Expansión de la CASEN sobre el CENSO (Región 04)

1 Variable CENSO

Necesitamos calcular las frecuencias a nivel censal de las respuestas correspondientes a la categoría: “Parquet, piso flotante, cerámico, madera, alfombra, flexit, cubrepiso u otro similar, sobre radier o vigas de madera” del campo P03C 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 Censo 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)

Hagamos un subset con la 1:

tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$REGION == 4) 
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA== 1)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave[,-c(1,2,4,5,6,7,8,9,10,11,12,14,15,16,17,18,19,20),drop=FALSE]

aterial de construcción del piso

names(tabla_con_clave_f)[2] <- "Tipo de piso"

# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de piso` == 1)

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

Veamos los primeros 100 registros:

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

	zona	unlist.c.	unlist.d.	Freq	anio
1	4101011001	1	4101	376	2017
2	4101021001	1	4101	532	2017
3	4101021002	1	4101	762	2017
4	4101021003	1	4101	738	2017
5	4101021004	1	4101	1306	2017
6	4101021005	1	4101	444	2017
7	4101031001	1	4101	1549	2017
8	4101031002	1	4101	876	2017
9	4101031003	1	4101	1441	2017
10	4101041001	1	4101	440	2017
11	4101041002	1	4101	480	2017
12	4101041003	1	4101	1578	2017
13	4101041004	1	4101	1157	2017
14	4101041005	1	4101	359	2017
15	4101041006	1	4101	343	2017
16	4101051001	1	4101	473	2017
17	4101051002	1	4101	969	2017
18	4101051003	1	4101	922	2017
19	4101051004	1	4101	859	2017
20	4101051005	1	4101	1684	2017
21	4101051006	1	4101	1322	2017
22	4101051007	1	4101	787	2017
23	4101051008	1	4101	1238	2017
24	4101051009	1	4101	1146	2017
25	4101051010	1	4101	1143	2017
26	4101051011	1	4101	951	2017
27	4101061001	1	4101	1359	2017
28	4101061002	1	4101	905	2017
29	4101061003	1	4101	1046	2017
30	4101061004	1	4101	1643	2017
31	4101061005	1	4101	68	2017
32	4101071001	1	4101	212	2017
33	4101091001	1	4101	290	2017
34	4101141001	1	4101	622	2017
35	4101141002	1	4101	672	2017
36	4101141003	1	4101	1357	2017
37	4101141004	1	4101	1096	2017
38	4101141005	1	4101	1850	2017
39	4101141006	1	4101	579	2017
40	4101151001	1	4101	1585	2017
41	4101151002	1	4101	545	2017
42	4101151003	1	4101	656	2017
43	4101151004	1	4101	894	2017
44	4101151005	1	4101	751	2017
45	4101151006	1	4101	1028	2017
46	4101161001	1	4101	1537	2017
47	4101161002	1	4101	735	2017
48	4101161003	1	4101	611	2017
49	4101161004	1	4101	1074	2017
50	4101161005	1	4101	1257	2017
51	4101161006	1	4101	1234	2017
52	4101161007	1	4101	1226	2017
53	4101161008	1	4101	1216	2017
54	4101161009	1	4101	1192	2017
55	4101161010	1	4101	1146	2017
56	4101171001	1	4101	607	2017
57	4101171002	1	4101	735	2017
58	4101171003	1	4101	954	2017
59	4101171004	1	4101	1210	2017
60	4101171005	1	4101	1252	2017
61	4101991999	1	4101	197	2017
255	4102011001	1	4102	1296	2017
256	4102011002	1	4102	561	2017
257	4102021001	1	4102	1011	2017
258	4102021002	1	4102	488	2017
259	4102021003	1	4102	497	2017
260	4102021004	1	4102	881	2017
261	4102021005	1	4102	543	2017
262	4102021006	1	4102	648	2017
263	4102031001	1	4102	634	2017
264	4102031002	1	4102	930	2017
265	4102031003	1	4102	622	2017
266	4102031004	1	4102	655	2017
267	4102041001	1	4102	534	2017
268	4102041002	1	4102	529	2017
269	4102051001	1	4102	1437	2017
270	4102051002	1	4102	1635	2017
271	4102051003	1	4102	1366	2017
272	4102051004	1	4102	5	2017
273	4102051005	1	4102	1485	2017
274	4102051006	1	4102	1182	2017
275	4102051007	1	4102	924	2017
276	4102051008	1	4102	742	2017
277	4102061001	1	4102	978	2017
278	4102061002	1	4102	1712	2017
279	4102061003	1	4102	81	2017
280	4102061004	1	4102	1279	2017
281	4102061005	1	4102	1852	2017
282	4102061006	1	4102	1375	2017
283	4102081001	1	4102	779	2017
284	4102081002	1	4102	863	2017
285	4102091001	1	4102	343	2017
286	4102091002	1	4102	735	2017
287	4102091003	1	4102	787	2017
288	4102091004	1	4102	599	2017
289	4102091005	1	4102	903	2017
290	4102091006	1	4102	946	2017
291	4102091007	1	4102	1648	2017
292	4102091008	1	4102	1655	2017
293	4102101001	1	4102	336	2017

Agregamos un cero a los códigos comunales de cuatro dígitos:

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

	zona	Freq	anio	código
1	4101011001	376	2017	04101
2	4101021001	532	2017	04101
3	4101021002	762	2017	04101
4	4101021003	738	2017	04101
5	4101021004	1306	2017	04101
6	4101021005	444	2017	04101
7	4101031001	1549	2017	04101
8	4101031002	876	2017	04101
9	4101031003	1441	2017	04101
10	4101041001	440	2017	04101
11	4101041002	480	2017	04101
12	4101041003	1578	2017	04101
13	4101041004	1157	2017	04101
14	4101041005	359	2017	04101
15	4101041006	343	2017	04101
16	4101051001	473	2017	04101
17	4101051002	969	2017	04101
18	4101051003	922	2017	04101
19	4101051004	859	2017	04101
20	4101051005	1684	2017	04101
21	4101051006	1322	2017	04101
22	4101051007	787	2017	04101
23	4101051008	1238	2017	04101
24	4101051009	1146	2017	04101
25	4101051010	1143	2017	04101
26	4101051011	951	2017	04101
27	4101061001	1359	2017	04101
28	4101061002	905	2017	04101
29	4101061003	1046	2017	04101
30	4101061004	1643	2017	04101
31	4101061005	68	2017	04101
32	4101071001	212	2017	04101
33	4101091001	290	2017	04101
34	4101141001	622	2017	04101
35	4101141002	672	2017	04101
36	4101141003	1357	2017	04101
37	4101141004	1096	2017	04101
38	4101141005	1850	2017	04101
39	4101141006	579	2017	04101
40	4101151001	1585	2017	04101
41	4101151002	545	2017	04101
42	4101151003	656	2017	04101
43	4101151004	894	2017	04101
44	4101151005	751	2017	04101
45	4101151006	1028	2017	04101
46	4101161001	1537	2017	04101
47	4101161002	735	2017	04101
48	4101161003	611	2017	04101
49	4101161004	1074	2017	04101
50	4101161005	1257	2017	04101
51	4101161006	1234	2017	04101
52	4101161007	1226	2017	04101
53	4101161008	1216	2017	04101
54	4101161009	1192	2017	04101
55	4101161010	1146	2017	04101
56	4101171001	607	2017	04101
57	4101171002	735	2017	04101
58	4101171003	954	2017	04101
59	4101171004	1210	2017	04101
60	4101171005	1252	2017	04101
61	4101991999	197	2017	04101
255	4102011001	1296	2017	04102
256	4102011002	561	2017	04102
257	4102021001	1011	2017	04102
258	4102021002	488	2017	04102
259	4102021003	497	2017	04102
260	4102021004	881	2017	04102
261	4102021005	543	2017	04102
262	4102021006	648	2017	04102
263	4102031001	634	2017	04102
264	4102031002	930	2017	04102
265	4102031003	622	2017	04102
266	4102031004	655	2017	04102
267	4102041001	534	2017	04102
268	4102041002	529	2017	04102
269	4102051001	1437	2017	04102
270	4102051002	1635	2017	04102
271	4102051003	1366	2017	04102
272	4102051004	5	2017	04102
273	4102051005	1485	2017	04102
274	4102051006	1182	2017	04102
275	4102051007	924	2017	04102
276	4102051008	742	2017	04102
277	4102061001	978	2017	04102
278	4102061002	1712	2017	04102
279	4102061003	81	2017	04102
280	4102061004	1279	2017	04102
281	4102061005	1852	2017	04102
282	4102061006	1375	2017	04102
283	4102081001	779	2017	04102
284	4102081002	863	2017	04102
285	4102091001	343	2017	04102
286	4102091002	735	2017	04102
287	4102091003	787	2017	04102
288	4102091004	599	2017	04102
289	4102091005	903	2017	04102
290	4102091006	946	2017	04102
291	4102091007	1648	2017	04102
292	4102091008	1655	2017	04102
293	4102101001	336	2017	04102

