001	186	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101091002	394	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101091003	373	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101091004	267	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101101001	44	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101101002	68	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101101003	156	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101101004	101	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101101005	52	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101101006	42	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101101007	105	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101101008	114	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101101009	33	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101101010	149	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111001	56	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111002	409	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111003	84	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111004	93	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111005	85	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111006	60	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111007	105	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111008	94	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111009	140	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111010	59	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111011	68	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111012	147	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111013	85	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111014	69	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111015	187	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111016	74	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111017	113	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111018	71	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101111019	152	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101121001	123	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101121002	152	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101121003	63	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101121004	375	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101121005	165	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101121006	213	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101121007	164	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101121008	276	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101121009	320	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101131001	114	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101131002	79	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101131003	143	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101131004	158	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101131005	102	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101131006	40	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101131007	202	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101131008	205	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101131009	173	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101141001	228	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101141002	101	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101141003	196	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101151001	497	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101151002	316	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101151003	210	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101151004	175	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101161001	190	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101161002	350	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101161003	292	2017	Santiago	425637.2	2017	13101	404495	172168109577
13101	13101171001	365	2017	Santiago	425637.2	2017	13101	404495	172168109577

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
13101011001	13101	211	2017	Santiago	425637.2	2017	13101	404495	172168109577	2174	0.0053746	13101
13101011002	13101	241	2017	Santiago	425637.2	2017	13101	404495	172168109577	2282	0.0056416	13101
13101011003	13101	141	2017	Santiago	425637.2	2017	13101	404495	172168109577	2209	0.0054611	13101
13101011004	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	1821	0.0045019	13101
13101011005	13101	96	2017	Santiago	425637.2	2017	13101	404495	172168109577	1741	0.0043041	13101
13101021001	13101	80	2017	Santiago	425637.2	2017	13101	404495	172168109577	3448	0.0085242	13101
13101021002	13101	29	2017	Santiago	425637.2	2017	13101	404495	172168109577	2856	0.0070607	13101
13101021003	13101	190	2017	Santiago	425637.2	2017	13101	404495	172168109577	2768	0.0068431	13101
13101021004	13101	391	2017	Santiago	425637.2	2017	13101	404495	172168109577	3279	0.0081064	13101
13101021005	13101	139	2017	Santiago	425637.2	2017	13101	404495	172168109577	2258	0.0055823	13101
13101021006	13101	326	2017	Santiago	425637.2	2017	13101	404495	172168109577	2478	0.0061262	13101
13101021007	13101	99	2017	Santiago	425637.2	2017	13101	404495	172168109577	2326	0.0057504	13101
13101021008	13101	71	2017	Santiago	425637.2	2017	13101	404495	172168109577	1541	0.0038097	13101
13101031001	13101	122	2017	Santiago	425637.2	2017	13101	404495	172168109577	2313	0.0057182	13101
13101031002	13101	265	2017	Santiago	425637.2	2017	13101	404495	172168109577	5950	0.0147097	13101
13101031003	13101	348	2017	Santiago	425637.2	2017	13101	404495	172168109577	4955	0.0122498	13101
13101031004	13101	359	2017	Santiago	425637.2	2017	13101	404495	172168109577	3903	0.0096491	13101
13101031005	13101	139	2017	Santiago	425637.2	2017	13101	404495	172168109577	1791	0.0044277	13101
13101031006	13101	174	2017	Santiago	425637.2	2017	13101	404495	172168109577	1697	0.0041954	13101
13101031007	13101	307	2017	Santiago	425637.2	2017	13101	404495	172168109577	2973	0.0073499	13101
13101041001	13101	300	2017	Santiago	425637.2	2017	13101	404495	172168109577	2790	0.0068975	13101
13101041002	13101	219	2017	Santiago	425637.2	2017	13101	404495	172168109577	2668	0.0065959	13101
13101041003	13101	266	2017	Santiago	425637.2	2017	13101	404495	172168109577	2729	0.0067467	13101
13101041004	13101	153	2017	Santiago	425637.2	2017	13101	404495	172168109577	2828	0.0069914	13101
13101041005	13101	219	2017	Santiago	425637.2	2017	13101	404495	172168109577	2550	0.0063042	13101
13101051001	13101	253	2017	Santiago	425637.2	2017	13101	404495	172168109577	3135	0.0077504	13101
13101051002	13101	370	2017	Santiago	425637.2	2017	13101	404495	172168109577	4424	0.0109371	13101
13101051003	13101	165	2017	Santiago	425637.2	2017	13101	404495	172168109577	2355	0.0058221	13101
13101061001	13101	417	2017	Santiago	425637.2	2017	13101	404495	172168109577	3968	0.0098098	13101
13101061002	13101	418	2017	Santiago	425637.2	2017	13101	404495	172168109577	4747	0.0117356	13101
13101061003	13101	227	2017	Santiago	425637.2	2017	13101	404495	172168109577	2864	0.0070804	13101
