date: 20-07-2021
1 Generación de ingresos expandidos a nivel Urbano
En los siguientes rpubs sólo llamaremos al rds ya construído llamado “Ingresos_expandidos_rural_17.rds”:
1.1 Variable CENSO
Necesitamos calcular las frecuencias a nivel censal de las respuestas correspondientes a la categoría: “ESCOLARIDAD” del campo ESCOLARIDAD del Censo de personas. Recordemos que ésta fué la más alta correlación en relación a los ingresos expandidos (ver punto 2 Correlaciones aquí).
1.1.1 Lectura y filtrado de la tabla censal de personas
Leemos la tabla Censo 2017 de personas que ya tiene integrada la clave zonal:
<- readRDS("../censo_personas_con_clave_17")
tabla_con_clave <- tabla_con_clave[c(1:100),]
r3_100 kbl(r3_100) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
kable_paper() %>%
scroll_box(width = "100%", height = "300px")
REGION | PROVINCIA | COMUNA | DC | AREA | ZC_LOC | ID_ZONA_LOC | NVIV | NHOGAR | PERSONAN | P07 | P08 | P09 | P10 | P10COMUNA | P10PAIS | P11 | P11COMUNA | P11PAIS | P12 | P12COMUNA | P12PAIS | P12A_LLEGADA | P12A_TRAMO | P13 | P14 | P15 | P15A | P16 | P16A | P16A_OTRO | P17 | P18 | P19 | P20 | P21M | P21A | P10PAIS_GRUPO | P11PAIS_GRUPO | P12PAIS_GRUPO | ESCOLARIDAD | P16A_GRUPO | REGION_15R | PROVINCIA_15R | COMUNA_15R | P10COMUNA_15R | P11COMUNA_15R | P12COMUNA_15R | clave |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 1 | 1 | 1 | 1 | 1 | 73 | 1 | 98 | 998 | 3 | 15101 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 4 | 6 | 2 | 1 | 2 | 98 | 7 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 4 | 2 | 15 | 152 | 15202 | 98 | 15101 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 3 | 1 | 1 | 1 | 1 | 78 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 3 | 98 | 98 | 98 | 1 | 2 | 98 | 7 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 3 | 1 | 2 | 2 | 2 | 78 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 3 | 98 | 98 | 98 | 1 | 2 | 98 | 7 | 98 | 1 | 1 | 3 | 1965 | 998 | 998 | 998 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 3 | 1 | 3 | 5 | 2 | 52 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 1 | 2 | 5 | 2 | 1 | 2 | 98 | 7 | 98 | 2 | 1 | 4 | 1995 | 998 | 998 | 998 | 2 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 3 | 1 | 4 | 11 | 1 | 44 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 1 | 3 | 5 | 2 | 1 | 2 | 98 | 1 | Z | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 3 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 9 | 1 | 1 | 1 | 1 | 39 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 8 | 5 | 1 | 1 | 2 | 98 | 8 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 8 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 9 | 1 | 2 | 2 | 2 | 35 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 6 | 5 | 2 | 1 | 2 | 98 | 1 | Z | 2 | 2 | 11 | 2004 | 998 | 998 | 998 | 6 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 9 | 1 | 3 | 5 | 1 | 13 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 1 | 7 | 5 | 2 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 7 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 9 | 1 | 4 | 5 | 1 | 12 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 1 | 6 | 5 | 2 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 6 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 10 | 1 | 1 | 1 | 2 | 65 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 4 | 5 | 2 | 1 | 2 | 98 | 6 | 98 | 3 | 3 | 9 | 1992 | 998 | 998 | 998 | 4 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 13 | 1 | 1 | 1 | 1 | 50 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 5 | 5 | 2 | 1 | 2 | 98 | 1 | Z | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 5 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 13 | 1 | 2 | 4 | 2 | 43 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 6 | 5 | 2 | 1 | 2 | 98 | 6 | 98 | 2 | 2 | 3 | 2002 | 998 | 998 | 998 | 6 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 13 | 1 | 3 | 5 | 1 | 15 | 3 | 15201 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 1 | 1 | 7 | 2 | 1 | 2 | 98 | 8 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 9 | 2 | 15 | 152 | 15202 | 15201 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 16 | 1 | 1 | 1 | 1 | 75 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 3 | 98 | 98 | 98 | 1 | 2 | 98 | 7 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 16 | 1 | 2 | 16 | 2 | 58 | 4 | 98 | 68 | 6 | 98 | 998 | 5 | 98 | 998 | 9999 | 1 | 3 | 98 | 98 | 98 | 1 | 2 | 98 | 7 | 98 | 4 | 4 | 99 | 9999 | 68 | 68 | 68 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 16 | 1 | 3 | 2 | 2 | 70 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 3 | 98 | 98 | 98 | 1 | 2 | 98 | 7 | 98 | 5 | 4 | 99 | 9999 | 998 | 998 | 998 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 17 | 1 | 1 | 1 | 2 | 43 | 2 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 8 | 5 | 1 | 1 | 2 | 98 | 1 | I | 3 | 3 | 9 | 2008 | 998 | 998 | 998 | 8 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 17 | 1 | 2 | 4 | 1 | 55 | 2 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 6 | 5 | 2 | 1 | 2 | 98 | 6 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 6 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 17 | 1 | 3 | 5 | 2 | 13 | 2 | 98 | 998 | 2 | 98 | 998 | 2 | 15101 | 998 | 9998 | 98 | 1 | 7 | 5 | 2 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 7 | 2 | 15 | 152 | 15202 | 98 | 98 | 15101 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 17 | 1 | 4 | 5 | 1 | 8 | 2 | 98 | 998 | 2 | 98 | 998 | 2 | 15101 | 998 | 9998 | 98 | 1 | 2 | 5 | 2 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 2 | 2 | 15 | 152 | 15202 | 98 | 98 | 15101 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 17 | 1 | 5 | 15 | 2 | 29 | 2 | 98 | 998 | 4 | 98 | 998 | 3 | 98 | 998 | 2015 | 1 | 2 | 6 | 5 | 2 | 1 | 2 | 98 | 6 | 98 | 5 | 5 | 11 | 2014 | 998 | 604 | 604 | 6 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 17 | 1 | 6 | 15 | 1 | 4 | 2 | 98 | 998 | 1 | 98 | 998 | 5 | 98 | 998 | 2015 | 1 | 1 | 0 | 1 | 2 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 68 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 17 | 1 | 7 | 15 | 2 | 2 | 2 | 98 | 998 | 1 | 98 | 998 | 3 | 98 | 998 | 2015 | 1 | 1 | 0 | 1 | 2 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 604 