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_urbano_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	375676.9	2017	1101	191468	71930106513
01107	Alto Hospicio	311571.7	2017	1107	108375	33766585496
01401	Pozo Almonte	316138.5	2017	1401	15711	4966851883
01405	Pica	330061.1	2017	1405	9296	3068247619
02101	Antofagasta	368221.4	2017	2101	361873	133249367039
02102	Mejillones	369770.7	2017	2102	13467	4979702302
02104	Taltal	383666.2	2017	2104	13317	5109282942
02201	Calama	434325.1	2017	2201	165731	71981127235
02203	San Pedro de Atacama	442861.0	2017	2203	10996	4869699464
02301	Tocopilla	286187.2	2017	2301	25186	7207910819
02302	María Elena	477748.0	2017	2302	6457	3084818966
03101	Copiapó	343121.0	2017	3101	153937	52819016037
03102	Caldera	318653.2	2017	3102	17662	5628052276
03103	Tierra Amarilla	333194.9	2017	3103	14019	4671058718
03201	Chañaral	286389.3	2017	3201	12219	3499391196
03202	Diego de Almagro	351583.9	2017	3202	13925	4895805596
03301	Vallenar	315981.5	2017	3301	51917	16404810756
03303	Freirina	289049.9	2017	3303	7041	2035200054
03304	Huasco	337414.8	2017	3304	10149	3424422750
04101	La Serena	279340.1	2017	4101	221054	61749247282
04102	Coquimbo	269078.6	2017	4102	227730	61277269093
04103	Andacollo	258539.7	2017	4103	11044	2855312920
04104	La Higuera	214257.0	2017	4104	4241	908664019
04106	Vicuña	254177.0	2017	4106	27771	7058750373
04201	Illapel	282139.3	2017	4201	30848	8703433491
04202	Canela	233397.3	2017	4202	9093	2122281844
04203	Los Vilos	285214.0	2017	4203	21382	6098444926
04204	Salamanca	262056.9	2017	4204	29347	7690585032
04301	Ovalle	280373.5	2017	4301	111272	31197719080
04302	Combarbalá	234537.3	2017	4302	13322	3124505460
04303	Monte Patria	225369.1	2017	4303	30751	6930326684
04304	Punitaqui	212496.1	2017	4304	10956	2328107498
05101	Valparaíso	306572.5	2017	5101	296655	90946261553
05102	Casablanca	348088.6	2017	5102	26867	9352095757
05103	Concón	333932.4	2017	5103	42152	14075920021
05105	Puchuncaví	296035.5	2017	5105	18546	5490274928
05107	Quintero	308224.7	2017	5107	31923	9839456903
05109	Viña del Mar	354715.9	2017	5109	334248	118563074323
05301	Los Andes	355446.2	2017	5301	66708	23711104774
05302	Calle Larga	246387.3	2017	5302	14832	3654416747
05303	Rinconada	279807.9	2017	5303	10207	2855998928
05304	San Esteban	219571.6	2017	5304	18855	4140022481
05401	La Ligua	259482.3	2017	5401	35390	9183080280
05402	Cabildo	262745.9	2017	5402	19388	5094117762
05403	Papudo	302317.1	2017	5403	6356	1921527704
05404	Petorca	237510.8	2017	5404	9826	2333781007
05405	Zapallar	294389.2	2017	5405	7339	2160521991
05501	Quillota	288694.2	2017	5501	90517	26131733924
05502	Calera	282823.6	2017	5502	50554	14297866792
05503	Hijuelas	268449.7	2017	5503	17988	4828872604

3 Unión Censo-Casen

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

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
comunas_con_ing_exp <-comunas_con_ing_exp[!(is.na(comunas_con_ing_exp$Ingresos_expandidos)),]
r3_100 <- comunas_con_ing_exp[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
04101	4101011001	376	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101021001	532	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101021002	762	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101021003	738	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101021004	1306	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101021005	444	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101031001	1549	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101031002	876	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101031003	1441	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101041001	440	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101041002	480	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101041003	1578	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101041004	1157	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101041005	359	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101041006	343	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101051001	473	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101051002	969	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101051003	922	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101051004	859	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101051005	1684	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101051006	1322	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101051007	787	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101051008	1238	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101051009	1146	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101051010	1143	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101051011	951	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101061001	1359	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101061002	905	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101061003	1046	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101061004	1643	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101061005	68	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101071001	212	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101091001	290	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101141001	622	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101141002	672	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101141003	1357	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101141004	1096	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101141005	1850	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101141006	579	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101151001	1585	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101151002	545	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101151003	656	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101151004	894	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101151005	751	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101151006	1028	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101161001	1537	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101161002	735	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101161003	611	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101161004	1074	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101161005	1257	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101161006	1234	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101161007	1226	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101161008	1216	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101161009	1192	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101161010	1146	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101171001	607	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101171002	735	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101171003	954	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101171004	1210	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101171005	1252	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101991999	197	2017	La Serena	279340.1	2017	4101	221054	61749247282
04102	4102011001	1296	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102011002	561	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102021001	1011	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102021002	488	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102021003	497	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102021004	881	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102021005	543	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102021006	648	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102031001	634	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102031002	930	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102031003	622	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102031004	655	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102041001	534	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102041002	529	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102051001	1437	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102051002	1635	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102051003	1366	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102051004	5	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102051005	1485	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102051006	1182	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102051007	924	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102051008	742	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102061001	978	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102061002	1712	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102061003	81	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102061004	1279	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102061005	1852	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102061006	1375	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102081001	779	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102081002	863	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102091001	343	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102091002	735	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102091003	787	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102091004	599	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102091005	903	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102091006	946	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102091007	1648	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102091008	1655	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102101001	336	2017	Coquimbo	269078.6	2017	4102	227730	61277269093

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
04101	4101011001	376	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101021001	532	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101021002	762	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101021003	738	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101021004	1306	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101021005	444	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101031001	1549	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101031002	876	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101031003	1441	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101041001	440	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101041002	480	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101041003	1578	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101041004	1157	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101041005	359	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101041006	343	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101051001	473	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101051002	969	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101051003	922	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101051004	859	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101051005	1684	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101051006	1322	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101051007	787	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101051008	1238	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101051009	1146	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101051010	1143	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101051011	951	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101061001	1359	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101061002	905	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101061003	1046	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101061004	1643	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101061005	68	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101071001	212	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101091001	290	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101141001	622	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101141002	672	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101141003	1357	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101141004	1096	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101141005	1850	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101141006	579	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101151001	1585	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101151002	545	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101151003	656	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101151004	894	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101151005	751	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101151006	1028	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101161001	1537	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101161002	735	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101161003	611	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101161004	1074	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101161005	1257	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101161006	1234	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101161007	1226	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101161008	1216	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101161009	1192	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101161010	1146	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101171001	607	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101171002	735	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101171003	954	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101171004	1210	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101171005	1252	2017	La Serena	279340.1	2017	4101	221054	61749247282
04101	4101991999	197	2017	La Serena	279340.1	2017	4101	221054	61749247282
04102	4102011001	1296	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102011002	561	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102021001	1011	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102021002	488	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102021003	497	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102021004	881	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102021005	543	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102021006	648	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102031001	634	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102031002	930	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102031003	622	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102031004	655	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102041001	534	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102041002	529	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102051001	1437	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102051002	1635	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102051003	1366	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102051004	5	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102051005	1485	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102051006	1182	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102051007	924	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102051008	742	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102061001	978	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102061002	1712	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102061003	81	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102061004	1279	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102061005	1852	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102061006	1375	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102081001	779	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102081002	863	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102091001	343	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102091002	735	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102091003	787	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102091004	599	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102091005	903	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102091006	946	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102091007	1648	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102091008	1655	2017	Coquimbo	269078.6	2017	4102	227730	61277269093
04102	4102101001	336	2017	Coquimbo	269078.6	2017	4102	227730	61277269093