13101071001	13101	390	2017	Santiago	425637.2	2017	13101	404495	172168109577	5009	0.0123833	13101
13101071002	13101	230	2017	Santiago	425637.2	2017	13101	404495	172168109577	3511	0.0086800	13101
13101071003	13101	218	2017	Santiago	425637.2	2017	13101	404495	172168109577	3368	0.0083264	13101
13101081001	13101	234	2017	Santiago	425637.2	2017	13101	404495	172168109577	3716	0.0091868	13101
13101081002	13101	433	2017	Santiago	425637.2	2017	13101	404495	172168109577	5244	0.0129643	13101
13101081003	13101	125	2017	Santiago	425637.2	2017	13101	404495	172168109577	3326	0.0082226	13101
13101081004	13101	216	2017	Santiago	425637.2	2017	13101	404495	172168109577	2794	0.0069074	13101
13101091001	13101	186	2017	Santiago	425637.2	2017	13101	404495	172168109577	3393	0.0083882	13101
13101091002	13101	394	2017	Santiago	425637.2	2017	13101	404495	172168109577	3321	0.0082102	13101
13101091003	13101	373	2017	Santiago	425637.2	2017	13101	404495	172168109577	3210	0.0079358	13101
13101091004	13101	267	2017	Santiago	425637.2	2017	13101	404495	172168109577	2956	0.0073079	13101
13101101001	13101	44	2017	Santiago	425637.2	2017	13101	404495	172168109577	2491	0.0061583	13101
13101101002	13101	68	2017	Santiago	425637.2	2017	13101	404495	172168109577	1820	0.0044994	13101
13101101003	13101	156	2017	Santiago	425637.2	2017	13101	404495	172168109577	2768	0.0068431	13101
13101101004	13101	101	2017	Santiago	425637.2	2017	13101	404495	172168109577	2423	0.0059902	13101
13101101005	13101	52	2017	Santiago	425637.2	2017	13101	404495	172168109577	1805	0.0044624	13101
13101101006	13101	42	2017	Santiago	425637.2	2017	13101	404495	172168109577	1296	0.0032040	13101
13101101007	13101	105	2017	Santiago	425637.2	2017	13101	404495	172168109577	2965	0.0073301	13101
13101101008	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	1624	0.0040149	13101
13101101009	13101	33	2017	Santiago	425637.2	2017	13101	404495	172168109577	1345	0.0033251	13101
13101101010	13101	149	2017	Santiago	425637.2	2017	13101	404495	172168109577	2930	0.0072436	13101
13101111001	13101	56	2017	Santiago	425637.2	2017	13101	404495	172168109577	2555	0.0063165	13101
13101111002	13101	409	2017	Santiago	425637.2	2017	13101	404495	172168109577	2594	0.0064129	13101
13101111003	13101	84	2017	Santiago	425637.2	2017	13101	404495	172168109577	2269	0.0056095	13101
13101111004	13101	93	2017	Santiago	425637.2	2017	13101	404495	172168109577	1996	0.0049345	13101
13101111005	13101	85	2017	Santiago	425637.2	2017	13101	404495	172168109577	1960	0.0048455	13101
13101111006	13101	60	2017	Santiago	425637.2	2017	13101	404495	172168109577	1677	0.0041459	13101
13101111007	13101	105	2017	Santiago	425637.2	2017	13101	404495	172168109577	1859	0.0045959	13101
13101111008	13101	94	2017	Santiago	425637.2	2017	13101	404495	172168109577	2552	0.0063091	13101
13101111009	13101	140	2017	Santiago	425637.2	2017	13101	404495	172168109577	3455	0.0085415	13101
13101111010	13101	59	2017	Santiago	425637.2	2017	13101	404495	172168109577	2560	0.0063289	13101
13101111011	13101	68	2017	Santiago	425637.2	2017	13101	404495	172168109577	1550	0.0038319	13101
13101111012	13101	147	2017	Santiago	425637.2	2017	13101	404495	172168109577	4104	0.0101460	13101
13101111013	13101	85	2017	Santiago	425637.2	2017	13101	404495	172168109577	3191	0.0078888	13101
13101111014	13101	69	2017	Santiago	425637.2	2017	13101	404495	172168109577	1894	0.0046824	13101
13101111015	13101	187	2017	Santiago	425637.2	2017	13101	404495	172168109577	2747	0.0067912	13101
13101111016	13101	74	2017	Santiago	425637.2	2017	13101	404495	172168109577	2452	0.0060619	13101
13101111017	13101	113	2017	Santiago	425637.2	2017	13101	404495	172168109577	1798	0.0044450	13101
13101111018	13101	71	2017	Santiago	425637.2	2017	13101	404495	172168109577	2724	0.0067343	13101
13101111019	13101	152	2017	Santiago	425637.2	2017	13101	404495	172168109577	2430	0.0060075	13101
13101121001	13101	123	2017	Santiago	425637.2	2017	13101	404495	172168109577	2320	0.0057355	13101
13101121002	13101	152	2017	Santiago	425637.2	2017	13101	404495	172168109577	1774	0.0043857	13101
13101121003	13101	63	2017	Santiago	425637.2	2017	13101	404495	172168109577	2011	0.0049716	13101
13101121004	13101	375	2017	Santiago	425637.2	2017	13101	404495	172168109577	4425	0.0109396	13101
13101121005	13101	165	2017	Santiago	425637.2	2017	13101	404495	172168109577	2095	0.0051793	13101
13101121006	13101	213	2017	Santiago	425637.2	2017	13101	404495	172168109577	3272	0.0080891	13101
13101121007	13101	164	2017	Santiago	425637.2	2017	13101	404495	172168109577	3067	0.0075823	13101
13101121008	13101	276	2017	Santiago	425637.2	2017	13101	404495	172168109577	4682	0.0115749	13101
13101121009	13101	320	2017	Santiago	425637.2	2017	13101	404495	172168109577	4441	0.0109791	13101
13101131001	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	1811	0.0044772	13101
13101131002	13101	79	2017	Santiago	425637.2	2017	13101	404495	172168109577	1759	0.0043486	13101
13101131003	13101	143	2017	Santiago	425637.2	2017	13101	404495	172168109577	3953	0.0097727	13101
13101131004	13101	158	2017	Santiago	425637.2	2017	13101	404495	172168109577	2442	0.0060372	13101
13101131005	13101	102	2017	Santiago	425637.2	2017	13101	404495	172168109577	3683	0.0091052	13101
13101131006	13101	40	2017	Santiago	425637.2	2017	13101	404495	172168109577	2197	0.0054315	13101
13101131007	13101	202	2017	Santiago	425637.2	2017	13101	404495	172168109577	2952	0.0072980	13101
13101131008	13101	205	2017	Santiago	425637.2	2017	13101	404495	172168109577	3025	0.0074785	13101
13101131009	13101	173	2017	Santiago	425637.2	2017	13101	404495	172168109577	3631	0.0089766	13101
13101141001	13101	228	2017	Santiago	425637.2	2017	13101	404495	172168109577	4373	0.0108110	13101
13101141002	13101	101	2017	Santiago	425637.2	2017	13101	404495	172168109577	3048	0.0075353	13101
13101141003	13101	196	2017	Santiago	425637.2	2017	13101	404495	172168109577	4067	0.0100545	13101
13101151001	13101	497	2017	Santiago	425637.2	2017	13101	404495	172168109577	4797	0.0118592	13101
13101151002	13101	316	2017	Santiago	425637.2	2017	13101	404495	172168109577	3915	0.0096787	13101
13101151003	13101	210	2017	Santiago	425637.2	2017	13101	404495	172168109577	3644	0.0090088	13101
13101151004	13101	175	2017	Santiago	425637.2	2017	13101	404495	172168109577	2038	0.0050384	13101
13101161001	13101	190	2017	Santiago	425637.2	2017	13101	404495	172168109577	2637	0.0065192	13101
13101161002	13101	350	2017	Santiago	425637.2	2017	13101	404495	172168109577	5341	0.0132041	13101
13101161003	13101	292	2017	Santiago	425637.2	2017	13101	404495	172168109577	5331	0.0131794	13101
13101171001	13101	365	2017	Santiago	425637.2	2017	13101	404495	172168109577	5638	0.0139384	13101

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
13101011001	13101	211	2017	Santiago	425637.2	2017	13101	404495	172168109577	2174	0.0053746	13101	925335221
13101011002	13101	241	2017	Santiago	425637.2	2017	13101	404495	172168109577	2282	0.0056416	13101	971304036
13101011003	13101	141	2017	Santiago	425637.2	2017	13101	404495	172168109577	2209	0.0054611	13101	940232522