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 17 | 1 | 8 | 15 | 1 | 16 | 2 | 98 | 998 | 6 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 4 | 5 | 2 | 1 | 2 | 98 | 6 | 98 | 98 | 98 | 98 | 9998 | 998 | 68 | 998 | 4 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 18 | 1 | 1 | 1 | 2 | 74 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 2 | 5 | 2 | 1 | 2 | 98 | 6 | 98 | 2 | 2 | 12 | 1976 | 998 | 998 | 998 | 2 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 19 | 1 | 1 | 1 | 1 | 68 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 3 | 98 | 98 | 98 | 1 | 2 | 98 | 7 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 20 | 1 | 1 | 1 | 1 | 74 | 1 | 98 | 998 | 3 | 15101 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 2 | 5 | 2 | 1 | 2 | 98 | 1 | Z | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 2 | 2 | 15 | 152 | 15202 | 98 | 15101 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 20 | 1 | 2 | 2 | 2 | 65 | 1 | 98 | 998 | 3 | 997 | 998 | 3 | 98 | 998 | 9999 | 2 | 2 | 2 | 5 | 2 | 1 | 2 | 98 | 6 | 98 | 2 | 2 | 9 | 1982 | 998 | 998 | 604 | 2 | 2 | 15 | 152 | 15202 | 98 | 997 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 25 | 1 | 1 | 1 | 2 | 76 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 3 | 98 | 98 | 98 | 1 | 2 | 98 | 6 | 98 | 8 | 6 | 3 | 1981 | 998 | 998 | 998 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 25 | 1 | 2 | 5 | 2 | 36 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 4 | 8 | 1 | 1 | 2 | 98 | 1 | A | 0 | 98 | 98 | 9998 | 998 | 998 | 998 | 12 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 28 | 1 | 1 | 1 | 2 | 31 | 1 | 98 | 998 | 2 | 98 | 998 | 5 | 98 | 998 | 2007 | 2 | 2 | 5 | 5 | 2 | 1 | 2 | 98 | 1 | A | 2 | 2 | 4 | 2008 | 998 | 998 | 68 | 5 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 28 | 1 | 2 | 4 | 1 | 35 | 1 | 98 | 998 | 2 | 98 | 998 | 5 | 98 | 998 | 2007 | 2 | 2 | 6 | 5 | 2 | 1 | 2 | 98 | 1 | F | 98 | 98 | 98 | 9998 | 998 | 998 | 68 | 6 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 28 | 1 | 3 | 5 | 1 | 11 | 1 | 98 | 998 | 2 | 98 | 998 | 5 | 98 | 998 | 2007 | 2 | 1 | 5 | 5 | 2 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 68 | 5 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 28 | 1 | 4 | 5 | 1 | 8 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 1 | 2 | 5 | 2 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 2 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 28 | 1 | 5 | 15 | 2 | 74 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 3 | 5 | 2 | 1 | 2 | 98 | 6 | 98 | 6 | 6 | 99 | 9999 | 998 | 998 | 998 | 3 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 33 | 1 | 1 | 1 | 1 | 41 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 8 | 5 | 1 | 1 | 2 | 98 | 1 | Z | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 8 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 33 | 1 | 2 | 2 | 2 | 47 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 8 | 5 | 1 | 1 | 2 | 98 | 1 | A | 2 | 1 | 4 | 1996 | 998 | 998 | 998 | 8 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 33 | 1 | 3 | 14 | 1 | 88 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 3 | 98 | 98 | 98 | 1 | 2 | 98 | 7 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 33 | 1 | 4 | 14 | 1 | 65 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 2 | 5 | 2 | 1 | 2 | 98 | 7 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 2 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 36 | 1 | 1 | 1 | 2 | 59 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 2 | 5 | 2 | 1 | 2 | 98 | 6 | 98 | 8 | 8 | 2 | 1998 | 998 | 998 | 998 | 2 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 36 | 1 | 2 | 2 | 1 | 56 | 1 | 98 | 998 | 99 | 99 | 999 | 1 | 98 | 998 | 9998 | 98 | 2 | 2 | 5 | 2 | 1 | 2 | 98 | 6 | 98 | 98 | 98 | 98 | 9998 | 998 | 999 | 998 | 2 | 2 | 15 | 152 | 15202 | 98 | 99 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 36 | 1 | 3 | 5 | 2 | 36 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 8 | 5 | 1 | 1 | 2 | 98 | 6 | 98 | 2 | 2 | 7 | 2010 | 998 | 998 | 998 | 8 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 36 | 1 | 4 | 12 | 2 | 13 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 1 | 7 | 5 | 2 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 7 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 36 | 1 | 5 | 12 | 2 | 6 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 1 | 0 | 3 | 1 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 36 | 1 | 6 | 5 | 1 | 24 | 1 | 98 | 998 | 3 | 15101 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 4 | 7 | 1 | 1 | 2 | 98 | 1 | Z | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 12 | 2 | 15 | 152 | 15202 | 98 | 15101 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 36 | 1 | 7 | 11 | 2 | 24 | 1 | 98 | 998 | 3 | 15101 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 4 | 7 | 1 | 1 | 2 | 98 | 1 | N | 2 | 2 | 11 | 2015 | 998 | 998 | 998 | 12 | 2 | 15 | 152 | 15202 | 98 | 15101 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 36 | 1 | 8 | 12 | 1 | 6 | 1 | 98 | 998 | 2 | 98 | 998 | 2 | 15101 | 998 | 9998 | 98 | 1 | 0 | 3 | 1 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 15101 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 36 | 1 | 9 | 12 | 2 | 1 | 1 | 98 | 998 | 1 | 98 | 998 | 2 | 15101 | 998 | 9998 | 98 | 3 | 98 | 98 | 98 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 15101 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 38 | 1 | 1 | 1 | 1 | 19 | 1 | 98 | 998 | 3 | 15101 | 998 | 2 | 15101 | 998 | 9998 | 98 | 1 | 1 | 8 | 2 | 1 | 2 | 98 | 1 | A | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 9 | 2 | 15 | 152 | 15202 | 98 | 15101 | 15101 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 39 | 1 | 1 | 1 | 1 | 21 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 1 | 7 | 2 | 1 | 2 | 98 | 1 | F | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 9 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 39 | 1 | 2 | 4 | 2 | 22 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 1 | 8 | 2 | 1 | 2 | 98 | 6 | 98 | 0 | 98 | 98 | 9998 | 998 | 998 | 998 | 9 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 43 | 1 | 1 | 1 | 2 | 26 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 8 | 5 | 1 | 1 | 2 | 