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
4101011001	04101	376	2017	La Serena	279340.1	2017	4101	221054	61749247282	1455	0.0065821	04101
4101021001	04101	532	2017	La Serena	279340.1	2017	4101	221054	61749247282	2431	0.0109973	04101
4101021002	04101	762	2017	La Serena	279340.1	2017	4101	221054	61749247282	2926	0.0132366	04101
4101021003	04101	738	2017	La Serena	279340.1	2017	4101	221054	61749247282	2699	0.0122097	04101
4101021004	04101	1306	2017	La Serena	279340.1	2017	4101	221054	61749247282	5323	0.0240801	04101
4101021005	04101	444	2017	La Serena	279340.1	2017	4101	221054	61749247282	2125	0.0096130	04101
4101031001	04101	1549	2017	La Serena	279340.1	2017	4101	221054	61749247282	4341	0.0196377	04101
4101031002	04101	876	2017	La Serena	279340.1	2017	4101	221054	61749247282	2583	0.0116849	04101
4101031003	04101	1441	2017	La Serena	279340.1	2017	4101	221054	61749247282	4026	0.0182127	04101
4101041001	04101	440	2017	La Serena	279340.1	2017	4101	221054	61749247282	1108	0.0050123	04101
4101041002	04101	480	2017	La Serena	279340.1	2017	4101	221054	61749247282	1015	0.0045916	04101
4101041003	04101	1578	2017	La Serena	279340.1	2017	4101	221054	61749247282	4721	0.0213568	04101
4101041004	04101	1157	2017	La Serena	279340.1	2017	4101	221054	61749247282	3560	0.0161047	04101
4101041005	04101	359	2017	La Serena	279340.1	2017	4101	221054	61749247282	812	0.0036733	04101
4101041006	04101	343	2017	La Serena	279340.1	2017	4101	221054	61749247282	821	0.0037140	04101
4101051001	04101	473	2017	La Serena	279340.1	2017	4101	221054	61749247282	1419	0.0064192	04101
4101051002	04101	969	2017	La Serena	279340.1	2017	4101	221054	61749247282	2920	0.0132094	04101
4101051003	04101	922	2017	La Serena	279340.1	2017	4101	221054	61749247282	3348	0.0151456	04101
4101051004	04101	859	2017	La Serena	279340.1	2017	4101	221054	61749247282	2851	0.0128973	04101
4101051005	04101	1684	2017	La Serena	279340.1	2017	4101	221054	61749247282	5493	0.0248491	04101
4101051006	04101	1322	2017	La Serena	279340.1	2017	4101	221054	61749247282	4295	0.0194296	04101
4101051007	04101	787	2017	La Serena	279340.1	2017	4101	221054	61749247282	2336	0.0105676	04101
4101051008	04101	1238	2017	La Serena	279340.1	2017	4101	221054	61749247282	4235	0.0191582	04101
4101051009	04101	1146	2017	La Serena	279340.1	2017	4101	221054	61749247282	3882	0.0175613	04101
4101051010	04101	1143	2017	La Serena	279340.1	2017	4101	221054	61749247282	3226	0.0145937	04101
4101051011	04101	951	2017	La Serena	279340.1	2017	4101	221054	61749247282	2966	0.0134175	04101
4101061001	04101	1359	2017	La Serena	279340.1	2017	4101	221054	61749247282	4424	0.0200132	04101
4101061002	04101	905	2017	La Serena	279340.1	2017	4101	221054	61749247282	3047	0.0137840	04101
4101061003	04101	1046	2017	La Serena	279340.1	2017	4101	221054	61749247282	3472	0.0157066	04101
4101061004	04101	1643	2017	La Serena	279340.1	2017	4101	221054	61749247282	5333	0.0241253	04101
4101061005	04101	68	2017	La Serena	279340.1	2017	4101	221054	61749247282	288	0.0013028	04101
4101071001	04101	212	2017	La Serena	279340.1	2017	4101	221054	61749247282	1056	0.0047771	04101
4101091001	04101	290	2017	La Serena	279340.1	2017	4101	221054	61749247282	1292	0.0058447	04101
4101141001	04101	622	2017	La Serena	279340.1	2017	4101	221054	61749247282	2872	0.0129923	04101
4101141002	04101	672	2017	La Serena	279340.1	2017	4101	221054	61749247282	2750	0.0124404	04101
4101141003	04101	1357	2017	La Serena	279340.1	2017	4101	221054	61749247282	4706	0.0212889	04101
4101141004	04101	1096	2017	La Serena	279340.1	2017	4101	221054	61749247282	3750	0.0169642	04101
4101141005	04101	1850	2017	La Serena	279340.1	2017	4101	221054	61749247282	5866	0.0265365	04101
4101141006	04101	579	2017	La Serena	279340.1	2017	4101	221054	61749247282	2114	0.0095633	04101
4101151001	04101	1585	2017	La Serena	279340.1	2017	4101	221054	61749247282	4957	0.0224244	04101
4101151002	04101	545	2017	La Serena	279340.1	2017	4101	221054	61749247282	1602	0.0072471	04101
4101151003	04101	656	2017	La Serena	279340.1	2017	4101	221054	61749247282	1900	0.0085952	04101
4101151004	04101	894	2017	La Serena	279340.1	2017	4101	221054	61749247282	2649	0.0119835	04101
4101151005	04101	751	2017	La Serena	279340.1	2017	4101	221054	61749247282	2047	0.0092602	04101
4101151006	04101	1028	2017	La Serena	279340.1	2017	4101	221054	61749247282	3173	0.0143540	04101
4101161001	04101	1537	2017	La Serena	279340.1	2017	4101	221054	61749247282	5756	0.0260389	04101
4101161002	04101	735	2017	La Serena	279340.1	2017	4101	221054	61749247282	3690	0.0166928	04101
4101161003	04101	611	2017	La Serena	279340.1	2017	4101	221054	61749247282	2952	0.0133542	04101
4101161004	04101	1074	2017	La Serena	279340.1	2017	4101	221054	61749247282	5185	0.0234558	04101
4101161005	04101	1257	2017	La Serena	279340.1	2017	4101	221054	61749247282	4746	0.0214699	04101
4101161006	04101	1234	2017	La Serena	279340.1	2017	4101	221054	61749247282	4464	0.0201942	04101
4101161007	04101	1226	2017	La Serena	279340.1	2017	4101	221054	61749247282	5497	0.0248672	04101
4101161008	04101	1216	2017	La Serena	279340.1	2017	4101	221054	61749247282	4352	0.0196875	04101
4101161009	04101	1192	2017	La Serena	279340.1	2017	4101	221054	61749247282	4309	0.0194930	04101
4101161010	04101	1146	2017	La Serena	279340.1	2017	4101	221054	61749247282	4752	0.0214970	04101
4101171001	04101	607	2017	La Serena	279340.1	2017	4101	221054	61749247282	2644	0.0119609	04101
4101171002	04101	735	2017	La Serena	279340.1	2017	4101	221054	61749247282	2927	0.0132411	04101
4101171003	04101	954	2017	La Serena	279340.1	2017	4101	221054	61749247282	5093	0.0230396	04101
4101171004	04101	1210	2017	La Serena	279340.1	2017	4101	221054	61749247282	4747	0.0214744	04101
4101171005	04101	1252	2017	La Serena	279340.1	2017	4101	221054	61749247282	4515	0.0204249	04101
4101991999	04101	197	2017	La Serena	279340.1	2017	4101	221054	61749247282	796	0.0036009	04101
4102011001	04102	1296	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	6389	0.0280552	04102
4102011002	04102	561	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2328	0.0102226	04102
4102021001	04102	1011	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4724	0.0207439	04102
4102021002	04102	488	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2101	0.0092258	04102
4102021003	04102	497	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2094	0.0091951	04102
4102021004	04102	881	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4472	0.0196373	04102
4102021005	04102	543	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2188	0.0096079	04102
4102021006	04102	648	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3498	0.0153603	04102
4102031001	04102	634	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2056	0.0090282	04102
4102031002	04102	930	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3483	0.0152944	04102
4102031003	04102	622	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2188	0.0096079	04102
4102031004	04102	655	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2324	0.0102051	04102
4102041001	04102	534	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	1800	0.0079041	04102
4102041002	04102	529	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	1873	0.0082247	04102
4102051001	04102	1437	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4645	0.0203970	04102
4102051002	04102	1635	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	5393	0.0236816	04102
4102051003	04102	1366	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4578	0.0201028	04102
4102051004	04102	5	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	15	0.0000659	04102
4102051005	04102	1485	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4613	0.0202564	04102
4102051006	04102	1182	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4305	0.0189040	04102
4102051007	04102	924	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2872	0.0126114	04102
4102051008	04102	742	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2455	0.0107803	04102
4102061001	04102	978	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4034	0.0177140	04102
4102061002	04102	1712	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	6820	0.0299477	04102
4102061003	04102	81	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	309	0.0013569	04102
4102061004	04102	1279	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4293	0.0188513	04102
4102061005	04102	1852	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	6111	0.0268344	04102
4102061006	04102	1375	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4633	0.0203443	04102
4102081001	04102	779	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2323	0.0102007	04102
4102081002	04102	863	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3139	0.0137839	04102
4102091001	04102	343	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	1423	0.0062486	04102
4102091002	04102	735	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2821	0.0123875	04102
4102091003	04102	787	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3087	0.0135555	04102
4102091004	04102	599	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2580	0.0113292	04102
4102091005	04102	903	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3863	0.0169631	04102
4102091006	04102	946	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3170	0.0139200	04102
4102091007	04102	1648	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	6290	0.0276204	04102
4102091008	04102	1655	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	5782	0.0253897	04102
4102101001	04102	336	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	1476	0.0064814	04102