13101011004	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	1821	0.0045019	13101	775085298
13101011005	13101	96	2017	Santiago	425637.2	2017	13101	404495	172168109577	1741	0.0043041	13101	741034324
13101021001	13101	80	2017	Santiago	425637.2	2017	13101	404495	172168109577	3448	0.0085242	13101	1467596983
13101021002	13101	29	2017	Santiago	425637.2	2017	13101	404495	172168109577	2856	0.0070607	13101	1215619775
13101021003	13101	190	2017	Santiago	425637.2	2017	13101	404495	172168109577	2768	0.0068431	13101	1178163704
13101021004	13101	391	2017	Santiago	425637.2	2017	13101	404495	172168109577	3279	0.0081064	13101	1395664301
13101021005	13101	139	2017	Santiago	425637.2	2017	13101	404495	172168109577	2258	0.0055823	13101	961088744
13101021006	13101	326	2017	Santiago	425637.2	2017	13101	404495	172168109577	2478	0.0061262	13101	1054728923
13101021007	13101	99	2017	Santiago	425637.2	2017	13101	404495	172168109577	2326	0.0057504	13101	990032072
13101021008	13101	71	2017	Santiago	425637.2	2017	13101	404495	172168109577	1541	0.0038097	13101	655906888
13101031001	13101	122	2017	Santiago	425637.2	2017	13101	404495	172168109577	2313	0.0057182	13101	984498788
13101031002	13101	265	2017	Santiago	425637.2	2017	13101	404495	172168109577	5950	0.0147097	13101	2532541198
13101031003	13101	348	2017	Santiago	425637.2	2017	13101	404495	172168109577	4955	0.0122498	13101	2109032208
13101031004	13101	359	2017	Santiago	425637.2	2017	13101	404495	172168109577	3903	0.0096491	13101	1661261899
13101031005	13101	139	2017	Santiago	425637.2	2017	13101	404495	172168109577	1791	0.0044277	13101	762316183
13101031006	13101	174	2017	Santiago	425637.2	2017	13101	404495	172168109577	1697	0.0041954	13101	722306288
13101031007	13101	307	2017	Santiago	425637.2	2017	13101	404495	172168109577	2973	0.0073499	13101	1265419325
13101041001	13101	300	2017	Santiago	425637.2	2017	13101	404495	172168109577	2790	0.0068975	13101	1187527722
13101041002	13101	219	2017	Santiago	425637.2	2017	13101	404495	172168109577	2668	0.0065959	13101	1135599986
13101041003	13101	266	2017	Santiago	425637.2	2017	13101	404495	172168109577	2729	0.0067467	13101	1161563854
13101041004	13101	153	2017	Santiago	425637.2	2017	13101	404495	172168109577	2828	0.0069914	13101	1203701934
13101041005	13101	219	2017	Santiago	425637.2	2017	13101	404495	172168109577	2550	0.0063042	13101	1085374799
13101051001	13101	253	2017	Santiago	425637.2	2017	13101	404495	172168109577	3135	0.0077504	13101	1334372547
13101051002	13101	370	2017	Santiago	425637.2	2017	13101	404495	172168109577	4424	0.0109371	13101	1883018867
13101051003	13101	165	2017	Santiago	425637.2	2017	13101	404495	172168109577	2355	0.0058221	13101	1002375550
13101061001	13101	417	2017	Santiago	425637.2	2017	13101	404495	172168109577	3968	0.0098098	13101	1688928315
13101061002	13101	418	2017	Santiago	425637.2	2017	13101	404495	172168109577	4747	0.0117356	13101	2020499675
13101061003	13101	227	2017	Santiago	425637.2	2017	13101	404495	172168109577	2864	0.0070804	13101	1219024873
13101071001	13101	390	2017	Santiago	425637.2	2017	13101	404495	172168109577	5009	0.0123833	13101	2132016615
13101071002	13101	230	2017	Santiago	425637.2	2017	13101	404495	172168109577	3511	0.0086800	13101	1494412126
13101071003	13101	218	2017	Santiago	425637.2	2017	13101	404495	172168109577	3368	0.0083264	13101	1433546009
13101081001	13101	234	2017	Santiago	425637.2	2017	13101	404495	172168109577	3716	0.0091868	13101	1581667747
13101081002	13101	433	2017	Santiago	425637.2	2017	13101	404495	172168109577	5244	0.0129643	13101	2232041352
13101081003	13101	125	2017	Santiago	425637.2	2017	13101	404495	172168109577	3326	0.0082226	13101	1415669248
13101081004	13101	216	2017	Santiago	425637.2	2017	13101	404495	172168109577	2794	0.0069074	13101	1189230270
13101091001	13101	186	2017	Santiago	425637.2	2017	13101	404495	172168109577	3393	0.0083882	13101	1444186939
13101091002	13101	394	2017	Santiago	425637.2	2017	13101	404495	172168109577	3321	0.0082102	13101	1413541062
13101091003	13101	373	2017	Santiago	425637.2	2017	13101	404495	172168109577	3210	0.0079358	13101	1366295336
13101091004	13101	267	2017	Santiago	425637.2	2017	13101	404495	172168109577	2956	0.0073079	13101	1258183493
13101101001	13101	44	2017	Santiago	425637.2	2017	13101	404495	172168109577	2491	0.0061583	13101	1060262206
13101101002	13101	68	2017	Santiago	425637.2	2017	13101	404495	172168109577	1820	0.0044994	13101	774659661
13101101003	13101	156	2017	Santiago	425637.2	2017	13101	404495	172168109577	2768	0.0068431	13101	1178163704
13101101004	13101	101	2017	Santiago	425637.2	2017	13101	404495	172168109577	2423	0.0059902	13101	1031318878
13101101005	13101	52	2017	Santiago	425637.2	2017	13101	404495	172168109577	1805	0.0044624	13101	768275103
13101101006	13101	42	2017	Santiago	425637.2	2017	13101	404495	172168109577	1296	0.0032040	13101	551625780
13101101007	13101	105	2017	Santiago	425637.2	2017	13101	404495	172168109577	2965	0.0073301	13101	1262014227
13101101008	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	1624	0.0040149	13101	691234774
13101101009	13101	33	2017	Santiago	425637.2	2017	13101	404495	172168109577	1345	0.0033251	13101	572482002
13101101010	13101	149	2017	Santiago	425637.2	2017	13101	404495	172168109577	2930	0.0072436	13101	1247116926
13101111001	13101	56	2017	Santiago	425637.2	2017	13101	404495	172168109577	2555	0.0063165	13101	1087502985
13101111002	13101	409	2017	Santiago	425637.2	2017	13101	404495	172168109577	2594	0.0064129	13101	1104102835
13101111003	13101	84	2017	Santiago	425637.2	2017	13101	404495	172168109577	2269	0.0056095	13101	965770753
13101111004	13101	93	2017	Santiago	425637.2	2017	13101	404495	172168109577	1996	0.0049345	13101	849571804
13101111005	13101	85	2017	Santiago	425637.2	2017	13101	404495	172168109577	1960	0.0048455	13101	834248865
13101111006	13101	60	2017	Santiago	425637.2	2017	13101	404495	172168109577	1677	0.0041459	13101	713793544
13101111007	13101	105	2017	Santiago	425637.2	2017	13101	404495	172168109577	1859	0.0045959	13101	791259511
13101111008	13101	94	2017	Santiago	425637.2	2017	13101	404495	172168109577	2552	0.0063091	13101	1086226074
13101111009	13101	140	2017	Santiago	425637.2	2017	13101	404495	172168109577	3455	0.0085415	13101	1470576444
13101111010	13101	59	2017	Santiago	425637.2	2017	13101	404495	172168109577	2560	0.0063289	13101	1089631171
13101111011	13101	68	2017	Santiago	425637.2	2017	13101	404495	172168109577	1550	0.0038319	13101	659737623
13101111012	13101	147	2017	Santiago	425637.2	2017	13101	404495	172168109577	4104	0.0101460	13101	1746814971
13101111013	13101	85	2017	Santiago	425637.2	2017	13101	404495	172168109577	3191	0.0078888	13101	1358208229
13101111014	13101	69	2017	Santiago	425637.2	2017	13101	404495	172168109577	1894	0.0046824	13101	806156812
13101111015	13101	187	2017	Santiago	425637.2	2017	13101	404495	172168109577	2747	0.0067912	13101	1169225323
13101111016	13101	74	2017	Santiago	425637.2	2017	13101	404495	172168109577	2452	0.0060619	13101	1043662356
13101111017	13101	113	2017	Santiago	425637.2	2017	13101	404495	172168109577	1798	0.0044450	13101	765295643