98 | 6 | 98 | 2 | 2 | 10 | 2013 | 998 | 998 | 998 | 8 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 43 | 1 | 2 | 2 | 1 | 24 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 8 | 5 | 1 | 1 | 2 | 98 | 1 | Z | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 8 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 43 | 1 | 3 | 13 | 2 | 71 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 1 | 5 | 2 | 1 | 2 | 98 | 6 | 98 | 3 | 3 | 12 | 1974 | 998 | 998 | 998 | 1 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 43 | 1 | 4 | 5 | 2 | 6 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 1 | 0 | 3 | 1 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 43 | 1 | 5 | 5 | 2 | 3 | 1 | 98 | 998 | 1 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 1 | 0 | 1 | 1 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 5 | 1 | 1 | 1 | 1 | 44 | 1 | 98 | 998 | 2 | 98 | 998 | 3 | 98 | 998 | 2005 | 2 | 2 | 4 | 7 | 1 | 1 | 2 | 98 | 6 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 604 | 12 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 5 | 1 | 2 | 2 | 2 | 42 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 3 | 5 | 2 | 1 | 2 | 98 | 1 | P | 3 | 3 | 12 | 2006 | 998 | 998 | 998 | 3 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 5 | 1 | 3 | 5 | 2 | 10 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 1 | 4 | 5 | 2 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 4 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 7 | 1 | 1 | 1 | 2 | 70 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 2 | 5 | 2 | 1 | 2 | 98 | 6 | 98 | 7 | 7 | 6 | 1994 | 998 | 998 | 998 | 2 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 7 | 1 | 2 | 5 | 1 | 44 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 5 | 5 | 2 | 1 | 2 | 98 | 7 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 5 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 8 | 1 | 1 | 1 | 1 | 58 | 1 | 98 | 998 | 2 | 98 | 998 | 3 | 98 | 998 | 2004 | 2 | 2 | 4 | 5 | 2 | 1 | 2 | 98 | 6 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 604 | 4 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 8 | 1 | 2 | 2 | 2 | 59 | 1 | 98 | 998 | 2 | 98 | 998 | 3 | 98 | 998 | 2004 | 2 | 2 | 2 | 5 | 2 | 1 | 2 | 98 | 6 | 98 | 3 | 3 | 7 | 1999 | 998 | 998 | 604 | 2 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 19 | 1 | 1 | 1 | 1 | 58 | 99 | 99 | 999 | 99 | 99 | 999 | 99 | 99 | 999 | 9999 | 99 | 99 | 99 | 99 | 99 | 99 | 99 | 99 | 99 | 99 | 98 | 98 | 98 | 9998 | 999 | 999 | 999 | 99 | 99 | 15 | 152 | 15202 | 99 | 99 | 99 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 21 | 1 | 1 | 1 | 1 | 53 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 8 | 5 | 1 | 1 | 2 | 98 | 1 | H | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 8 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 21 | 1 | 2 | 2 | 2 | 46 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 3 | 5 | 2 | 1 | 2 | 98 | 6 | 98 | 3 | 3 | 2 | 1990 | 998 | 998 | 998 | 3 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 22 | 1 | 1 | 1 | 2 | 73 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 3 | 98 | 98 | 98 | 1 | 2 | 98 | 6 | 98 | 6 | 5 | 3 | 1979 | 998 | 998 | 998 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 30 | 1 | 1 | 1 | 1 | 57 | 1 | 98 | 998 | 2 | 98 | 998 | 2 | 997 | 998 | 9998 | 98 | 2 | 3 | 5 | 2 | 1 | 2 | 98 | 6 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 3 | 2 | 15 | 152 | 15202 | 98 | 98 | 997 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 3 | 1 | 1 | 2 | 2 | 64 | 1 | 98 | 998 | 2 | 98 | 998 | 3 | 98 | 998 | 1974 | 4 | 3 | 98 | 98 | 98 | 1 | 2 | 98 | 1 | A | 12 | 10 | 99 | 9999 | 998 | 998 | 604 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 3 | 1 | 2 | 1 | 1 | 74 | 2 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 3 | 98 | 98 | 98 | 1 | 2 | 98 | 99 | 99 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 3 | 1 | 3 | 5 | 2 | 38 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 8 | 5 | 1 | 1 | 2 | 98 | 2 | A | 0 | 98 | 98 | 9998 | 998 | 998 | 998 | 8 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 3 | 1 | 4 | 14 | 1 | 38 | 99 | 99 | 999 | 99 | 99 | 999 | 99 | 99 | 999 | 9999 | 99 | 99 | 99 | 99 | 99 | 99 | 99 | 99 | 8 | 98 | 98 | 98 | 98 | 9998 | 999 | 999 | 999 | 99 | 99 | 15 | 152 | 15202 | 99 | 99 | 99 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 9 | 1 | 1 | 1 | 2 | 79 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 3 | 98 | 98 | 98 | 1 | 2 | 98 | 8 | 98 | 2 | 2 | 99 | 9999 | 998 | 998 | 998 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 19 | 1 | 1 | 1 | 1 | 46 | 99 | 99 | 999 | 99 | 99 | 999 | 99 | 99 | 999 | 9999 | 99 | 99 | 99 | 99 | 99 | 99 | 99 | 99 | 99 | 99 | 98 | 98 | 98 | 9998 | 999 | 999 | 999 | 99 | 99 | 15 | 152 | 15202 | 99 | 99 | 99 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 20 | 1 | 1 | 1 | 2 | 58 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 8 | 5 | 1 | 1 | 2 | 98 | 1 | A | 3 | 3 | 7 | 1982 | 998 | 998 | 998 | 8 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 21 | 1 | 1 | 1 | 2 | 45 | 1 | 98 | 998 | 6 | 98 | 998 | 2 | 997 | 998 | 9998 | 98 | 2 | 4 | 5 | 2 | 1 | 2 | 98 | 1 | A | 6 | 6 | 2 | 2007 | 998 | 68 | 998 | 4 | 2 | 15 | 152 | 15202 | 98 | 98 | 997 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 21 | 1 | 2 | 5 | 2 | 10 | 1 | 98 | 998 | 6 | 98 | 998 | 2 | 3201 | 998 | 9998 | 98 | 1 | 4 | 5 | 2 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 98 | 9998 | 998 | 68 | 998 | 4 | 2 | 15 | 152 | 15202 | 98 | 98 | 3201 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 24 | 1 | 1 | 1 | 1 | 67 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 3 | 98 | 98 | 98 | 1 | 2 | 98 | 8 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 24 | 1 | 2 | 2 | 2 | 53 | 1 | 98 | 998 | 2 | 98 | 998 | 3 | 98 | 998 | 9999 | 99 | 3 | 98 | 98 | 98 | 1 | 2 | 98 | 8 | 98 | 0 | 98 | 98 | 9998 | 998 | 998 | 604 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 27 | 1 | 1 | 1 | 1 | 48 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 4 | 7 | 1 | 1 | 2 | 98 | 8 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 12 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 31 | 1 | 1 | 1 | 1 | 49 | 1 | 98 | 998 | 4 | 98 | 998 | 3 | 98 | 998 | 2001 | 2 | 2 | 8 | 5 | 1 | 1 | 2 | 98 | 1 | A | 98 | 98 | 98 | 9998 | 998 | 604 | 604 | 8 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 42 | 1 | 1 | 1 | 1 | 46 | 1 | 98 | 998 | 2 | 98 | 998 | 3 | 98 | 998 | 1992 | 3 | 2 | 8 | 5 | 1 | 1 | 2 | 98 | 2 | A | 98 | 98 | 98 | 9998 | 998 | 998 | 604 | 8 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 42 | 1 | 2 | 2 | 2 | 24 | 1 | 98 | 998 | 6 | 98 | 998 | 5 | 98 | 998 | 2013 | 1 | 2 | 7 | 5 | 2 | 1 | 2 | 98 | 6 | 98 | 2 | 2 | 6 | 2016 | 998 | 68 | 68 | 7 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 42 | 1 | 3 | 6 | 2 | 2 | 1 | 98 | 998 | 1 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 3 | 98 | 98 | 98 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 42 | 1 | 4 | 5 | 1 | 0 | 1 | 98 | 998 | 1 | 98 | 998 | 2 | 15101 | 998 | 9998 | 98 | 99 | 99 | 99 | 99 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 99 | 2 | 15 | 152 | 15202 | 98 | 98 | 15101 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 42 | 1 | 5 | 5 | 2 | 13 | 1 | 98 | 998 | 2 | 98 | 998 | 3 | 98 | 998 | 9999 | 99 | 1 | 7 | 5 | 2 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 604 | 7 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 42 | 1 | 6 | 5 | 1 | 6 | 1 | 98 | 998 | 2 | 98 | 998 | 2 | 15101 | 998 | 9998 | 98 | 1 | 0 | 3 | 1 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 15101 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 15 | 4094 | 2 | 1 | 1 | 1 | 1 | 41 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 4 | 12 | 1 | 1 | 2 | 98 | 1 | O | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 16 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012015 |
15 | 152 | 15202 | 1 | 2 | 15 | 4094 | 8 | 1 | 1 | 17 | 1 | 70 | 2 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 3 | 98 | 98 | 98 | 1 | 2 | 98 | 7 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 0 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012015 |
15 | 152 | 15202 | 1 | 2 | 15 | 4094 | 8 | 1 | 2 | 17 | 1 | 47 | 2 | 98 | 998 | 3 | 15101 | 998 | 2 | 8101 | 998 | 9998 | 98 | 2 | 4 | 8 | 1 | 1 | 2 | 98 | 1 | Z | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 12 | 2 | 15 | 152 | 15202 | 98 | 15101 | 8101 | 15202012015 |
15 | 152 | 15202 | 1 | 2 | 15 | 4094 | 8 | 1 | 3 | 17 | 1 | 19 | 2 | 98 | 998 | 3 | 15101 | 998 | 2 | 15101 | 998 | 9998 | 98 | 1 | 99 | 7 | 99 | 1 | 2 | 98 | 1 | I | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 99 | 2 | 15 | 152 | 15202 | 98 | 15101 | 15101 | 15202012015 |
15 | 152 | 15202 | 1 | 2 | 15 | 4094 | 8 | 1 | 4 | 17 | 1 | 43 | 2 | 98 | 998 | 3 | 4302 | 998 | 2 | 8101 | 998 | 9998 | 98 | 99 | 4 | 8 | 1 | 1 | 2 | 98 | 1 | N | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 12 | 2 | 15 | 152 | 15202 | 98 | 4302 | 8101 | 15202012015 |
15 | 152 | 15202 | 1 | 2 | 15 | 4094 | 8 | 1 | 5 | 17 | 2 | 35 | 2 | 98 | 998 | 6 | 98 | 998 | 5 | 98 | 998 | 2016 | 1 | 2 | 8 | 5 | 1 | 1 | 2 | 98 | 1 | I | 2 | 2 | 3 | 2007 | 998 | 68 | 68 | 8 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012015 |
15 | 152 | 15202 | 1 | 2 | 15 | 4094 | 8 | 1 | 6 | 17 | 1 | 36 | 3 | 13123 | 998 | 3 | 13123 | 998 | 2 | 12101 | 998 | 9998 | 98 | 2 | 5 | 12 | 1 | 2 | 98 | 98 | 1 | J | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 17 | 98 | 15 | 152 | 15202 | 13123 | 13123 | 12101 | 15202012015 |
15 | 152 | 15202 | 1 | 2 | 15 | 4094 | 8 | 1 | 7 | 17 | 2 | 25 | 2 | 98 | 998 | 3 | 15101 | 998 | 2 | 15101 | 998 | 9998 | 98 | 2 | 5 | 12 | 1 | 1 | 2 | 98 | 1 | Q | 1 | 1 | 12 | 2011 | 998 | 998 | 998 | 17 | 2 | 15 | 152 | 15202 | 98 | 15101 | 15101 | 15202012015 |
15 | 152 | 15202 | 1 | 2 | 15 | 4094 | 9 | 1 | 1 | 1 | 1 | 72 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 1 | 5 | 2 | 1 | 2 | 98 | 1 | G | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 1 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012015 |
15 | 152 | 15202 | 1 | 2 | 15 | 4094 | 12 | 1 | 1 | 1 | 1 | 21 | 1 | 98 | 998 | 3 | 15101 | 998 | 2 | 15101 | 998 | 9998 | 98 | 2 | 4 | 8 | 1 | 1 | 2 | 98 | 1 | N | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 12 | 2 | 15 | 152 | 15202 | 98 | 15101 | 15101 | 15202012015 |
15 | 152 | 15202 | 1 | 2 | 15 | 4094 | 15 | 1 | 1 | 1 | 1 | 61 | 1 | 98 | 998 | 2 | 98 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 3 | 7 | 2 | 1 | 2 | 98 | 4 | 98 | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 11 | 2 | 15 | 152 | 15202 | 98 | 98 | 98 | 15202012015 |
15 | 152 | 15202 | 1 | 2 | 15 | 4094 | 15 | 1 | 2 | 5 | 2 | 31 | 1 | 98 | 998 | 3 | 15101 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 4 | 12 | 1 | 1 | 2 | 98 | 1 | P | 1 | 1 | 10 | 2007 | 998 | 998 | 998 | 16 | 2 | 15 | 152 | 15202 | 98 | 15101 | 98 | 15202012015 |
15 | 152 | 15202 | 1 | 2 | 15 | 4094 | 16 | 1 | 1 | 1 | 1 | 34 | 1 | 98 | 998 | 3 | 15101 | 998 | 1 | 98 | 998 | 9998 | 98 | 2 | 5 | 12 | 1 | 1 | 2 | 98 | 1 | O | 98 | 98 | 98 | 9998 | 998 | 998 | 998 | 17 | 2 | 15 | 152 | 15202 | 98 | 15101 | 98 | 15202012015 |
Despleguemos los códigos de regiones de nuestra tabla:
<- unique(tabla_con_clave$REGION)
regiones regiones
## [1] 15 14 13 12 11 10 9 16 8 7 6 5 4 3 2 1
Hagamos un subset con la region = 1, y área URBANA = 1.
<- filter(tabla_con_clave, tabla_con_clave$REGION == 10)
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA== 2) tabla_con_clave
1.1.2 Cálculo de frecuencias
Obtenemos las frecuencias a la pregunta ESCOLARIDAD por zona:
<- tabla_con_clave[,c("clave","ESCOLARIDAD","COMUNA") ] tabla_con_clave_f
Renombramos y filtramos por la categoria Trabajo por un sueldo
== 1:
names(tabla_con_clave_f)[2] <- "ESCOLARIDAD"
<- filter(tabla_con_clave_f, tabla_con_clave_f$ESCOLARIDAD == 14) tabla_con_clave_ff
# Determinamos las frecuencias por zona:
<- tabla_con_clave_ff$clave
b <- tabla_con_clave_ff$ESCOLARIDAD
c <- tabla_con_clave_ff$COMUNA
d = xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
cross_tab <- as.data.frame(cross_tab)
tabla <-tabla[!(tabla$Freq == 0),]
d names(d)[1] <- "zona"
$anio <- "2017"
d
head(d,5)
## zona unlist.c. unlist.d. Freq anio
## 1 10101032002 14 10101 6 2017
## 2 10101032011 14 10101 20 2017
## 3 10101032019 14 10101 15 2017
## 4 10101042010 14 10101 1 2017
## 5 10101062003 14 10101 5 2017
Agregamos un cero a los códigos comunales de cuatro dígitos:
<- d$unlist.d.
