Análisis diferenciados para zonas Urbanas y Rurales a nivel nacional.
Abstract
Nuestro objetivo es expandir variables de la CASEN sobre las del CENSO, ambos del año 2017, para poder realizar predicciones a nivel de Zona Censal, tanto a nivel urbano como a nivel zonal.
El primer paso será construir nuestra tabla de trabajo.
El segundo, será calcular las correlaciones entre el ingreso total promedio por comuna multiplicado por la población de la misma, y la frecuencia de categorías específicas de respuestas de variables de calidad de la vivienda. Ésto lo haremos para la pregunta P01: Tipo de vivienda y la P03B: Material en la cubierta del techo. Para ésta última también calcularemos la correlación entre la frecuencia de respuestas dividida por la cantidad de personas a nivel comunal y los ingresos expandidos.
En específico, expandiremos los ingresos promedios comunales obtenidos de la CASEN sobre la categoría de respuesta: “tejas o tejuelas de arcilla, metálicas, de cemento, de madera, asfálticas o plásticas” del campo P03B del CENSO de viviendas, que fue la categoría de respuesta que más alto correlaciona con los ingresos expandidos (obtenidos de la multiplicación del ingreso promedio y los habitantes), ambos a nivel comunal.
Por último calcularemos regresiones lineales.
Ensayaremos diferentes modelos dentro del análisis de regresión cuya variable independiente será: “frecuencia de población que posee la variable Censal respecto a la zona” y la dependiente: “ingreso expandido por zona”
Lo anterior para elegir el que posea el mayor coeficiente de determinación y así contruir una tabla de valores predichos.
Repetiremos los mismos pasos señalados pero a nivel rural.
Necesitamos calcular las frecuencias a nivel censal de las respuestas correspondientes a la categoría: “tejas o tejuelas de arcilla, metálicas, de cemento, de madera, asfálticas o plásticas” del campo P03B del Censo de viviendas. Recordemos que ésta fué la más alta correlación en relación a los ingresos expandidos (ver punto 1.2 aquí).
Leemos la tabla Casen 2017 de viviendas que ya tiene integrada la clave zonal:
Filtramos por área = 1 -URBANO-
tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 1)
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 | 15201 | 1 | 1 | 1 | 1767 | 1 | 1 | 1 | 2 | 1 | 3 | 2 | 1 | 1 | 1 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 2 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 3 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 4 | 1 | 1 | 2 | 3 | 1 | 99 | 1 | 2 | 3 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 5 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 6 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 7 | 5 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 8 | 1 | 1 | 3 | 3 | 1 | 99 | 1 | 1 | 4 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 9 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 10 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 11 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 12 | 1 | 1 | 4 | 3 | 3 | 3 | 4 | 1 | 1 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 13 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 14 | 1 | 1 | 2 | 3 | 1 | 2 | 1 | 1 | 1 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 15 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 2 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 16 | 1 | 1 | 5 | 1 | 5 | 2 | 1 | 1 | 2 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 17 | 1 | 1 | 5 | 3 | 1 | 3 | 1 | 1 | 1 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 18 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 19 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 20 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 21 | 4 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 22 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 23 | 1 | 1 | 5 | 3 | 1 | 2 | 1 | 1 | 1 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 24 | 1 | 1 | 3 | 1 | 1 | 6 | 1 | 1 | 4 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 25 | 1 | 1 | 5 | 6 | 1 | 1 | 4 | 2 | 4 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 26 | 4 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 27 | 1 | 1 | 5 | 99 | 3 | 3 | 1 | 1 | 4 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 28 | 1 | 1 | 6 | 3 | 2 | 1 | 1 | 1 | 3 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 29 | 1 | 1 | 2 | 3 | 1 | 99 | 1 | 1 | 2 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 30 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 31 | 1 | 1 | 2 | 3 | 1 | 1 | 1 | 1 | 1 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 32 | 1 | 1 | 3 | 1 | 1 | 2 | 1 | 1 | 6 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 33 | 8 | 1 | 98 | 98 | 98 | 98 | 98 | 98 | 8 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 34 | 3 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 35 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 36 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 37 | 1 | 1 | 5 | 3 | 3 | 1 | 1 | 1 | 4 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 38 | 1 | 1 | 1 | 1 | 4 | 2 | 1 | 1 | 1 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 39 | 1 | 1 | 5 | 3 | 4 | 3 | 1 | 1 | 7 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 40 | 1 | 1 | 5 | 3 | 2 | 1 | 1 | 1 | 7 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 41 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 42 | 1 | 1 | 2 | 3 | 1 | 2 | 1 | 1 | 2 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 43 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 44 | 1 | 1 | 2 | 3 | 1 | 1 | 1 | 1 | 1 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 45 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 46 | 1 | 1 | 5 | 6 | 1 | 1 | 1 | 1 | 2 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 47 | 1 | 1 | 5 | 3 | 4 | 0 | 1 | 1 | 3 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 48 | 1 | 1 | 3 | 3 | 1 | 1 | 1 | 1 | 1 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 49 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 50 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 51 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 52 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 53 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 54 | 1 | 1 | 5 | 3 | 4 | 1 | 1 | 1 | 2 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 55 | 1 | 1 | 2 | 2 | 1 | 4 | 1 | 1 | 4 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 56 | 8 | 1 | 98 | 98 | 98 | 98 | 98 | 98 | 4 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 57 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 58 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 59 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 60 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 61 | 1 | 1 | 2 | 3 | 1 | 1 | 1 | 1 | 3 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 62 | 1 | 1 | 5 | 3 | 1 | 2 | 1 | 1 | 2 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 63 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 64 | 1 | 1 | 5 | 3 | 2 | 4 | 1 | 1 | 1 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 65 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 66 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 67 | 5 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 68 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 69 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 70 | 1 | 1 | 1 | 3 | 3 | 1 | 1 | 1 | 1 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 71 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 72 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 73 | 1 | 1 | 2 | 3 | 1 | 1 | 1 | 1 | 1 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 74 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 75 | 1 | 1 | 5 | 3 | 4 | 1 | 1 | 1 | 2 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 76 | 1 | 1 | 5 | 3 | 4 | 1 | 1 | 1 | 1 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 77 | 1 | 1 | 3 | 6 | 1 | 1 | 1 | 1 | 3 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 78 | 5 | 1 | 3 | 3 | 1 | 1 | 1 | 1 | 3 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 79 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 80 | 1 | 1 | 2 | 1 | 1 | 3 | 1 | 1 | 5 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 81 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 82 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 83 | 1 | 1 | 5 | 3 | 2 | 1 | 1 | 1 | 2 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 84 | 4 | 1 | 5 | 3 | 2 | 0 | 1 | 1 | 2 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 85 | 8 | 1 | 98 | 98 | 98 | 98 | 98 | 98 | 8 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 86 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 87 | 1 | 1 | 3 | 3 | 1 | 2 | 1 | 1 | 1 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 88 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 89 | 5 | 1 | 4 | 3 | 2 | 1 | 2 | 1 | 2 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 90 | 1 | 1 | 5 | 3 | 3 | 2 | 1 | 3 | 6 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 91 | 1 | 1 | 2 | 3 | 1 | 1 | 1 | 1 | 1 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 92 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 93 | 5 | 1 | 4 | 3 | 5 | 1 | 1 | 1 | 5 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 94 | 1 | 1 | 5 | 3 | 3 | 99 | 1 | 1 | 3 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 95 | 1 | 1 | 5 | 3 | 2 | 2 | 1 | 1 | 2 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 96 | 1 | 1 | 2 | 3 | 4 | 6 | 1 | 1 | 1 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 97 | 1 | 1 | 5 | 1 | 1 | 1 | 1 | 1 | 1 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 98 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 99 | 1 | 1 | 2 | 7 | 4 | 1 | 1 | 1 | 1 | 15 | 152 | 15201 | 15201011001 |
15 | 152 | 15201 | 1 | 1 | 1 | 1767 | 100 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15201 | 15201011001 |
Despleguemos los códigos de regiones de nuestra tabla:
regiones <- unique(tabla_con_clave$REGION)
regiones
## [1] 15 14 13 12 11 10 9 16 8 7 6 5 4 3 2 1
Hagamos un subset con la 2:
tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 2)
tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE]
names(tabla_con_clave_f)[2] <- "Tipo de techo"
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab = xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona"
d$anio <- "2017"
Agregamos un cero a los códigos comunales de cuatro dígitos:
codigos <- d$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 | 2101011001 | 240 | 2017 | 02101 |
2 | 2101011002 | 442 | 2017 | 02101 |
3 | 2101011003 | 410 | 2017 | 02101 |
4 | 2101011004 | 872 | 2017 | 02101 |
5 | 2101011005 | 542 | 2017 | 02101 |
6 | 2101011006 | 97 | 2017 | 02101 |
7 | 2101011008 | 318 | 2017 | 02101 |
8 | 2101011009 | 462 | 2017 | 02101 |
9 | 2101011010 | 264 | 2017 | 02101 |
10 | 2101011011 | 79 | 2017 | 02101 |
11 | 2101011012 | 282 | 2017 | 02101 |
12 | 2101011013 | 735 | 2017 | 02101 |
13 | 2101011014 | 516 | 2017 | 02101 |
14 | 2101011015 | 424 | 2017 | 02101 |
15 | 2101011016 | 121 | 2017 | 02101 |
16 | 2101011017 | 317 | 2017 | 02101 |
17 | 2101011018 | 745 | 2017 | 02101 |
18 | 2101011019 | 166 | 2017 | 02101 |
19 | 2101011020 | 604 | 2017 | 02101 |
20 | 2101011021 | 124 | 2017 | 02101 |
21 | 2101011022 | 136 | 2017 | 02101 |
22 | 2101021001 | 287 | 2017 | 02101 |
23 | 2101021002 | 267 | 2017 | 02101 |
24 | 2101021003 | 194 | 2017 | 02101 |
25 | 2101021004 | 351 | 2017 | 02101 |
26 | 2101021005 | 301 | 2017 | 02101 |
27 | 2101031001 | 295 | 2017 | 02101 |
28 | 2101031002 | 256 | 2017 | 02101 |
29 | 2101031003 | 123 | 2017 | 02101 |
30 | 2101031004 | 352 | 2017 | 02101 |
31 | 2101031005 | 168 | 2017 | 02101 |
32 | 2101031006 | 327 | 2017 | 02101 |
33 | 2101041001 | 324 | 2017 | 02101 |
34 | 2101041002 | 311 | 2017 | 02101 |
35 | 2101041003 | 232 | 2017 | 02101 |
36 | 2101041004 | 232 | 2017 | 02101 |
37 | 2101041005 | 252 | 2017 | 02101 |
38 | 2101051001 | 389 | 2017 | 02101 |
39 | 2101051002 | 332 | 2017 | 02101 |
40 | 2101051003 | 318 | 2017 | 02101 |
41 | 2101061001 | 262 | 2017 | 02101 |
42 | 2101061002 | 302 | 2017 | 02101 |
43 | 2101061003 | 356 | 2017 | 02101 |
44 | 2101071001 | 315 | 2017 | 02101 |
45 | 2101071002 | 223 | 2017 | 02101 |
46 | 2101071003 | 390 | 2017 | 02101 |
47 | 2101071004 | 273 | 2017 | 02101 |
48 | 2101071005 | 224 | 2017 | 02101 |
49 | 2101081001 | 242 | 2017 | 02101 |
50 | 2101081002 | 305 | 2017 | 02101 |
51 | 2101081003 | 198 | 2017 | 02101 |
52 | 2101081004 | 111 | 2017 | 02101 |
53 | 2101091001 | 293 | 2017 | 02101 |
54 | 2101091002 | 376 | 2017 | 02101 |
55 | 2101091003 | 93 | 2017 | 02101 |
56 | 2101091004 | 303 | 2017 | 02101 |
57 | 2101091005 | 429 | 2017 | 02101 |
58 | 2101091006 | 200 | 2017 | 02101 |
59 | 2101091007 | 153 | 2017 | 02101 |
60 | 2101091008 | 287 | 2017 | 02101 |
61 | 2101091009 | 210 | 2017 | 02101 |
62 | 2101091010 | 251 | 2017 | 02101 |
63 | 2101101001 | 375 | 2017 | 02101 |
64 | 2101101002 | 134 | 2017 | 02101 |
65 | 2101101003 | 175 | 2017 | 02101 |
66 | 2101141001 | 589 | 2017 | 02101 |
67 | 2101141002 | 454 | 2017 | 02101 |
68 | 2101141003 | 204 | 2017 | 02101 |
69 | 2101141004 | 436 | 2017 | 02101 |
70 | 2101141005 | 360 | 2017 | 02101 |
71 | 2101141006 | 436 | 2017 | 02101 |
72 | 2101141007 | 196 | 2017 | 02101 |
73 | 2101141008 | 180 | 2017 | 02101 |
74 | 2101141009 | 396 | 2017 | 02101 |
75 | 2101151001 | 229 | 2017 | 02101 |
76 | 2101151002 | 278 | 2017 | 02101 |
77 | 2101151003 | 240 | 2017 | 02101 |
78 | 2101151004 | 453 | 2017 | 02101 |
79 | 2101161001 | 291 | 2017 | 02101 |
80 | 2101161002 | 258 | 2017 | 02101 |
81 | 2101161003 | 184 | 2017 | 02101 |
82 | 2101161004 | 254 | 2017 | 02101 |
83 | 2101161005 | 257 | 2017 | 02101 |
84 | 2101171001 | 114 | 2017 | 02101 |
85 | 2101171002 | 240 | 2017 | 02101 |
86 | 2101171003 | 393 | 2017 | 02101 |
87 | 2101171004 | 385 | 2017 | 02101 |
88 | 2101181001 | 559 | 2017 | 02101 |
89 | 2101181002 | 484 | 2017 | 02101 |
90 | 2101181003 | 348 | 2017 | 02101 |
91 | 2101181004 | 115 | 2017 | 02101 |
92 | 2101991999 | 150 | 2017 | 02101 |
248 | 2102011001 | 267 | 2017 | 02102 |
249 | 2102011002 | 432 | 2017 | 02102 |
250 | 2102991999 | 8 | 2017 | 02102 |
406 | 2104011001 | 109 | 2017 | 02104 |
407 | 2104021001 | 176 | 2017 | 02104 |
408 | 2104031001 | 346 | 2017 | 02104 |
409 | 2104991999 | 7 | 2017 | 02104 |
565 | 2201011001 | 354 | 2017 | 02201 |
Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí
h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_urbano_nacional.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 | tipo |
---|---|---|---|---|---|---|---|
01101 | Iquique | 356487.6 | 2017 | 1101 | 191468 | 68255976664 | Urbano |
01107 | Alto Hospicio | 301933.4 | 2017 | 1107 | 108375 | 32722034397 | Urbano |
01401 | Pozo Almonte | 299998.6 | 2017 | 1401 | 15711 | 4713278189 | Urbano |
01405 | Pica | 330061.1 | 2017 | 1405 | 9296 | 3068247619 | Urbano |
02101 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02102 | Mejillones | 369770.7 | 2017 | 2102 | 13467 | 4979702302 | Urbano |
02104 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano |
02201 | Calama | 416281.1 | 2017 | 2201 | 165731 | 68990679686 | Urbano |
02203 | San Pedro de Atacama | 437934.7 | 2017 | 2203 | 10996 | 4815529626 | Urbano |
02301 | Tocopilla | 271720.8 | 2017 | 2301 | 25186 | 6843559467 | Urbano |
02302 | María Elena | 466266.9 | 2017 | 2302 | 6457 | 3010685220 | Urbano |
03101 | Copiapó | 330574.6 | 2017 | 3101 | 153937 | 50887663717 | Urbano |
03102 | Caldera | 299314.8 | 2017 | 3102 | 17662 | 5286498241 | Urbano |
03103 | Tierra Amarilla | 315860.6 | 2017 | 3103 | 14019 | 4428049932 | Urbano |
03201 | Chañaral | 286389.3 | 2017 | 3201 | 12219 | 3499391196 | Urbano |
03202 | Diego de Almagro | 325861.5 | 2017 | 3202 | 13925 | 4537621312 | Urbano |
03301 | Vallenar | 311577.0 | 2017 | 3301 | 51917 | 16176145007 | Urbano |
03303 | Freirina | 289049.9 | 2017 | 3303 | 7041 | 2035200054 | Urbano |
03304 | Huasco | 337414.8 | 2017 | 3304 | 10149 | 3424422750 | Urbano |
04101 | La Serena | 272136.8 | 2017 | 4101 | 221054 | 60156924947 | Urbano |
04102 | Coquimbo | 264340.0 | 2017 | 4102 | 227730 | 60198159091 | Urbano |
04103 | Andacollo | 251267.7 | 2017 | 4103 | 11044 | 2775000288 | Urbano |
04104 | La Higuera | 214257.0 | 2017 | 4104 | 4241 | 908664019 | Urbano |
04106 | Vicuña | 245957.4 | 2017 | 4106 | 27771 | 6830481918 | Urbano |
04201 | Illapel | 270316.5 | 2017 | 4201 | 30848 | 8338722128 | Urbano |
04202 | Canela | 233397.3 | 2017 | 4202 | 9093 | 2122281844 | Urbano |
04203 | Los Vilos | 282415.6 | 2017 | 4203 | 21382 | 6038609501 | Urbano |
04204 | Salamanca | 262056.9 | 2017 | 4204 | 29347 | 7690585032 | Urbano |
04301 | Ovalle | 274771.4 | 2017 | 4301 | 111272 | 30574361012 | Urbano |
04302 | Combarbalá | 228990.4 | 2017 | 4302 | 13322 | 3050610572 | Urbano |
04303 | Monte Patria | 225369.1 | 2017 | 4303 | 30751 | 6930326684 | Urbano |
04304 | Punitaqui | 212496.1 | 2017 | 4304 | 10956 | 2328107498 | Urbano |
05101 | Valparaíso | 297929.0 | 2017 | 5101 | 296655 | 88382118059 | Urbano |
05102 | Casablanca | 341641.8 | 2017 | 5102 | 26867 | 9178890241 | Urbano |
05103 | Concón | 318496.3 | 2017 | 5103 | 42152 | 13425257057 | Urbano |
05105 | Puchuncaví | 296035.5 | 2017 | 5105 | 18546 | 5490274928 | Urbano |
05107 | Quintero | 308224.7 | 2017 | 5107 | 31923 | 9839456903 | Urbano |
05109 | Viña del Mar | 337006.1 | 2017 | 5109 | 334248 | 112643604611 | Urbano |
05301 | Los Andes | 339720.2 | 2017 | 5301 | 66708 | 22662055502 | Urbano |
05302 | Calle Larga | 246387.3 | 2017 | 5302 | 14832 | 3654416747 | Urbano |
05303 | Rinconada | 273904.7 | 2017 | 5303 | 10207 | 2795744821 | Urbano |
05304 | San Esteban | 219571.6 | 2017 | 5304 | 18855 | 4140022481 | Urbano |
05401 | La Ligua | 250134.4 | 2017 | 5401 | 35390 | 8852256241 | Urbano |
05402 | Cabildo | 262745.9 | 2017 | 5402 | 19388 | 5094117762 | Urbano |
05403 | Papudo | 294355.2 | 2017 | 5403 | 6356 | 1870921373 | Urbano |
05404 | Petorca | 237510.8 | 2017 | 5404 | 9826 | 2333781007 | Urbano |
05405 | Zapallar | 294389.2 | 2017 | 5405 | 7339 | 2160521991 | Urbano |
05501 | Quillota | 286029.5 | 2017 | 5501 | 90517 | 25890529852 | Urbano |
05502 | Calera | 277181.9 | 2017 | 5502 | 50554 | 14012652087 | Urbano |
05503 | Hijuelas | 254094.0 | 2017 | 5503 | 17988 | 4570642363 | Urbano |
Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.
comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
kable_paper() %>%
scroll_box(width = "100%", height = "300px")
código | zona | Freq | anio | comuna.x | promedio_i | año | comuna.y | personas | Ingresos_expandidos | tipo |
---|---|---|---|---|---|---|---|---|---|---|
02101 | 2101011001 | 240 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011002 | 442 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011003 | 410 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011004 | 872 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011005 | 542 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011006 | 97 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011008 | 318 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011009 | 462 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011010 | 264 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011011 | 79 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011012 | 282 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011013 | 735 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011014 | 516 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011015 | 424 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011016 | 121 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011017 | 317 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011018 | 745 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011019 | 166 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011020 | 604 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011021 | 124 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011022 | 136 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101021001 | 287 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101021002 | 267 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101021003 | 194 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101021004 | 351 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101021005 | 301 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101031001 | 295 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101031002 | 256 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101031003 | 123 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101031004 | 352 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101031005 | 168 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101031006 | 327 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101041001 | 324 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101041002 | 311 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101041003 | 232 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101041004 | 232 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101041005 | 252 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101051001 | 389 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101051002 | 332 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101051003 | 318 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101061001 | 262 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101061002 | 302 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101061003 | 356 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101071001 | 315 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101071002 | 223 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101071003 | 390 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101071004 | 273 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101071005 | 224 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101081001 | 242 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101081002 | 305 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101081003 | 198 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101081004 | 111 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101091001 | 293 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101091002 | 376 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101091003 | 93 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101091004 | 303 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101091005 | 429 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101091006 | 200 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101091007 | 153 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101091008 | 287 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101091009 | 210 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101091010 | 251 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101101001 | 375 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101101002 | 134 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101101003 | 175 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101141001 | 589 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101141002 | 454 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101141003 | 204 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101141004 | 436 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101141005 | 360 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101141006 | 436 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101141007 | 196 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101141008 | 180 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101141009 | 396 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101151001 | 229 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101151002 | 278 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101151003 | 240 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101151004 | 453 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101161001 | 291 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101161002 | 258 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101161003 | 184 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101161004 | 254 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101161005 | 257 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101171001 | 114 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101171002 | 240 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101171003 | 393 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101171004 | 385 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101181001 | 559 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101181002 | 484 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101181003 | 348 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101181004 | 115 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101991999 | 150 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02102 | 2102011001 | 267 | 2017 | Mejillones | 369770.7 | 2017 | 2102 | 13467 | 4979702302 | Urbano |
02102 | 2102011002 | 432 | 2017 | Mejillones | 369770.7 | 2017 | 2102 | 13467 | 4979702302 | Urbano |
02102 | 2102991999 | 8 | 2017 | Mejillones | 369770.7 | 2017 | 2102 | 13467 | 4979702302 | Urbano |
02104 | 2104011001 | 109 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano |
02104 | 2104021001 | 176 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano |
02104 | 2104031001 | 346 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano |
02104 | 2104991999 | 7 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano |
02201 | 2201011001 | 354 | 2017 | Calama | 416281.1 | 2017 | 2201 | 165731 | 68990679686 | Urbano |
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 |
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 | tipo |
---|---|---|---|---|---|---|---|---|---|---|
02101 | 2101011001 | 240 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011002 | 442 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011003 | 410 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011004 | 872 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011005 | 542 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011006 | 97 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011008 | 318 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011009 | 462 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011010 | 264 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011011 | 79 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011012 | 282 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011013 | 735 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011014 | 516 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011015 | 424 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011016 | 121 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011017 | 317 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011018 | 745 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011019 | 166 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011020 | 604 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011021 | 124 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101011022 | 136 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101021001 | 287 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101021002 | 267 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101021003 | 194 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101021004 | 351 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101021005 | 301 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101031001 | 295 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101031002 | 256 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101031003 | 123 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101031004 | 352 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101031005 | 168 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101031006 | 327 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101041001 | 324 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101041002 | 311 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101041003 | 232 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101041004 | 232 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101041005 | 252 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101051001 | 389 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101051002 | 332 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101051003 | 318 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101061001 | 262 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101061002 | 302 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101061003 | 356 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101071001 | 315 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101071002 | 223 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101071003 | 390 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101071004 | 273 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101071005 | 224 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101081001 | 242 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101081002 | 305 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101081003 | 198 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101081004 | 111 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101091001 | 293 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101091002 | 376 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101091003 | 93 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101091004 | 303 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101091005 | 429 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101091006 | 200 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101091007 | 153 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101091008 | 287 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101091009 | 210 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101091010 | 251 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101101001 | 375 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101101002 | 134 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101101003 | 175 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101141001 | 589 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101141002 | 454 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101141003 | 204 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101141004 | 436 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101141005 | 360 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101141006 | 436 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101141007 | 196 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101141008 | 180 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101141009 | 396 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101151001 | 229 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101151002 | 278 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101151003 | 240 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101151004 | 453 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101161001 | 291 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101161002 | 258 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101161003 | 184 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101161004 | 254 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101161005 | 257 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101171001 | 114 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101171002 | 240 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101171003 | 393 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101171004 | 385 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101181001 | 559 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101181002 | 484 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101181003 | 348 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101181004 | 115 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02101 | 2101991999 | 150 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano |
02102 | 2102011001 | 267 | 2017 | Mejillones | 369770.7 | 2017 | 2102 | 13467 | 4979702302 | Urbano |
02102 | 2102011002 | 432 | 2017 | Mejillones | 369770.7 | 2017 | 2102 | 13467 | 4979702302 | Urbano |
02102 | 2102991999 | 8 | 2017 | Mejillones | 369770.7 | 2017 | 2102 | 13467 | 4979702302 | Urbano |
02104 | 2104011001 | 109 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano |
02104 | 2104021001 | 176 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano |
02104 | 2104031001 | 346 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano |
02104 | 2104991999 | 7 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano |
02201 | 2201011001 | 354 | 2017 | Calama | 416281.1 | 2017 | 2201 | 165731 | 68990679686 | Urbano |
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 | tipo | Freq.y | p_poblacional | código.y |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2101011001 | 02101 | 240 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4618 | 0.0127614 | 02101 |
2101011002 | 02101 | 442 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3644 | 0.0100698 | 02101 |
2101011003 | 02101 | 410 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5645 | 0.0155994 | 02101 |
2101011004 | 02101 | 872 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4385 | 0.0121175 | 02101 |
2101011005 | 02101 | 542 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2383 | 0.0065852 | 02101 |
2101011006 | 02101 | 97 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1466 | 0.0040511 | 02101 |
2101011008 | 02101 | 318 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 6487 | 0.0179262 | 02101 |
2101011009 | 02101 | 462 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 6152 | 0.0170004 | 02101 |
2101011010 | 02101 | 264 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4495 | 0.0124215 | 02101 |
2101011011 | 02101 | 79 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2445 | 0.0067565 | 02101 |
2101011012 | 02101 | 282 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4275 | 0.0118135 | 02101 |
2101011013 | 02101 | 735 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2947 | 0.0081437 | 02101 |
2101011014 | 02101 | 516 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 6247 | 0.0172630 | 02101 |
2101011015 | 02101 | 424 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2887 | 0.0079779 | 02101 |
2101011016 | 02101 | 121 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1643 | 0.0045403 | 02101 |
2101011017 | 02101 | 317 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4126 | 0.0114018 | 02101 |
2101011018 | 02101 | 745 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4567 | 0.0126204 | 02101 |
2101011019 | 02101 | 166 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1843 | 0.0050929 | 02101 |
2101011020 | 02101 | 604 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2874 | 0.0079420 | 02101 |
2101011021 | 02101 | 124 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2753 | 0.0076076 | 02101 |
2101011022 | 02101 | 136 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2805 | 0.0077513 | 02101 |
2101021001 | 02101 | 287 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3642 | 0.0100643 | 02101 |
2101021002 | 02101 | 267 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4658 | 0.0128719 | 02101 |
2101021003 | 02101 | 194 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2620 | 0.0072401 | 02101 |
2101021004 | 02101 | 351 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4532 | 0.0125237 | 02101 |
2101021005 | 02101 | 301 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5748 | 0.0158840 | 02101 |
2101031001 | 02101 | 295 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3779 | 0.0104429 | 02101 |
2101031002 | 02101 | 256 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2511 | 0.0069389 | 02101 |
2101031003 | 02101 | 123 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2087 | 0.0057672 | 02101 |
2101031004 | 02101 | 352 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3200 | 0.0088429 | 02101 |
2101031005 | 02101 | 168 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3138 | 0.0086716 | 02101 |
2101031006 | 02101 | 327 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3335 | 0.0092159 | 02101 |
2101041001 | 02101 | 324 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4379 | 0.0121009 | 02101 |
2101041002 | 02101 | 311 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4125 | 0.0113990 | 02101 |
2101041003 | 02101 | 232 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2518 | 0.0069582 | 02101 |
2101041004 | 02101 | 232 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2923 | 0.0080774 | 02101 |
2101041005 | 02101 | 252 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4618 | 0.0127614 | 02101 |
2101051001 | 02101 | 389 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4321 | 0.0119407 | 02101 |
2101051002 | 02101 | 332 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5027 | 0.0138916 | 02101 |
2101051003 | 02101 | 318 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5139 | 0.0142011 | 02101 |
2101061001 | 02101 | 262 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3741 | 0.0103379 | 02101 |
2101061002 | 02101 | 302 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2750 | 0.0075994 | 02101 |
2101061003 | 02101 | 356 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3370 | 0.0093127 | 02101 |
2101071001 | 02101 | 315 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4201 | 0.0116090 | 02101 |
2101071002 | 02101 | 223 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2844 | 0.0078591 | 02101 |
2101071003 | 02101 | 390 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 7624 | 0.0210682 | 02101 |
2101071004 | 02101 | 273 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3724 | 0.0102909 | 02101 |
2101071005 | 02101 | 224 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3220 | 0.0088981 | 02101 |
2101081001 | 02101 | 242 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2981 | 0.0082377 | 02101 |
2101081002 | 02101 | 305 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4598 | 0.0127061 | 02101 |
2101081003 | 02101 | 198 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3327 | 0.0091938 | 02101 |
2101081004 | 02101 | 111 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2616 | 0.0072291 | 02101 |
2101091001 | 02101 | 293 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2670 | 0.0073783 | 02101 |
2101091002 | 02101 | 376 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3682 | 0.0101748 | 02101 |
2101091003 | 02101 | 93 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3061 | 0.0084588 | 02101 |
2101091004 | 02101 | 303 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4034 | 0.0111476 | 02101 |