13101111018	13101	71	2017	Santiago	425637.2	2017	13101	404495	172168109577	2724	0.0067343	13101	1159435668
13101111019	13101	152	2017	Santiago	425637.2	2017	13101	404495	172168109577	2430	0.0060075	13101	1034298338
13101121001	13101	123	2017	Santiago	425637.2	2017	13101	404495	172168109577	2320	0.0057355	13101	987478249
13101121002	13101	152	2017	Santiago	425637.2	2017	13101	404495	172168109577	1774	0.0043857	13101	755080351
13101121003	13101	63	2017	Santiago	425637.2	2017	13101	404495	172168109577	2011	0.0049716	13101	855956361
13101121004	13101	375	2017	Santiago	425637.2	2017	13101	404495	172168109577	4425	0.0109396	13101	1883444505
13101121005	13101	165	2017	Santiago	425637.2	2017	13101	404495	172168109577	2095	0.0051793	13101	891709884
13101121006	13101	213	2017	Santiago	425637.2	2017	13101	404495	172168109577	3272	0.0080891	13101	1392684840
13101121007	13101	164	2017	Santiago	425637.2	2017	13101	404495	172168109577	3067	0.0075823	13101	1305429219
13101121008	13101	276	2017	Santiago	425637.2	2017	13101	404495	172168109577	4682	0.0115749	13101	1992833259
13101121009	13101	320	2017	Santiago	425637.2	2017	13101	404495	172168109577	4441	0.0109791	13101	1890254699
13101131001	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	1811	0.0044772	13101	770828926
13101131002	13101	79	2017	Santiago	425637.2	2017	13101	404495	172168109577	1759	0.0043486	13101	748695793
13101131003	13101	143	2017	Santiago	425637.2	2017	13101	404495	172168109577	3953	0.0097727	13101	1682543757
13101131004	13101	158	2017	Santiago	425637.2	2017	13101	404495	172168109577	2442	0.0060372	13101	1039405984
13101131005	13101	102	2017	Santiago	425637.2	2017	13101	404495	172168109577	3683	0.0091052	13101	1567621720
13101131006	13101	40	2017	Santiago	425637.2	2017	13101	404495	172168109577	2197	0.0054315	13101	935124876
13101131007	13101	202	2017	Santiago	425637.2	2017	13101	404495	172168109577	2952	0.0072980	13101	1256480944
13101131008	13101	205	2017	Santiago	425637.2	2017	13101	404495	172168109577	3025	0.0074785	13101	1287552458
13101131009	13101	173	2017	Santiago	425637.2	2017	13101	404495	172168109577	3631	0.0089766	13101	1545488587
13101141001	13101	228	2017	Santiago	425637.2	2017	13101	404495	172168109577	4373	0.0108110	13101	1861311371
13101141002	13101	101	2017	Santiago	425637.2	2017	13101	404495	172168109577	3048	0.0075353	13101	1297342113
13101141003	13101	196	2017	Santiago	425637.2	2017	13101	404495	172168109577	4067	0.0100545	13101	1731066396
13101151001	13101	497	2017	Santiago	425637.2	2017	13101	404495	172168109577	4797	0.0118592	13101	2041781534
13101151002	13101	316	2017	Santiago	425637.2	2017	13101	404495	172168109577	3915	0.0096787	13101	1666369545
13101151003	13101	210	2017	Santiago	425637.2	2017	13101	404495	172168109577	3644	0.0090088	13101	1551021870
13101151004	13101	175	2017	Santiago	425637.2	2017	13101	404495	172168109577	2038	0.0050384	13101	867448565
13101161001	13101	190	2017	Santiago	425637.2	2017	13101	404495	172168109577	2637	0.0065192	13101	1122405234
13101161002	13101	350	2017	Santiago	425637.2	2017	13101	404495	172168109577	5341	0.0132041	13101	2273328158
13101161003	13101	292	2017	Santiago	425637.2	2017	13101	404495	172168109577	5331	0.0131794	13101	2269071786
13101171001	13101	365	2017	Santiago	425637.2	2017	13101	404495	172168109577	5638	0.0139384	13101	2399742399

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 
## -1.023e+09 -4.734e+08  3.601e+07  3.496e+08  3.887e+09 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 695788136   13318991   52.24   <2e-16 ***
## Freq.x        1219470      35857   34.01   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 496700000 on 2377 degrees of freedom
## Multiple R-squared:  0.3273, Adjusted R-squared:  0.327 
## F-statistic:  1157 on 1 and 2377 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 
## -1.023e+09 -4.734e+08  3.601e+07  3.496e+08  3.887e+09 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 695788136   13318991   52.24   <2e-16 ***
## Freq.x        1219470      35857   34.01   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 496700000 on 2377 degrees of freedom
## Multiple R-squared:  0.3273, Adjusted R-squared:  0.327 
## F-statistic:  1157 on 1 and 2377 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 
## -1.023e+09 -4.734e+08  3.601e+07  3.496e+08  3.887e+09 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 695788136   13318991   52.24   <2e-16 ***
## Freq.x        1219470      35857   34.01   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 496700000 on 2377 degrees of freedom
## Multiple R-squared:  0.3273, Adjusted R-squared:  0.327 
## F-statistic:  1157 on 1 and 2377 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 
## -989556130 -252675066  -18045205  240326295 3779622656 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -518001233   26687817  -19.41   <2e-16 ***
## log(Freq.x)  317038765    5367227   59.07   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 385500000 on 2377 degrees of freedom
## Multiple R-squared:  0.5948, Adjusted R-squared:  0.5946 
## F-statistic:  3489 on 1 and 2377 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 
## -1.076e+09 -3.356e+08 -2.209e+07  2.928e+08  3.912e+09 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  257199757   17150451   15.00   <2e-16 ***
## sqrt(Freq.x)  54825105    1108496   49.46   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 425200000 on 2377 degrees of freedom
## Multiple R-squared:  0.5072, Adjusted R-squared:  0.507 
## F-statistic:  2446 on 1 and 2377 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 
## -18269  -7351   1503   6406  43503 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  14594.01     335.93   43.44   <2e-16 ***
## sqrt(Freq.x)  1086.82      21.71   50.05   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8328 on 2377 degrees of freedom
## Multiple R-squared:  0.5132, Adjusted R-squared:  0.513 
## F-statistic:  2506 on 1 and 2377 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 
## -4.3054 -0.5761  0.3389  0.7307  2.4432 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  18.748721   0.040166  466.78   <2e-16 ***
## sqrt(Freq.x)  0.112380   0.002596   43.29   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9958 on 2377 degrees of freedom
## Multiple R-squared:  0.4408, Adjusted R-squared:  0.4406 
## F-statistic:  1874 on 1 and 2377 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 
## -18435  -4267    463   4663  41000 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -3355.58     444.58  -7.548 6.27e-14 ***
## log(Freq.x)  6828.54      89.41  76.373  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6422 on 2377 degrees of freedom
## Multiple R-squared:  0.7105, Adjusted R-squared:  0.7103 
## F-statistic:  5833 on 1 and 2377 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 
## -2.4783 -0.4360  0.1116  0.4717  2.7674 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.511254   0.045436  363.39   <2e-16 ***
## log(Freq.x)  0.786405   0.009138   86.06   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6564 on 2377 degrees of freedom
