1 Nivel nacional URBANO (código 1)
1.1 Pregunta P15: Completó el nivel especificado
Categorías de respuesta:
1 Sala cuna o jardín infantil
2 Prekínder
3 Kínder
4 Especial o diferencial
5 Educación básica
6 Primaria o preparatorio (sistema antiguo)
7 Científico-humanista
8 Técnica profesional
9 Humanidades (sistema antiguo)
10 Técnica comercial, industrial/normalista (sistema antiguo)
11 Técnico superior (1-3 años)
12 Profesional (4 o más años)
13 Magíster
14 Doctorado
1.2 Generación de tabla de contingencia para la variable P15
tabla_con_clave <- readRDS("censos/censo_personas_con_clave_17")
tabla_con_clave_u <- filter(tabla_con_clave, tabla_con_clave$AREA == 1)
b <- tabla_con_clave_u$COMUNA
c <- tabla_con_clave_u$P15
cross_tab = xtabs( ~ unlist(b) + unlist(c))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2017"
d_t <- filter(d,d$unlist.c. == 1)
for(i in 2:14){
d_i <- filter(d,d$unlist.c. == i)
d_t = merge( x = d_t, y = d_i, by = "unlist.b.", all.x = TRUE)
}codigos <- d_t$unlist.b.
rango <- seq(1:nrow(d_t))
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_t,cadena)
comuna_corr <- comuna_corr[,-c(1),drop=FALSE]
names(comuna_corr)[ncol(comuna_corr)] <- "código"quitar <- seq(3,(ncol(comuna_corr)-1),3)
comuna_corr <- comuna_corr[,-c(quitar),drop=FALSE]
names(comuna_corr)[2] <- "Sala cuna"
names(comuna_corr)[4] <- "Prekínder"
names(comuna_corr)[6] <- "Kínder"
names(comuna_corr)[8] <- "Especial"
names(comuna_corr)[10] <- "Educación básica"
names(comuna_corr)[12] <- "Primaria"
names(comuna_corr)[14] <- "Científico-humanista"
names(comuna_corr)[16] <- "Técnica profesional"
names(comuna_corr)[18] <- "Humanidades"
names(comuna_corr)[20] <- "Técnica comercial"
names(comuna_corr)[22] <- "Técnico superior "
names(comuna_corr)[24] <- "Profesional"
names(comuna_corr)[26] <- "Magíster"
names(comuna_corr)[28] <- "Doctorado"quitar <- seq(1,(ncol(comuna_corr)-1),2)
comuna_corr <- comuna_corr[,-c(quitar),drop=FALSE]
ingresos_expandidos_2017 <- readRDS("Ingresos_expandidos_urbano_17.rds")
df_2017_2 = merge( x = comuna_corr, y = ingresos_expandidos_2017, by = "código", all.x = TRUE)union_final_urb <- df_2017_2[,-c(1,(ncol(df_2017_2)-1))]
write_xlsx(union_final_urb, "P15_urbano.xlsx")data_sum <- summary(union_final_urb)
kbl(head(data_sum)) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
kable_paper() %>%
scroll_box(width = "100%", height = "500px")| Sala cuna | Prekínder | Kínder | Especial | Educación básica | Primaria | Científico-humanista | Técnica profesional | Humanidades | Técnica comercial | Técnico superior | Profesional | Magíster | Doctorado | ingresos_expandidos | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Min. : 21.0 | Min. : 8.0 | Min. : 15.0 | Min. : 1.00 | Min. : 354 | Min. : 11 | Min. : 201 | Min. : 53.0 | Min. : 3.0 | Min. : 1.0 | Min. : 26 | Min. : 21 | Min. : 1.0 | Min. : 1.0 | Min. :7.054e+08 | |