codigos <- seq(1:nrow(d))
rango <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
cadena <- as.data.frame(codigos)
codigos <- as.data.frame(cadena)
cadena <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
comuna_corr names(comuna_corr)[4] <- "código"
1.1.3 Tabla de frecuencias:
head(comuna_corr,5)
## zona Freq anio código
## 1 10101032002 6 2017 10101
## 2 10101032011 20 2017 10101
## 3 10101032019 15 2017 10101
## 4 10101042010 1 2017 10101
## 5 10101062003 5 2017 10101
1.2 Variable CASEN
1.2.1 Tabla de ingresos expandidos
Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí
<- readRDS("../ingresos_expandidos_rural_17.rds")
h_y_m_2017_censo <- head(h_y_m_2017_censo,50)
tablamadre kbl(tablamadre) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
kable_paper() %>%
scroll_box(width = "100%", height = "300px")
código | personas | comuna | promedio_i | año | ingresos_expandidos | |
---|---|---|---|---|---|---|
1 | 01101 | 191468 | Iquique | 272529.7 | 2017 | 52180713221 |
3 | 01401 | 15711 | Pozo Almonte | 243272.4 | 2017 | 3822052676 |
4 | 01402 | 1250 | Camiña | 226831.0 | 2017 | 283538750 |
6 | 01404 | 2730 | Huara | 236599.7 | 2017 | 645917134 |
7 | 01405 | 9296 | Pica | 269198.0 | 2017 | 2502464414 |
10 | 02103 | 10186 | Sierra Gorda | 322997.9 | 2017 | 3290056742 |
11 | 02104 | 13317 | Taltal | 288653.8 | 2017 | 3844002134 |
12 | 02201 | 165731 | Calama | 238080.9 | 2017 | 39457387800 |
14 | 02203 | 10996 | San Pedro de Atacama | 271472.6 | 2017 | 2985112297 |
15 | 02301 | 25186 | Tocopilla | 166115.9 | 2017 | 4183793832 |
17 | 03101 | 153937 | Copiapó | 251396.0 | 2017 | 38699138722 |
19 | 03103 | 14019 | Tierra Amarilla | 287819.4 | 2017 | 4034940816 |
21 | 03202 | 13925 | Diego de Almagro | 326439.0 | 2017 | 4545663075 |
22 | 03301 | 51917 | Vallenar | 217644.6 | 2017 | 11299454698 |
23 | 03302 | 5299 | Alto del Carmen | 196109.9 | 2017 | 1039186477 |
24 | 03303 | 7041 | Freirina | 202463.8 | 2017 | 1425547554 |
25 | 03304 | 10149 | Huasco | 205839.6 | 2017 | 2089066548 |
26 | 04101 | 221054 | La Serena | 200287.4 | 2017 | 44274327972 |
27 | 04102 | 227730 | Coquimbo | 206027.8 | 2017 | 46918711304 |
28 | 04103 | 11044 | Andacollo | 217096.4 | 2017 | 2397612293 |
29 | 04104 | 4241 | La Higuera | 231674.2 | 2017 | 982530309 |
30 | 04105 | 4497 | Paiguano | 174868.5 | 2017 | 786383423 |
31 | 04106 | 27771 | Vicuña | 169077.1 | 2017 | 4695441470 |
32 | 04201 | 30848 | Illapel | 165639.6 | 2017 | 5109649759 |
33 | 04202 | 9093 | Canela | 171370.3 | 2017 | 1558270441 |
34 | 04203 | 21382 | Los Vilos | 173238.5 | 2017 | 3704185607 |
35 | 04204 | 29347 | Salamanca | 193602.0 | 2017 | 5681637894 |
36 | 04301 | 111272 | Ovalle | 230819.8 | 2017 | 25683781418 |
37 | 04302 | 13322 | Combarbalá | 172709.2 | 2017 | 2300832587 |
38 | 04303 | 30751 | Monte Patria | 189761.6 | 2017 | 5835357638 |
39 | 04304 | 10956 | Punitaqui | 165862.0 | 2017 | 1817183694 |
40 | 04305 | 4278 | Río Hurtado | 182027.2 | 2017 | 778712384 |
41 | 05101 | 296655 | Valparaíso | 251998.5 | 2017 | 74756602991 |
42 | 05102 | 26867 | Casablanca | 252317.7 | 2017 | 6779018483 |
45 | 05105 | 18546 | Puchuncaví | 231606.0 | 2017 | 4295363979 |
46 | 05107 | 31923 | Quintero | 285125.8 | 2017 | 9102071069 |
49 | 05301 | 66708 | Los Andes | 280548.0 | 2017 | 18714795984 |
50 | 05302 | 14832 | Calle Larga | 234044.6 | 2017 | 3471349123 |
51 | 05303 | 10207 | Rinconada | 246136.9 | 2017 | 2512319225 |
52 | 05304 | 18855 | San Esteban | 211907.3 | 2017 | 3995512770 |
53 | 05401 | 35390 | La Ligua | 172675.9 | 2017 | 6111000517 |
54 | 05402 | 19388 | Cabildo | 212985.0 | 2017 | 4129354103 |
56 | 05404 | 9826 | Petorca | 270139.8 | 2017 | 2654393853 |
57 | 05405 | 7339 | Zapallar | 235661.4 | 2017 | 1729518700 |
58 | 05501 | 90517 | Quillota | 212067.6 | 2017 | 19195726144 |
59 | 05502 | 50554 | Calera | 226906.2 | 2017 | 11471016698 |
60 | 05503 | 17988 | Hijuelas | 215402.0 | 2017 | 3874650405 |
61 | 05504 | 22098 | La Cruz | 243333.4 | 2017 | 5377180726 |
62 | 05506 | 22120 | Nogales | 219800.7 | 2017 | 4861992055 |
63 | 05601 | 91350 | San Antonio | 230261.5 | 2017 | 21034388728 |
1.3 Unión Censo-Casen:
y creamos la columna multipob:
= merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
comunas_censo_casen <- comunas_censo_casen[,-c(4)]
comunas_censo_casen head(comunas_censo_casen,5)
## código zona Freq personas comuna promedio_i año
## 1 10101 10101072036 3 245902 Puerto Montt 176237.4 2017
## 2 10101 10101142049 33 245902 Puerto Montt 176237.4 2017
## 3 10101 10101152002 14 245902 Puerto Montt 176237.4 2017
## 4 10101 10101152006 7 245902 Puerto Montt 176237.4 2017
## 5 10101 10101142047 2 245902 Puerto Montt 176237.4 2017
## ingresos_expandidos
## 1 43337141298
## 2 43337141298
## 3 43337141298
## 4 43337141298
## 5 43337141298
1.4 Unión de la proporcion zonal por comuna con la tabla censo-casen:
unimos a nuestra tabla de proporciones zonales por comuna:
Para calcular la variable multipob, debemos multiplicarla por su proporcion 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.
1.5 Ingreso promedio expandido por zona (multi_pob)
En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:
\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]
<- readRDS("../tabla_de_prop_pob.rds")
tabla_de_prop_pob names(tabla_de_prop_pob)[1] <- "zona"
= merge( x = comunas_censo_casen, y = tabla_de_prop_pob, by = "zona", all.x = TRUE)
comunas_censo_casen head(comunas_censo_casen,5)
## zona código.x Freq.x personas comuna promedio_i año
## 1 10101032002 10101 6 245902 Puerto Montt 176237.4 2017
## 2 10101032011 10101 20 245902 Puerto Montt 176237.4 2017
## 3 10101032019 10101 15 245902 Puerto Montt 176237.4 2017
## 4 10101042010 10101 1 245902 Puerto Montt 176237.4 2017
## 5 10101062003 10101 5 245902 Puerto Montt 176237.4 2017
## ingresos_expandidos Freq.y p código.y
## 1 43337141298 129 5.245992e-04 10101
## 2 43337141298 426 1.732397e-03 10101
## 3 43337141298 829 3.371262e-03 10101
## 4 43337141298 6 2.439996e-05 10101
## 5 43337141298 158 6.425324e-04 10101
$multipob <- comunas_censo_casen$ingresos_expandidos*comunas_censo_casen$p comunas_censo_casen
head(comunas_censo_casen,5)
## zona código.x Freq.x personas comuna promedio_i año
## 1 10101032002 10101 6 245902 Puerto Montt 176237.4 2017
## 2 10101032011 10101 20 245902 Puerto Montt 176237.4 2017
## 3 10101032019 10101 15 245902 Puerto Montt 176237.4 2017
## 4 10101042010 10101 1 245902 Puerto Montt 176237.4 2017
## 5 10101062003 10101 5 245902 Puerto Montt 176237.4 2017
## ingresos_expandidos Freq.y p código.y multipob
## 1 43337141298 129 5.245992e-04 10101 22734631
## 2 43337141298 426 1.732397e-03 10101 75077153
## 3 43337141298 829 3.371262e-03 10101 146100846
## 4 43337141298 6 2.439996e-05 10101 1057425
## 5 43337141298 158 6.425324e-04 10101 27845517
1.6 Análisis de regresión
Aplicaremos un análisis de regresión donde:
\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]
\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]
1.6.1 Diagrama de dispersión loess
scatter.smooth(x=comunas_censo_casen$Freq.x, y=comunas_censo_casen$multipob, main="multi_pob ~ Freq.x",
xlab = "Freq.x",
ylab = "multi_pob",
col = 2)
1.6.2 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.