## Multiple R-squared:  0.757,  Adjusted R-squared:  0.7569 
## F-statistic:  7407 on 1 and 2377 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 (0.7569).

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 
## -2.4783 -0.4360  0.1116  0.4717  2.7674 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.511254   0.045436  363.39   <2e-16 ***
## log(Freq.x)  0.786405   0.009138   86.06   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6564 on 2377 degrees of freedom
## Multiple R-squared:  0.757,  Adjusted R-squared:  0.7569 
## F-statistic:  7407 on 1 and 2377 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

\[ \hat Y = e^{16.511254+0.786405 \cdot ln{X}} \]

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

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- exp(16.511254+0.786405 * log(h_y_m_comuna_corr_01$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
13101011001	13101	211	2017	Santiago	425637.2	2017	13101	404495	172168109577	2174	0.0053746	13101	925335221	996721544
13101011002	13101	241	2017	Santiago	425637.2	2017	13101	404495	172168109577	2282	0.0056416	13101	971304036	1106564195
13101011003	13101	141	2017	Santiago	425637.2	2017	13101	404495	172168109577	2209	0.0054611	13101	940232522	725944063
13101011004	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	1821	0.0045019	13101	775085298	614195685
13101011005	13101	96	2017	Santiago	425637.2	2017	13101	404495	172168109577	1741	0.0043041	13101	741034324	536555326
13101021001	13101	80	2017	Santiago	425637.2	2017	13101	404495	172168109577	3448	0.0085242	13101	1467596983	464885481
13101021002	13101	29	2017	Santiago	425637.2	2017	13101	404495	172168109577	2856	0.0070607	13101	1215619775	209306926
13101021003	13101	190	2017	Santiago	425637.2	2017	13101	404495	172168109577	2768	0.0068431	13101	1178163704	917845804
13101021004	13101	391	2017	Santiago	425637.2	2017	13101	404495	172168109577	3279	0.0081064	13101	1395664301	1619001549
13101021005	13101	139	2017	Santiago	425637.2	2017	13101	404495	172168109577	2258	0.0055823	13101	961088744	717834051
13101021006	13101	326	2017	Santiago	425637.2	2017	13101	404495	172168109577	2478	0.0061262	13101	1054728923	1403309251
13101021007	13101	99	2017	Santiago	425637.2	2017	13101	404495	172168109577	2326	0.0057504	13101	990032072	549697797
13101021008	13101	71	2017	Santiago	425637.2	2017	13101	404495	172168109577	1541	0.0038097	13101	655906888	423238653
13101031001	13101	122	2017	Santiago	425637.2	2017	13101	404495	172168109577	2313	0.0057182	13101	984498788	647843796
13101031002	13101	265	2017	Santiago	425637.2	2017	13101	404495	172168109577	5950	0.0147097	13101	2532541198	1192337417
13101031003	13101	348	2017	Santiago	425637.2	2017	13101	404495	172168109577	4955	0.0122498	13101	2109032208	1477260641
13101031004	13101	359	2017	Santiago	425637.2	2017	13101	404495	172168109577	3903	0.0096491	13101	1661261899	1513859433
13101031005	13101	139	2017	Santiago	425637.2	2017	13101	404495	172168109577	1791	0.0044277	13101	762316183	717834051
13101031006	13101	174	2017	Santiago	425637.2	2017	13101	404495	172168109577	1697	0.0041954	13101	722306288	856496580
13101031007	13101	307	2017	Santiago	425637.2	2017	13101	404495	172168109577	2973	0.0073499	13101	1265419325	1338580690
13101041001	13101	300	2017	Santiago	425637.2	2017	13101	404495	172168109577	2790	0.0068975	13101	1187527722	1314519527
13101041002	13101	219	2017	Santiago	425637.2	2017	13101	404495	172168109577	2668	0.0065959	13101	1135599986	1026321568
13101041003	13101	266	2017	Santiago	425637.2	2017	13101	404495	172168109577	2729	0.0067467	13101	1161563854	1195874334
13101041004	13101	153	2017	Santiago	425637.2	2017	13101	404495	172168109577	2828	0.0069914	13101	1203701934	774103028
13101041005	13101	219	2017	Santiago	425637.2	2017	13101	404495	172168109577	2550	0.0063042	13101	1085374799	1026321568
13101051001	13101	253	2017	Santiago	425637.2	2017	13101	404495	172168109577	3135	0.0077504	13101	1334372547	1149668137
13101051002	13101	370	2017	Santiago	425637.2	2017	13101	404495	172168109577	4424	0.0109371	13101	1883018867	1550219439
13101051003	13101	165	2017	Santiago	425637.2	2017	13101	404495	172168109577	2355	0.0058221	13101	1002375550	821461025
13101061001	13101	417	2017	Santiago	425637.2	2017	13101	404495	172168109577	3968	0.0098098	13101	1688928315	1703078223
13101061002	13101	418	2017	Santiago	425637.2	2017	13101	404495	172168109577	4747	0.0117356	13101	2020499675	1706289174
13101061003	13101	227	2017	Santiago	425637.2	2017	13101	404495	172168109577	2864	0.0070804	13101	1219024873	1055691478
13101071001	13101	390	2017	Santiago	425637.2	2017	13101	404495	172168109577	5009	0.0123833	13101	2132016615	1615744416
13101071002	13101	230	2017	Santiago	425637.2	2017	13101	404495	172168109577	3511	0.0086800	13101	1494412126	1066647894
13101071003	13101	218	2017	Santiago	425637.2	2017	13101	404495	172168109577	3368	0.0083264	13101	1433546009	1022634359
13101081001	13101	234	2017	Santiago	425637.2	2017	13101	404495	172168109577	3716	0.0091868	13101	1581667747	1081209114
13101081002	13101	433	2017	Santiago	425637.2	2017	13101	404495	172168109577	5244	0.0129643	13101	2232041352	1754259211
13101081003	13101	125	2017	Santiago	425637.2	2017	13101	404495	172168109577	3326	0.0082226	13101	1415669248	660339110
13101081004	13101	216	2017	Santiago	425637.2	2017	13101	404495	172168109577	2794	0.0069074	13101	1189230270	1015249078
13101091001	13101	186	2017	Santiago	425637.2	2017	13101	404495	172168109577	3393	0.0083882	13101	1444186939	902615586
13101091002	13101	394	2017	Santiago	425637.2	2017	13101	404495	172168109577	3321	0.0082102	13101	1413541062	1628762298
13101091003	13101	373	2017	Santiago	425637.2	2017	13101	404495	172168109577	3210	0.0079358	13101	1366295336	1560095504
13101091004	13101	267	2017	Santiago	425637.2	2017	13101	404495	172168109577	2956	0.0073079	13101	1258183493	1199408411
13101101001	13101	44	2017	Santiago	425637.2	2017	13101	404495	172168109577	2491	0.0061583	13101	1060262206	290513222
13101101002	13101	68	2017	Santiago	425637.2	2017	13101	404495	172168109577	1820	0.0044994	13101	774659661	409110544
13101101003	13101	156	2017	Santiago	425637.2	2017	13101	404495	172168109577	2768	0.0068431	13101	1178163704	786014669
13101101004	13101	101	2017	Santiago	425637.2	2017	13101	404495	172168109577	2423	0.0059902	13101	1031318878	558412140
13101101005	13101	52	2017	Santiago	425637.2	2017	13101	404495	172168109577	1805	0.0044624	13101	768275103	331298989