2101091005 | 02101 | 429 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3472 | 0.0095945 | 02101 |
2101091006 | 02101 | 200 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4565 | 0.0126149 | 02101 |
2101091007 | 02101 | 153 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1884 | 0.0052062 | 02101 |
2101091008 | 02101 | 287 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2451 | 0.0067731 | 02101 |
2101091009 | 02101 | 210 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2064 | 0.0057037 | 02101 |
2101091010 | 02101 | 251 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2643 | 0.0073037 | 02101 |
2101101001 | 02101 | 375 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3814 | 0.0105396 | 02101 |
2101101002 | 02101 | 134 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1578 | 0.0043606 | 02101 |
2101101003 | 02101 | 175 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2526 | 0.0069803 | 02101 |
2101141001 | 02101 | 589 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 7735 | 0.0213749 | 02101 |
2101141002 | 02101 | 454 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 6365 | 0.0175890 | 02101 |
2101141003 | 02101 | 204 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3005 | 0.0083040 | 02101 |
2101141004 | 02101 | 436 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4535 | 0.0125320 | 02101 |
2101141005 | 02101 | 360 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4116 | 0.0113742 | 02101 |
2101141006 | 02101 | 436 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 7236 | 0.0199960 | 02101 |
2101141007 | 02101 | 196 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2982 | 0.0082405 | 02101 |
2101141008 | 02101 | 180 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2989 | 0.0082598 | 02101 |
2101141009 | 02101 | 396 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 6367 | 0.0175946 | 02101 |
2101151001 | 02101 | 229 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2879 | 0.0079558 | 02101 |
2101151002 | 02101 | 278 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3675 | 0.0101555 | 02101 |
2101151003 | 02101 | 240 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3768 | 0.0104125 | 02101 |
2101151004 | 02101 | 453 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 8961 | 0.0247628 | 02101 |
2101161001 | 02101 | 291 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2974 | 0.0082184 | 02101 |
2101161002 | 02101 | 258 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3413 | 0.0094315 | 02101 |
2101161003 | 02101 | 184 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1805 | 0.0049879 | 02101 |
2101161004 | 02101 | 254 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2437 | 0.0067344 | 02101 |
2101161005 | 02101 | 257 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3265 | 0.0090225 | 02101 |
2101171001 | 02101 | 114 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2503 | 0.0069168 | 02101 |
2101171002 | 02101 | 240 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4463 | 0.0123331 | 02101 |
2101171003 | 02101 | 393 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4570 | 0.0126287 | 02101 |
2101171004 | 02101 | 385 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5586 | 0.0154364 | 02101 |
2101181001 | 02101 | 559 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5341 | 0.0147593 | 02101 |
2101181002 | 02101 | 484 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5876 | 0.0162377 | 02101 |
2101181003 | 02101 | 348 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4235 | 0.0117030 | 02101 |
2101181004 | 02101 | 115 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4475 | 0.0123662 | 02101 |
2101991999 | 02101 | 150 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4121 | 0.0113880 | 02101 |
2102011001 | 02102 | 267 | 2017 | Mejillones | 369770.7 | 2017 | 2102 | 13467 | 4979702302 | Urbano | 5020 | 0.3727631 | 02102 |
2102011002 | 02102 | 432 | 2017 | Mejillones | 369770.7 | 2017 | 2102 | 13467 | 4979702302 | Urbano | 7764 | 0.5765204 | 02102 |
2102991999 | 02102 | 8 | 2017 | Mejillones | 369770.7 | 2017 | 2102 | 13467 | 4979702302 | Urbano | 170 | 0.0126234 | 02102 |
2104011001 | 02104 | 109 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano | 2174 | 0.1632500 | 02104 |
2104021001 | 02104 | 176 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano | 2812 | 0.2111587 | 02104 |
2104031001 | 02104 | 346 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano | 5947 | 0.4465721 | 02104 |
2104991999 | 02104 | 7 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano | 190 | 0.0142675 | 02104 |
2201011001 | 02201 | 354 | 2017 | Calama | 416281.1 | 2017 | 2201 | 165731 | 68990679686 | Urbano | 3387 | 0.0204367 | 02201 |
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 | tipo | Freq.y | p_poblacional | código.y | multi_pob |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2101011001 | 02101 | 240 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4618 | 0.0127614 | 02101 | 1605125412 |
2101011002 | 02101 | 442 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3644 | 0.0100698 | 02101 | 1266582287 |
2101011003 | 02101 | 410 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5645 | 0.0155994 | 02101 | 1962090288 |
2101011004 | 02101 | 872 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4385 | 0.0121175 | 02101 | 1524139223 |
2101011005 | 02101 | 542 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2383 | 0.0065852 | 02101 | 828283642 |
2101011006 | 02101 | 97 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1466 | 0.0040511 | 02101 | 509552589 |
2101011008 | 02101 | 318 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 6487 | 0.0179262 | 02101 | 2254752826 |
2101011009 | 02101 | 462 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 6152 | 0.0170004 | 02101 | 2138313455 |
2101011010 | 02101 | 264 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4495 | 0.0124215 | 02101 | 1562373046 |
2101011011 | 02101 | 79 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2445 | 0.0067565 | 02101 | 849833615 |
2101011012 | 02101 | 282 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4275 | 0.0118135 | 02101 | 1485905400 |
2101011013 | 02101 | 735 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2947 | 0.0081437 | 02101 | 1024318880 |
2101011014 | 02101 | 516 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 6247 | 0.0172630 | 02101 | 2171333575 |
2101011015 | 02101 | 424 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2887 | 0.0079779 | 02101 | 1003464068 |
2101011016 | 02101 | 121 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1643 | 0.0045403 | 02101 | 571074286 |
2101011017 | 02101 | 317 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4126 | 0.0114018 | 02101 | 1434115949 |
2101011018 | 02101 | 745 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4567 | 0.0126204 | 02101 | 1587398821 |
2101011019 | 02101 | 166 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1843 | 0.0050929 | 02101 | 640590328 |
2101011020 | 02101 | 604 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2874 | 0.0079420 | 02101 | 998945525 |
2101011021 | 02101 | 124 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2753 | 0.0076076 | 02101 | 956888320 |
2101011022 | 02101 | 136 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2805 | 0.0077513 | 02101 | 974962490 |
2101021001 | 02101 | 287 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3642 | 0.0100643 | 02101 | 1265887127 |
2101021002 | 02101 | 267 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4658 | 0.0128719 | 02101 | 1619028621 |
2101021003 | 02101 | 194 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2620 | 0.0072401 | 02101 | 910660152 |
2101021004 | 02101 | 351 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4532 | 0.0125237 | 02101 | 1575233514 |
2101021005 | 02101 | 301 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5748 | 0.0158840 | 02101 | 1997891050 |
2101031001 | 02101 | 295 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3779 | 0.0104429 | 02101 | 1313505616 |
2101031002 | 02101 | 256 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2511 | 0.0069389 | 02101 | 872773909 |
2101031003 | 02101 | 123 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2087 | 0.0057672 | 02101 | 725399899 |
2101031004 | 02101 | 352 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3200 | 0.0088429 | 02101 | 1112256674 |
2101031005 | 02101 | 168 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3138 | 0.0086716 | 02101 | 1090706701 |
2101031006 | 02101 | 327 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3335 | 0.0092159 | 02101 | 1159180002 |
2101041001 | 02101 | 324 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4379 | 0.0121009 | 02101 | 1522053742 |
2101041002 | 02101 | 311 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4125 | 0.0113990 | 02101 | 1433768368 |
2101041003 | 02101 | 232 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2518 | 0.0069582 | 02101 | 875206970 |
2101041004 | 02101 | 232 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2923 | 0.0080774 | 02101 | 1015976955 |
2101041005 | 02101 | 252 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4618 | 0.0127614 | 02101 | 1605125412 |
2101051001 | 02101 | 389 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4321 | 0.0119407 | 02101 | 1501894090 |
2101051002 | 02101 | 332 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5027 | 0.0138916 | 02101 | 1747285718 |
2101051003 | 02101 | 318 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5139 | 0.0142011 | 02101 | 1786214702 |
2101061001 | 02101 | 262 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3741 | 0.0103379 | 02101 | 1300297568 |
2101061002 | 02101 | 302 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2750 | 0.0075994 | 02101 | 955845579 |
2101061003 | 02101 | 356 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3370 | 0.0093127 | 02101 | 1171345309 |
2101071001 | 02101 | 315 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4201 | 0.0116090 | 02101 | 1460184464 |
2101071002 | 02101 | 223 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2844 | 0.0078591 | 02101 | 988518119 |
2101071003 | 02101 | 390 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 7624 | 0.0210682 | 02101 | 2649951525 |
2101071004 | 02101 | 273 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3724 | 0.0102909 | 02101 | 1294388704 |
2101071005 | 02101 | 224 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3220 | 0.0088981 | 02101 | 1119208278 |
2101081001 | 02101 | 242 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2981 | 0.0082377 | 02101 | 1036136608 |
2101081002 | 02101 | 305 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4598 | 0.0127061 | 02101 | 1598173808 |
2101081003 | 02101 | 198 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3327 | 0.0091938 | 02101 | 1156399360 |
2101081004 | 02101 | 111 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2616 | 0.0072291 | 02101 | 909269831 |
2101091001 | 02101 | 293 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2670 | 0.0073783 | 02101 | 928039162 |
2101091002 | 02101 | 376 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3682 | 0.0101748 | 02101 | 1279790335 |
2101091003 | 02101 | 93 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3061 | 0.0084588 | 02101 | 1063943024 |
2101091004 | 02101 | 303 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4034 | 0.0111476 | 02101 | 1402138569 |
2101091005 | 02101 | 429 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3472 | 0.0095945 | 02101 | 1206798491 |
2101091006 | 02101 | 200 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4565 | 0.0126149 | 02101 | 1586703661 |
2101091007 | 02101 | 153 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1884 | 0.0052062 | 02101 | 654841117 |
2101091008 | 02101 | 287 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2451 | 0.0067731 | 02101 | 851919096 |
2101091009 | 02101 | 210 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2064 | 0.0057037 | 02101 | 717405554 |
2101091010 | 02101 | 251 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2643 | 0.0073037 | 02101 | 918654496 |
2101101001 | 02101 | 375 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3814 | 0.0105396 | 02101 | 1325670923 |
2101101002 | 02101 | 134 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1578 | 0.0043606 | 02101 | 548481572 |
2101101003 | 02101 | 175 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2526 | 0.0069803 | 02101 | 877987612 |
2101141001 | 02101 | 589 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 7735 | 0.0213749 | 02101 | 2688532928 |
2101141002 | 02101 | 454 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 6365 | 0.0175890 | 02101 | 2212348040 |
2101141003 | 02101 | 204 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3005 | 0.0083040 | 02101 | 1044478533 |
2101141004 | 02101 | 436 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4535 | 0.0125320 | 02101 | 1576276255 |
2101141005 | 02101 | 360 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4116 | 0.0113742 | 02101 | 1430640146 |
2101141006 | 02101 | 436 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 7236 | 0.0199960 | 02101 | 2515090403 |
2101141007 | 02101 | 196 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2982 | 0.0082405 | 02101 | 1036484188 |
2101141008 | 02101 | 180 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2989 | 0.0082598 | 02101 | 1038917249 |
2101141009 | 02101 | 396 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 6367 | 0.0175946 | 02101 | 2213043200 |
2101151001 | 02101 | 229 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2879 | 0.0079558 | 02101 | 1000683426 |
2101151002 | 02101 | 278 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3675 | 0.0101555 | 02101 | 1277357274 |
2101151003 | 02101 | 240 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3768 | 0.0104125 | 02101 | 1309682233 |
2101151004 | 02101 | 453 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 8961 | 0.0247628 | 02101 | 3114666266 |
2101161001 | 02101 | 291 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2974 | 0.0082184 | 02101 | 1033703546 |
2101161002 | 02101 | 258 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3413 | 0.0094315 | 02101 | 1186291258 |
2101161003 | 02101 | 184 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1805 | 0.0049879 | 02101 | 627382280 |
2101161004 | 02101 | 254 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2437 | 0.0067344 | 02101 | 847052973 |
2101161005 | 02101 | 257 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3265 | 0.0090225 | 02101 | 1134849387 |
2101171001 | 02101 | 114 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2503 | 0.0069168 | 02101 | 869993267 |
2101171002 | 02101 | 240 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4463 | 0.0123331 | 02101 | 1551250479 |
2101171003 | 02101 | 393 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4570 | 0.0126287 | 02101 | 1588441562 |
2101171004 | 02101 | 385 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5586 | 0.0154364 | 02101 | 1941583056 |
2101181001 | 02101 | 559 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5341 | 0.0147593 | 02101 | 1856425904 |
2101181002 | 02101 | 484 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5876 | 0.0162377 | 02101 | 2042381317 |
2101181003 | 02101 | 348 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4235 | 0.0117030 | 02101 | 1472002191 |
2101181004 | 02101 | 115 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4475 | 0.0123662 | 02101 | 1555421442 |
2101991999 | 02101 | 150 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4121 | 0.0113880 | 02101 | 1432378048 |
2102011001 | 02102 | 267 | 2017 | Mejillones | 369770.7 | 2017 | 2102 | 13467 | 4979702302 | Urbano | 5020 | 0.3727631 | 02102 | 1856249020 |
2102011002 | 02102 | 432 | 2017 | Mejillones | 369770.7 | 2017 | 2102 | 13467 | 4979702302 | Urbano | 7764 | 0.5765204 | 02102 | 2870899879 |
2102991999 | 02102 | 8 | 2017 | Mejillones | 369770.7 | 2017 | 2102 | 13467 | 4979702302 | Urbano | 170 | 0.0126234 | 02102 | 62861023 |
2104011001 | 02104 | 109 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano | 2174 | 0.1632500 | 02104 | 818139039 |
2104021001 | 02104 | 176 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano | 2812 | 0.2111587 | 02104 | 1058236880 |
2104031001 | 02104 | 346 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano | 5947 | 0.4465721 | 02104 | 2238027997 |
2104991999 | 02104 | 7 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano | 190 | 0.0142675 | 02104 | 71502492 |
2201011001 | 02201 | 354 | 2017 | Calama | 416281.1 | 2017 | 2201 | 165731 | 68990679686 | Urbano | 3387 | 0.0204367 | 02201 | 1409944018 |
Aplicaremos un análisis de regresión donde:
\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]
\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]
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)
Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.
Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.
linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod)
##
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.562e+09 -2.740e+08 2.337e+06 2.146e+08 1.340e+09
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 469548368 80042316 5.866 2.66e-08 ***
## Freq.x 2880607 231096 12.465 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 466500000 on 153 degrees of freedom
## Multiple R-squared: 0.5039, Adjusted R-squared: 0.5006
## F-statistic: 155.4 on 1 and 153 DF, p-value: < 2.2e-16
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.
\[ \hat Y = \beta_0 + \beta_1 X^2 \]
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)"
modelos1 <- cbind(modelo,dato,sintaxis)
modelos1
## modelo dato
## [1,] "cuadrático" "0.500608794420092"
## sintaxis
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
\[ \hat Y = \beta_0 + \beta_1 X^3 \]
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)"
modelos2 <- cbind(modelo,dato,sintaxis)
modelos2
## modelo dato
## [1,] "cúbico" "0.500608794420092"
## sintaxis
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
\[ \hat Y = \beta_0 + \beta_1 ln X \]
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)"
modelos3 <- cbind(modelo,dato,sintaxis)
modelos3
## modelo dato
## [1,] "logarítmico" "0.409374856166196"
## sintaxis
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
\[ \hat Y = \beta_0 + \beta_1 e^X \]
No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.
\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]
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)"
modelos5 <- cbind(modelo,dato,sintaxis)
modelos5
## modelo dato
## [1,] "raíz cuadrada" "0.543531488486724"
## sintaxis
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]
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)"
modelos6 <- cbind(modelo,dato,sintaxis)
modelos6
## modelo dato
## [1,] "raíz-raíz" "0.629998732218126"
## sintaxis
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]
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)"
modelos7 <- cbind(modelo,dato,sintaxis)
modelos7
## modelo dato
## [1,] "log-raíz" "0.615607359693425"
## sintaxis
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]
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)"
modelos8 <- cbind(modelo,dato,sintaxis)
modelos8
## modelo dato
## [1,] "raíz-log" "0.581881737589711"
## sintaxis
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]
linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
dato <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,dato,sintaxis)
modelos9
## modelo dato
## [1,] "log-log" "0.7430801516998"
## sintaxis
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$dato ),]
## modelo dato
## 3 logarítmico 0.409374856166196
## 1 cuadrático 0.500608794420092
## 2 cúbico 0.500608794420092
## 4 raíz cuadrada 0.543531488486724
## 7 raíz-log 0.581881737589711
## 6 log-raíz 0.615607359693425
## 5 raíz-raíz 0.629998732218126
## 8 log-log 0.7430801516998
## sintaxis
## 3 linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 1 linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2 linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 4 linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 7 linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 6 linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 8 linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
metodo <- 8
switch (metodo,
case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)