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
4101011001	04101	376	2017	La Serena	279340.1	2017	4101	221054	61749247282	1455	0.0065821	04101	406439851
4101021001	04101	532	2017	La Serena	279340.1	2017	4101	221054	61749247282	2431	0.0109973	04101	679075792
4101021002	04101	762	2017	La Serena	279340.1	2017	4101	221054	61749247282	2926	0.0132366	04101	817349143
4101021003	04101	738	2017	La Serena	279340.1	2017	4101	221054	61749247282	2699	0.0122097	04101	753938940
4101021004	04101	1306	2017	La Serena	279340.1	2017	4101	221054	61749247282	5323	0.0240801	04101	1486927372
4101021005	04101	444	2017	La Serena	279340.1	2017	4101	221054	61749247282	2125	0.0096130	04101	593597720
4101031001	04101	1549	2017	La Serena	279340.1	2017	4101	221054	61749247282	4341	0.0196377	04101	1212615390
4101031002	04101	876	2017	La Serena	279340.1	2017	4101	221054	61749247282	2583	0.0116849	04101	721535488
4101031003	04101	1441	2017	La Serena	279340.1	2017	4101	221054	61749247282	4026	0.0182127	04101	1124623257
4101041001	04101	440	2017	La Serena	279340.1	2017	4101	221054	61749247282	1108	0.0050123	04101	309508835
4101041002	04101	480	2017	La Serena	279340.1	2017	4101	221054	61749247282	1015	0.0045916	04101	283530205
4101041003	04101	1578	2017	La Serena	279340.1	2017	4101	221054	61749247282	4721	0.0213568	04101	1318764630
4101041004	04101	1157	2017	La Serena	279340.1	2017	4101	221054	61749247282	3560	0.0161047	04101	994450769
4101041005	04101	359	2017	La Serena	279340.1	2017	4101	221054	61749247282	812	0.0036733	04101	226824164
4101041006	04101	343	2017	La Serena	279340.1	2017	4101	221054	61749247282	821	0.0037140	04101	229338225
4101051001	04101	473	2017	La Serena	279340.1	2017	4101	221054	61749247282	1419	0.0064192	04101	396383607
4101051002	04101	969	2017	La Serena	279340.1	2017	4101	221054	61749247282	2920	0.0132094	04101	815673103
4101051003	04101	922	2017	La Serena	279340.1	2017	4101	221054	61749247282	3348	0.0151456	04101	935230667
4101051004	04101	859	2017	La Serena	279340.1	2017	4101	221054	61749247282	2851	0.0128973	04101	796398636
4101051005	04101	1684	2017	La Serena	279340.1	2017	4101	221054	61749247282	5493	0.0248491	04101	1534415190
4101051006	04101	1322	2017	La Serena	279340.1	2017	4101	221054	61749247282	4295	0.0194296	04101	1199765745
4101051007	04101	787	2017	La Serena	279340.1	2017	4101	221054	61749247282	2336	0.0105676	04101	652538482
4101051008	04101	1238	2017	La Serena	279340.1	2017	4101	221054	61749247282	4235	0.0191582	04101	1183005339
4101051009	04101	1146	2017	La Serena	279340.1	2017	4101	221054	61749247282	3882	0.0175613	04101	1084398283
4101051010	04101	1143	2017	La Serena	279340.1	2017	4101	221054	61749247282	3226	0.0145937	04101	901151175
4101051011	04101	951	2017	La Serena	279340.1	2017	4101	221054	61749247282	2966	0.0134175	04101	828522748
4101061001	04101	1359	2017	La Serena	279340.1	2017	4101	221054	61749247282	4424	0.0200132	04101	1235800619
4101061002	04101	905	2017	La Serena	279340.1	2017	4101	221054	61749247282	3047	0.0137840	04101	851149296
4101061003	04101	1046	2017	La Serena	279340.1	2017	4101	221054	61749247282	3472	0.0157066	04101	969868840
4101061004	04101	1643	2017	La Serena	279340.1	2017	4101	221054	61749247282	5333	0.0241253	04101	1489720773
4101061005	04101	68	2017	La Serena	279340.1	2017	4101	221054	61749247282	288	0.0013028	04101	80449950
4101071001	04101	212	2017	La Serena	279340.1	2017	4101	221054	61749247282	1056	0.0047771	04101	294983150
4101091001	04101	290	2017	La Serena	279340.1	2017	4101	221054	61749247282	1292	0.0058447	04101	360907414
4101141001	04101	622	2017	La Serena	279340.1	2017	4101	221054	61749247282	2872	0.0129923	04101	802264778
4101141002	04101	672	2017	La Serena	279340.1	2017	4101	221054	61749247282	2750	0.0124404	04101	768185285
4101141003	04101	1357	2017	La Serena	279340.1	2017	4101	221054	61749247282	4706	0.0212889	04101	1314574528
4101141004	04101	1096	2017	La Serena	279340.1	2017	4101	221054	61749247282	3750	0.0169642	04101	1047525389
4101141005	04101	1850	2017	La Serena	279340.1	2017	4101	221054	61749247282	5866	0.0265365	04101	1638609048
4101141006	04101	579	2017	La Serena	279340.1	2017	4101	221054	61749247282	2114	0.0095633	04101	590524979
4101151001	04101	1585	2017	La Serena	279340.1	2017	4101	221054	61749247282	4957	0.0224244	04101	1384688894
4101151002	04101	545	2017	La Serena	279340.1	2017	4101	221054	61749247282	1602	0.0072471	04101	447502846
4101151003	04101	656	2017	La Serena	279340.1	2017	4101	221054	61749247282	1900	0.0085952	04101	530746197
4101151004	04101	894	2017	La Serena	279340.1	2017	4101	221054	61749247282	2649	0.0119835	04101	739971935
4101151005	04101	751	2017	La Serena	279340.1	2017	4101	221054	61749247282	2047	0.0092602	04101	571809192
4101151006	04101	1028	2017	La Serena	279340.1	2017	4101	221054	61749247282	3173	0.0143540	04101	886346149
4101161001	04101	1537	2017	La Serena	279340.1	2017	4101	221054	61749247282	5756	0.0260389	04101	1607881637
4101161002	04101	735	2017	La Serena	279340.1	2017	4101	221054	61749247282	3690	0.0166928	04101	1030764983
4101161003	04101	611	2017	La Serena	279340.1	2017	4101	221054	61749247282	2952	0.0133542	04101	824611986
4101161004	04101	1074	2017	La Serena	279340.1	2017	4101	221054	61749247282	5185	0.0234558	04101	1448378438
4101161005	04101	1257	2017	La Serena	279340.1	2017	4101	221054	61749247282	4746	0.0214699	04101	1325748132
4101161006	04101	1234	2017	La Serena	279340.1	2017	4101	221054	61749247282	4464	0.0201942	04101	1246974223
4101161007	04101	1226	2017	La Serena	279340.1	2017	4101	221054	61749247282	5497	0.0248672	04101	1535532550
4101161008	04101	1216	2017	La Serena	279340.1	2017	4101	221054	61749247282	4352	0.0196875	04101	1215688131
4101161009	04101	1192	2017	La Serena	279340.1	2017	4101	221054	61749247282	4309	0.0194930	04101	1203676507
4101161010	04101	1146	2017	La Serena	279340.1	2017	4101	221054	61749247282	4752	0.0214970	04101	1327424173
4101171001	04101	607	2017	La Serena	279340.1	2017	4101	221054	61749247282	2644	0.0119609	04101	738575234
4101171002	04101	735	2017	La Serena	279340.1	2017	4101	221054	61749247282	2927	0.0132411	04101	817628484
4101171003	04101	954	2017	La Serena	279340.1	2017	4101	221054	61749247282	5093	0.0230396	04101	1422679148
4101171004	04101	1210	2017	La Serena	279340.1	2017	4101	221054	61749247282	4747	0.0214744	04101	1326027472
4101171005	04101	1252	2017	La Serena	279340.1	2017	4101	221054	61749247282	4515	0.0204249	04101	1261220568
4101991999	04101	197	2017	La Serena	279340.1	2017	4101	221054	61749247282	796	0.0036009	04101	222354723
4102011001	04102	1296	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	6389	0.0280552	04102	1719143162
4102011002	04102	561	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2328	0.0102226	04102	626414976
4102021001	04102	1011	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4724	0.0207439	04102	1271127296
4102021002	04102	488	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2101	0.0092258	04102	565334134
4102021003	04102	497	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2094	0.0091951	04102	563450584
4102021004	04102	881	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4472	0.0196373	04102	1203319490
4102021005	04102	543	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2188	0.0096079	04102	588743972
4102021006	04102	648	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3498	0.0153603	04102	941236935
4102031001	04102	634	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2056	0.0090282	04102	553225597
4102031002	04102	930	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3483	0.0152944	04102	937200756
4102031003	04102	622	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2188	0.0096079	04102	588743972
4102031004	04102	655	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2324	0.0102051	04102	625338661
4102041001	04102	534	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	1800	0.0079041	04102	484341476
4102041002	04102	529	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	1873	0.0082247	04102	503984214
4102051001	04102	1437	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4645	0.0203970	04102	1249870087
4102051002	04102	1635	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	5393	0.0236816	04102	1451140878
4102051003	04102	1366	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4578	0.0201028	04102	1231841821
4102051004	04102	5	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	15	0.0000659	04102	4036179
4102051005	04102	1485	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4613	0.0202564	04102	1241259572
4102051006	04102	1182	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4305	0.0189040	04102	1158383364
4102051007	04102	924	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2872	0.0126114	04102	772793733
4102051008	04102	742	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2455	0.0107803	04102	660587958
4102061001	04102	978	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4034	0.0177140	04102	1085463064
4102061002	04102	1712	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	6820	0.0299477	04102	1835116037
4102061003	04102	81	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	309	0.0013569	04102	83145287
4102061004	04102	1279	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4293	0.0188513	04102	1155154421
4102061005	04102	1852	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	6111	0.0268344	04102	1644339312
4102061006	04102	1375	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4633	0.0203443	04102	1246641144
4102081001	04102	779	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2323	0.0102007	04102	625069583
4102081002	04102	863	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3139	0.0137839	04102	844637719
4102091001	04102	343	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	1423	0.0062486	04102	382898845
4102091002	04102	735	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2821	0.0123875	04102	759070725
4102091003	04102	787	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3087	0.0135555	04102	830645632
4102091004	04102	599	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2580	0.0113292	04102	694222783
4102091005	04102	903	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3863	0.0169631	04102	1039450624
4102091006	04102	946	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3170	0.0139200	04102	852979155
4102091007	04102	1648	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	6290	0.0276204	04102	1692504381
4102091008	04102	1655	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	5782	0.0253897	04102	1555812453
4102101001	04102	336	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	1476	0.0064814	04102	397160010

