tabla_con_clave <- readRDS("censos/censo_personas_con_clave_17")
tabla_con_clave_uu <- filter(tabla_con_clave, tabla_con_clave$AREA == 1)

tabla_con_clave_uu$P15U <-paste0(tabla_con_clave_uu$P15, tabla_con_clave_uu$P15A)

b <- tabla_con_clave_uu$COMUNA
c <- tabla_con_clave_uu$P15U
cross_tab =  xtabs( ~ unlist(b) + unlist(c))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2017"

dd <- mutate_if(d, is.factor, as.character)
ddd <- mutate_if(dd, is.character, as.integer)
ddd <- sort(unique(ddd$unlist.c.))
 
d_t <- filter(dd,dd$unlist.c. == 11)
for(i in ddd[2:27]){
  d_i <- filter(dd,dd$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]

categorias <- c(
"Sala cuna o jardín infantil(sí)",
"Sala cuna o jardín infantil(no)",
"Prekínder(sí)",
"Kínder(sí)",
"Kínder(no)",
"Especial o diferencial(sí)",
"Especial o diferencial(no)",
"Educación básica(sí)",
"Educación básica(no)",
"Primaria o preparatorio (sistema antiguo)(sí)",
"Primaria o preparatorio (sistema antiguo)(no)",
"Científico-humanista(sí)",
"Científico-humanista(no)",
"Técnica profesional(sí)",
"Técnica profesional(no)",
"Humanidades (sistema antiguo)(sí)",
"Humanidades (sistema antiguo)(no)",
"Técnica comercial, industrial/normalista (sistema antiguo)(sí)",
"Técnica comercial, industrial/normalista (sistema antiguo)(no)",
"Técnico superior (1-3 años)(sí)",
"Técnico superior (1-3 años)(no)",
"Profesional (4 o más años)(sí)",
"Profesional (4 o más años)(no)",
"Magíster(sí)",
"Magíster(no)",
"Doctorado(sí)",
"Doctorado(no)")

III <- seq(2,54,2)
comuna_corr$sn

for (i in 1:100) {
      names(comuna_corr)[III[i]] <- paste0(categorias[i])
      
} 

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_A__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")

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 

i <- 1
f <- 10
for (cc in 1:2) {
III <- seq(i,f)
print(paste0(i-1,"-",f-1))
df_2017_2_sub_subset <- union_final_urb[,c(III,ncol(union_final_urb))]
chart.Correlation(df_2017_2_sub_subset, histogram=TRUE, method = c( "kendall"), pch=20)

i <- i+10
f <- f+10
}

# print("21 - 28")
df_2017_2_sub_subset <- union_final_urb[,c(22:28)]
chart.Correlation(df_2017_2_sub_subset, histogram=TRUE, method = c( "kendall"), pch=20)

i <- 1
f <- 10
for (cc in 1:2) {
III <- seq(i,f)
print(paste0(i-1,"-",f-1))
df_2017_2_sub_subset <- union_final_urb[,c(III,ncol(union_final_urb))]
chart.Correlation(df_2017_2_sub_subset, histogram=TRUE, method = c( "pearson"), pch=20)

i <- i+10
f <- f+10
}

# print("21 - 23")
df_2017_2_sub_subset <- union_final_urb[,c(22:28)]
chart.Correlation(df_2017_2_sub_subset, histogram=TRUE, method = c( "pearson"), pch=20)

i <- 1
f <- 10
for (cc in 1:2) {
III <- seq(i,f)
print(paste0(i-1,"-",f-1))
df_2017_2_sub_subset <- union_final_urb[,c(III,ncol(union_final_urb))]
chart.Correlation(df_2017_2_sub_subset, histogram=TRUE, method = c( "spearman"), pch=20)

i <- i+10
f <- f+10
}

# print("21 - 23")
df_2017_2_sub_subset <- union_final_urb[,c(22:28)]
chart.Correlation(df_2017_2_sub_subset, histogram=TRUE, method = c( "spearman"), pch=20)

i <- 1
f <- 10
for (cc in 1:2) {
III <- seq(i,f)
print(paste0(i-1,"-",f-1))
df_2017_2_sub_subset <- union_final_urb[,c(III,ncol(union_final_urb))]
chart.Correlation(df_2017_2_sub_subset, histogram=TRUE, method = c( "kendall"), pch=20)

i <- i+10
f <- f+10
}

# print("21 - 28")
df_2017_2_sub_subset <- union_final_urb[,c(22:28)]
chart.Correlation(df_2017_2_sub_subset, histogram=TRUE, method = c( "kendall"), pch=20)

i <- 1
f <- 10
for (cc in 1:2) {
III <- seq(i,f)
print(paste0(i-1,"-",f-1))
df_2017_2_sub_subset <- union_final_urb[,c(III,ncol(union_final_urb))]
chart.Correlation(df_2017_2_sub_subset, histogram=TRUE, method = c( "pearson"), pch=20)

i <- i+10
f <- f+10
}

# print("21 - 23")
df_2017_2_sub_subset <- union_final_urb[,c(22:28)]
chart.Correlation(df_2017_2_sub_subset, histogram=TRUE, method = c( "pearson"), pch=20)

i <- 1
f <- 10
for (cc in 1:2) {
III <- seq(i,f)
print(paste0(i-1,"-",f-1))
df_2017_2_sub_subset <- union_final_urb[,c(III,ncol(union_final_urb))]
chart.Correlation(df_2017_2_sub_subset, histogram=TRUE, method = c( "spearman"), pch=20)

i <- i+10
f <- f+10
}

# print("21 - 23")
df_2017_2_sub_subset <- union_final_urb[,c(22:28)]
chart.Correlation(df_2017_2_sub_subset, histogram=TRUE, method = c( "spearman"), pch=20)