##
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.47838 -0.21365 0.04744 0.22507 1.82867
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.20627 0.17460 98.55 <2e-16 ***
## log(Freq.x) 0.66382 0.03142 21.13 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4088 on 153 degrees of freedom
## Multiple R-squared: 0.7447, Adjusted R-squared: 0.7431
## F-statistic: 446.4 on 1 and 153 DF, p-value: < 2.2e-16
aa <- linearMod$coefficients[1]
aa
## (Intercept)
## 17.20627
bb <- linearMod$coefficients[2]
bb
## log(Freq.x)
## 0.6638183
Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.6545895).
Desplegamos una curva suavizada por loess en el diagrama de dispersión.
scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")
Observemos nuevamente el resultado sobre log-log.
linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod)
##
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.47838 -0.21365 0.04744 0.22507 1.82867
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.20627 0.17460 98.55 <2e-16 ***
## log(Freq.x) 0.66382 0.03142 21.13 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4088 on 153 degrees of freedom
## Multiple R-squared: 0.7447, Adjusted R-squared: 0.7431
## F-statistic: 446.4 on 1 and 153 DF, p-value: < 2.2e-16
ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")
par(mfrow = c (2,2))
plot(linearMod)
\[ \hat Y = e^{17.361982+0.641075 \cdot ln{X}} \]
Esta nueva variable se llamará: est_ing
h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))
r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
kable_paper() %>%
scroll_box(width = "100%", height = "300px")
zona | código.x | Freq.x | anio | comuna.x | promedio_i | año | comuna.y | personas | Ingresos_expandidos | tipo | Freq.y | p_poblacional | código.y | multi_pob | est_ing |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2101011001 | 02101 | 240 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4618 | 0.0127614 | 02101 | 1605125412 | 1128793584 |
2101011002 | 02101 | 442 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3644 | 0.0100698 | 02101 | 1266582287 | 1693037107 |
2101011003 | 02101 | 410 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5645 | 0.0155994 | 02101 | 1962090288 | 1610647489 |
2101011004 | 02101 | 872 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4385 | 0.0121175 | 02101 | 1524139223 | 2658002773 |
2101011005 | 02101 | 542 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2383 | 0.0065852 | 02101 | 828283642 | 1938498751 |
2101011006 | 02101 | 97 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1466 | 0.0040511 | 02101 | 509552589 | 618645945 |
2101011008 | 02101 | 318 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 6487 | 0.0179262 | 02101 | 2254752826 | 1360641377 |
2101011009 | 02101 | 462 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 6152 | 0.0170004 | 02101 | 2138313455 | 1743511717 |
2101011010 | 02101 | 264 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4495 | 0.0124215 | 02101 | 1562373046 | 1202518470 |
2101011011 | 02101 | 79 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2445 | 0.0067565 | 02101 | 849833615 | 539841567 |
2101011012 | 02101 | 282 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4275 | 0.0118135 | 02101 | 1485905400 | 1256339321 |
2101011013 | 02101 | 735 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2947 | 0.0081437 | 02101 | 1024318880 | 2372907239 |
2101011014 | 02101 | 516 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 6247 | 0.0172630 | 02101 | 2171333575 | 1876261121 |
2101011015 | 02101 | 424 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2887 | 0.0079779 | 02101 | 1003464068 | 1646949561 |
2101011016 | 02101 | 121 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1643 | 0.0045403 | 02101 | 571074286 | 716436632 |
2101011017 | 02101 | 317 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4126 | 0.0114018 | 02101 | 1434115949 | 1357799564 |
2101011018 | 02101 | 745 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4567 | 0.0126204 | 02101 | 1587398821 | 2394289533 |
2101011019 | 02101 | 166 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1843 | 0.0050929 | 02101 | 640590328 | 883761589 |
2101011020 | 02101 | 604 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2874 | 0.0079420 | 02101 | 998945525 | 2083003414 |
2101011021 | 02101 | 124 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2753 | 0.0076076 | 02101 | 956888320 | 728179359 |
2101011022 | 02101 | 136 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2805 | 0.0077513 | 02101 | 974962490 | 774228070 |
2101021001 | 02101 | 287 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3642 | 0.0100643 | 02101 | 1265887127 | 1271082494 |
2101021002 | 02101 | 267 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4658 | 0.0128719 | 02101 | 1619028621 | 1211572297 |
2101021003 | 02101 | 194 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2620 | 0.0072401 | 02101 | 910660152 | 980102288 |
2101021004 | 02101 | 351 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4532 | 0.0125237 | 02101 | 1575233514 | 1452807931 |
2101021005 | 02101 | 301 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5748 | 0.0158840 | 02101 | 1997891050 | 1311911544 |
2101031001 | 02101 | 295 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3779 | 0.0104429 | 02101 | 1313505616 | 1294493302 |
2101031002 | 02101 | 256 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2511 | 0.0069389 | 02101 | 872773909 | 1178204055 |
2101031003 | 02101 | 123 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2087 | 0.0057672 | 02101 | 725399899 | 724275839 |
2101031004 | 02101 | 352 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3200 | 0.0088429 | 02101 | 1112256674 | 1455554197 |
2101031005 | 02101 | 168 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3138 | 0.0086716 | 02101 | 1090706701 | 890815510 |
2101031006 | 02101 | 327 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3335 | 0.0092159 | 02101 | 1159180002 | 1386084069 |
2101041001 | 02101 | 324 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4379 | 0.0121009 | 02101 | 1522053742 | 1377629640 |
2101041002 | 02101 | 311 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4125 | 0.0113990 | 02101 | 1433768368 | 1340684908 |
2101041003 | 02101 | 232 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2518 | 0.0069582 | 02101 | 875206970 | 1103674388 |
2101041004 | 02101 | 232 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2923 | 0.0080774 | 02101 | 1015976955 | 1103674388 |
2101041005 | 02101 | 252 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4618 | 0.0127614 | 02101 | 1605125412 | 1165951210 |
2101051001 | 02101 | 389 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4321 | 0.0119407 | 02101 | 1501894090 | 1555402176 |
2101051002 | 02101 | 332 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5027 | 0.0138916 | 02101 | 1747285718 | 1400117084 |
2101051003 | 02101 | 318 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5139 | 0.0142011 | 02101 | 1786214702 | 1360641377 |
2101061001 | 02101 | 262 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3741 | 0.0103379 | 02101 | 1300297568 | 1196463366 |
2101061002 | 02101 | 302 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2750 | 0.0075994 | 02101 | 955845579 | 1314803190 |
2101061003 | 02101 | 356 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3370 | 0.0093127 | 02101 | 1171345309 | 1466513143 |
2101071001 | 02101 | 315 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4201 | 0.0116090 | 02101 | 1460184464 | 1352106877 |
2101071002 | 02101 | 223 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2844 | 0.0078591 | 02101 | 988518119 | 1075064430 |
2101071003 | 02101 | 390 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 7624 | 0.0210682 | 02101 | 2649951525 | 1558055284 |
2101071004 | 02101 | 273 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3724 | 0.0102909 | 02101 | 1294388704 | 1229578049 |
2101071005 | 02101 | 224 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3220 | 0.0088981 | 02101 | 1119208278 | 1078262235 |
2101081001 | 02101 | 242 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2981 | 0.0082377 | 02101 | 1036136608 | 1135029152 |
2101081002 | 02101 | 305 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4598 | 0.0127061 | 02101 | 1598173808 | 1323458880 |
2101081003 | 02101 | 198 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3327 | 0.0091938 | 02101 | 1156399360 | 993470855 |
2101081004 | 02101 | 111 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2616 | 0.0072291 | 02101 | 909269831 | 676565052 |
2101091001 | 02101 | 293 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2670 | 0.0073783 | 02101 | 928039162 | 1288660823 |
2101091002 | 02101 | 376 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3682 | 0.0101748 | 02101 | 1279790335 | 1520700111 |
2101091003 | 02101 | 93 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3061 | 0.0084588 | 02101 | 1063943024 | 601591566 |
2101091004 | 02101 | 303 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4034 | 0.0111476 | 02101 | 1402138569 | 1317691618 |
2101091005 | 02101 | 429 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3472 | 0.0095945 | 02101 | 1206798491 | 1659816537 |
2101091006 | 02101 | 200 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4565 | 0.0126149 | 02101 | 1586703661 | 1000121051 |
2101091007 | 02101 | 153 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1884 | 0.0052062 | 02101 | 654841117 | 837191664 |
2101091008 | 02101 | 287 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2451 | 0.0067731 | 02101 | 851919096 | 1271082494 |
2101091009 | 02101 | 210 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2064 | 0.0057037 | 02101 | 717405554 | 1033043035 |
2101091010 | 02101 | 251 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2643 | 0.0073037 | 02101 | 918654496 | 1162877809 |
2101101001 | 02101 | 375 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3814 | 0.0105396 | 02101 | 1325670923 | 1518014152 |
2101101002 | 02101 | 134 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1578 | 0.0043606 | 02101 | 548481572 | 766651223 |
2101101003 | 02101 | 175 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2526 | 0.0069803 | 02101 | 877987612 | 915285222 |
2101141001 | 02101 | 589 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 7735 | 0.0213749 | 02101 | 2688532928 | 2048518990 |
2101141002 | 02101 | 454 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 6365 | 0.0175890 | 02101 | 2212348040 | 1723411803 |
2101141003 | 02101 | 204 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3005 | 0.0083040 | 02101 | 1044478533 | 1013354780 |
2101141004 | 02101 | 436 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4535 | 0.0125320 | 02101 | 1576276255 | 1677745942 |
2101141005 | 02101 | 360 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4116 | 0.0113742 | 02101 | 1430640146 | 1477430770 |
2101141006 | 02101 | 436 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 7236 | 0.0199960 | 02101 | 2515090403 | 1677745942 |
2101141007 | 02101 | 196 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2982 | 0.0082405 | 02101 | 1036484188 | 986798037 |
2101141008 | 02101 | 180 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2989 | 0.0082598 | 02101 | 1038917249 | 932562413 |
2101141009 | 02101 | 396 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 6367 | 0.0175946 | 02101 | 2213043200 | 1573926195 |
2101151001 | 02101 | 229 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2879 | 0.0079558 | 02101 | 1000683426 | 1094179892 |
2101151002 | 02101 | 278 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3675 | 0.0101555 | 02101 | 1277357274 | 1244481418 |
2101151003 | 02101 | 240 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3768 | 0.0104125 | 02101 | 1309682233 | 1128793584 |
2101151004 | 02101 | 453 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 8961 | 0.0247628 | 02101 | 3114666266 | 1720890974 |
2101161001 | 02101 | 291 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2974 | 0.0082184 | 02101 | 1033703546 | 1282814945 |
2101161002 | 02101 | 258 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3413 | 0.0094315 | 02101 | 1186291258 | 1184306320 |
2101161003 | 02101 | 184 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1805 | 0.0049879 | 02101 | 627382280 | 946268241 |
2101161004 | 02101 | 254 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2437 | 0.0067344 | 02101 | 847052973 | 1172085741 |
2101161005 | 02101 | 257 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3265 | 0.0090225 | 02101 | 1134849387 | 1181257183 |
2101171001 | 02101 | 114 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2503 | 0.0069168 | 02101 | 869993267 | 688648840 |
2101171002 | 02101 | 240 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4463 | 0.0123331 | 02101 | 1551250479 | 1128793584 |
2101171003 | 02101 | 393 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4570 | 0.0126287 | 02101 | 1588441562 | 1566000922 |
2101171004 | 02101 | 385 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5586 | 0.0154364 | 02101 | 1941583056 | 1544766728 |
2101181001 | 02101 | 559 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5341 | 0.0147593 | 02101 | 1856425904 | 1978650108 |
2101181002 | 02101 | 484 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5876 | 0.0162377 | 02101 | 2042381317 | 1798192776 |
2101181003 | 02101 | 348 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4235 | 0.0117030 | 02101 | 1472002191 | 1444553304 |
2101181004 | 02101 | 115 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4475 | 0.0123662 | 02101 | 1555421442 | 692652930 |
2101991999 | 02101 | 150 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4121 | 0.0113880 | 02101 | 1432378048 | 826258506 |
2102011001 | 02102 | 267 | 2017 | Mejillones | 369770.7 | 2017 | 2102 | 13467 | 4979702302 | Urbano | 5020 | 0.3727631 | 02102 | 1856249020 | 1211572297 |
2102011002 | 02102 | 432 | 2017 | Mejillones | 369770.7 | 2017 | 2102 | 13467 | 4979702302 | Urbano | 7764 | 0.5765204 | 02102 | 2870899879 | 1667512519 |
2102991999 | 02102 | 8 | 2017 | Mejillones | 369770.7 | 2017 | 2102 | 13467 | 4979702302 | Urbano | 170 | 0.0126234 | 02102 | 62861023 | 118052272 |
2104011001 | 02104 | 109 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano | 2174 | 0.1632500 | 02104 | 818139039 | 668448159 |
2104021001 | 02104 | 176 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano | 2812 | 0.2111587 | 02104 | 1058236880 | 918753800 |
2104031001 | 02104 | 346 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano | 5947 | 0.4465721 | 02104 | 2238027997 | 1439036926 |
2104991999 | 02104 | 7 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano | 190 | 0.0142675 | 02104 | 71502492 | 108038422 |
2201011001 | 02201 | 354 | 2017 | Calama | 416281.1 | 2017 | 2201 | 165731 | 68990679686 | Urbano | 3387 | 0.0204367 | 02201 | 1409944018 | 1461038873 |
\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]
h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing /( h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional)
r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
kable_paper() %>%
scroll_box(width = "100%", height = "300px")
zona | código.x | Freq.x | anio | comuna.x | promedio_i | año | comuna.y | personas | Ingresos_expandidos | tipo | Freq.y | p_poblacional | código.y | multi_pob | est_ing | ing_medio_zona |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2101011001 | 02101 | 240 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4618 | 0.0127614 | 02101 | 1605125412 | 1128793584 | 244433.4 |
2101011002 | 02101 | 442 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3644 | 0.0100698 | 02101 | 1266582287 | 1693037107 | 464609.5 |
2101011003 | 02101 | 410 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5645 | 0.0155994 | 02101 | 1962090288 | 1610647489 | 285322.9 |
2101011004 | 02101 | 872 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4385 | 0.0121175 | 02101 | 1524139223 | 2658002773 | 606158.0 |
2101011005 | 02101 | 542 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2383 | 0.0065852 | 02101 | 828283642 | 1938498751 | 813469.9 |
2101011006 | 02101 | 97 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1466 | 0.0040511 | 02101 | 509552589 | 618645945 | 421995.9 |
2101011008 | 02101 | 318 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 6487 | 0.0179262 | 02101 | 2254752826 | 1360641377 | 209748.9 |
2101011009 | 02101 | 462 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 6152 | 0.0170004 | 02101 | 2138313455 | 1743511717 | 283405.7 |
2101011010 | 02101 | 264 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4495 | 0.0124215 | 02101 | 1562373046 | 1202518470 | 267523.6 |
2101011011 | 02101 | 79 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2445 | 0.0067565 | 02101 | 849833615 | 539841567 | 220794.1 |
2101011012 | 02101 | 282 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4275 | 0.0118135 | 02101 | 1485905400 | 1256339321 | 293880.5 |
2101011013 | 02101 | 735 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2947 | 0.0081437 | 02101 | 1024318880 | 2372907239 | 805194.2 |
2101011014 | 02101 | 516 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 6247 | 0.0172630 | 02101 | 2171333575 | 1876261121 | 300345.9 |
2101011015 | 02101 | 424 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2887 | 0.0079779 | 02101 | 1003464068 | 1646949561 | 570470.9 |
2101011016 | 02101 | 121 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1643 | 0.0045403 | 02101 | 571074286 | 716436632 | 436053.9 |
2101011017 | 02101 | 317 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4126 | 0.0114018 | 02101 | 1434115949 | 1357799564 | 329083.8 |
2101011018 | 02101 | 745 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4567 | 0.0126204 | 02101 | 1587398821 | 2394289533 | 524258.7 |
2101011019 | 02101 | 166 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1843 | 0.0050929 | 02101 | 640590328 | 883761589 | 479523.4 |
2101011020 | 02101 | 604 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2874 | 0.0079420 | 02101 | 998945525 | 2083003414 | 724775.0 |
2101011021 | 02101 | 124 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2753 | 0.0076076 | 02101 | 956888320 | 728179359 | 264503.9 |
2101011022 | 02101 | 136 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2805 | 0.0077513 | 02101 | 974962490 | 774228070 | 276017.1 |
2101021001 | 02101 | 287 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3642 | 0.0100643 | 02101 | 1265887127 | 1271082494 | 349006.7 |
2101021002 | 02101 | 267 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4658 | 0.0128719 | 02101 | 1619028621 | 1211572297 | 260105.7 |
2101021003 | 02101 | 194 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2620 | 0.0072401 | 02101 | 910660152 | 980102288 | 374084.8 |
2101021004 | 02101 | 351 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4532 | 0.0125237 | 02101 | 1575233514 | 1452807931 | 320566.6 |
2101021005 | 02101 | 301 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5748 | 0.0158840 | 02101 | 1997891050 | 1311911544 | 228237.9 |
2101031001 | 02101 | 295 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3779 | 0.0104429 | 02101 | 1313505616 | 1294493302 | 342549.2 |
2101031002 | 02101 | 256 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2511 | 0.0069389 | 02101 | 872773909 | 1178204055 | 469217.1 |
2101031003 | 02101 | 123 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2087 | 0.0057672 | 02101 | 725399899 | 724275839 | 347041.6 |
2101031004 | 02101 | 352 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3200 | 0.0088429 | 02101 | 1112256674 | 1455554197 | 454860.7 |
2101031005 | 02101 | 168 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3138 | 0.0086716 | 02101 | 1090706701 | 890815510 | 283880.0 |
2101031006 | 02101 | 327 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3335 | 0.0092159 | 02101 | 1159180002 | 1386084069 | 415617.4 |
2101041001 | 02101 | 324 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4379 | 0.0121009 | 02101 | 1522053742 | 1377629640 | 314599.1 |
2101041002 | 02101 | 311 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4125 | 0.0113990 | 02101 | 1433768368 | 1340684908 | 325014.5 |
2101041003 | 02101 | 232 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2518 | 0.0069582 | 02101 | 875206970 | 1103674388 | 438313.9 |
2101041004 | 02101 | 232 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2923 | 0.0080774 | 02101 | 1015976955 | 1103674388 | 377582.8 |