7 Análisis de regresión

Aplicaremos un análisis de regresión donde:

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

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

7.1 Diagrama de dispersión loess

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

7.2 Outliers

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

7.3 Modelo lineal

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

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

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -294701357  -86138039  -16269587   51612906  476023651 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 47712923   19496083   2.447   0.0153 *  
## Freq.x        942288      19737  47.741   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 128700000 on 191 degrees of freedom
## Multiple R-squared:  0.9227, Adjusted R-squared:  0.9223 
## F-statistic:  2279 on 1 and 191 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

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
dato <- datos$adj.r.squared
modelo <- "cuadrático"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
latex <- "$$ '\'hat Y = '\'beta_0 + '\'beta_1  X^2  $$"
modelos1 <- cbind(modelo,dato,sintaxis,latex)
 
### 8.2 Modelo cúbico
 
linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
dato <- datos$adj.r.squared
modelo <- "cúbico"
sintaxis <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
latex <- "$$ '\'hat Y = '\'beta_0 + '\'beta_1  X^3  $$"
modelos2 <- cbind(modelo,dato,sintaxis,latex)
 
### 8.3 Modelo logarítmico
 
linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
dato <- datos$adj.r.squared
modelo <- "logarítmico"
sintaxis <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
latex <- "$$ '\'hat Y = '\'beta_0 + '\'beta_1 ln X  $$"
modelos3 <- cbind(modelo,dato,sintaxis,latex)
 
### 8.5 Modelo con raíz cuadrada 
 
linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
dato <- datos$adj.r.squared
modelo <- "raíz cuadrada"
sintaxis <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
latex <- "$$ '\'hat Y = '\'beta_0 + '\'beta_1  '\'sqrt {X}  $$"
modelos5 <- cbind(modelo,dato,sintaxis,latex)
 
### 8.6 Modelo raíz-raíz
 
linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
dato <- datos$adj.r.squared
modelo <- "raíz-raíz"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
latex <- "$$ '\'hat Y = {'\'beta_0}^2 + 2  '\'beta_0  '\'beta_1 '\'sqrt{X}+  '\'beta_1^2 X  $$"
modelos6 <- cbind(modelo,dato,sintaxis,latex)
 
### 8.7 Modelo log-raíz
 
linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
dato <- datos$adj.r.squared
modelo <- "log-raíz"
sintaxis <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
latex <- "$$ '\'hat Y = e^{'\'beta_0 + '\'beta_1 '\'sqrt{X}} $$"
modelos7 <- cbind(modelo,dato,sintaxis,latex)
 
### 8.8 Modelo raíz-log
 
linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
dato <- datos$adj.r.squared
modelo <- "raíz-log"
sintaxis <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
latex <- "$$ '\'hat Y = {'\'beta_0}^2 + 2  '\'beta_0  '\'beta_1 '\'ln{X}+  '\'beta_1^2 ln^2X  $$"
modelos8 <- cbind(modelo,dato,sintaxis,latex)
 
### 8.9 Modelo log-log
 
linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
dato <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
latex <- "$$ '\'hat Y = e^{'\'beta_0+'\'beta_1  ln{X}} $$"
modelos9 <- cbind(modelo,dato,sintaxis,latex)
 
modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)

modelos_bind <<- modelos_bind[order(modelos_bind$dato ),]
h_y_m_comuna_corr_01 <<- h_y_m_comuna_corr_01

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

	modelo	dato	sintaxis	latex
3	logarítmico	0.63999970331006	linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)	\[ ''hat Y = ''beta_0 + ''beta_1 ln X \]
6	log-raíz	0.816408583872768	linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)	\[ ''hat Y = e^{''beta_0 + ''beta_1 ''sqrt{X}} \]
7	raíz-log	0.825843712907017	linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)	\[ ''hat Y = {''beta_0}^2 + 2 ''beta_0 ''beta_1 ''ln{X}+ ''beta_1^2 ln^2X \]
4	raíz cuadrada	0.88509550225697	linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)	\[ ''hat Y = ''beta_0 + ''beta_1 ''sqrt {X} \]
1	cuadrático	0.92227444130024	linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)	\[ ''hat Y = ''beta_0 + ''beta_1 X^2 \]
2	cúbico	0.92227444130024	linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)	\[ ''hat Y = ''beta_0 + ''beta_1 X^3 \]
5	raíz-raíz	0.945887021141637	linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)	\[ ''hat Y = {''beta_0}^2 + 2 ''beta_0 ''beta_1 ''sqrt{X}+ ''beta_1^2 X \]
8	log-log	0.958780429287661	linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)	\[ ''hat Y = e^{''beta_0+''beta_1 ln{X}} \]

h_y_m_comuna_corr <- h_y_m_comuna_corr_01
metodo <- 8


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

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.53509 -0.09912 -0.00938  0.09334  1.84502 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 13.97259    0.09578  145.89   <2e-16 ***
## log(Freq.x)  0.97595    0.01460   66.83   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2122 on 191 degrees of freedom
## Multiple R-squared:  0.959,  Adjusted R-squared:  0.9588 
## F-statistic:  4467 on 1 and 191 DF,  p-value: < 2.2e-16

aa <- linearMod$coefficients[1]
aa

## (Intercept) 
##    13.97259

bb <- linearMod$coefficients[2]
bb

## log(Freq.x) 
##   0.9759537

9 Modelo log-log (log-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.9588).

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$Freq.x), y=log(h_y_m_comuna_corr$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)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.53509 -0.09912 -0.00938  0.09334  1.84502 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 13.97259    0.09578  145.89   <2e-16 ***
## log(Freq.x)  0.97595    0.01460   66.83   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2122 on 191 degrees of freedom
## Multiple R-squared:  0.959,  Adjusted R-squared:  0.9588 
## F-statistic:  4467 on 1 and 191 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr, 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

\[ \hat Y = e^{13.97259 +0.9759537 \cdot ln{X}} \]