13101101006	13101	42	2017	Santiago	425637.2	2017	13101	404495	172168109577	1296	0.0032040	13101	551625780	280077267
13101101007	13101	105	2017	Santiago	425637.2	2017	13101	404495	172168109577	2965	0.0073301	13101	1262014227	575731342
13101101008	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	1624	0.0040149	13101	691234774	614195685
13101101009	13101	33	2017	Santiago	425637.2	2017	13101	404495	172168109577	1345	0.0033251	13101	572482002	231693290
13101101010	13101	149	2017	Santiago	425637.2	2017	13101	404495	172168109577	2930	0.0072436	13101	1247116926	758142859
13101111001	13101	56	2017	Santiago	425637.2	2017	13101	404495	172168109577	2555	0.0063165	13101	1087502985	351180430
13101111002	13101	409	2017	Santiago	425637.2	2017	13101	404495	172168109577	2594	0.0064129	13101	1104102835	1677330984
13101111003	13101	84	2017	Santiago	425637.2	2017	13101	404495	172168109577	2269	0.0056095	13101	965770753	483069206
13101111004	13101	93	2017	Santiago	425637.2	2017	13101	404495	172168109577	1996	0.0049345	13101	849571804	523324822
13101111005	13101	85	2017	Santiago	425637.2	2017	13101	404495	172168109577	1960	0.0048455	13101	834248865	487585960
13101111006	13101	60	2017	Santiago	425637.2	2017	13101	404495	172168109577	1677	0.0041459	13101	713793544	370760565
13101111007	13101	105	2017	Santiago	425637.2	2017	13101	404495	172168109577	1859	0.0045959	13101	791259511	575731342
13101111008	13101	94	2017	Santiago	425637.2	2017	13101	404495	172168109577	2552	0.0063091	13101	1086226074	527744980
13101111009	13101	140	2017	Santiago	425637.2	2017	13101	404495	172168109577	3455	0.0085415	13101	1470576444	721892150
13101111010	13101	59	2017	Santiago	425637.2	2017	13101	404495	172168109577	2560	0.0063289	13101	1089631171	365892391
13101111011	13101	68	2017	Santiago	425637.2	2017	13101	404495	172168109577	1550	0.0038319	13101	659737623	409110544
13101111012	13101	147	2017	Santiago	425637.2	2017	13101	404495	172168109577	4104	0.0101460	13101	1746814971	750128541
13101111013	13101	85	2017	Santiago	425637.2	2017	13101	404495	172168109577	3191	0.0078888	13101	1358208229	487585960
13101111014	13101	69	2017	Santiago	425637.2	2017	13101	404495	172168109577	1894	0.0046824	13101	806156812	413834431
13101111015	13101	187	2017	Santiago	425637.2	2017	13101	404495	172168109577	2747	0.0067912	13101	1169225323	906429644
13101111016	13101	74	2017	Santiago	425637.2	2017	13101	404495	172168109577	2452	0.0060619	13101	1043662356	437239785
13101111017	13101	113	2017	Santiago	425637.2	2017	13101	404495	172168109577	1798	0.0044450	13101	765295643	609954802
13101111018	13101	71	2017	Santiago	425637.2	2017	13101	404495	172168109577	2724	0.0067343	13101	1159435668	423238653
13101111019	13101	152	2017	Santiago	425637.2	2017	13101	404495	172168109577	2430	0.0060075	13101	1034298338	770121429
13101121001	13101	123	2017	Santiago	425637.2	2017	13101	404495	172168109577	2320	0.0057355	13101	987478249	652016116
13101121002	13101	152	2017	Santiago	425637.2	2017	13101	404495	172168109577	1774	0.0043857	13101	755080351	770121429
13101121003	13101	63	2017	Santiago	425637.2	2017	13101	404495	172168109577	2011	0.0049716	13101	855956361	385262649
13101121004	13101	375	2017	Santiago	425637.2	2017	13101	404495	172168109577	4425	0.0109396	13101	1883444505	1566670120
13101121005	13101	165	2017	Santiago	425637.2	2017	13101	404495	172168109577	2095	0.0051793	13101	891709884	821461025
13101121006	13101	213	2017	Santiago	425637.2	2017	13101	404495	172168109577	3272	0.0080891	13101	1392684840	1004143690
13101121007	13101	164	2017	Santiago	425637.2	2017	13101	404495	172168109577	3067	0.0075823	13101	1305429219	817543327
13101121008	13101	276	2017	Santiago	425637.2	2017	13101	404495	172168109577	4682	0.0115749	13101	1992833259	1231089446
13101121009	13101	320	2017	Santiago	425637.2	2017	13101	404495	172168109577	4441	0.0109791	13101	1890254699	1382957934
13101131001	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	1811	0.0044772	13101	770828926	614195685
13101131002	13101	79	2017	Santiago	425637.2	2017	13101	404495	172168109577	1759	0.0043486	13101	748695793	460309496
13101131003	13101	143	2017	Santiago	425637.2	2017	13101	404495	172168109577	3953	0.0097727	13101	1682543757	734029540
13101131004	13101	158	2017	Santiago	425637.2	2017	13101	404495	172168109577	2442	0.0060372	13101	1039405984	793928565
13101131005	13101	102	2017	Santiago	425637.2	2017	13101	404495	172168109577	3683	0.0091052	13101	1567621720	562755463
13101131006	13101	40	2017	Santiago	425637.2	2017	13101	404495	172168109577	2197	0.0054315	13101	935124876	269534579
13101131007	13101	202	2017	Santiago	425637.2	2017	13101	404495	172168109577	2952	0.0072980	13101	1256480944	963133181
13101131008	13101	205	2017	Santiago	425637.2	2017	13101	404495	172168109577	3025	0.0074785	13101	1287552458	974364150
13101131009	13101	173	2017	Santiago	425637.2	2017	13101	404495	172168109577	3631	0.0089766	13101	1545488587	852623203
13101141001	13101	228	2017	Santiago	425637.2	2017	13101	404495	172168109577	4373	0.0108110	13101	1861311371	1059347034
13101141002	13101	101	2017	Santiago	425637.2	2017	13101	404495	172168109577	3048	0.0075353	13101	1297342113	558412140
13101141003	13101	196	2017	Santiago	425637.2	2017	13101	404495	172168109577	4067	0.0100545	13101	1731066396	940563535
13101151001	13101	497	2017	Santiago	425637.2	2017	13101	404495	172168109577	4797	0.0118592	13101	2041781534	1955125533
13101151002	13101	316	2017	Santiago	425637.2	2017	13101	404495	172168109577	3915	0.0096787	13101	1666369545	1369345130
13101151003	13101	210	2017	Santiago	425637.2	2017	13101	404495	172168109577	3644	0.0090088	13101	1551021870	993004842
13101151004	13101	175	2017	Santiago	425637.2	2017	13101	404495	172168109577	2038	0.0050384	13101	867448565	860365205
13101161001	13101	190	2017	Santiago	425637.2	2017	13101	404495	172168109577	2637	0.0065192	13101	1122405234	917845804
13101161002	13101	350	2017	Santiago	425637.2	2017	13101	404495	172168109577	5341	0.0132041	13101	2273328158	1483933133
13101161003	13101	292	2017	Santiago	425637.2	2017	13101	404495	172168109577	5331	0.0131794	13101	2269071786	1286873634
13101171001	13101	365	2017	Santiago	425637.2	2017	13101	404495	172168109577	5638	0.0139384	13101	2399742399	1533721203

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
13101011001	13101	211	2017	Santiago	425637.2	2017	13101	404495	172168109577	2174	0.0053746	13101	925335221	996721544	458473.57