2101041005 | 02101 | 252 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4618 | 0.0127614 | 02101 | 1605125412 | 1165951210 | 252479.7 |
2101051001 | 02101 | 389 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4321 | 0.0119407 | 02101 | 1501894090 | 1555402176 | 359963.5 |
2101051002 | 02101 | 332 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5027 | 0.0138916 | 02101 | 1747285718 | 1400117084 | 278519.4 |
2101051003 | 02101 | 318 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5139 | 0.0142011 | 02101 | 1786214702 | 1360641377 | 264767.7 |
2101061001 | 02101 | 262 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3741 | 0.0103379 | 02101 | 1300297568 | 1196463366 | 319824.5 |
2101061002 | 02101 | 302 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2750 | 0.0075994 | 02101 | 955845579 | 1314803190 | 478110.3 |
2101061003 | 02101 | 356 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3370 | 0.0093127 | 02101 | 1171345309 | 1466513143 | 435167.1 |
2101071001 | 02101 | 315 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4201 | 0.0116090 | 02101 | 1460184464 | 1352106877 | 321853.6 |
2101071002 | 02101 | 223 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2844 | 0.0078591 | 02101 | 988518119 | 1075064430 | 378011.4 |
2101071003 | 02101 | 390 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 7624 | 0.0210682 | 02101 | 2649951525 | 1558055284 | 204361.9 |
2101071004 | 02101 | 273 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3724 | 0.0102909 | 02101 | 1294388704 | 1229578049 | 330176.7 |
2101071005 | 02101 | 224 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3220 | 0.0088981 | 02101 | 1119208278 | 1078262235 | 334864.0 |
2101081001 | 02101 | 242 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2981 | 0.0082377 | 02101 | 1036136608 | 1135029152 | 380754.5 |
2101081002 | 02101 | 305 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4598 | 0.0127061 | 02101 | 1598173808 | 1323458880 | 287833.6 |
2101081003 | 02101 | 198 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3327 | 0.0091938 | 02101 | 1156399360 | 993470855 | 298608.6 |
2101081004 | 02101 | 111 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2616 | 0.0072291 | 02101 | 909269831 | 676565052 | 258625.8 |
2101091001 | 02101 | 293 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2670 | 0.0073783 | 02101 | 928039162 | 1288660823 | 482644.5 |
2101091002 | 02101 | 376 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3682 | 0.0101748 | 02101 | 1279790335 | 1520700111 | 413009.3 |
2101091003 | 02101 | 93 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3061 | 0.0084588 | 02101 | 1063943024 | 601591566 | 196534.3 |
2101091004 | 02101 | 303 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4034 | 0.0111476 | 02101 | 1402138569 | 1317691618 | 326646.4 |
2101091005 | 02101 | 429 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3472 | 0.0095945 | 02101 | 1206798491 | 1659816537 | 478057.8 |
2101091006 | 02101 | 200 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4565 | 0.0126149 | 02101 | 1586703661 | 1000121051 | 219084.6 |
2101091007 | 02101 | 153 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1884 | 0.0052062 | 02101 | 654841117 | 837191664 | 444369.2 |
2101091008 | 02101 | 287 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2451 | 0.0067731 | 02101 | 851919096 | 1271082494 | 518597.5 |
2101091009 | 02101 | 210 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2064 | 0.0057037 | 02101 | 717405554 | 1033043035 | 500505.3 |
2101091010 | 02101 | 251 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2643 | 0.0073037 | 02101 | 918654496 | 1162877809 | 439984.0 |
2101101001 | 02101 | 375 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3814 | 0.0105396 | 02101 | 1325670923 | 1518014152 | 398011.1 |
2101101002 | 02101 | 134 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1578 | 0.0043606 | 02101 | 548481572 | 766651223 | 485837.3 |
2101101003 | 02101 | 175 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2526 | 0.0069803 | 02101 | 877987612 | 915285222 | 362345.7 |
2101141001 | 02101 | 589 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 7735 | 0.0213749 | 02101 | 2688532928 | 2048518990 | 264837.6 |
2101141002 | 02101 | 454 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 6365 | 0.0175890 | 02101 | 2212348040 | 1723411803 | 270763.8 |
2101141003 | 02101 | 204 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3005 | 0.0083040 | 02101 | 1044478533 | 1013354780 | 337222.9 |
2101141004 | 02101 | 436 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4535 | 0.0125320 | 02101 | 1576276255 | 1677745942 | 369955.0 |
2101141005 | 02101 | 360 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4116 | 0.0113742 | 02101 | 1430640146 | 1477430770 | 358948.2 |
2101141006 | 02101 | 436 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 7236 | 0.0199960 | 02101 | 2515090403 | 1677745942 | 231861.0 |
2101141007 | 02101 | 196 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2982 | 0.0082405 | 02101 | 1036484188 | 986798037 | 330918.2 |
2101141008 | 02101 | 180 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2989 | 0.0082598 | 02101 | 1038917249 | 932562413 | 311998.1 |
2101141009 | 02101 | 396 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 6367 | 0.0175946 | 02101 | 2213043200 | 1573926195 | 247200.6 |
2101151001 | 02101 | 229 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2879 | 0.0079558 | 02101 | 1000683426 | 1094179892 | 380055.5 |
2101151002 | 02101 | 278 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3675 | 0.0101555 | 02101 | 1277357274 | 1244481418 | 338634.4 |
2101151003 | 02101 | 240 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3768 | 0.0104125 | 02101 | 1309682233 | 1128793584 | 299573.7 |
2101151004 | 02101 | 453 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 8961 | 0.0247628 | 02101 | 3114666266 | 1720890974 | 192042.3 |
2101161001 | 02101 | 291 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2974 | 0.0082184 | 02101 | 1033703546 | 1282814945 | 431343.3 |
2101161002 | 02101 | 258 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3413 | 0.0094315 | 02101 | 1186291258 | 1184306320 | 346998.6 |
2101161003 | 02101 | 184 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 1805 | 0.0049879 | 02101 | 627382280 | 946268241 | 524248.3 |
2101161004 | 02101 | 254 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2437 | 0.0067344 | 02101 | 847052973 | 1172085741 | 480954.3 |
2101161005 | 02101 | 257 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 3265 | 0.0090225 | 02101 | 1134849387 | 1181257183 | 361793.9 |
2101171001 | 02101 | 114 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 2503 | 0.0069168 | 02101 | 869993267 | 688648840 | 275129.4 |
2101171002 | 02101 | 240 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4463 | 0.0123331 | 02101 | 1551250479 | 1128793584 | 252922.6 |
2101171003 | 02101 | 393 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4570 | 0.0126287 | 02101 | 1588441562 | 1566000922 | 342669.8 |
2101171004 | 02101 | 385 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5586 | 0.0154364 | 02101 | 1941583056 | 1544766728 | 276542.6 |
2101181001 | 02101 | 559 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5341 | 0.0147593 | 02101 | 1856425904 | 1978650108 | 370464.4 |
2101181002 | 02101 | 484 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 5876 | 0.0162377 | 02101 | 2042381317 | 1798192776 | 306023.3 |
2101181003 | 02101 | 348 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4235 | 0.0117030 | 02101 | 1472002191 | 1444553304 | 341098.8 |
2101181004 | 02101 | 115 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4475 | 0.0123662 | 02101 | 1555421442 | 692652930 | 154782.8 |
2101991999 | 02101 | 150 | 2017 | Antofagasta | 347580.2 | 2017 | 2101 | 361873 | 125779893517 | Urbano | 4121 | 0.0113880 | 02101 | 1432378048 | 826258506 | 200499.5 |
2102011001 | 02102 | 267 | 2017 | Mejillones | 369770.7 | 2017 | 2102 | 13467 | 4979702302 | Urbano | 5020 | 0.3727631 | 02102 | 1856249020 | 1211572297 | 241349.1 |
2102011002 | 02102 | 432 | 2017 | Mejillones | 369770.7 | 2017 | 2102 | 13467 | 4979702302 | Urbano | 7764 | 0.5765204 | 02102 | 2870899879 | 1667512519 | 214774.9 |
2102991999 | 02102 | 8 | 2017 | Mejillones | 369770.7 | 2017 | 2102 | 13467 | 4979702302 | Urbano | 170 | 0.0126234 | 02102 | 62861023 | 118052272 | 694425.1 |
2104011001 | 02104 | 109 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano | 2174 | 0.1632500 | 02104 | 818139039 | 668448159 | 307473.9 |
2104021001 | 02104 | 176 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano | 2812 | 0.2111587 | 02104 | 1058236880 | 918753800 | 326726.1 |
2104031001 | 02104 | 346 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano | 5947 | 0.4465721 | 02104 | 2238027997 | 1439036926 | 241977.0 |
2104991999 | 02104 | 7 | 2017 | Taltal | 376328.9 | 2017 | 2104 | 13317 | 5011572025 | Urbano | 190 | 0.0142675 | 02104 | 71502492 | 108038422 | 568623.3 |
2201011001 | 02201 | 354 | 2017 | Calama | 416281.1 | 2017 | 2201 | 165731 | 68990679686 | Urbano | 3387 | 0.0204367 | 02201 | 1409944018 | 1461038873 | 431366.7 |
Guardamos:
saveRDS(h_y_m_comuna_corr_01, "casen_censo_urbano_nivel_nacional_17_r02.rds")
tabla_con_clave <- readRDS("../censo_viviendas_con_clave_17.rds")
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA == 2)
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
kable_paper() %>%
scroll_box(width = "100%", height = "300px")
REGION | PROVINCIA | COMUNA | DC | AREA | ZC_LOC | ID_ZONA_LOC | NVIV | P01 | P02 | P03A | P03B | P03C | P04 | P05 | CANT_HOG | CANT_PER | REGION_15R | PROVINCIA_15R | COMUNA_15R | clave |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 1 | 3 | 1 | 5 | 3 | 5 | 1 | 4 | 1 | 1 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 2 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 3 | 1 | 1 | 5 | 3 | 5 | 2 | 3 | 1 | 4 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 4 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 5 | 3 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 6 | 3 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 7 | 3 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 8 | 3 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 9 | 3 | 1 | 5 | 3 | 5 | 1 | 4 | 1 | 4 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 10 | 1 | 1 | 5 | 3 | 4 | 1 | 4 | 1 | 1 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 11 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 12 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 13 | 1 | 1 | 5 | 3 | 4 | 1 | 4 | 1 | 3 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 14 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 15 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 16 | 1 | 1 | 5 | 3 | 5 | 1 | 3 | 1 | 3 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 17 | 1 | 1 | 5 | 3 | 5 | 2 | 4 | 1 | 8 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 18 | 3 | 1 | 5 | 3 | 5 | 1 | 1 | 1 | 1 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 19 | 3 | 1 | 5 | 3 | 5 | 1 | 3 | 1 | 1 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 20 | 1 | 1 | 5 | 3 | 5 | 2 | 1 | 1 | 2 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 21 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 22 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 23 | 3 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 24 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 25 | 1 | 1 | 5 | 3 | 5 | 1 | 4 | 1 | 2 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 26 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 27 | 3 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 28 | 3 | 1 | 5 | 3 | 5 | 1 | 4 | 1 | 5 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 29 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 30 | 3 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 31 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 32 | 3 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 33 | 1 | 1 | 5 | 3 | 5 | 3 | 4 | 1 | 4 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 34 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 35 | 3 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 36 | 1 | 1 | 5 | 3 | 5 | 3 | 2 | 1 | 9 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 37 | 3 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 38 | 1 | 1 | 5 | 3 | 5 | 99 | 4 | 1 | 1 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 39 | 1 | 1 | 5 | 3 | 5 | 1 | 4 | 1 | 2 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 40 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 41 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 42 | 3 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 43 | 3 | 1 | 5 | 3 | 5 | 2 | 1 | 1 | 5 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 44 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 6 | 13225 | 45 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012006 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 1 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 2 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 3 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 4 | 3 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 5 | 3 | 1 | 5 | 3 | 5 | 2 | 3 | 1 | 3 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 6 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 7 | 1 | 1 | 5 | 99 | 5 | 2 | 4 | 1 | 2 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 8 | 3 | 1 | 5 | 3 | 5 | 3 | 3 | 1 | 2 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 9 | 3 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 10 | 3 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 11 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 12 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 13 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 14 | 3 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 15 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 16 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 17 | 3 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 18 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 19 | 3 | 1 | 99 | 99 | 99 | 99 | 99 | 1 | 1 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 20 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 21 | 1 | 1 | 5 | 3 | 5 | 1 | 4 | 1 | 2 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 22 | 3 | 1 | 5 | 3 | 5 | 2 | 4 | 1 | 1 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 23 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 24 | 3 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 25 | 3 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 26 | 1 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 27 | 1 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 28 | 3 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 29 | 1 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 8 | 13910 | 30 | 1 | 1 | 5 | 1 | 4 | 2 | 4 | 1 | 1 | 15 | 152 | 15202 | 15202012008 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 1 | 3 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 2 | 3 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 3 | 3 | 1 | 5 | 3 | 5 | 2 | 3 | 1 | 4 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 4 | 3 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 5 | 3 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 6 | 3 | 3 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 7 | 3 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 8 | 3 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 9 | 3 | 1 | 5 | 3 | 5 | 1 | 4 | 1 | 1 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 10 | 3 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 11 | 3 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 12 | 3 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 13 | 3 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 14 | 3 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 15 | 3 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 16 | 3 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 17 | 3 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 18 | 3 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 19 | 3 | 1 | 99 | 99 | 99 | 99 | 99 | 1 | 1 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 20 | 3 | 1 | 5 | 3 | 5 | 3 | 99 | 1 | 1 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 21 | 3 | 1 | 5 | 99 | 5 | 1 | 4 | 1 | 2 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 22 | 3 | 2 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 23 | 3 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 24 | 3 | 1 | 5 | 3 | 5 | 1 | 2 | 1 | 2 | 15 | 152 | 15202 | 15202012012 |
15 | 152 | 15202 | 1 | 2 | 12 | 8394 | 25 | 3 | 4 | 98 | 98 | 98 | 98 | 98 | 0 | 0 | 15 | 152 | 15202 | 15202012012 |
Despleguemos los códigos de regiones de nuestra tabla:
regiones <- unique(tabla_con_clave$REGION)
regiones
## [1] 15 14 13 12 11 10 9 16 8 7 6 5 4 3 2 1
Hagamos un subset con la 2:
tabla_con_clave_r <- filter(tabla_con_clave, tabla_con_clave$REGION == 2)
tabla_con_clave_f <- tabla_con_clave_r[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE]
names(tabla_con_clave_f)[2] <- "Tipo de techo"
# Ahora filtramos por Tipo de techo = 1.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Tipo de techo` == 1)
# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Tipo de techo`
d <- tabla_con_clave_ff$COMUNA
cross_tab = xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona"
d$anio <- "2017"
Veamos los primeros 100 registros:
r3_100 <- d[c(1:100),]
kbl(r3_100) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
kable_paper() %>%
scroll_box(width = "100%", height = "300px")
zona | unlist.c. | unlist.d. | Freq | anio | |
---|---|---|---|---|---|
1 | 2101012001 | 1 | 2101 | 1 | 2017 |
2 | 2101012005 | 1 | 2101 | 1 | 2017 |
3 | 2101012012 | 1 | 2101 | 1 | 2017 |
4 | 2101012013 | 1 | 2101 | 10 | 2017 |
5 | 2101012901 | 1 | 2101 | 1 | 2017 |
6 | 2101102007 | 1 | 2101 | 18 | 2017 |
7 | 2101102014 | 1 | 2101 | 5 | 2017 |
8 | 2101102016 | 1 | 2101 | 1 | 2017 |
9 | 2101122014 | 1 | 2101 | 12 | 2017 |
10 | 2101122019 | 1 | 2101 | 2 | 2017 |
70 | 2102012001 | 1 | 2102 | 5 | 2017 |
71 | 2102022003 | 1 | 2102 | 20 | 2017 |
72 | 2102022006 | 1 | 2102 | 16 | 2017 |
73 | 2102022008 | 1 | 2102 | 4 | 2017 |
74 | 2102022901 | 1 | 2102 | 1 | 2017 |
134 | 2103012008 | 1 | 2103 | 18 | 2017 |
135 | 2103012901 | 1 | 2103 | 1 | 2017 |
136 | 2103032002 | 1 | 2103 | 29 | 2017 |
196 | 2104012008 | 1 | 2104 | 8 | 2017 |
197 | 2104012014 | 1 | 2104 | 10 | 2017 |
198 | 2104012901 | 1 | 2104 | 6 | 2017 |
199 | 2104022015 | 1 | 2104 | 8 | 2017 |
200 | 2104022022 | 1 | 2104 | 2 | 2017 |
201 | 2104032901 | 1 | 2104 | 1 | 2017 |
202 | 2104042020 | 1 | 2104 | 27 | 2017 |
203 | 2104072901 | 1 | 2104 | 1 | 2017 |
263 | 2201032006 | 1 | 2201 | 2 | 2017 |
264 | 2201032013 | 1 | 2201 | 2 | 2017 |
265 | 2201082002 | 1 | 2201 | 45 | 2017 |
266 | 2201082012 | 1 | 2201 | 6 | 2017 |
267 | 2201122002 | 1 | 2201 | 1 | 2017 |
268 | 2201132004 | 1 | 2201 | 30 | 2017 |
269 | 2201132010 | 1 | 2201 | 9 | 2017 |
270 | 2201132901 | 1 | 2201 | 1 | 2017 |
271 | 2201142002 | 1 | 2201 | 2 | 2017 |
272 | 2201152001 | 1 | 2201 | 3 | 2017 |
273 | 2201152003 | 1 | 2201 | 8 | 2017 |
274 | 2201152014 | 1 | 2201 | 8 | 2017 |
275 | 2201152015 | 1 | 2201 | 1 | 2017 |
335 | 2202012005 | 1 | 2202 | 20 | 2017 |
395 | 2203012013 | 1 | 2203 | 2 | 2017 |
396 | 2203012014 | 1 | 2203 | 147 | 2017 |
397 | 2203012901 | 1 | 2203 | 4 | 2017 |
398 | 2203022008 | 1 | 2203 | 3 | 2017 |
399 | 2203022017 | 1 | 2203 | 50 | 2017 |
400 | 2203032012 | 1 | 2203 | 11 | 2017 |
401 | 2203032015 | 1 | 2203 | 19 | 2017 |
461 | 2301032003 | 1 | 2301 | 1 | 2017 |
462 | 2301032005 | 1 | 2301 | 1 | 2017 |
463 | 2301032006 | 1 | 2301 | 2 | 2017 |
464 | 2301032018 | 1 | 2301 | 2 | 2017 |
465 | 2301032020 | 1 | 2301 | 1 | 2017 |
466 | 2301032023 | 1 | 2301 | 2 | 2017 |
467 | 2301052004 | 1 | 2301 | 9 | 2017 |
468 | 2301052007 | 1 | 2301 | 1 | 2017 |
469 | 2301052008 | 1 | 2301 | 4 | 2017 |
470 | 2301052901 | 1 | 2301 | 3 | 2017 |
530 | 2302012011 | 1 | 2302 | 9 | 2017 |
531 | 2302012901 | 1 | 2302 | 3 | 2017 |
NA | NA | NA | NA | NA | NA |
NA.1 | NA | NA | NA | NA | NA |
NA.2 | NA | NA | NA | NA | NA |
NA.3 | NA | NA | NA | NA | NA |
NA.4 | NA | NA | NA | NA | NA |
NA.5 | NA | NA | NA | NA | NA |
NA.6 | NA | NA | NA | NA | NA |
NA.7 | NA | NA | NA | NA | NA |
NA.8 | NA | NA | NA | NA | NA |