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

r3_100 <- h_y_m_comuna_corr[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
4101011001	04101	376	2017	La Serena	279340.1	2017	4101	221054	61749247282	1455	0.0065821	04101	406439851	381488742
4101021001	04101	532	2017	La Serena	279340.1	2017	4101	221054	61749247282	2431	0.0109973	04101	679075792	535280186
4101021002	04101	762	2017	La Serena	279340.1	2017	4101	221054	61749247282	2926	0.0132366	04101	817349143	760102649
4101021003	04101	738	2017	La Serena	279340.1	2017	4101	221054	61749247282	2699	0.0122097	04101	753938940	736729137
4101021004	04101	1306	2017	La Serena	279340.1	2017	4101	221054	61749247282	5323	0.0240801	04101	1486927372	1285979091
4101021005	04101	444	2017	La Serena	279340.1	2017	4101	221054	61749247282	2125	0.0096130	04101	593597720	448684253
4101031001	04101	1549	2017	La Serena	279340.1	2017	4101	221054	61749247282	4341	0.0196377	04101	1212615390	1519008209
4101031002	04101	876	2017	La Serena	279340.1	2017	4101	221054	61749247282	2583	0.0116849	04101	721535488	870894203
4101031003	04101	1441	2017	La Serena	279340.1	2017	4101	221054	61749247282	4026	0.0182127	04101	1124623257	1415557239
4101041001	04101	440	2017	La Serena	279340.1	2017	4101	221054	61749247282	1108	0.0050123	04101	309508835	444738823
4101041002	04101	480	2017	La Serena	279340.1	2017	4101	221054	61749247282	1015	0.0045916	04101	283530205	484155567
4101041003	04101	1578	2017	La Serena	279340.1	2017	4101	221054	61749247282	4721	0.0213568	04101	1318764630	1546756662
4101041004	04101	1157	2017	La Serena	279340.1	2017	4101	221054	61749247282	3560	0.0161047	04101	994450769	1142586686
4101041005	04101	359	2017	La Serena	279340.1	2017	4101	221054	61749247282	812	0.0036733	04101	226824164	364646040
4101041006	04101	343	2017	La Serena	279340.1	2017	4101	221054	61749247282	821	0.0037140	04101	229338225	348776565
4101051001	04101	473	2017	La Serena	279340.1	2017	4101	221054	61749247282	1419	0.0064192	04101	396383607	477263532
4101051002	04101	969	2017	La Serena	279340.1	2017	4101	221054	61749247282	2920	0.0132094	04101	815673103	961017665
4101051003	04101	922	2017	La Serena	279340.1	2017	4101	221054	61749247282	3348	0.0151456	04101	935230667	915498723
4101051004	04101	859	2017	La Serena	279340.1	2017	4101	221054	61749247282	2851	0.0128973	04101	796398636	854395818
4101051005	04101	1684	2017	La Serena	279340.1	2017	4101	221054	61749247282	5493	0.0248491	04101	1534415190	1648079415
4101051006	04101	1322	2017	La Serena	279340.1	2017	4101	221054	61749247282	4295	0.0194296	04101	1199765745	1301352714
4101051007	04101	787	2017	La Serena	279340.1	2017	4101	221054	61749247282	2336	0.0105676	04101	652538482	784431245
4101051008	04101	1238	2017	La Serena	279340.1	2017	4101	221054	61749247282	4235	0.0191582	04101	1183005339	1220589952
4101051009	04101	1146	2017	La Serena	279340.1	2017	4101	221054	61749247282	3882	0.0175613	04101	1084398283	1131983717
4101051010	04101	1143	2017	La Serena	279340.1	2017	4101	221054	61749247282	3226	0.0145937	04101	901151175	1129091574
4101051011	04101	951	2017	La Serena	279340.1	2017	4101	221054	61749247282	2966	0.0134175	04101	828522748	943591295
4101061001	04101	1359	2017	La Serena	279340.1	2017	4101	221054	61749247282	4424	0.0200132	04101	1235800619	1336887174
4101061002	04101	905	2017	La Serena	279340.1	2017	4101	221054	61749247282	3047	0.0137840	04101	851149296	899020823
4101061003	04101	1046	2017	La Serena	279340.1	2017	4101	221054	61749247282	3472	0.0157066	04101	969868840	1035477704
4101061004	04101	1643	2017	La Serena	279340.1	2017	4101	221054	61749247282	5333	0.0241253	04101	1489720773	1608907274
4101061005	04101	68	2017	La Serena	279340.1	2017	4101	221054	61749247282	288	0.0013028	04101	80449950	71888837
4101071001	04101	212	2017	La Serena	279340.1	2017	4101	221054	61749247282	1056	0.0047771	04101	294983150	218078929
4101091001	04101	290	2017	La Serena	279340.1	2017	4101	221054	61749247282	1292	0.0058447	04101	360907414	296076579
4101141001	04101	622	2017	La Serena	279340.1	2017	4101	221054	61749247282	2872	0.0129923	04101	802264778	623487412
4101141002	04101	672	2017	La Serena	279340.1	2017	4101	221054	61749247282	2750	0.0124404	04101	768185285	672355761
4101141003	04101	1357	2017	La Serena	279340.1	2017	4101	221054	61749247282	4706	0.0212889	04101	1314574528	1334966993
4101141004	04101	1096	2017	La Serena	279340.1	2017	4101	221054	61749247282	3750	0.0169642	04101	1047525389	1083757186
4101141005	04101	1850	2017	La Serena	279340.1	2017	4101	221054	61749247282	5866	0.0265365	04101	1638609048	1806450125
4101141006	04101	579	2017	La Serena	279340.1	2017	4101	221054	61749247282	2114	0.0095633	04101	590524979	581385226
4101151001	04101	1585	2017	La Serena	279340.1	2017	4101	221054	61749247282	4957	0.0224244	04101	1384688894	1553452719
4101151002	04101	545	2017	La Serena	279340.1	2017	4101	221054	61749247282	1602	0.0072471	04101	447502846	548042093
4101151003	04101	656	2017	La Serena	279340.1	2017	4101	221054	61749247282	1900	0.0085952	04101	530746197	656727725
4101151004	04101	894	2017	La Serena	279340.1	2017	4101	221054	61749247282	2649	0.0119835	04101	739971935	888354695
4101151005	04101	751	2017	La Serena	279340.1	2017	4101	221054	61749247282	2047	0.0092602	04101	571809192	749392021
4101151006	04101	1028	2017	La Serena	279340.1	2017	4101	221054	61749247282	3173	0.0143540	04101	886346149	1018083635
4101161001	04101	1537	2017	La Serena	279340.1	2017	4101	221054	61749247282	5756	0.0260389	04101	1607881637	1507522449
4101161002	04101	735	2017	La Serena	279340.1	2017	4101	221054	61749247282	3690	0.0166928	04101	1030764983	733806175
4101161003	04101	611	2017	La Serena	279340.1	2017	4101	221054	61749247282	2952	0.0133542	04101	824611986	612723946
4101161004	04101	1074	2017	La Serena	279340.1	2017	4101	221054	61749247282	5185	0.0234558	04101	1448378438	1062520886
4101161005	04101	1257	2017	La Serena	279340.1	2017	4101	221054	61749247282	4746	0.0214699	04101	1325748132	1238868944
4101161006	04101	1234	2017	La Serena	279340.1	2017	4101	221054	61749247282	4464	0.0201942	04101	1246974223	1216740886
4101161007	04101	1226	2017	La Serena	279340.1	2017	4101	221054	61749247282	5497	0.0248672	04101	1535532550	1209041855
4101161008	04101	1216	2017	La Serena	279340.1	2017	4101	221054	61749247282	4352	0.0196875	04101	1215688131	1199416366
4101161009	04101	1192	2017	La Serena	279340.1	2017	4101	221054	61749247282	4309	0.0194930	04101	1203676507	1176307395
4101161010	04101	1146	2017	La Serena	279340.1	2017	4101	221054	61749247282	4752	0.0214970	04101	1327424173	1131983717
4101171001	04101	607	2017	La Serena	279340.1	2017	4101	221054	61749247282	2644	0.0119609	04101	738575234	608808808
4101171002	04101	735	2017	La Serena	279340.1	2017	4101	221054	61749247282	2927	0.0132411	04101	817628484	733806175
4101171003	04101	954	2017	La Serena	279340.1	2017	4101	221054	61749247282	5093	0.0230396	04101	1422679148	946496237
4101171004	04101	1210	2017	La Serena	279340.1	2017	4101	221054	61749247282	4747	0.0214744	04101	1326027472	1193640160
4101171005	04101	1252	2017	La Serena	279340.1	2017	4101	221054	61749247282	4515	0.0204249	04101	1261220568	1234059331
4101991999	04101	197	2017	La Serena	279340.1	2017	4101	221054	61749247282	796	0.0036009	04101	222354723	203006721
4102011001	04102	1296	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	6389	0.0280552	04102	1719143162	1276368279
4102011002	04102	561	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2328	0.0102226	04102	626414976	563739026
4102021001	04102	1011	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4724	0.0207439	04102	1271127296	1001649180
4102021002	04102	488	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2101	0.0092258	04102	565334134	492029221
4102021003	04102	497	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2094	0.0091951	04102	563450584	500883376
4102021004	04102	881	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4472	0.0196373	04102	1203319490	875745198
4102021005	04102	543	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2188	0.0096079	04102	588743972	546079203
4102021006	04102	648	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3498	0.0153603	04102	941236935	648910283
4102031001	04102	634	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2056	0.0090282	04102	553225597	635224157
4102031002	04102	930	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3483	0.0152944	04102	937200756	923250492
4102031003	04102	622	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2188	0.0096079	04102	588743972	623487412
4102031004	04102	655	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2324	0.0102051	04102	625338661	655750671
4102041001	04102	534	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	1800	0.0079041	04102	484341476	537244040
4102041002	04102	529	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	1873	0.0082247	04102	503984214	532334072
4102051001	04102	1437	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4645	0.0203970	04102	1249870087	1411722223
4102051002	04102	1635	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	5393	0.0236816	04102	1451140878	1601261206
4102051003	04102	1366	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4578	0.0201028	04102	1231841821	1343607274
4102051004	04102	5	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	15	0.0000659	04102	4036179	5628335
4102051005	04102	1485	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4613	0.0202564	04102	1241259572	1457725678
4102051006	04102	1182	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4305	0.0189040	04102	1158383364	1166675367
4102051007	04102	924	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2872	0.0126114	04102	772793733	917436816
4102051008	04102	742	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2455	0.0107803	04102	660587958	740625976
4102061001	04102	978	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4034	0.0177140	04102	1085463064	969727923
4102061002	04102	1712	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	6820	0.0299477	04102	1835116037	1674817911
4102061003	04102	81	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	309	0.0013569	04102	83145287	85272819
4102061004	04102	1279	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4293	0.0188513	04102	1155154421	1260025800
4102061005	04102	1852	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	6111	0.0268344	04102	1644339312	1808356059
4102061006	04102	1375	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4633	0.0203443	04102	1246641144	1352246186
4102081001	04102	779	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2323	0.0102007	04102	625069583	776648145
4102081002	04102	863	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3139	0.0137839	04102	844637719	858278492
4102091001	04102	343	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	1423	0.0062486	04102	382898845	348776565
4102091002	04102	735	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2821	0.0123875	04102	759070725	733806175
4102091003	04102	787	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3087	0.0135555	04102	830645632	784431245
4102091004	04102	599	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2580	0.0113292	04102	694222783	600976665
4102091005	04102	903	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3863	0.0169631	04102	1039450624	897081760
4102091006	04102	946	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3170	0.0139200	04102	852979155	938749236
4102091007	04102	1648	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	6290	0.0276204	04102	1692504381	1613685611
4102091008	04102	1655	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	5782	0.0253897	04102	1555812453	1620374698
4102101001	04102	336	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	1476	0.0064814	04102	397160010	341828119