1.6.3 Modelo lineal
Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.
<- lm( multipob~(Freq.x) , data=comunas_censo_casen)
linearMod summary(linearMod)
##
## Call:
## lm(formula = multipob ~ (Freq.x), data = comunas_censo_casen)
##
## Residuals:
## Min 1Q Median 3Q Max
## -182659732 -14331675 -5298482 8899782 124670492
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17572146 1078516 16.29 <2e-16 ***
## Freq.x 4831427 122420 39.47 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25870000 on 797 degrees of freedom
## (58 observations deleted due to missingness)
## Multiple R-squared: 0.6615, Adjusted R-squared: 0.6611
## F-statistic: 1558 on 1 and 797 DF, p-value: < 2.2e-16
1.6.4 Gráfica de la recta de regresión lineal
ggplot(comunas_censo_casen, aes(x = Freq.x , y = multipob)) +
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.
1.7 Modelos alternativos
### 8.1 Modelo cuadrático
<- lm( multipob~(Freq.x^2) , data=comunas_censo_casen)
linearMod <- summary(linearMod)
datos <- datos$adj.r.squared
dato <- "cuadrático"
modelo <- "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
sintaxis
<- cbind(modelo,dato,sintaxis)
modelos1
<- cbind(modelo,dato,sintaxis)
modelos1
### 8.2 Modelo cúbico
<- lm( multipob~(Freq.x^3) , data=comunas_censo_casen)
linearMod <- summary(linearMod)
datos <- datos$adj.r.squared
dato <- "cúbico"
modelo <- "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
sintaxis
<- cbind(modelo,dato,sintaxis)
modelos2
### 8.3 Modelo logarítmico
<- lm( multipob~log(Freq.x) , data=comunas_censo_casen)
linearMod <- summary(linearMod)
datos <- datos$adj.r.squared
dato <- "logarítmico"
modelo <- "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
sintaxis
<- cbind(modelo,dato,sintaxis)
modelos3
### 8.5 Modelo con raíz cuadrada
<- lm( multipob~sqrt(Freq.x) , data=comunas_censo_casen)
linearMod <- summary(linearMod)
datos <- datos$adj.r.squared
dato <- "raíz cuadrada"
modelo <- "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
sintaxis
<- cbind(modelo,dato,sintaxis)
modelos5
### 8.6 Modelo raíz-raíz
<- lm( sqrt(multipob)~sqrt(Freq.x) , data=comunas_censo_casen)
linearMod <- summary(linearMod)
datos <- datos$adj.r.squared
dato <- "raíz-raíz"
modelo <- "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
sintaxis
<- cbind(modelo,dato,sintaxis)
modelos6
### 8.7 Modelo log-raíz
<- lm( log(multipob)~sqrt(Freq.x) , data=comunas_censo_casen)
linearMod <- summary(linearMod)
datos <- datos$adj.r.squared
dato <- "log-raíz"
modelo <- "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
sintaxis
<- cbind(modelo,dato,sintaxis)
modelos7
### 8.8 Modelo raíz-log
<- lm( sqrt(multipob)~log(Freq.x) , data=comunas_censo_casen)
linearMod <- summary(linearMod)
datos <- datos$adj.r.squared
dato <- "raíz-log"
modelo <- "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
sintaxis
<- cbind(modelo,dato,sintaxis)
modelos8
### 8.9 Modelo log-log
<- lm( log(multipob)~log(Freq.x) , data=comunas_censo_casen)
linearMod <- summary(linearMod)
datos <- datos$adj.r.squared
dato <- "log-log"
modelo <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
sintaxis
<- cbind(modelo,dato,sintaxis)
modelos9
<- rbind(modelos1, modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind
<<- modelos_bind[order(modelos_bind$dato, decreasing = T ),]
modelos_bind <<- comunas_censo_casen
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 | |
---|---|---|---|
1 | cuadrático | 0.661084681186056 | linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01) |
2 | cúbico | 0.661084681186056 | linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01) |
4 | raíz cuadrada | 0.657679252752928 | linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01) |
5 | raíz-raíz | 0.608798497404002 | linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01) |
7 | raíz-log | 0.558632942330874 | linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01) |
3 | logarítmico | 0.523769487714094 | linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01) |
8 | log-log | 0.463409631224975 | linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01) |
6 | log-raíz | 0.439092616742394 | linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01) |
Elegimos el 1 pues tiene el ma alto \(R^2\)
<- h_y_m_comuna_corr_01
h_y_m_comuna_corr <- 1
metodo
switch (metodo,
case = linearMod <- lm( multipob~(Freq.x^2) , data=h_y_m_comuna_corr),
case = linearMod <- lm( multipob~(Freq.x^3) , data=h_y_m_comuna_corr),
case = linearMod <- lm( multipob~log(Freq.x) , data=h_y_m_comuna_corr),
case = linearMod <- lm( multipob~sqrt(Freq.x) , data=h_y_m_comuna_corr),
case = linearMod <- lm( sqrt(multipob)~sqrt(Freq.x) , data=h_y_m_comuna_corr),
case = linearMod <- lm( log(multipob)~sqrt(Freq.x) , data=h_y_m_comuna_corr),
case = linearMod <- lm( sqrt(multipob)~log(Freq.x) , data=h_y_m_comuna_corr),
case = linearMod <- lm( log(multipob)~log(Freq.x) , data=h_y_m_comuna_corr)
)summary(linearMod)
##
## Call:
## lm(formula = multipob ~ (Freq.x^2), data = h_y_m_comuna_corr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -182659732 -14331675 -5298482 8899782 124670492
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17572146 1078516 16.29 <2e-16 ***
## Freq.x 4831427 122420 39.47 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25870000 on 797 degrees of freedom
## (58 observations deleted due to missingness)
## Multiple R-squared: 0.6615, Adjusted R-squared: 0.6611
## F-statistic: 1558 on 1 and 797 DF, p-value: < 2.2e-16
<- linearMod$coefficients[1]
aa aa
## (Intercept)
## 17572146
<- linearMod$coefficients[2]
bb bb
## Freq.x
## 4831427
1.8 Modelo cuadrático (cuadrático)
Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.6611).