NA.9 | NA | NA | NA | NA | NA |
NA.10 | NA | NA | NA | NA | NA |
NA.11 | NA | NA | NA | NA | NA |
NA.12 | NA | NA | NA | NA | NA |
NA.13 | NA | NA | NA | NA | NA |
NA.14 | NA | NA | NA | NA | NA |
NA.15 | NA | NA | NA | NA | NA |
NA.16 | NA | NA | NA | NA | NA |
NA.17 | NA | NA | NA | NA | NA |
NA.18 | NA | NA | NA | NA | NA |
NA.19 | NA | NA | NA | NA | NA |
NA.20 | NA | NA | NA | NA | NA |
NA.21 | NA | NA | NA | NA | NA |
NA.22 | NA | NA | NA | NA | NA |
NA.23 | NA | NA | NA | NA | NA |
NA.24 | NA | NA | NA | NA | NA |
NA.25 | NA | NA | NA | NA | NA |
NA.26 | NA | NA | NA | NA | NA |
NA.27 | NA | NA | NA | NA | NA |
NA.28 | NA | NA | NA | NA | NA |
NA.29 | NA | NA | NA | NA | NA |
NA.30 | NA | NA | NA | NA | NA |
NA.31 | NA | NA | NA | NA | NA |
NA.32 | NA | NA | NA | NA | NA |
NA.33 | NA | NA | NA | NA | NA |
NA.34 | NA | NA | NA | NA | NA |
NA.35 | NA | NA | NA | NA | NA |
NA.36 | NA | NA | NA | NA | NA |
NA.37 | NA | NA | NA | NA | NA |
NA.38 | NA | NA | NA | NA | NA |
NA.39 | NA | NA | NA | NA | NA |
NA.40 | NA | NA | NA | NA | NA |
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 | 2101012001 | 1 | 2017 | 02101 |
2 | 2101012005 | 1 | 2017 | 02101 |
3 | 2101012012 | 1 | 2017 | 02101 |
4 | 2101012013 | 10 | 2017 | 02101 |
5 | 2101012901 | 1 | 2017 | 02101 |
6 | 2101102007 | 18 | 2017 | 02101 |
7 | 2101102014 | 5 | 2017 | 02101 |
8 | 2101102016 | 1 | 2017 | 02101 |
9 | 2101122014 | 12 | 2017 | 02101 |
10 | 2101122019 | 2 | 2017 | 02101 |
70 | 2102012001 | 5 | 2017 | 02102 |
71 | 2102022003 | 20 | 2017 | 02102 |
72 | 2102022006 | 16 | 2017 | 02102 |
73 | 2102022008 | 4 | 2017 | 02102 |
74 | 2102022901 | 1 | 2017 | 02102 |
134 | 2103012008 | 18 | 2017 | 02103 |
135 | 2103012901 | 1 | 2017 | 02103 |
136 | 2103032002 | 29 | 2017 | 02103 |
196 | 2104012008 | 8 | 2017 | 02104 |
197 | 2104012014 | 10 | 2017 | 02104 |
198 | 2104012901 | 6 | 2017 | 02104 |
199 | 2104022015 | 8 | 2017 | 02104 |
200 | 2104022022 | 2 | 2017 | 02104 |
201 | 2104032901 | 1 | 2017 | 02104 |
202 | 2104042020 | 27 | 2017 | 02104 |
203 | 2104072901 | 1 | 2017 | 02104 |
263 | 2201032006 | 2 | 2017 | 02201 |
264 | 2201032013 | 2 | 2017 | 02201 |
265 | 2201082002 | 45 | 2017 | 02201 |
266 | 2201082012 | 6 | 2017 | 02201 |
267 | 2201122002 | 1 | 2017 | 02201 |
268 | 2201132004 | 30 | 2017 | 02201 |
269 | 2201132010 | 9 | 2017 | 02201 |
270 | 2201132901 | 1 | 2017 | 02201 |
271 | 2201142002 | 2 | 2017 | 02201 |
272 | 2201152001 | 3 | 2017 | 02201 |
273 | 2201152003 | 8 | 2017 | 02201 |
274 | 2201152014 | 8 | 2017 | 02201 |
275 | 2201152015 | 1 | 2017 | 02201 |
335 | 2202012005 | 20 | 2017 | 02202 |
395 | 2203012013 | 2 | 2017 | 02203 |
396 | 2203012014 | 147 | 2017 | 02203 |
397 | 2203012901 | 4 | 2017 | 02203 |
398 | 2203022008 | 3 | 2017 | 02203 |
399 | 2203022017 | 50 | 2017 | 02203 |
400 | 2203032012 | 11 | 2017 | 02203 |
401 | 2203032015 | 19 | 2017 | 02203 |
461 | 2301032003 | 1 | 2017 | 02301 |
462 | 2301032005 | 1 | 2017 | 02301 |
463 | 2301032006 | 2 | 2017 | 02301 |
464 | 2301032018 | 2 | 2017 | 02301 |
465 | 2301032020 | 1 | 2017 | 02301 |
466 | 2301032023 | 2 | 2017 | 02301 |
467 | 2301052004 | 9 | 2017 | 02301 |
468 | 2301052007 | 1 | 2017 | 02301 |
469 | 2301052008 | 4 | 2017 | 02301 |
470 | 2301052901 | 3 | 2017 | 02301 |
530 | 2302012011 | 9 | 2017 | 02302 |
531 | 2302012901 | 3 | 2017 | 02302 |
NA | NA | NA | NA | NA |
NA.1 | NA | NA | NA | NA |
NA.2 | NA | NA | NA | NA |
NA.3 | NA | NA | NA | NA |
NA.4 | NA | NA | NA | NA |
NA.5 | NA | NA | NA | NA |
NA.6 | NA | NA | NA | NA |
NA.7 | NA | NA | NA | NA |
NA.8 | NA | NA | NA | NA |
NA.9 | NA | NA | NA | NA |
NA.10 | NA | NA | NA | NA |
NA.11 | NA | NA | NA | NA |
NA.12 | NA | NA | NA | NA |
NA.13 | NA | NA | NA | NA |
NA.14 | NA | NA | NA | NA |
NA.15 | NA | NA | NA | NA |
NA.16 | NA | NA | NA | NA |
NA.17 | NA | NA | NA | NA |
NA.18 | NA | NA | NA | NA |
NA.19 | NA | NA | NA | NA |
NA.20 | NA | NA | NA | NA |
NA.21 | NA | NA | NA | NA |
NA.22 | NA | NA | NA | NA |
NA.23 | NA | NA | NA | NA |
NA.24 | NA | NA | NA | NA |
NA.25 | NA | NA | NA | NA |
NA.26 | NA | NA | NA | NA |
NA.27 | NA | NA | NA | NA |
NA.28 | NA | NA | NA | NA |
NA.29 | NA | NA | NA | NA |
NA.30 | NA | NA | NA | NA |
NA.31 | NA | NA | NA | NA |
NA.32 | NA | NA | NA | NA |
NA.33 | NA | NA | NA | NA |
NA.34 | NA | NA | NA | NA |
NA.35 | NA | NA | NA | NA |
NA.36 | NA | NA | NA | NA |
NA.37 | NA | NA | NA | NA |
NA.38 | NA | NA | NA | NA |
NA.39 | NA | NA | NA | NA |
NA.40 | NA | NA | NA | NA |
Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí
h_y_m_2017_censo <- readRDS("ingresos_expandidos_17_rural_nacional.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 | tipo |
---|---|---|---|---|---|---|---|
01101 | Iquique | 289375.3 | 2017 | 1101 | 191468 | 55406102543 | Rural |
01401 | Pozo Almonte | 263069.6 | 2017 | 1401 | 15711 | 4133086727 | Rural |
01402 | Camiña | 262850.3 | 2017 | 1402 | 1250 | 328562901 | Rural |
01404 | Huara | 253968.5 | 2017 | 1404 | 2730 | 693334131 | Rural |
01405 | Pica | 290496.7 | 2017 | 1405 | 9296 | 2700457509 | Rural |
02103 | Sierra Gorda | 403458.5 | 2017 | 2103 | 10186 | 4109628188 | Rural |
02104 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural |
02201 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
02203 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural |
02301 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural |
03101 | Copiapó | 308502.8 | 2017 | 3101 | 153937 | 47489990283 | Rural |
03103 | Tierra Amarilla | 312457.3 | 2017 | 3103 | 14019 | 4380339153 | Rural |
03202 | Diego de Almagro | 374511.6 | 2017 | 3202 | 13925 | 5215073473 | Rural |
03301 | Vallenar | 254290.6 | 2017 | 3301 | 51917 | 13202005308 | Rural |
03302 | Alto del Carmen | 227130.4 | 2017 | 3302 | 5299 | 1203563833 | Rural |
03303 | Freirina | 214803.3 | 2017 | 3303 | 7041 | 1512429891 | Rural |
03304 | Huasco | 227560.7 | 2017 | 3304 | 10149 | 2309513927 | Rural |
04101 | La Serena | 233184.2 | 2017 | 4101 | 221054 | 51546306303 | Rural |
04102 | Coquimbo | 231810.7 | 2017 | 4102 | 227730 | 52790242466 | Rural |
04103 | Andacollo | 242908.2 | 2017 | 4103 | 11044 | 2682678345 | Rural |
04104 | La Higuera | 250699.6 | 2017 | 4104 | 4241 | 1063217069 | Rural |
04105 | Paiguano | 205942.1 | 2017 | 4105 | 4497 | 926121774 | Rural |
04106 | Vicuña | 176130.6 | 2017 | 4106 | 27771 | 4891322768 | Rural |
04201 | Illapel | 191976.8 | 2017 | 4201 | 30848 | 5922099530 | Rural |
04202 | Canela | 171370.3 | 2017 | 4202 | 9093 | 1558270441 | Rural |
04203 | Los Vilos | 173238.5 | 2017 | 4203 | 21382 | 3704185607 | Rural |
04204 | Salamanca | 223234.2 | 2017 | 4204 | 29347 | 6551254640 | Rural |
04301 | Ovalle | 241393.7 | 2017 | 4301 | 111272 | 26860360045 | Rural |
04302 | Combarbalá | 179139.6 | 2017 | 4302 | 13322 | 2386498044 | Rural |
04303 | Monte Patria | 201205.8 | 2017 | 4303 | 30751 | 6187280931 | Rural |
04304 | Punitaqui | 171931.7 | 2017 | 4304 | 10956 | 1883683880 | Rural |
04305 | Río Hurtado | 182027.2 | 2017 | 4305 | 4278 | 778712384 | Rural |
05101 | Valparaíso | 331716.1 | 2017 | 5101 | 296655 | 98405237576 | Rural |
05102 | Casablanca | 268917.1 | 2017 | 5102 | 26867 | 7224996933 | Rural |
05105 | Puchuncaví | 279614.4 | 2017 | 5105 | 18546 | 5185728335 | Rural |
05107 | Quintero | 334628.2 | 2017 | 5107 | 31923 | 10682335196 | Rural |
05301 | Los Andes | 324402.1 | 2017 | 5301 | 66708 | 21640215030 | Rural |
05302 | Calle Larga | 242743.8 | 2017 | 5302 | 14832 | 3600375502 | Rural |
05303 | Rinconada | 326532.5 | 2017 | 5303 | 10207 | 3332917471 | Rural |
05304 | San Esteban | 223168.6 | 2017 | 5304 | 18855 | 4207844130 | Rural |
05401 | La Ligua | 181468.0 | 2017 | 5401 | 35390 | 6422154059 | Rural |
05402 | Cabildo | 231277.8 | 2017 | 5402 | 19388 | 4484014285 | Rural |
05404 | Petorca | 298208.9 | 2017 | 5404 | 9826 | 2930200178 | Rural |
05405 | Zapallar | 292882.3 | 2017 | 5405 | 7339 | 2149463129 | Rural |
05501 | Quillota | 220926.8 | 2017 | 5501 | 90517 | 19997628209 | Rural |
05502 | Calera | 226906.2 | 2017 | 5502 | 50554 | 11471016698 | Rural |
05503 | Hijuelas | 253739.9 | 2017 | 5503 | 17988 | 4564273201 | Rural |
05504 | La Cruz | 291124.1 | 2017 | 5504 | 22098 | 6433259569 | Rural |
05506 | Nogales | 264475.3 | 2017 | 5506 | 22120 | 5850194593 | Rural |
05601 | San Antonio | 266331.2 | 2017 | 5601 | 91350 | 24329353815 | Rural |
Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.
comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)
r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
kable_paper() %>%
scroll_box(width = "100%", height = "300px")
código | zona | Freq | anio | comuna.x | promedio_i | año | comuna.y | personas | Ingresos_expandidos | tipo | |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 02101 | 2101012001 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA |
2 | 02101 | 2101012005 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA |
3 | 02101 | 2101012012 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA |
4 | 02101 | 2101012013 | 10 | 2017 | NA | NA | NA | NA | NA | NA | NA |
5 | 02101 | 2101012901 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA |
6 | 02101 | 2101102007 | 18 | 2017 | NA | NA | NA | NA | NA | NA | NA |
7 | 02101 | 2101102014 | 5 | 2017 | NA | NA | NA | NA | NA | NA | NA |
8 | 02101 | 2101102016 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA |
9 | 02101 | 2101122014 | 12 | 2017 | NA | NA | NA | NA | NA | NA | NA |
10 | 02101 | 2101122019 | 2 | 2017 | NA | NA | NA | NA | NA | NA | NA |
11 | 02102 | 2102012001 | 5 | 2017 | NA | NA | NA | NA | NA | NA | NA |
12 | 02102 | 2102022003 | 20 | 2017 | NA | NA | NA | NA | NA | NA | NA |
13 | 02102 | 2102022006 | 16 | 2017 | NA | NA | NA | NA | NA | NA | NA |
14 | 02102 | 2102022008 | 4 | 2017 | NA | NA | NA | NA | NA | NA | NA |
15 | 02102 | 2102022901 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA |
16 | 02103 | 2103012901 | 1 | 2017 | Sierra Gorda | 403458.5 | 2017 | 2103 | 10186 | 4109628188 | Rural |
17 | 02103 | 2103032002 | 29 | 2017 | Sierra Gorda | 403458.5 | 2017 | 2103 | 10186 | 4109628188 | Rural |
18 | 02103 | 2103012008 | 18 | 2017 | Sierra Gorda | 403458.5 | 2017 | 2103 | 10186 | 4109628188 | Rural |
19 | 02104 | 2104012901 | 6 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural |
20 | 02104 | 2104022015 | 8 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural |
21 | 02104 | 2104032901 | 1 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural |
22 | 02104 | 2104042020 | 27 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural |
23 | 02104 | 2104072901 | 1 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural |
24 | 02104 | 2104022022 | 2 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural |
25 | 02104 | 2104012008 | 8 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural |
26 | 02104 | 2104012014 | 10 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural |
27 | 02201 | 2201082012 | 6 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
28 | 02201 | 2201122002 | 1 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
29 | 02201 | 2201032013 | 2 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
30 | 02201 | 2201082002 | 45 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
31 | 02201 | 2201132901 | 1 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
32 | 02201 | 2201142002 | 2 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
33 | 02201 | 2201152001 | 3 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
34 | 02201 | 2201152003 | 8 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
35 | 02201 | 2201152014 | 8 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
36 | 02201 | 2201152015 | 1 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
37 | 02201 | 2201032006 | 2 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
38 | 02201 | 2201132004 | 30 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
39 | 02201 | 2201132010 | 9 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
40 | 02202 | 2202012005 | 20 | 2017 | NA | NA | NA | NA | NA | NA | NA |
41 | 02203 | 2203012901 | 4 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural |
42 | 02203 | 2203022008 | 3 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural |
43 | 02203 | 2203012013 | 2 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural |
44 | 02203 | 2203012014 | 147 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural |
45 | 02203 | 2203032015 | 19 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural |
46 | 02203 | 2203022017 | 50 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural |
47 | 02203 | 2203032012 | 11 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural |
48 | 02301 | 2301032003 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural |
49 | 02301 | 2301032005 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural |
50 | 02301 | 2301032020 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural |
51 | 02301 | 2301032023 | 2 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural |
52 | 02301 | 2301032006 | 2 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural |
53 | 02301 | 2301032018 | 2 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural |
54 | 02301 | 2301052008 | 4 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural |
55 | 02301 | 2301052901 | 3 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural |
56 | 02301 | 2301052004 | 9 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural |
57 | 02301 | 2301052007 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural |
58 | 02302 | 2302012901 | 3 | 2017 | NA | NA | NA | NA | NA | NA | NA |
59 | 02302 | 2302012011 | 9 | 2017 | NA | NA | NA | NA | NA | NA | NA |
NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.4 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.5 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.6 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.7 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.8 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.9 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.10 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.11 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.12 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.13 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.14 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.15 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.16 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.17 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.18 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.19 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.20 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.21 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.22 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.23 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.24 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.25 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.26 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.27 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.28 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.29 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.30 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.31 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.32 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.33 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.34 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.35 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.36 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.37 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.38 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.39 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.40 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
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 |
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 | tipo | |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 02101 | 2101012001 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA |
2 | 02101 | 2101012005 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA |
3 | 02101 | 2101012012 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA |
4 | 02101 | 2101012013 | 10 | 2017 | NA | NA | NA | NA | NA | NA | NA |
5 | 02101 | 2101012901 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA |
6 | 02101 | 2101102007 | 18 | 2017 | NA | NA | NA | NA | NA | NA | NA |
7 | 02101 | 2101102014 | 5 | 2017 | NA | NA | NA | NA | NA | NA | NA |
8 | 02101 | 2101102016 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA |
9 | 02101 | 2101122014 | 12 | 2017 | NA | NA | NA | NA | NA | NA | NA |
10 | 02101 | 2101122019 | 2 | 2017 | NA | NA | NA | NA | NA | NA | NA |
11 | 02102 | 2102012001 | 5 | 2017 | NA | NA | NA | NA | NA | NA | NA |
12 | 02102 | 2102022003 | 20 | 2017 | NA | NA | NA | NA | NA | NA | NA |
13 | 02102 | 2102022006 | 16 | 2017 | NA | NA | NA | NA | NA | NA | NA |
14 | 02102 | 2102022008 | 4 | 2017 | NA | NA | NA | NA | NA | NA | NA |
15 | 02102 | 2102022901 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA |
16 | 02103 | 2103012901 | 1 | 2017 | Sierra Gorda | 403458.5 | 2017 | 2103 | 10186 | 4109628188 | Rural |
17 | 02103 | 2103032002 | 29 | 2017 | Sierra Gorda | 403458.5 | 2017 | 2103 | 10186 | 4109628188 | Rural |
18 | 02103 | 2103012008 | 18 | 2017 | Sierra Gorda | 403458.5 | 2017 | 2103 | 10186 | 4109628188 | Rural |
19 | 02104 | 2104012901 | 6 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural |
20 | 02104 | 2104022015 | 8 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural |
21 | 02104 | 2104032901 | 1 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural |
22 | 02104 | 2104042020 | 27 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural |
23 | 02104 | 2104072901 | 1 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural |
24 | 02104 | 2104022022 | 2 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural |
25 | 02104 | 2104012008 | 8 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural |
26 | 02104 | 2104012014 | 10 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural |
27 | 02201 | 2201082012 | 6 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
28 | 02201 | 2201122002 | 1 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
29 | 02201 | 2201032013 | 2 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
30 | 02201 | 2201082002 | 45 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
31 | 02201 | 2201132901 | 1 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
32 | 02201 | 2201142002 | 2 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
33 | 02201 | 2201152001 | 3 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
34 | 02201 | 2201152003 | 8 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
35 | 02201 | 2201152014 | 8 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
36 | 02201 | 2201152015 | 1 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
37 | 02201 | 2201032006 | 2 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
38 | 02201 | 2201132004 | 30 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
39 | 02201 | 2201132010 | 9 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural |
40 | 02202 | 2202012005 | 20 | 2017 | NA | NA | NA | NA | NA | NA | NA |
41 | 02203 | 2203012901 | 4 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural |
42 | 02203 | 2203022008 | 3 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural |
43 | 02203 | 2203012013 | 2 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural |
44 | 02203 | 2203012014 | 147 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural |
45 | 02203 | 2203032015 | 19 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural |
46 | 02203 | 2203022017 | 50 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural |
47 | 02203 | 2203032012 | 11 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural |
48 | 02301 | 2301032003 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural |
49 | 02301 | 2301032005 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural |
50 | 02301 | 2301032020 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural |
51 | 02301 | 2301032023 | 2 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural |
52 | 02301 | 2301032006 | 2 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural |
53 | 02301 | 2301032018 | 2 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural |
54 | 02301 | 2301052008 | 4 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural |
55 | 02301 | 2301052901 | 3 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural |