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

r3_100 <- h_y_m_comuna_corr[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
4101011001	04101	376	2017	La Serena	279340.1	2017	4101	221054	61749247282	1455	0.0065821	04101	406439851	381488742	262191.6
4101021001	04101	532	2017	La Serena	279340.1	2017	4101	221054	61749247282	2431	0.0109973	04101	679075792	535280186	220189.3
4101021002	04101	762	2017	La Serena	279340.1	2017	4101	221054	61749247282	2926	0.0132366	04101	817349143	760102649	259775.3
4101021003	04101	738	2017	La Serena	279340.1	2017	4101	221054	61749247282	2699	0.0122097	04101	753938940	736729137	272963.7
4101021004	04101	1306	2017	La Serena	279340.1	2017	4101	221054	61749247282	5323	0.0240801	04101	1486927372	1285979091	241589.2
4101021005	04101	444	2017	La Serena	279340.1	2017	4101	221054	61749247282	2125	0.0096130	04101	593597720	448684253	211145.5
4101031001	04101	1549	2017	La Serena	279340.1	2017	4101	221054	61749247282	4341	0.0196377	04101	1212615390	1519008209	349921.3
4101031002	04101	876	2017	La Serena	279340.1	2017	4101	221054	61749247282	2583	0.0116849	04101	721535488	870894203	337163.8
4101031003	04101	1441	2017	La Serena	279340.1	2017	4101	221054	61749247282	4026	0.0182127	04101	1124623257	1415557239	351603.9
4101041001	04101	440	2017	La Serena	279340.1	2017	4101	221054	61749247282	1108	0.0050123	04101	309508835	444738823	401388.8
4101041002	04101	480	2017	La Serena	279340.1	2017	4101	221054	61749247282	1015	0.0045916	04101	283530205	484155567	477000.6
4101041003	04101	1578	2017	La Serena	279340.1	2017	4101	221054	61749247282	4721	0.0213568	04101	1318764630	1546756662	327633.3
4101041004	04101	1157	2017	La Serena	279340.1	2017	4101	221054	61749247282	3560	0.0161047	04101	994450769	1142586686	320951.3
4101041005	04101	359	2017	La Serena	279340.1	2017	4101	221054	61749247282	812	0.0036733	04101	226824164	364646040	449071.5
4101041006	04101	343	2017	La Serena	279340.1	2017	4101	221054	61749247282	821	0.0037140	04101	229338225	348776565	424819.2
4101051001	04101	473	2017	La Serena	279340.1	2017	4101	221054	61749247282	1419	0.0064192	04101	396383607	477263532	336337.9
4101051002	04101	969	2017	La Serena	279340.1	2017	4101	221054	61749247282	2920	0.0132094	04101	815673103	961017665	329115.6
4101051003	04101	922	2017	La Serena	279340.1	2017	4101	221054	61749247282	3348	0.0151456	04101	935230667	915498723	273446.5
4101051004	04101	859	2017	La Serena	279340.1	2017	4101	221054	61749247282	2851	0.0128973	04101	796398636	854395818	299682.9
4101051005	04101	1684	2017	La Serena	279340.1	2017	4101	221054	61749247282	5493	0.0248491	04101	1534415190	1648079415	300032.7
4101051006	04101	1322	2017	La Serena	279340.1	2017	4101	221054	61749247282	4295	0.0194296	04101	1199765745	1301352714	302992.5
4101051007	04101	787	2017	La Serena	279340.1	2017	4101	221054	61749247282	2336	0.0105676	04101	652538482	784431245	335801.0
4101051008	04101	1238	2017	La Serena	279340.1	2017	4101	221054	61749247282	4235	0.0191582	04101	1183005339	1220589952	288214.9
4101051009	04101	1146	2017	La Serena	279340.1	2017	4101	221054	61749247282	3882	0.0175613	04101	1084398283	1131983717	291598.1
4101051010	04101	1143	2017	La Serena	279340.1	2017	4101	221054	61749247282	3226	0.0145937	04101	901151175	1129091574	349997.4
4101051011	04101	951	2017	La Serena	279340.1	2017	4101	221054	61749247282	2966	0.0134175	04101	828522748	943591295	318136.0
4101061001	04101	1359	2017	La Serena	279340.1	2017	4101	221054	61749247282	4424	0.0200132	04101	1235800619	1336887174	302189.7
4101061002	04101	905	2017	La Serena	279340.1	2017	4101	221054	61749247282	3047	0.0137840	04101	851149296	899020823	295051.1
4101061003	04101	1046	2017	La Serena	279340.1	2017	4101	221054	61749247282	3472	0.0157066	04101	969868840	1035477704	298236.7
4101061004	04101	1643	2017	La Serena	279340.1	2017	4101	221054	61749247282	5333	0.0241253	04101	1489720773	1608907274	301689.0
4101061005	04101	68	2017	La Serena	279340.1	2017	4101	221054	61749247282	288	0.0013028	04101	80449950	71888837	249614.0
4101071001	04101	212	2017	La Serena	279340.1	2017	4101	221054	61749247282	1056	0.0047771	04101	294983150	218078929	206514.1
4101091001	04101	290	2017	La Serena	279340.1	2017	4101	221054	61749247282	1292	0.0058447	04101	360907414	296076579	229161.4
4101141001	04101	622	2017	La Serena	279340.1	2017	4101	221054	61749247282	2872	0.0129923	04101	802264778	623487412	217091.7
4101141002	04101	672	2017	La Serena	279340.1	2017	4101	221054	61749247282	2750	0.0124404	04101	768185285	672355761	244493.0
4101141003	04101	1357	2017	La Serena	279340.1	2017	4101	221054	61749247282	4706	0.0212889	04101	1314574528	1334966993	283673.4
4101141004	04101	1096	2017	La Serena	279340.1	2017	4101	221054	61749247282	3750	0.0169642	04101	1047525389	1083757186	289001.9
4101141005	04101	1850	2017	La Serena	279340.1	2017	4101	221054	61749247282	5866	0.0265365	04101	1638609048	1806450125	307952.6
4101141006	04101	579	2017	La Serena	279340.1	2017	4101	221054	61749247282	2114	0.0095633	04101	590524979	581385226	275016.7
4101151001	04101	1585	2017	La Serena	279340.1	2017	4101	221054	61749247282	4957	0.0224244	04101	1384688894	1553452719	313385.7
4101151002	04101	545	2017	La Serena	279340.1	2017	4101	221054	61749247282	1602	0.0072471	04101	447502846	548042093	342098.7
4101151003	04101	656	2017	La Serena	279340.1	2017	4101	221054	61749247282	1900	0.0085952	04101	530746197	656727725	345646.2
4101151004	04101	894	2017	La Serena	279340.1	2017	4101	221054	61749247282	2649	0.0119835	04101	739971935	888354695	335354.7
4101151005	04101	751	2017	La Serena	279340.1	2017	4101	221054	61749247282	2047	0.0092602	04101	571809192	749392021	366092.8
4101151006	04101	1028	2017	La Serena	279340.1	2017	4101	221054	61749247282	3173	0.0143540	04101	886346149	1018083635	320858.4
4101161001	04101	1537	2017	La Serena	279340.1	2017	4101	221054	61749247282	5756	0.0260389	04101	1607881637	1507522449	261904.5
4101161002	04101	735	2017	La Serena	279340.1	2017	4101	221054	61749247282	3690	0.0166928	04101	1030764983	733806175	198863.5
4101161003	04101	611	2017	La Serena	279340.1	2017	4101	221054	61749247282	2952	0.0133542	04101	824611986	612723946	207562.3
4101161004	04101	1074	2017	La Serena	279340.1	2017	4101	221054	61749247282	5185	0.0234558	04101	1448378438	1062520886	204922.1
4101161005	04101	1257	2017	La Serena	279340.1	2017	4101	221054	61749247282	4746	0.0214699	04101	1325748132	1238868944	261034.3