13101011002	13101	241	2017	Santiago	425637.2	2017	13101	404495	172168109577	2282	0.0056416	13101	971304036	1106564195	484909.81
13101011003	13101	141	2017	Santiago	425637.2	2017	13101	404495	172168109577	2209	0.0054611	13101	940232522	725944063	328630.18
13101011004	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	1821	0.0045019	13101	775085298	614195685	337284.84
13101011005	13101	96	2017	Santiago	425637.2	2017	13101	404495	172168109577	1741	0.0043041	13101	741034324	536555326	308188.01
13101021001	13101	80	2017	Santiago	425637.2	2017	13101	404495	172168109577	3448	0.0085242	13101	1467596983	464885481	134827.58
13101021002	13101	29	2017	Santiago	425637.2	2017	13101	404495	172168109577	2856	0.0070607	13101	1215619775	209306926	73286.74
13101021003	13101	190	2017	Santiago	425637.2	2017	13101	404495	172168109577	2768	0.0068431	13101	1178163704	917845804	331591.69
13101021004	13101	391	2017	Santiago	425637.2	2017	13101	404495	172168109577	3279	0.0081064	13101	1395664301	1619001549	493748.57
13101021005	13101	139	2017	Santiago	425637.2	2017	13101	404495	172168109577	2258	0.0055823	13101	961088744	717834051	317907.02
13101021006	13101	326	2017	Santiago	425637.2	2017	13101	404495	172168109577	2478	0.0061262	13101	1054728923	1403309251	566307.20
13101021007	13101	99	2017	Santiago	425637.2	2017	13101	404495	172168109577	2326	0.0057504	13101	990032072	549697797	236327.51
13101021008	13101	71	2017	Santiago	425637.2	2017	13101	404495	172168109577	1541	0.0038097	13101	655906888	423238653	274651.95
13101031001	13101	122	2017	Santiago	425637.2	2017	13101	404495	172168109577	2313	0.0057182	13101	984498788	647843796	280088.11
13101031002	13101	265	2017	Santiago	425637.2	2017	13101	404495	172168109577	5950	0.0147097	13101	2532541198	1192337417	200392.84
13101031003	13101	348	2017	Santiago	425637.2	2017	13101	404495	172168109577	4955	0.0122498	13101	2109032208	1477260641	298135.35
13101031004	13101	359	2017	Santiago	425637.2	2017	13101	404495	172168109577	3903	0.0096491	13101	1661261899	1513859433	387870.72
13101031005	13101	139	2017	Santiago	425637.2	2017	13101	404495	172168109577	1791	0.0044277	13101	762316183	717834051	400800.70
13101031006	13101	174	2017	Santiago	425637.2	2017	13101	404495	172168109577	1697	0.0041954	13101	722306288	856496580	504712.19
13101031007	13101	307	2017	Santiago	425637.2	2017	13101	404495	172168109577	2973	0.0073499	13101	1265419325	1338580690	450245.78
13101041001	13101	300	2017	Santiago	425637.2	2017	13101	404495	172168109577	2790	0.0068975	13101	1187527722	1314519527	471153.95
13101041002	13101	219	2017	Santiago	425637.2	2017	13101	404495	172168109577	2668	0.0065959	13101	1135599986	1026321568	384678.25
13101041003	13101	266	2017	Santiago	425637.2	2017	13101	404495	172168109577	2729	0.0067467	13101	1161563854	1195874334	438209.72
13101041004	13101	153	2017	Santiago	425637.2	2017	13101	404495	172168109577	2828	0.0069914	13101	1203701934	774103028	273728.09
13101041005	13101	219	2017	Santiago	425637.2	2017	13101	404495	172168109577	2550	0.0063042	13101	1085374799	1026321568	402479.05
13101051001	13101	253	2017	Santiago	425637.2	2017	13101	404495	172168109577	3135	0.0077504	13101	1334372547	1149668137	366720.30
13101051002	13101	370	2017	Santiago	425637.2	2017	13101	404495	172168109577	4424	0.0109371	13101	1883018867	1550219439	350411.27
13101051003	13101	165	2017	Santiago	425637.2	2017	13101	404495	172168109577	2355	0.0058221	13101	1002375550	821461025	348815.72
13101061001	13101	417	2017	Santiago	425637.2	2017	13101	404495	172168109577	3968	0.0098098	13101	1688928315	1703078223	429203.18
13101061002	13101	418	2017	Santiago	425637.2	2017	13101	404495	172168109577	4747	0.0117356	13101	2020499675	1706289174	359445.79
13101061003	13101	227	2017	Santiago	425637.2	2017	13101	404495	172168109577	2864	0.0070804	13101	1219024873	1055691478	368607.36
13101071001	13101	390	2017	Santiago	425637.2	2017	13101	404495	172168109577	5009	0.0123833	13101	2132016615	1615744416	322568.26
13101071002	13101	230	2017	Santiago	425637.2	2017	13101	404495	172168109577	3511	0.0086800	13101	1494412126	1066647894	303801.74
13101071003	13101	218	2017	Santiago	425637.2	2017	13101	404495	172168109577	3368	0.0083264	13101	1433546009	1022634359	303632.53
13101081001	13101	234	2017	Santiago	425637.2	2017	13101	404495	172168109577	3716	0.0091868	13101	1581667747	1081209114	290960.47
13101081002	13101	433	2017	Santiago	425637.2	2017	13101	404495	172168109577	5244	0.0129643	13101	2232041352	1754259211	334526.93
13101081003	13101	125	2017	Santiago	425637.2	2017	13101	404495	172168109577	3326	0.0082226	13101	1415669248	660339110	198538.52
13101081004	13101	216	2017	Santiago	425637.2	2017	13101	404495	172168109577	2794	0.0069074	13101	1189230270	1015249078	363367.60
13101091001	13101	186	2017	Santiago	425637.2	2017	13101	404495	172168109577	3393	0.0083882	13101	1444186939	902615586	266022.87
13101091002	13101	394	2017	Santiago	425637.2	2017	13101	404495	172168109577	3321	0.0082102	13101	1413541062	1628762298	490443.33
13101091003	13101	373	2017	Santiago	425637.2	2017	13101	404495	172168109577	3210	0.0079358	13101	1366295336	1560095504	486011.06
13101091004	13101	267	2017	Santiago	425637.2	2017	13101	404495	172168109577	2956	0.0073079	13101	1258183493	1199408411	405753.86
13101101001	13101	44	2017	Santiago	425637.2	2017	13101	404495	172168109577	2491	0.0061583	13101	1060262206	290513222	116625.14
13101101002	13101	68	2017	Santiago	425637.2	2017	13101	404495	172168109577	1820	0.0044994	13101	774659661	409110544	224786.01
13101101003	13101	156	2017	Santiago	425637.2	2017	13101	404495	172168109577	2768	0.0068431	13101	1178163704	786014669	283964.84
13101101004	13101	101	2017	Santiago	425637.2	2017	13101	404495	172168109577	2423	0.0059902	13101	1031318878	558412140	230463.12
13101101005	13101	52	2017	Santiago	425637.2	2017	13101	404495	172168109577	1805	0.0044624	13101	768275103	331298989	183545.15
13101101006	13101	42	2017	Santiago	425637.2	2017	13101	404495	172168109577	1296	0.0032040	13101	551625780	280077267	216109.00
13101101007	13101	105	2017	Santiago	425637.2	2017	13101	404495	172168109577	2965	0.0073301	13101	1262014227	575731342	194175.83
13101101008	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	1624	0.0040149	13101	691234774	614195685	378199.31
13101101009	13101	33	2017	Santiago	425637.2	2017	13101	404495	172168109577	1345	0.0033251	13101	572482002	231693290	172262.67