1.8.1 Diagrama de dispersión sobre cuadrático
Desplegamos una curva suavizada por loess en el diagrama de dispersión.
scatter.smooth(x= (comunas_censo_casen$Freq.x)^2, y= (comunas_censo_casen$multipob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")
ggplot(comunas_censo_casen, aes(x = (Freq.x)^2 , y = (multipob))) + geom_point() + stat_smooth(method = "lm", col = "red")
1.8.2 Análisis de residuos
par(mfrow = c (2,2))
plot(linearMod)
1.8.3 Ecuación del modelo
Modelo cuadrático
\[ \hat Y = \17572146 + \4831427 X^2 \]
1.9 10 Aplicación la regresión a los valores de la variable a nivel de zona
Esta nueva variable se llamará: est_ing
$est_ing = aa + bb * (h_y_m_comuna_corr$Freq.x)^2 h_y_m_comuna_corr
1.10 11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona
\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]
$ing_medio_zona <- h_y_m_comuna_corr$est_ing /( h_y_m_comuna_corr$personas * h_y_m_comuna_corr$p)
h_y_m_comuna_corr
<- h_y_m_comuna_corr[c(1:100),]
r3_100 kbl(r3_100) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
kable_paper() %>%
scroll_box(width = "100%", height = "300px")
zona | código.x | Freq.x | personas | comuna | promedio_i | año | ingresos_expandidos | Freq.y | p | código.y | multipob | est_ing | ing_medio_zona |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
10101032002 | 10101 | 6 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 129 | 0.0005246 | 10101 | 22734631 | 191503503 | 1484523.28 |
10101032011 | 10101 | 20 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 426 | 0.0017324 | 10101 | 75077153 | 1950142781 | 4577799.96 |
10101032019 | 10101 | 15 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 829 | 0.0033713 | 10101 | 146100846 | 1104643128 | 1332500.76 |
10101042010 | 10101 | 1 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 6 | 0.0000244 | 10101 | 1057425 | 22403572 | 3733928.72 |
10101062003 | 10101 | 5 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 158 | 0.0006425 | 10101 | 27845517 | 138357810 | 875682.34 |
10101062008 | 10101 | 31 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 581 | 0.0023627 | 10101 | 102393958 | 4660573097 | 8021640.44 |
10101062013 | 10101 | 12 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 571 | 0.0023221 | 10101 | 100631584 | 713297574 | 1249207.66 |
10101062039 | 10101 | 3 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 67 | 0.0002725 | 10101 | 11807909 | 61054985 | 911268.43 |
10101072014 | 10101 | 38 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 997 | 0.0040545 | 10101 | 175708737 | 6994152139 | 7015197.73 |
10101072028 | 10101 | 2 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 145 | 0.0005897 | 10101 | 25554430 | 36897852 | 254467.95 |
10101072029 | 10101 | 42 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 1051 | 0.0042741 | 10101 | 185225559 | 8540208647 | 8125793.19 |
10101072036 | 10101 | 3 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 118 | 0.0004799 | 10101 | 20796019 | 61054985 | 517415.13 |
10101072045 | 10101 | 2 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 113 | 0.0004595 | 10101 | 19914832 | 36897852 | 326529.66 |
10101082016 | 10101 | 2 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 121 | 0.0004921 | 10101 | 21324731 | 36897852 | 304940.93 |
10101082017 | 10101 | 2 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 38 | 0.0001545 | 10101 | 6697023 | 36897852 | 970996.11 |
10101082018 | 10101 | 8 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 623 | 0.0025335 | 10101 | 109795931 | 326783447 | 524532.02 |
10101082030 | 10101 | 4 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 176 | 0.0007157 | 10101 | 31017791 | 94874971 | 539062.34 |
10101082034 | 10101 | 1 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 66 | 0.0002684 | 10101 | 11631672 | 22403572 | 339448.07 |
10101082042 | 10101 | 13 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 253 | 0.0010289 | 10101 | 44588075 | 834083239 | 3296771.70 |
10101082045 | 10101 | 2 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 123 | 0.0005002 | 10101 | 21677206 | 36897852 | 299982.54 |
10101092004 | 10101 | 4 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 97 | 0.0003945 | 10101 | 17095033 | 94874971 | 978092.49 |
10101092008 | 10101 | 38 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 752 | 0.0030581 | 10101 | 132530562 | 6994152139 | 9300734.23 |
10101092037 | 10101 | 6 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 276 | 0.0011224 | 10101 | 48641536 | 191503503 | 693853.27 |
10101092040 | 10101 | 15 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 509 | 0.0020699 | 10101 | 89704862 | 1104643128 | 2170222.26 |
10101092041 | 10101 | 61 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 1683 | 0.0068442 | 10101 | 296607627 | 17995310481 | 10692400.76 |
10101092044 | 10101 | 23 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 530 | 0.0021553 | 10101 | 93405848 | 2573396811 | 4855465.68 |
10101102005 | 10101 | 3 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 147 | 0.0005978 | 10101 | 25906905 | 61054985 | 415340.03 |
10101102007 | 10101 | 16 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 824 | 0.0033509 | 10101 | 145219658 | 1254417352 | 1522351.16 |
10101102026 | 10101 | 2 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 245 | 0.0009963 | 10101 | 43178175 | 36897852 | 150603.48 |
10101102035 | 10101 | 22 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 940 | 0.0038227 | 10101 | 165663202 | 2355982614 | 2506364.48 |
10101102037 | 10101 | 4 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 164 | 0.0006669 | 10101 | 28902942 | 94874971 | 578505.92 |
10101112025 | 10101 | 18 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 1078 | 0.0043839 | 10101 | 189983971 | 1582954360 | 1468417.77 |
10101122024 | 10101 | 10 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 952 | 0.0038715 | 10101 | 167778052 | 500714805 | 525960.93 |
10101132022 | 10101 | 9 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 703 | 0.0028589 | 10101 | 123894927 | 408917699 | 581675.25 |
10101132023 | 10101 | 2 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 603 | 0.0024522 | 10101 | 106271182 | 36897852 | 61190.47 |
10101132027 | 10101 | 1 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 105 | 0.0004270 | 10101 | 18504932 | 22403572 | 213367.36 |
10101132049 | 10101 | 60 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 1883 | 0.0076575 | 10101 | 331855117 | 17410707863 | 9246260.15 |
10101142015 | 10101 | 1 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 124 | 0.0005043 | 10101 | 21853444 | 22403572 | 180673.97 |
10101142027 | 10101 | 4 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 192 | 0.0007808 | 10101 | 33837590 | 94874971 | 494140.47 |
10101142038 | 10101 | 1 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 53 | 0.0002155 | 10101 | 9340585 | 22403572 | 422708.91 |
10101142046 | 10101 | 6 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 317 | 0.0012891 | 10101 | 55867271 | 191503503 | 604112.00 |
10101142047 | 10101 | 2 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 263 | 0.0010695 | 10101 | 46350449 | 36897852 | 140296.02 |
10101142049 | 10101 | 33 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 973 | 0.0039569 | 10101 | 171479038 | 5278995700 | 5425483.76 |
10101152002 | 10101 | 14 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 554 | 0.0022529 | 10101 | 97635547 | 964531757 | 1741032.05 |
10101152006 | 10101 | 7 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 214 | 0.0008703 | 10101 | 37714814 | 254312049 | 1188374.06 |
10101152020 | 10101 | 32 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 617 | 0.0025091 | 10101 | 108738506 | 4964952972 | 8046925.40 |