56 | 02301 | 2301052004 | 9 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural |
57 | 02301 | 2301052007 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural |
58 | 02302 | 2302012901 | 3 | 2017 | NA | NA | NA | NA | NA | NA | NA |
59 | 02302 | 2302012011 | 9 | 2017 | NA | NA | NA | NA | NA | NA | NA |
NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.4 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.5 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.6 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.7 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.8 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.9 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.10 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.11 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.12 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.13 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.14 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.15 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.16 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.17 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.18 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.19 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.20 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.21 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.22 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.23 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.24 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.25 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.26 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.27 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.28 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.29 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.30 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.31 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.32 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.33 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.34 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.35 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.36 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.37 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.38 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.39 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.40 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
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 | tipo | Freq.y | p_poblacional | código.y |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2101012001 | 02101 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 6 | 0.0000166 | 02101 |
2101012005 | 02101 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 59 | 0.0001630 | 02101 |
2101012012 | 02101 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 53 | 0.0001465 | 02101 |
2101012013 | 02101 | 10 | 2017 | NA | NA | NA | NA | NA | NA | NA | 121 | 0.0003344 | 02101 |
2101012901 | 02101 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 16 | 0.0000442 | 02101 |
2101102007 | 02101 | 18 | 2017 | NA | NA | NA | NA | NA | NA | NA | 401 | 0.0011081 | 02101 |
2101102014 | 02101 | 5 | 2017 | NA | NA | NA | NA | NA | NA | NA | 78 | 0.0002155 | 02101 |
2101102016 | 02101 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 9 | 0.0000249 | 02101 |
2101122014 | 02101 | 12 | 2017 | NA | NA | NA | NA | NA | NA | NA | 268 | 0.0007406 | 02101 |
2101122019 | 02101 | 2 | 2017 | NA | NA | NA | NA | NA | NA | NA | 1438 | 0.0039738 | 02101 |
2102012001 | 02102 | 5 | 2017 | NA | NA | NA | NA | NA | NA | NA | 31 | 0.0023019 | 02102 |
2102022003 | 02102 | 20 | 2017 | NA | NA | NA | NA | NA | NA | NA | 293 | 0.0217569 | 02102 |
2102022006 | 02102 | 16 | 2017 | NA | NA | NA | NA | NA | NA | NA | 127 | 0.0094305 | 02102 |
2102022008 | 02102 | 4 | 2017 | NA | NA | NA | NA | NA | NA | NA | 31 | 0.0023019 | 02102 |
2102022901 | 02102 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 12 | 0.0008911 | 02102 |
2103012008 | 02103 | 18 | 2017 | Sierra Gorda | 403458.5 | 2017 | 2103 | 10186 | 4109628188 | Rural | 2684 | 0.2634989 | 02103 |
2103012901 | 02103 | 1 | 2017 | Sierra Gorda | 403458.5 | 2017 | 2103 | 10186 | 4109628188 | Rural | 8 | 0.0007854 | 02103 |
2103032002 | 02103 | 29 | 2017 | Sierra Gorda | 403458.5 | 2017 | 2103 | 10186 | 4109628188 | Rural | 942 | 0.0924799 | 02103 |
2104012008 | 02104 | 8 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 59 | 0.0044304 | 02104 |
2104012014 | 02104 | 10 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 19 | 0.0014267 | 02104 |
2104012901 | 02104 | 6 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 26 | 0.0019524 | 02104 |
2104022015 | 02104 | 8 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 59 | 0.0044304 | 02104 |
2104022022 | 02104 | 2 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 57 | 0.0042802 | 02104 |
2104032901 | 02104 | 1 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 11 | 0.0008260 | 02104 |
2104042020 | 02104 | 27 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 729 | 0.0547421 | 02104 |
2104072901 | 02104 | 1 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 56 | 0.0042052 | 02104 |
2201032006 | 02201 | 2 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 36 | 0.0002172 | 02201 |
2201032013 | 02201 | 2 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 13 | 0.0000784 | 02201 |
2201082002 | 02201 | 45 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 1364 | 0.0082302 | 02201 |
2201082012 | 02201 | 6 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 227 | 0.0013697 | 02201 |
2201122002 | 02201 | 1 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 151 | 0.0009111 | 02201 |
2201132004 | 02201 | 30 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 542 | 0.0032704 | 02201 |
2201132010 | 02201 | 9 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 108 | 0.0006517 | 02201 |
2201132901 | 02201 | 1 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 42 | 0.0002534 | 02201 |
2201142002 | 02201 | 2 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 122 | 0.0007361 | 02201 |
2201152001 | 02201 | 3 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 29 | 0.0001750 | 02201 |
2201152003 | 02201 | 8 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 119 | 0.0007180 | 02201 |
2201152014 | 02201 | 8 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 60 | 0.0003620 | 02201 |
2201152015 | 02201 | 1 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 39 | 0.0002353 | 02201 |
2202012005 | 02202 | 20 | 2017 | NA | NA | NA | NA | NA | NA | NA | 240 | 0.7476636 | 02202 |
2203012013 | 02203 | 2 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 27 | 0.0024554 | 02203 |
2203012014 | 02203 | 147 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 2621 | 0.2383594 | 02203 |
2203012901 | 02203 | 4 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 40 | 0.0036377 | 02203 |
2203022008 | 02203 | 3 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 83 | 0.0075482 | 02203 |
2203022017 | 02203 | 50 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 742 | 0.0674791 | 02203 |
2203032012 | 02203 | 11 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 1475 | 0.1341397 | 02203 |
2203032015 | 02203 | 19 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 408 | 0.0371044 | 02203 |
2301032003 | 02301 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 23 | 0.0009132 | 02301 |
2301032005 | 02301 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 37 | 0.0014691 | 02301 |
2301032006 | 02301 | 2 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 118 | 0.0046851 | 02301 |
2301032018 | 02301 | 2 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 30 | 0.0011911 | 02301 |
2301032020 | 02301 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 13 | 0.0005162 | 02301 |
2301032023 | 02301 | 2 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 50 | 0.0019852 | 02301 |
2301052004 | 02301 | 9 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 71 | 0.0028190 | 02301 |
2301052007 | 02301 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 15 | 0.0005956 | 02301 |
2301052008 | 02301 | 4 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 23 | 0.0009132 | 02301 |
2301052901 | 02301 | 3 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 93 | 0.0036925 | 02301 |
2302012011 | 02302 | 9 | 2017 | NA | NA | NA | NA | NA | NA | NA | 141 | 0.0218368 | 02302 |
2302012901 | 02302 | 3 | 2017 | NA | NA | NA | NA | NA | NA | NA | 57 | 0.0088276 | 02302 |
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 | tipo | Freq.y | p_poblacional | código.y | multi_pob |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2101012001 | 02101 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 6 | 0.0000166 | 02101 | NA |
2101012005 | 02101 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 59 | 0.0001630 | 02101 | NA |
2101012012 | 02101 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 53 | 0.0001465 | 02101 | NA |
2101012013 | 02101 | 10 | 2017 | NA | NA | NA | NA | NA | NA | NA | 121 | 0.0003344 | 02101 | NA |
2101012901 | 02101 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 16 | 0.0000442 | 02101 | NA |
2101102007 | 02101 | 18 | 2017 | NA | NA | NA | NA | NA | NA | NA | 401 | 0.0011081 | 02101 | NA |
2101102014 | 02101 | 5 | 2017 | NA | NA | NA | NA | NA | NA | NA | 78 | 0.0002155 | 02101 | NA |
2101102016 | 02101 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 9 | 0.0000249 | 02101 | NA |
2101122014 | 02101 | 12 | 2017 | NA | NA | NA | NA | NA | NA | NA | 268 | 0.0007406 | 02101 | NA |
2101122019 | 02101 | 2 | 2017 | NA | NA | NA | NA | NA | NA | NA | 1438 | 0.0039738 | 02101 | NA |
2102012001 | 02102 | 5 | 2017 | NA | NA | NA | NA | NA | NA | NA | 31 | 0.0023019 | 02102 | NA |
2102022003 | 02102 | 20 | 2017 | NA | NA | NA | NA | NA | NA | NA | 293 | 0.0217569 | 02102 | NA |
2102022006 | 02102 | 16 | 2017 | NA | NA | NA | NA | NA | NA | NA | 127 | 0.0094305 | 02102 | NA |
2102022008 | 02102 | 4 | 2017 | NA | NA | NA | NA | NA | NA | NA | 31 | 0.0023019 | 02102 | NA |
2102022901 | 02102 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 12 | 0.0008911 | 02102 | NA |
2103012008 | 02103 | 18 | 2017 | Sierra Gorda | 403458.5 | 2017 | 2103 | 10186 | 4109628188 | Rural | 2684 | 0.2634989 | 02103 | 1082882590 |
2103012901 | 02103 | 1 | 2017 | Sierra Gorda | 403458.5 | 2017 | 2103 | 10186 | 4109628188 | Rural | 8 | 0.0007854 | 02103 | 3227668 |
2103032002 | 02103 | 29 | 2017 | Sierra Gorda | 403458.5 | 2017 | 2103 | 10186 | 4109628188 | Rural | 942 | 0.0924799 | 02103 | 380057898 |
2104012008 | 02104 | 8 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 59 | 0.0044304 | 02104 | 20384144 |
2104012014 | 02104 | 10 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 19 | 0.0014267 | 02104 | 6564385 |
2104012901 | 02104 | 6 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 26 | 0.0019524 | 02104 | 8982843 |
2104022015 | 02104 | 8 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 59 | 0.0044304 | 02104 | 20384144 |
2104022022 | 02104 | 2 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 57 | 0.0042802 | 02104 | 19693156 |
2104032901 | 02104 | 1 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 11 | 0.0008260 | 02104 | 3800434 |
2104042020 | 02104 | 27 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 729 | 0.0547421 | 02104 | 251865098 |
2104072901 | 02104 | 1 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 56 | 0.0042052 | 02104 | 19347662 |
2201032006 | 02201 | 2 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 36 | 0.0002172 | 02201 | 11160901 |
2201032013 | 02201 | 2 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 13 | 0.0000784 | 02201 | 4030325 |
2201082002 | 02201 | 45 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 1364 | 0.0082302 | 02201 | 422874126 |
2201082012 | 02201 | 6 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 227 | 0.0013697 | 02201 | 70375679 |
2201122002 | 02201 | 1 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 151 | 0.0009111 | 02201 | 46813778 |
2201132004 | 02201 | 30 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 542 | 0.0032704 | 02201 | 168033560 |
2201132010 | 02201 | 9 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 108 | 0.0006517 | 02201 | 33482702 |
2201132901 | 02201 | 1 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 42 | 0.0002534 | 02201 | 13021051 |
2201142002 | 02201 | 2 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 122 | 0.0007361 | 02201 | 37823052 |
2201152001 | 02201 | 3 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 29 | 0.0001750 | 02201 | 8990726 |
2201152003 | 02201 | 8 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 119 | 0.0007180 | 02201 | 36892977 |
2201152014 | 02201 | 8 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 60 | 0.0003620 | 02201 | 18601501 |
2201152015 | 02201 | 1 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 39 | 0.0002353 | 02201 | 12090976 |
2202012005 | 02202 | 20 | 2017 | NA | NA | NA | NA | NA | NA | NA | 240 | 0.7476636 | 02202 | NA |
2203012013 | 02203 | 2 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 27 | 0.0024554 | 02203 | 9615995 |
2203012014 | 02203 | 147 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 2621 | 0.2383594 | 02203 | 933463770 |
2203012901 | 02203 | 4 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 40 | 0.0036377 | 02203 | 14245918 |
2203022008 | 02203 | 3 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 83 | 0.0075482 | 02203 | 29560280 |
2203022017 | 02203 | 50 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 742 | 0.0674791 | 02203 | 264261777 |
2203032012 | 02203 | 11 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 1475 | 0.1341397 | 02203 | 525318222 |
2203032015 | 02203 | 19 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 408 | 0.0371044 | 02203 | 145308363 |
2301032003 | 02301 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 23 | 0.0009132 | 02301 | 4145016 |
2301032005 | 02301 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 37 | 0.0014691 | 02301 | 6668068 |
2301032006 | 02301 | 2 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 118 | 0.0046851 | 02301 | 21265732 |
2301032018 | 02301 | 2 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 30 | 0.0011911 | 02301 | 5406542 |
2301032020 | 02301 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 13 | 0.0005162 | 02301 | 2342835 |
2301032023 | 02301 | 2 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 50 | 0.0019852 | 02301 | 9010903 |
2301052004 | 02301 | 9 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 71 | 0.0028190 | 02301 | 12795483 |
2301052007 | 02301 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 15 | 0.0005956 | 02301 | 2703271 |
2301052008 | 02301 | 4 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 23 | 0.0009132 | 02301 | 4145016 |
2301052901 | 02301 | 3 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 93 | 0.0036925 | 02301 | 16760280 |
2302012011 | 02302 | 9 | 2017 | NA | NA | NA | NA | NA | NA | NA | 141 | 0.0218368 | 02302 | NA |
2302012901 | 02302 | 3 | 2017 | NA | NA | NA | NA | NA | NA | NA | 57 | 0.0088276 | 02302 | NA |
Aplicaremos un análisis de regresión donde:
\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]
\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]
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)
Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.
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
## -111631952 -55937792 -36457073 -23657073 926683414
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 32620989 30018819 1.087 0.284
## Freq.x 6865455 1107886 6.197 2.76e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 172400000 on 39 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.4961, Adjusted R-squared: 0.4832
## F-statistic: 38.4 on 1 and 39 DF, p-value: 2.759e-07
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.
\[ \hat Y = \beta_0 + \beta_1 X^2 \]
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)"
modelos1 <- cbind(modelo,dato,sintaxis)
modelos1
## modelo dato
## [1,] "cuadrático" "0.483214321192485"
## sintaxis
## [1,] "linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)"
\[ \hat Y = \beta_0 + \beta_1 X^3 \]
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)"
modelos2 <- cbind(modelo,dato,sintaxis)
modelos2
## modelo dato
## [1,] "cúbico" "0.483214321192485"
## sintaxis
## [1,] "linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)"
\[ \hat Y = \beta_0 + \beta_1 ln X \]
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)"
modelos3 <- cbind(modelo,dato,sintaxis)
modelos3
## modelo dato
## [1,] "logarítmico" "0.42457206343907"
## sintaxis
## [1,] "linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)"
\[ \hat Y = \beta_0 + \beta_1 e^X \]
No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.
\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]
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)"
modelos5 <- cbind(modelo,dato,sintaxis)
modelos5
## modelo dato
## [1,] "raíz cuadrada" "0.531898928627414"
## sintaxis
## [1,] "linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]
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)"
modelos6 <- cbind(modelo,dato,sintaxis)
modelos6
## modelo dato
## [1,] "raíz-raíz" "0.638527969918706"
## sintaxis
## [1,] "linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]
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)"
modelos7 <- cbind(modelo,dato,sintaxis)
modelos7
## modelo dato
## [1,] "log-raíz" "0.599584061810376"
## sintaxis
## [1,] "linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)"
\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]
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)"
modelos8 <- cbind(modelo,dato,sintaxis)
modelos8
## modelo dato
## [1,] "raíz-log" "0.577599181706715"
## sintaxis
## [1,] "linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]
linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
datos <- summary(linearMod)
dato <- datos$adj.r.squared
modelo <- "log-log"
sintaxis <- "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos9 <- cbind(modelo,dato,sintaxis)
modelos9
## modelo dato
## [1,] "log-log" "0.640590862494669"
## sintaxis
## [1,] "linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)"
modelos_bind <- rbind(modelos1,modelos2,modelos3,modelos5,modelos6,modelos7,modelos8,modelos9)
modelos_bind <- as.data.frame(modelos_bind)
modelos_bind[order(modelos_bind$dato ),]
## modelo dato
## 3 logarítmico 0.42457206343907
## 1 cuadrático 0.483214321192485
## 2 cúbico 0.483214321192485
## 4 raíz cuadrada 0.531898928627414
## 7 raíz-log 0.577599181706715
## 6 log-raíz 0.599584061810376
## 5 raíz-raíz 0.638527969918706
## 8 log-log 0.640590862494669
## sintaxis
## 3 linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 1 linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
## 2 linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
## 4 linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 7 linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
## 6 linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 5 linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
## 8 linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
metodo <- 8
switch (metodo,
case = linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01),
case = linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01),
case = linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01),
case = linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
case = linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
case = linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01),
case = linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01),
case = linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
)
summary(linearMod)
##
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1627 -0.6956 -0.1130 0.5815 2.3421
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.5055 0.2385 65.023 < 2e-16 ***
## log(Freq.x) 1.0225 0.1203 8.503 2.05e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.005 on 39 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.6496, Adjusted R-squared: 0.6406
## F-statistic: 72.29 on 1 and 39 DF, p-value: 2.048e-10
aa <- linearMod$coefficients[1]
aa
## (Intercept)
## 15.50554
bb <- linearMod$coefficients[2]
bb
## log(Freq.x)
## 1.022457
Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.6545895).