4101161006	04101	1234	2017	La Serena	279340.1	2017	4101	221054	61749247282	4464	0.0201942	04101	1246974223	1216740886	272567.4
4101161007	04101	1226	2017	La Serena	279340.1	2017	4101	221054	61749247282	5497	0.0248672	04101	1535532550	1209041855	219945.8
4101161008	04101	1216	2017	La Serena	279340.1	2017	4101	221054	61749247282	4352	0.0196875	04101	1215688131	1199416366	275601.2
4101161009	04101	1192	2017	La Serena	279340.1	2017	4101	221054	61749247282	4309	0.0194930	04101	1203676507	1176307395	272988.5
4101161010	04101	1146	2017	La Serena	279340.1	2017	4101	221054	61749247282	4752	0.0214970	04101	1327424173	1131983717	238212.1
4101171001	04101	607	2017	La Serena	279340.1	2017	4101	221054	61749247282	2644	0.0119609	04101	738575234	608808808	230260.5
4101171002	04101	735	2017	La Serena	279340.1	2017	4101	221054	61749247282	2927	0.0132411	04101	817628484	733806175	250702.5
4101171003	04101	954	2017	La Serena	279340.1	2017	4101	221054	61749247282	5093	0.0230396	04101	1422679148	946496237	185842.6
4101171004	04101	1210	2017	La Serena	279340.1	2017	4101	221054	61749247282	4747	0.0214744	04101	1326027472	1193640160	251451.5
4101171005	04101	1252	2017	La Serena	279340.1	2017	4101	221054	61749247282	4515	0.0204249	04101	1261220568	1234059331	273324.3
4101991999	04101	197	2017	La Serena	279340.1	2017	4101	221054	61749247282	796	0.0036009	04101	222354723	203006721	255033.6
4102011001	04102	1296	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	6389	0.0280552	04102	1719143162	1276368279	199775.9
4102011002	04102	561	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2328	0.0102226	04102	626414976	563739026	242155.9
4102021001	04102	1011	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4724	0.0207439	04102	1271127296	1001649180	212034.1
4102021002	04102	488	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2101	0.0092258	04102	565334134	492029221	234188.1
4102021003	04102	497	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2094	0.0091951	04102	563450584	500883376	239199.3
4102021004	04102	881	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4472	0.0196373	04102	1203319490	875745198	195828.5
4102021005	04102	543	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2188	0.0096079	04102	588743972	546079203	249579.2
4102021006	04102	648	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3498	0.0153603	04102	941236935	648910283	185508.9
4102031001	04102	634	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2056	0.0090282	04102	553225597	635224157	308961.2
4102031002	04102	930	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3483	0.0152944	04102	937200756	923250492	265073.4
4102031003	04102	622	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2188	0.0096079	04102	588743972	623487412	284957.7
4102031004	04102	655	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2324	0.0102051	04102	625338661	655750671	282164.7
4102041001	04102	534	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	1800	0.0079041	04102	484341476	537244040	298468.9
4102041002	04102	529	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	1873	0.0082247	04102	503984214	532334072	284214.7
4102051001	04102	1437	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4645	0.0203970	04102	1249870087	1411722223	303923.0
4102051002	04102	1635	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	5393	0.0236816	04102	1451140878	1601261206	296914.7
4102051003	04102	1366	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4578	0.0201028	04102	1231841821	1343607274	293492.2
4102051004	04102	5	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	15	0.0000659	04102	4036179	5628335	375222.3
4102051005	04102	1485	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4613	0.0202564	04102	1241259572	1457725678	316003.8
4102051006	04102	1182	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4305	0.0189040	04102	1158383364	1166675367	271004.7
4102051007	04102	924	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2872	0.0126114	04102	772793733	917436816	319441.8
4102051008	04102	742	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2455	0.0107803	04102	660587958	740625976	301680.6
4102061001	04102	978	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4034	0.0177140	04102	1085463064	969727923	240388.7
4102061002	04102	1712	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	6820	0.0299477	04102	1835116037	1674817911	245574.5
4102061003	04102	81	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	309	0.0013569	04102	83145287	85272819	275963.8
4102061004	04102	1279	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4293	0.0188513	04102	1155154421	1260025800	293507.1
4102061005	04102	1852	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	6111	0.0268344	04102	1644339312	1808356059	295918.2
4102061006	04102	1375	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	4633	0.0203443	04102	1246641144	1352246186	291872.7
4102081001	04102	779	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2323	0.0102007	04102	625069583	776648145	334329.8
4102081002	04102	863	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3139	0.0137839	04102	844637719	858278492	273424.2
4102091001	04102	343	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	1423	0.0062486	04102	382898845	348776565	245099.5
4102091002	04102	735	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2821	0.0123875	04102	759070725	733806175	260122.7
4102091003	04102	787	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3087	0.0135555	04102	830645632	784431245	254108.0
4102091004	04102	599	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	2580	0.0113292	04102	694222783	600976665	232936.7
4102091005	04102	903	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3863	0.0169631	04102	1039450624	897081760	232224.1
4102091006	04102	946	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	3170	0.0139200	04102	852979155	938749236	296135.4
4102091007	04102	1648	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	6290	0.0276204	04102	1692504381	1613685611	256547.8
4102091008	04102	1655	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	5782	0.0253897	04102	1555812453	1620374698	280244.7
4102101001	04102	336	2017	Coquimbo	269078.6	2017	4102	227730	61277269093	1476	0.0064814	04102	397160010	341828119	231590.9

Guardamos:

saveRDS(h_y_m_comuna_corr, "P03C/region_04_P03C_u.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