13101101010	13101	149	2017	Santiago	425637.2	2017	13101	404495	172168109577	2930	0.0072436	13101	1247116926	758142859	258751.83
13101111001	13101	56	2017	Santiago	425637.2	2017	13101	404495	172168109577	2555	0.0063165	13101	1087502985	351180430	137448.31
13101111002	13101	409	2017	Santiago	425637.2	2017	13101	404495	172168109577	2594	0.0064129	13101	1104102835	1677330984	646619.50
13101111003	13101	84	2017	Santiago	425637.2	2017	13101	404495	172168109577	2269	0.0056095	13101	965770753	483069206	212899.61
13101111004	13101	93	2017	Santiago	425637.2	2017	13101	404495	172168109577	1996	0.0049345	13101	849571804	523324822	262186.78
13101111005	13101	85	2017	Santiago	425637.2	2017	13101	404495	172168109577	1960	0.0048455	13101	834248865	487585960	248768.35
13101111006	13101	60	2017	Santiago	425637.2	2017	13101	404495	172168109577	1677	0.0041459	13101	713793544	370760565	221085.61
13101111007	13101	105	2017	Santiago	425637.2	2017	13101	404495	172168109577	1859	0.0045959	13101	791259511	575731342	309699.48
13101111008	13101	94	2017	Santiago	425637.2	2017	13101	404495	172168109577	2552	0.0063091	13101	1086226074	527744980	206796.62
13101111009	13101	140	2017	Santiago	425637.2	2017	13101	404495	172168109577	3455	0.0085415	13101	1470576444	721892150	208941.29
13101111010	13101	59	2017	Santiago	425637.2	2017	13101	404495	172168109577	2560	0.0063289	13101	1089631171	365892391	142926.72
13101111011	13101	68	2017	Santiago	425637.2	2017	13101	404495	172168109577	1550	0.0038319	13101	659737623	409110544	263942.29
13101111012	13101	147	2017	Santiago	425637.2	2017	13101	404495	172168109577	4104	0.0101460	13101	1746814971	750128541	182779.86
13101111013	13101	85	2017	Santiago	425637.2	2017	13101	404495	172168109577	3191	0.0078888	13101	1358208229	487585960	152800.36
13101111014	13101	69	2017	Santiago	425637.2	2017	13101	404495	172168109577	1894	0.0046824	13101	806156812	413834431	218497.59
13101111015	13101	187	2017	Santiago	425637.2	2017	13101	404495	172168109577	2747	0.0067912	13101	1169225323	906429644	329970.75
13101111016	13101	74	2017	Santiago	425637.2	2017	13101	404495	172168109577	2452	0.0060619	13101	1043662356	437239785	178319.65
13101111017	13101	113	2017	Santiago	425637.2	2017	13101	404495	172168109577	1798	0.0044450	13101	765295643	609954802	339240.71
13101111018	13101	71	2017	Santiago	425637.2	2017	13101	404495	172168109577	2724	0.0067343	13101	1159435668	423238653	155373.95
13101111019	13101	152	2017	Santiago	425637.2	2017	13101	404495	172168109577	2430	0.0060075	13101	1034298338	770121429	316922.40
13101121001	13101	123	2017	Santiago	425637.2	2017	13101	404495	172168109577	2320	0.0057355	13101	987478249	652016116	281041.43
13101121002	13101	152	2017	Santiago	425637.2	2017	13101	404495	172168109577	1774	0.0043857	13101	755080351	770121429	434115.80
13101121003	13101	63	2017	Santiago	425637.2	2017	13101	404495	172168109577	2011	0.0049716	13101	855956361	385262649	191577.65
13101121004	13101	375	2017	Santiago	425637.2	2017	13101	404495	172168109577	4425	0.0109396	13101	1883444505	1566670120	354049.74
13101121005	13101	165	2017	Santiago	425637.2	2017	13101	404495	172168109577	2095	0.0051793	13101	891709884	821461025	392105.50
13101121006	13101	213	2017	Santiago	425637.2	2017	13101	404495	172168109577	3272	0.0080891	13101	1392684840	1004143690	306889.88
13101121007	13101	164	2017	Santiago	425637.2	2017	13101	404495	172168109577	3067	0.0075823	13101	1305429219	817543327	266561.24
13101121008	13101	276	2017	Santiago	425637.2	2017	13101	404495	172168109577	4682	0.0115749	13101	1992833259	1231089446	262940.93
13101121009	13101	320	2017	Santiago	425637.2	2017	13101	404495	172168109577	4441	0.0109791	13101	1890254699	1382957934	311406.88
13101131001	13101	114	2017	Santiago	425637.2	2017	13101	404495	172168109577	1811	0.0044772	13101	770828926	614195685	339147.26
13101131002	13101	79	2017	Santiago	425637.2	2017	13101	404495	172168109577	1759	0.0043486	13101	748695793	460309496	261688.17
13101131003	13101	143	2017	Santiago	425637.2	2017	13101	404495	172168109577	3953	0.0097727	13101	1682543757	734029540	185689.23
13101131004	13101	158	2017	Santiago	425637.2	2017	13101	404495	172168109577	2442	0.0060372	13101	1039405984	793928565	325114.07
13101131005	13101	102	2017	Santiago	425637.2	2017	13101	404495	172168109577	3683	0.0091052	13101	1567621720	562755463	152798.12
13101131006	13101	40	2017	Santiago	425637.2	2017	13101	404495	172168109577	2197	0.0054315	13101	935124876	269534579	122683.01
13101131007	13101	202	2017	Santiago	425637.2	2017	13101	404495	172168109577	2952	0.0072980	13101	1256480944	963133181	326264.63
13101131008	13101	205	2017	Santiago	425637.2	2017	13101	404495	172168109577	3025	0.0074785	13101	1287552458	974364150	322103.85
13101131009	13101	173	2017	Santiago	425637.2	2017	13101	404495	172168109577	3631	0.0089766	13101	1545488587	852623203	234817.74
13101141001	13101	228	2017	Santiago	425637.2	2017	13101	404495	172168109577	4373	0.0108110	13101	1861311371	1059347034	242247.21
13101141002	13101	101	2017	Santiago	425637.2	2017	13101	404495	172168109577	3048	0.0075353	13101	1297342113	558412140	183206.08
13101141003	13101	196	2017	Santiago	425637.2	2017	13101	404495	172168109577	4067	0.0100545	13101	1731066396	940563535	231267.16
13101151001	13101	497	2017	Santiago	425637.2	2017	13101	404495	172168109577	4797	0.0118592	13101	2041781534	1955125533	407572.55
13101151002	13101	316	2017	Santiago	425637.2	2017	13101	404495	172168109577	3915	0.0096787	13101	1666369545	1369345130	349768.87
13101151003	13101	210	2017	Santiago	425637.2	2017	13101	404495	172168109577	3644	0.0090088	13101	1551021870	993004842	272504.07
13101151004	13101	175	2017	Santiago	425637.2	2017	13101	404495	172168109577	2038	0.0050384	13101	867448565	860365205	422161.53
13101161001	13101	190	2017	Santiago	425637.2	2017	13101	404495	172168109577	2637	0.0065192	13101	1122405234	917845804	348064.39
13101161002	13101	350	2017	Santiago	425637.2	2017	13101	404495	172168109577	5341	0.0132041	13101	2273328158	1483933133	277838.07
13101161003	13101	292	2017	Santiago	425637.2	2017	13101	404495	172168109577	5331	0.0131794	13101	2269071786	1286873634	241394.42
13101171001	13101	365	2017	Santiago	425637.2	2017	13101	404495	172168109577	5638	0.0139384	13101	2399742399	1533721203	272032.85

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "casen_censo_region_13.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