10101152031 | 10101 | 37 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 642 | 0.0026108 | 10101 | 113144443 | 6631795145 | 10329898.98 |
10101152033 | 10101 | 22 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 700 | 0.0028467 | 10101 | 123366215 | 2355982614 | 3365689.45 |
10101152049 | 10101 | 1 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 96 | 0.0003904 | 10101 | 16918795 | 22403572 | 233370.54 |
10101152901 | 10101 | 1 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 46 | 0.0001871 | 10101 | 8106923 | 22403572 | 487034.18 |
10101162006 | 10101 | 3 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 233 | 0.0009475 | 10101 | 41063326 | 61054985 | 262038.56 |
10101162010 | 10101 | 6 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 351 | 0.0014274 | 10101 | 61859345 | 191503503 | 545594.03 |
10101162020 | 10101 | 10 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 205 | 0.0008337 | 10101 | 36128677 | 500714805 | 2442511.24 |
10101162031 | 10101 | 26 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 762 | 0.0030988 | 10101 | 134292936 | 3283616519 | 4309208.03 |
10101162033 | 10101 | 3 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 168 | 0.0006832 | 10101 | 29607892 | 61054985 | 363422.53 |
10101172029 | 10101 | 29 | 245902 | Puerto Montt | 176237.4 | 2017 | 43337141298 | 854 | 0.0034729 | 10101 | 150506782 | 4080801906 | 4778456.56 |
10102012001 | 10102 | 3 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 80 | 0.0023540 | 10102 | 12435515 | 61054985 | 763187.31 |
10102012004 | 10102 | 2 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 283 | 0.0083272 | 10102 | 43990633 | 36897852 | 130381.10 |
10102012008 | 10102 | 7 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 697 | 0.0205090 | 10102 | 108344420 | 254312049 | 364866.64 |
10102012033 | 10102 | 29 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 1490 | 0.0438429 | 10102 | 231611458 | 4080801906 | 2738793.23 |
10102022001 | 10102 | 1 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 164 | 0.0048257 | 10102 | 25492805 | 22403572 | 136607.15 |
10102022002 | 10102 | 4 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 308 | 0.0090628 | 10102 | 47876731 | 94874971 | 308035.62 |
10102022003 | 10102 | 3 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 184 | 0.0054142 | 10102 | 28601683 | 61054985 | 331820.57 |
10102022004 | 10102 | 2 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 74 | 0.0021774 | 10102 | 11502851 | 36897852 | 498619.62 |
10102022010 | 10102 | 4 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 294 | 0.0086509 | 10102 | 45700516 | 94874971 | 322703.98 |
10102022013 | 10102 | 1 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 450 | 0.0132411 | 10102 | 69949769 | 22403572 | 49785.72 |
10102032002 | 10102 | 1 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 54 | 0.0015889 | 10102 | 8393972 | 22403572 | 414880.97 |
10102032011 | 10102 | 1 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 163 | 0.0047962 | 10102 | 25337361 | 22403572 | 137445.23 |
10102032016 | 10102 | 1 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 95 | 0.0027954 | 10102 | 14767174 | 22403572 | 235827.08 |
10102032018 | 10102 | 4 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 470 | 0.0138296 | 10102 | 73058648 | 94874971 | 201861.64 |
10102032034 | 10102 | 7 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 695 | 0.0204502 | 10102 | 108033533 | 254312049 | 365916.62 |
10102032901 | 10102 | 2 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 89 | 0.0026188 | 10102 | 13834510 | 36897852 | 414582.61 |
10102042014 | 10102 | 2 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 87 | 0.0025600 | 10102 | 13523622 | 36897852 | 424113.24 |
10102042019 | 10102 | 6 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 336 | 0.0098867 | 10102 | 52229161 | 191503503 | 569950.90 |
10102042028 | 10102 | 4 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 171 | 0.0050316 | 10102 | 26580912 | 94874971 | 554824.39 |
10102042029 | 10102 | 5 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 183 | 0.0053847 | 10102 | 28446239 | 138357810 | 756053.61 |
10102042035 | 10102 | 3 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 271 | 0.0079741 | 10102 | 42125305 | 61054985 | 225295.15 |
10102042043 | 10102 | 3 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 86 | 0.0025305 | 10102 | 13368178 | 61054985 | 709941.69 |
10102042901 | 10102 | 1 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 127 | 0.0037369 | 10102 | 19741379 | 22403572 | 176406.08 |
10102052012 | 10102 | 2 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 302 | 0.0088863 | 10102 | 46944067 | 36897852 | 122178.32 |
10102052019 | 10102 | 5 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 332 | 0.0097690 | 10102 | 51607385 | 138357810 | 416740.39 |
10102052042 | 10102 | 8 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 410 | 0.0120641 | 10102 | 63732012 | 326783447 | 797032.80 |
10102052043 | 10102 | 4 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 273 | 0.0080330 | 10102 | 42436193 | 94874971 | 347527.37 |
10102062015 | 10102 | 18 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 1129 | 0.0332205 | 10102 | 175496199 | 1582954360 | 1402085.35 |
10102062019 | 10102 | 7 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 362 | 0.0106518 | 10102 | 56270703 | 254312049 | 702519.47 |
10102062038 | 10102 | 2 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 357 | 0.0105046 | 10102 | 55493484 | 36897852 | 103355.33 |
10102062039 | 10102 | 4 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 643 | 0.0189201 | 10102 | 99950448 | 94874971 | 147550.50 |
10102072022 | 10102 | 2 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 148 | 0.0043549 | 10102 | 23005702 | 36897852 | 249309.81 |
10102082022 | 10102 | 5 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 565 | 0.0166250 | 10102 | 87825821 | 138357810 | 244881.08 |
10102102023 | 10102 | 3 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 630 | 0.0185376 | 10102 | 97929677 | 61054985 | 96912.67 |
10102112023 | 10102 | 6 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 650 | 0.0191261 | 10102 | 101038556 | 191503503 | 294620.77 |
10102122023 | 10102 | 2 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 511 | 0.0150360 | 10102 | 79431849 | 36897852 | 72207.15 |
10102132023 | 10102 | 6 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 775 | 0.0228042 | 10102 | 120469047 | 191503503 | 247101.29 |
10102142901 | 10102 | 4 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 55 | 0.0016184 | 10102 | 8549416 | 94874971 | 1724999.47 |
10102152006 | 10102 | 10 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 586 | 0.0172429 | 10102 | 91090144 | 500714805 | 854462.12 |
10102162009 | 10102 | 4 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 163 | 0.0047962 | 10102 | 25337361 | 94874971 | 582055.04 |
10102162030 | 10102 | 8 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 341 | 0.0100338 | 10102 | 53006381 | 326783447 | 958309.23 |
10102162031 | 10102 | 3 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 361 | 0.0106223 | 10102 | 56115259 | 61054985 | 169127.38 |
10102162036 | 10102 | 1 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 260 | 0.0076504 | 10102 | 40415422 | 22403572 | 86167.59 |
10102162040 | 10102 | 3 | 33985 | Calbuco | 155443.9 | 2017 | 5282762017 | 271 | 0.0079741 | 10102 | 42125305 | 61054985 | 225295.15 |
Guardamos:
saveRDS(h_y_m_comuna_corr, "Rural/region_10_ESCOLARIDAD_r.rds")
1.11 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