Desplegamos una curva suavizada por loess en el diagrama de dispersión.
scatter.smooth(x=log(h_y_m_comuna_corr_01$Freq.x), y=log(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")
Observemos nuevamente el resultado sobre log-log.
linearMod <- lm(log( multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod)
##
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1627 -0.6956 -0.1130 0.5815 2.3421
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.5055 0.2385 65.023 < 2e-16 ***
## log(Freq.x) 1.0225 0.1203 8.503 2.05e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.005 on 39 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.6496, Adjusted R-squared: 0.6406
## F-statistic: 72.29 on 1 and 39 DF, p-value: 2.048e-10
ggplot(h_y_m_comuna_corr_01, aes(x = log(Freq.x) , y = log(multi_pob))) + geom_point() + stat_smooth(method = "lm", col = "red")
par(mfrow = c (2,2))
plot(linearMod)
\[ \hat Y = e^{17.361982+0.641075 \cdot ln{X}} \]
Esta nueva variable se llamará: est_ing
h_y_m_comuna_corr_01$est_ing <- exp(aa+bb * log(h_y_m_comuna_corr_01$Freq.x))
r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
kable_paper() %>%
scroll_box(width = "100%", height = "300px")
zona | código.x | Freq.x | anio | comuna.x | promedio_i | año | comuna.y | personas | Ingresos_expandidos | tipo | Freq.y | p_poblacional | código.y | multi_pob | est_ing | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2101012001 | 02101 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 6 | 0.0000166 | 02101 | NA | 5419631 |
2 | 2101012005 | 02101 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 59 | 0.0001630 | 02101 | NA | 5419631 |
3 | 2101012012 | 02101 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 53 | 0.0001465 | 02101 | NA | 5419631 |
4 | 2101012013 | 02101 | 10 | 2017 | NA | NA | NA | NA | NA | NA | NA | 121 | 0.0003344 | 02101 | NA | 57072532 |
5 | 2101012901 | 02101 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 16 | 0.0000442 | 02101 | NA | 5419631 |
6 | 2101102007 | 02101 | 18 | 2017 | NA | NA | NA | NA | NA | NA | NA | 401 | 0.0011081 | 02101 | NA | 104095608 |
7 | 2101102014 | 02101 | 5 | 2017 | NA | NA | NA | NA | NA | NA | NA | 78 | 0.0002155 | 02101 | NA | 28095501 |
8 | 2101102016 | 02101 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 9 | 0.0000249 | 02101 | NA | 5419631 |
9 | 2101122014 | 02101 | 12 | 2017 | NA | NA | NA | NA | NA | NA | NA | 268 | 0.0007406 | 02101 | NA | 68768031 |
10 | 2101122019 | 02101 | 2 | 2017 | NA | NA | NA | NA | NA | NA | NA | 1438 | 0.0039738 | 02101 | NA | 11009309 |
11 | 2102012001 | 02102 | 5 | 2017 | NA | NA | NA | NA | NA | NA | NA | 31 | 0.0023019 | 02102 | NA | 28095501 |
12 | 2102022003 | 02102 | 20 | 2017 | NA | NA | NA | NA | NA | NA | NA | 293 | 0.0217569 | 02102 | NA | 115935782 |
13 | 2102022006 | 02102 | 16 | 2017 | NA | NA | NA | NA | NA | NA | NA | 127 | 0.0094305 | 02102 | NA | 92285003 |
14 | 2102022008 | 02102 | 4 | 2017 | NA | NA | NA | NA | NA | NA | NA | 31 | 0.0023019 | 02102 | NA | 22364048 |
15 | 2102022901 | 02102 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 12 | 0.0008911 | 02102 | NA | 5419631 |
16 | 2103012008 | 02103 | 18 | 2017 | Sierra Gorda | 403458.5 | 2017 | 2103 | 10186 | 4109628188 | Rural | 2684 | 0.2634989 | 02103 | 1082882590 | 104095608 |
17 | 2103012901 | 02103 | 1 | 2017 | Sierra Gorda | 403458.5 | 2017 | 2103 | 10186 | 4109628188 | Rural | 8 | 0.0007854 | 02103 | 3227668 | 5419631 |
18 | 2103032002 | 02103 | 29 | 2017 | Sierra Gorda | 403458.5 | 2017 | 2103 | 10186 | 4109628188 | Rural | 942 | 0.0924799 | 02103 | 380057898 | 169515497 |
19 | 2104012008 | 02104 | 8 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 59 | 0.0044304 | 02104 | 20384144 | 45429795 |
20 | 2104012014 | 02104 | 10 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 19 | 0.0014267 | 02104 | 6564385 | 57072532 |
21 | 2104012901 | 02104 | 6 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 26 | 0.0019524 | 02104 | 8982843 | 33852928 |
22 | 2104022015 | 02104 | 8 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 59 | 0.0044304 | 02104 | 20384144 | 45429795 |
23 | 2104022022 | 02104 | 2 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 57 | 0.0042802 | 02104 | 19693156 | 11009309 |
24 | 2104032901 | 02104 | 1 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 11 | 0.0008260 | 02104 | 3800434 | 5419631 |
25 | 2104042020 | 02104 | 27 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 729 | 0.0547421 | 02104 | 251865098 | 157571701 |
26 | 2104072901 | 02104 | 1 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 56 | 0.0042052 | 02104 | 19347662 | 5419631 |
27 | 2201032006 | 02201 | 2 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 36 | 0.0002172 | 02201 | 11160901 | 11009309 |
28 | 2201032013 | 02201 | 2 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 13 | 0.0000784 | 02201 | 4030325 | 11009309 |
29 | 2201082002 | 02201 | 45 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 1364 | 0.0082302 | 02201 | 422874126 | 265649575 |
30 | 2201082012 | 02201 | 6 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 227 | 0.0013697 | 02201 | 70375679 | 33852928 |
31 | 2201122002 | 02201 | 1 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 151 | 0.0009111 | 02201 | 46813778 | 5419631 |
32 | 2201132004 | 02201 | 30 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 542 | 0.0032704 | 02201 | 168033560 | 175494419 |
33 | 2201132010 | 02201 | 9 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 108 | 0.0006517 | 02201 | 33482702 | 51243886 |
34 | 2201132901 | 02201 | 1 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 42 | 0.0002534 | 02201 | 13021051 | 5419631 |
35 | 2201142002 | 02201 | 2 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 122 | 0.0007361 | 02201 | 37823052 | 11009309 |
36 | 2201152001 | 02201 | 3 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 29 | 0.0001750 | 02201 | 8990726 | 16665022 |
37 | 2201152003 | 02201 | 8 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 119 | 0.0007180 | 02201 | 36892977 | 45429795 |
38 | 2201152014 | 02201 | 8 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 60 | 0.0003620 | 02201 | 18601501 | 45429795 |
39 | 2201152015 | 02201 | 1 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 39 | 0.0002353 | 02201 | 12090976 | 5419631 |
40 | 2202012005 | 02202 | 20 | 2017 | NA | NA | NA | NA | NA | NA | NA | 240 | 0.7476636 | 02202 | NA | 115935782 |
41 | 2203012013 | 02203 | 2 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 27 | 0.0024554 | 02203 | 9615995 | 11009309 |
42 | 2203012014 | 02203 | 147 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 2621 | 0.2383594 | 02203 | 933463770 | 891167661 |
43 | 2203012901 | 02203 | 4 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 40 | 0.0036377 | 02203 | 14245918 | 22364048 |
44 | 2203022008 | 02203 | 3 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 83 | 0.0075482 | 02203 | 29560280 | 16665022 |
45 | 2203022017 | 02203 | 50 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 742 | 0.0674791 | 02203 | 264261777 | 295865422 |
46 | 2203032012 | 02203 | 11 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 1475 | 0.1341397 | 02203 | 525318222 | 62914304 |
47 | 2203032015 | 02203 | 19 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 408 | 0.0371044 | 02203 | 145308363 | 110012195 |
48 | 2301032003 | 02301 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 23 | 0.0009132 | 02301 | 4145016 | 5419631 |
49 | 2301032005 | 02301 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 37 | 0.0014691 | 02301 | 6668068 | 5419631 |
50 | 2301032006 | 02301 | 2 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 118 | 0.0046851 | 02301 | 21265732 | 11009309 |
51 | 2301032018 | 02301 | 2 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 30 | 0.0011911 | 02301 | 5406542 | 11009309 |
52 | 2301032020 | 02301 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 13 | 0.0005162 | 02301 | 2342835 | 5419631 |
53 | 2301032023 | 02301 | 2 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 50 | 0.0019852 | 02301 | 9010903 | 11009309 |
54 | 2301052004 | 02301 | 9 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 71 | 0.0028190 | 02301 | 12795483 | 51243886 |
55 | 2301052007 | 02301 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 15 | 0.0005956 | 02301 | 2703271 | 5419631 |
56 | 2301052008 | 02301 | 4 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 23 | 0.0009132 | 02301 | 4145016 | 22364048 |
57 | 2301052901 | 02301 | 3 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 93 | 0.0036925 | 02301 | 16760280 | 16665022 |
58 | 2302012011 | 02302 | 9 | 2017 | NA | NA | NA | NA | NA | NA | NA | 141 | 0.0218368 | 02302 | NA | 51243886 |
59 | 2302012901 | 02302 | 3 | 2017 | NA | NA | NA | NA | NA | NA | NA | 57 | 0.0088276 | 02302 | NA | 16665022 |
NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.4 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.5 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.6 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.7 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.8 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.9 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.10 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.11 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.12 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.13 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.14 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.15 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.16 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.17 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.18 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.19 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.20 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.21 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.22 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.23 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.24 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.25 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.26 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.27 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.28 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.29 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.30 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.31 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.32 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.33 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.34 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.35 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.36 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.37 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.38 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.39 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.40 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]
h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing /( h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional)
r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
kable_paper() %>%
scroll_box(width = "100%", height = "300px")
zona | código.x | Freq.x | anio | comuna.x | promedio_i | año | comuna.y | personas | Ingresos_expandidos | tipo | Freq.y | p_poblacional | código.y | multi_pob | est_ing | ing_medio_zona | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2101012001 | 02101 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 6 | 0.0000166 | 02101 | NA | 5419631 | NA |
2 | 2101012005 | 02101 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 59 | 0.0001630 | 02101 | NA | 5419631 | NA |
3 | 2101012012 | 02101 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 53 | 0.0001465 | 02101 | NA | 5419631 | NA |
4 | 2101012013 | 02101 | 10 | 2017 | NA | NA | NA | NA | NA | NA | NA | 121 | 0.0003344 | 02101 | NA | 57072532 | NA |
5 | 2101012901 | 02101 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 16 | 0.0000442 | 02101 | NA | 5419631 | NA |
6 | 2101102007 | 02101 | 18 | 2017 | NA | NA | NA | NA | NA | NA | NA | 401 | 0.0011081 | 02101 | NA | 104095608 | NA |
7 | 2101102014 | 02101 | 5 | 2017 | NA | NA | NA | NA | NA | NA | NA | 78 | 0.0002155 | 02101 | NA | 28095501 | NA |
8 | 2101102016 | 02101 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 9 | 0.0000249 | 02101 | NA | 5419631 | NA |
9 | 2101122014 | 02101 | 12 | 2017 | NA | NA | NA | NA | NA | NA | NA | 268 | 0.0007406 | 02101 | NA | 68768031 | NA |
10 | 2101122019 | 02101 | 2 | 2017 | NA | NA | NA | NA | NA | NA | NA | 1438 | 0.0039738 | 02101 | NA | 11009309 | NA |
11 | 2102012001 | 02102 | 5 | 2017 | NA | NA | NA | NA | NA | NA | NA | 31 | 0.0023019 | 02102 | NA | 28095501 | NA |
12 | 2102022003 | 02102 | 20 | 2017 | NA | NA | NA | NA | NA | NA | NA | 293 | 0.0217569 | 02102 | NA | 115935782 | NA |
13 | 2102022006 | 02102 | 16 | 2017 | NA | NA | NA | NA | NA | NA | NA | 127 | 0.0094305 | 02102 | NA | 92285003 | NA |
14 | 2102022008 | 02102 | 4 | 2017 | NA | NA | NA | NA | NA | NA | NA | 31 | 0.0023019 | 02102 | NA | 22364048 | NA |
15 | 2102022901 | 02102 | 1 | 2017 | NA | NA | NA | NA | NA | NA | NA | 12 | 0.0008911 | 02102 | NA | 5419631 | NA |
16 | 2103012008 | 02103 | 18 | 2017 | Sierra Gorda | 403458.5 | 2017 | 2103 | 10186 | 4109628188 | Rural | 2684 | 0.2634989 | 02103 | 1082882590 | 104095608 | 38783.76 |
17 | 2103012901 | 02103 | 1 | 2017 | Sierra Gorda | 403458.5 | 2017 | 2103 | 10186 | 4109628188 | Rural | 8 | 0.0007854 | 02103 | 3227668 | 5419631 | 677453.86 |
18 | 2103032002 | 02103 | 29 | 2017 | Sierra Gorda | 403458.5 | 2017 | 2103 | 10186 | 4109628188 | Rural | 942 | 0.0924799 | 02103 | 380057898 | 169515497 | 179952.76 |
19 | 2104012008 | 02104 | 8 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 59 | 0.0044304 | 02104 | 20384144 | 45429795 | 769996.52 |
20 | 2104012014 | 02104 | 10 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 19 | 0.0014267 | 02104 | 6564385 | 57072532 | 3003817.47 |
21 | 2104012901 | 02104 | 6 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 26 | 0.0019524 | 02104 | 8982843 | 33852928 | 1302035.69 |
22 | 2104022015 | 02104 | 8 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 59 | 0.0044304 | 02104 | 20384144 | 45429795 | 769996.52 |
23 | 2104022022 | 02104 | 2 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 57 | 0.0042802 | 02104 | 19693156 | 11009309 | 193145.77 |
24 | 2104032901 | 02104 | 1 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 11 | 0.0008260 | 02104 | 3800434 | 5419631 | 492693.72 |
25 | 2104042020 | 02104 | 27 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 729 | 0.0547421 | 02104 | 251865098 | 157571701 | 216147.74 |
26 | 2104072901 | 02104 | 1 | 2017 | Taltal | 345494.0 | 2017 | 2104 | 13317 | 4600943086 | Rural | 56 | 0.0042052 | 02104 | 19347662 | 5419631 | 96779.12 |
27 | 2201032006 | 02201 | 2 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 36 | 0.0002172 | 02201 | 11160901 | 11009309 | 305814.14 |
28 | 2201032013 | 02201 | 2 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 13 | 0.0000784 | 02201 | 4030325 | 11009309 | 846869.93 |
29 | 2201082002 | 02201 | 45 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 1364 | 0.0082302 | 02201 | 422874126 | 265649575 | 194757.75 |
30 | 2201082012 | 02201 | 6 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 227 | 0.0013697 | 02201 | 70375679 | 33852928 | 149131.84 |
31 | 2201122002 | 02201 | 1 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 151 | 0.0009111 | 02201 | 46813778 | 5419631 | 35891.60 |
32 | 2201132004 | 02201 | 30 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 542 | 0.0032704 | 02201 | 168033560 | 175494419 | 323790.44 |
33 | 2201132010 | 02201 | 9 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 108 | 0.0006517 | 02201 | 33482702 | 51243886 | 474480.42 |
34 | 2201132901 | 02201 | 1 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 42 | 0.0002534 | 02201 | 13021051 | 5419631 | 129038.83 |
35 | 2201142002 | 02201 | 2 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 122 | 0.0007361 | 02201 | 37823052 | 11009309 | 90240.24 |
36 | 2201152001 | 02201 | 3 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 29 | 0.0001750 | 02201 | 8990726 | 16665022 | 574655.92 |
37 | 2201152003 | 02201 | 8 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 119 | 0.0007180 | 02201 | 36892977 | 45429795 | 381762.98 |
38 | 2201152014 | 02201 | 8 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 60 | 0.0003620 | 02201 | 18601501 | 45429795 | 757163.25 |
39 | 2201152015 | 02201 | 1 | 2017 | Calama | 310025.0 | 2017 | 2201 | 165731 | 51380756402 | Rural | 39 | 0.0002353 | 02201 | 12090976 | 5419631 | 138964.90 |
40 | 2202012005 | 02202 | 20 | 2017 | NA | NA | NA | NA | NA | NA | NA | 240 | 0.7476636 | 02202 | NA | 115935782 | NA |
41 | 2203012013 | 02203 | 2 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 27 | 0.0024554 | 02203 | 9615995 | 11009309 | 407752.19 |
42 | 2203012014 | 02203 | 147 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 2621 | 0.2383594 | 02203 | 933463770 | 891167661 | 340010.55 |
43 | 2203012901 | 02203 | 4 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 40 | 0.0036377 | 02203 | 14245918 | 22364048 | 559101.21 |
44 | 2203022008 | 02203 | 3 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 83 | 0.0075482 | 02203 | 29560280 | 16665022 | 200783.39 |
45 | 2203022017 | 02203 | 50 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 742 | 0.0674791 | 02203 | 264261777 | 295865422 | 398740.46 |
46 | 2203032012 | 02203 | 11 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 1475 | 0.1341397 | 02203 | 525318222 | 62914304 | 42653.77 |
47 | 2203032015 | 02203 | 19 | 2017 | San Pedro de Atacama | 356147.9 | 2017 | 2203 | 10996 | 3916202829 | Rural | 408 | 0.0371044 | 02203 | 145308363 | 110012195 | 269637.73 |
48 | 2301032003 | 02301 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 23 | 0.0009132 | 02301 | 4145016 | 5419631 | 235636.13 |
49 | 2301032005 | 02301 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 37 | 0.0014691 | 02301 | 6668068 | 5419631 | 146476.51 |
50 | 2301032006 | 02301 | 2 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 118 | 0.0046851 | 02301 | 21265732 | 11009309 | 93299.23 |
51 | 2301032018 | 02301 | 2 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 30 | 0.0011911 | 02301 | 5406542 | 11009309 | 366976.97 |
52 | 2301032020 | 02301 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 13 | 0.0005162 | 02301 | 2342835 | 5419631 | 416894.69 |
53 | 2301032023 | 02301 | 2 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 50 | 0.0019852 | 02301 | 9010903 | 11009309 | 220186.18 |
54 | 2301052004 | 02301 | 9 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 71 | 0.0028190 | 02301 | 12795483 | 51243886 | 721744.87 |
55 | 2301052007 | 02301 | 1 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 15 | 0.0005956 | 02301 | 2703271 | 5419631 | 361308.73 |
56 | 2301052008 | 02301 | 4 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 23 | 0.0009132 | 02301 | 4145016 | 22364048 | 972349.93 |
57 | 2301052901 | 02301 | 3 | 2017 | Tocopilla | 180218.1 | 2017 | 2301 | 25186 | 4538972205 | Rural | 93 | 0.0036925 | 02301 | 16760280 | 16665022 | 179193.78 |
58 | 2302012011 | 02302 | 9 | 2017 | NA | NA | NA | NA | NA | NA | NA | 141 | 0.0218368 | 02302 | NA | 51243886 | NA |
59 | 2302012901 | 02302 | 3 | 2017 | NA | NA | NA | NA | NA | NA | NA | 57 | 0.0088276 | 02302 | NA | 16665022 | NA |
NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.4 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.5 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.6 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.7 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.8 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.9 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.10 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.11 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.12 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.13 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.14 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.15 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.16 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.17 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.18 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.19 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.20 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.21 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.22 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.23 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.24 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.25 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.26 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.27 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.28 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.29 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.30 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.31 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.32 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.33 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.34 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.35 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.36 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.37 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.38 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.39 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
NA.40 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
Guardamos:
saveRDS(h_y_m_comuna_corr_01, "casen_censo_rural_nivel_nacional_17_r02.rds")
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