| 1st Qu.: 158.5 | 1st Qu.: 79.0 | 1st Qu.: 110.5 | 1st Qu.: 16.75 | 1st Qu.: 1838 | 1st Qu.: 177 | 1st Qu.: 1311 | 1st Qu.: 610.5 | 1st Qu.: 59.5 | 1st Qu.: 19.0 | 1st Qu.: 269 | 1st Qu.: 370 | 1st Qu.: 14.0 | 1st Qu.: 2.0 | 1st Qu.:2.954e+09 | |
| Median : 396.0 | Median : 200.0 | Median : 290.0 | Median : 50.00 | Median : 4117 | Median : 422 | Median : 3290 | Median : 1669.0 | Median : 180.0 | Median : 56.0 | Median : 793 | Median : 1152 | Median : 45.5 | Median : 8.0 | Median :5.697e+09 | |
| Mean : 1314.5 | Mean : 711.8 | Mean : 996.1 | Mean : 208.16 | Mean : 10975 | Mean : 1283 | Mean : 10695 | Mean : 6189.3 | Mean : 1071.3 | Mean : 351.5 | Mean : 3478 | Mean : 7451 | Mean : 656.6 | Mean : 125.0 | Mean :1.784e+10 | |
| 3rd Qu.: 1501.0 | 3rd Qu.: 814.0 | 3rd Qu.: 1080.5 | 3rd Qu.: 239.25 | 3rd Qu.: 12292 | 3rd Qu.: 1230 | 3rd Qu.: 11595 | 3rd Qu.: 5862.0 | 3rd Qu.: 848.5 | 3rd Qu.: 293.0 | 3rd Qu.: 3294 | 3rd Qu.: 5454 | 3rd Qu.: 212.0 | 3rd Qu.: 41.5 | 3rd Qu.:1.857e+10 | |
| Max. :14959.0 | Max. :8913.0 | Max. :12389.0 | Max. :2854.00 | Max. :127389 | Max. :11635 | Max. :131079 | Max. :87139.0 | Max. :13380.0 | Max. :4988.0 | Max. :49500 | Max. :137640 | Max. :27574.0 | Max. :3668.0 | Max. :1.870e+11 |
Graficas:
library(plotly)
df_2017_fig <- df_2017_2[,-c((ncol(df_2017_2)-1))]
fig <- plot_ly(df_2017_fig, x = df_2017_fig$código, y = df_2017_fig[,2]
, name = colnames(df_2017_fig[2]), type = 'scatter', mode = 'lines',
width=7000, height=400)
grafica_fn <- function(g){
fig <<- fig %>% add_trace(y = ~df_2017_fig[,g]
, name = colnames(df_2017_fig[g]), mode = 'lines',
width=7000, height=400)
}
for (g in 3:(ncol(df_2017_2)-1)) {
grafica_fn(g)
}
fig <- fig %>% layout(autosize = T )
fig 2 Correlaciones
La distribución es asimétrica, poseyendo un sesgo positivo.
2.1 Kendall
df_2017_2f <- filter(union_final_urb, union_final_urb$ingresos_expandidos != 'is.na')
III <- seq(1,(ncol(df_2017_2f)-1),1)
my_data <- df_2017_2f[, c(III)]
tabla <- cor(x=my_data, y=df_2017_2f$ingresos_expandidos, method=c("kendall"), use = "pairwise")
data.frame(tabla)## tabla
## Sala cuna 0.8478057
## Prekínder 0.8451455
## Kínder 0.8562985
## Especial 0.8044501
## Educación básica 0.8346435
## Primaria 0.7741262
## Científico-humanista 0.8476307
## Técnica profesional 0.8158829
## Humanidades 0.8012709
## Técnica comercial 0.7739275
## Técnico superior 0.8463225
## Profesional 0.8396709
## Magíster 0.7628141
## Doctorado 0.7247702
3 Nivel nacional RURAL (código 2)
tabla_con_clave <- readRDS("censos/censo_personas_con_clave_17")
tabla_con_clave_u <- filter(tabla_con_clave, tabla_con_clave$AREA == 2)
b <- tabla_con_clave_u$COMUNA
c <- tabla_con_clave_u$P15
cross_tab = xtabs( ~ unlist(b) + unlist(c))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2017"
d_t <- filter(d,d$unlist.c. == 1)
for(i in 2:14){
d_i <- filter(d,d$unlist.c. == i)
d_t = merge( x = d_t, y = d_i, by = "unlist.b.", all.x = TRUE)
}codigos <- d_t$unlist.b.
rango <- seq(1:nrow(d_t))
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_t,cadena)
comuna_corr <- comuna_corr[,-c(1),drop=FALSE]
names(comuna_corr)[ncol(comuna_corr)] <- "código"quitar <- seq(3,(ncol(comuna_corr)-1),3)
comuna_corr <- comuna_corr[,-c(quitar),drop=FALSE]
names(comuna_corr)[2] <- "Sala cuna"
names(comuna_corr)[4] <- "Prekínder"
names(comuna_corr)[6] <- "Kínder"
names(comuna_corr)[8] <- "Especial"
names(comuna_corr)[10] <- "Educación básica"
names(comuna_corr)[12] <- "Primaria"
names(comuna_corr)[14] <- "Científico-humanista"
names(comuna_corr)[16] <- "Técnica profesional"
names(comuna_corr)[18] <- "Humanidades"
names(comuna_corr)[20] <- "Técnica comercial"
names(comuna_corr)[22] <- "Técnico superior "
names(comuna_corr)[24] <- "Profesional"
names(comuna_corr)[26] <- "Magíster"
names(comuna_corr)[28] <- "Doctorado"quitar <- seq(1,(ncol(comuna_corr)-1),2)
comuna_corr <- comuna_corr[,-c(quitar),drop=FALSE]
ingresos_expandidos_2017 <- readRDS("Ingresos_expandidos_rural_17.rds")
df_2017_2 = merge( x = comuna_corr, y = ingresos_expandidos_2017, by = "código", all.x = TRUE)union_final_urb <- df_2017_2[,-c(1,(ncol(df_2017_2)-1))]
write_xlsx(union_final_urb, "P15_rural.xlsx")data_sum <- summary(union_final_urb)
kbl(head(data_sum)) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
kable_paper() %>%
scroll_box(width = "100%", height = "500px")| Sala cuna | Prekínder | Kínder | Especial | Educación básica | Primaria | Científico-humanista | Técnica profesional | Humanidades | Técnica comercial | Técnico superior | Profesional | Magíster | Doctorado | ingresos_expandidos | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Min. : 1.0 | Min. : 1.00 | Min. : 1.0 | Min. : 1.00 | Min. : 3 | Min. : 1.0 | Min. : 2 | Min. : 2.0 | Min. : 1.00 | Min. : 1.0 | Min. : 1.0 | Min. : 8.0 | Min. : 1.00 | Min. : 1.000 | Min. :2.792e+08 | |
| 1st Qu.: 46.0 | 1st Qu.: 29.00 | 1st Qu.: 47.0 | 1st Qu.: 8.00 | 1st Qu.: 1030 | 1st Qu.: 87.0 | 1st Qu.: 537 | 1st Qu.: 256.0 | 1st Qu.: 24.00 | 1st Qu.: 8.0 | 1st Qu.: 101.0 | 1st Qu.: 141.5 | 1st Qu.: 7.00 | 1st Qu.: 2.000 | 1st Qu.:1.809e+09 | |
| Median : 95.0 | Median : 62.50 | Median : 95.0 | Median : 17.00 | Median : 2113 | Median : 196.0 | Median : 968 | Median : 520.0 | Median : 50.00 | Median : 17.0 | Median : 196.0 | Median : 298.0 | Median : 15.00 | Median : 3.000 | Median :3.546e+09 | |
| Mean :129.5 | Mean : 88.39 | Mean : 131.9 | Mean : 23.02 | Mean : 2663 | Mean : 241.1 | Mean : 1278 | Mean : 748.4 | Mean : 74.21 | Mean : 23.8 | Mean : 274.1 | Mean : 499.5 | Mean : 40.74 | Mean : 9.056 | Mean :8.206e+09 | |
| 3rd Qu.:165.0 | 3rd Qu.:118.00 | 3rd Qu.: 171.5 | 3rd Qu.: 31.50 | 3rd Qu.: 3554 | 3rd Qu.: 334.0 | 3rd Qu.: 1643 | 3rd Qu.: 983.0 | 3rd Qu.: 98.25 | 3rd Qu.: 31.5 | 3rd Qu.: 336.0 | 3rd Qu.: 520.5 | 3rd Qu.: 32.00 | 3rd Qu.: 8.000 | 3rd Qu.:7.252e+09 | |
| Max. :983.0 | Max. :773.00 | Max. :1029.0 | Max. :205.00 | Max. :18864 | Max. :1625.0 | Max. :10113 | Max. :6247.0 | Max. :586.00 | Max. :278.0 | Max. :2061.0 | Max. :7201.0 | Max. :1407.00 | Max. :157.000 | Max. :7.585e+10 |
Graficas:
library(plotly)
df_2017_fig <- df_2017_2[,-c((ncol(df_2017_2)-1))]
fig <- plot_ly(df_2017_fig, x = df_2017_fig$código, y = df_2017_fig[,2]
, name = colnames(df_2017_fig[2]), type = 'scatter', mode = 'lines',
width=7000, height=400)
grafica_fn <- function(g){
fig <<- fig %>% add_trace(y = ~df_2017_fig[,g]
, name = colnames(df_2017_fig[g]), mode = 'lines',
width=7000, height=400)
}
for (g in 3:(ncol(df_2017_2)-1)) {
grafica_fn(g)
}
fig <- fig %>% layout(autosize = T )
fig 4 Correlaciones
La distribución es asimétrica, poseyendo un sesgo positivo.
4.1 Kendall
df_2017_2f <- filter(union_final_urb, union_final_urb$ingresos_expandidos != 'is.na')
III <- seq(1,(ncol(df_2017_2f)-1),1)
my_data <- df_2017_2f[, c(III)]
tabla <- cor(x=my_data, y=df_2017_2f$ingresos_expandidos, method=c("kendall"), use = "pairwise")
data.frame(tabla)## tabla
## Sala cuna 0.3717124
## Prekínder 0.4129805
## Kínder 0.3745238
## Especial 0.3858292
## Educación básica 0.2472100
## Primaria 0.2255109
## Científico-humanista 0.4105004
## Técnica profesional 0.4022984
## Humanidades 0.4434557
## Técnica comercial 0.4451786
## Técnico superior 0.4819257
## Profesional 0.4942858
## Magíster 0.4674843
## Doctorado 0.4523087
