Lectura de bases de datos Casen
direccion <- switch(2,"C:/Users/enamo/Desktop/Shiny-R/Casen_en_pandemia_2020/casen/","C:/Users/enamo/Desktop/Shiny-R/Casen_en_pandemia_2020/casen/")
# dataset_06 <<- readRDS(paste0(direccion,"casen_2006_c.rds"))
# dataset_06 <- mutate_if(dataset_06, is.factor, as.character)
# dataset_09 <<- readRDS(paste0(direccion,"casen_2009_c.rds"))
# dataset_09 <- mutate_if(dataset_09, is.factor, as.character)
# dataset_11 <<- readRDS(paste0(direccion,"casen_2011_c.rds"))
# dataset_11 <- mutate_if(dataset_11, is.factor, as.character)
# dataset_13 <<- readRDS(paste0(direccion,"casen_2013_c.rds"))
# dataset_13 <- mutate_if(dataset_13, is.factor, as.character)
# dataset_15 <<- readRDS(paste0(direccion,"casen_2015_c.rds"))
# dataset_15 <- mutate_if(dataset_15, is.factor, as.character)
# dataset_17 <<- readRDS(paste0(direccion,"casen_2017_c.rds"))
# dataset_17 <- mutate_if(dataset_17, is.factor, as.character)
dataset_20 <<- readRDS(paste0(direccion,"casen_2020_c.rds"))
dataset_20 <- mutate_if(dataset_20, is.factor, as.character)1 Homologación de pobreza
dataset_20$pobreza[dataset_20$pobreza == "No pobres"] <- "No pobre"
dataset_20$pobreza[dataset_20$pobreza == "Pobres no extremos"] <- "Pobre"
dataset_20$pobreza[dataset_20$pobreza == "Pobres extremos"] <- "Pobre extremo"2 subset de columas utilizadas
dataset_2020 <- dataset_20[,c("comuna","r3","expc")]3 Cantidad de miembros de pueblos originarios por comuna
pob_tot <- dataset_2020
tabla_matp <-xtabs(pob_tot$expc~comuna+r3, data = pob_tot)
tabla_matp <- as.data.frame(tabla_matp)
tabla_matp <- mutate_if(tabla_matp, is.factor, as.character)
pueblos <- c(sort(unique(tabla_matp$r3)[-8]),"No pertenece a ninguno de estos pueblos indígenas")
receptaculo <- data.frame()
receptaculo2 <- data.frame()
for (i in unique(tabla_matp$comuna)) {
for (j in pueblos) {
dataset1 <- filter(tabla_matp, comuna == i)
dataset1 <- filter(dataset1, r3 == j)
receptaculo <- rbind(receptaculo,dataset1)
}
}4 Total de poblaciones
df1 <- filter(receptaculo, r3 == pueblos[1])
df2 <- filter(receptaculo, r3 == pueblos[2])
df3 <- filter(receptaculo, r3 == pueblos[3])
df4 <- filter(receptaculo, r3 == pueblos[4])
df5 <- filter(receptaculo, r3 == pueblos[5])
df6 <- filter(receptaculo, r3 == pueblos[6])
df7 <- filter(receptaculo, r3 == pueblos[7])
df8 <- filter(receptaculo, r3 == pueblos[8])
df9 <- filter(receptaculo, r3 == pueblos[9])
df10 <- filter(receptaculo, r3 == pueblos[10])
# df11 <- filter(receptaculo, r3 == pueblos[11])
names(df1)[3] <- c(pueblos[1])
names(df2)[3] <- c(pueblos[2])
names(df3)[3] <- c(pueblos[3])
names(df4)[3] <- c(pueblos[4])
names(df5)[3] <- c(pueblos[5])
names(df6)[3] <- c(pueblos[6])
names(df7)[3] <- c(pueblos[7])
names(df8)[3] <- c(pueblos[8])
names(df9)[3] <- c(pueblos[9])
names(df10)[3] <- c(pueblos[10])
# names(df11)[3] <- c(pueblos[11])
df <- merge(x=df1, y= df2[,c(1,3)], by= "comuna")
df <- merge(x=df, y= df3[,c(1,3)], by= "comuna")
df <- merge(x=df, y= df4[,c(1,3)], by= "comuna")
df <- merge(x=df, y= df5[,c(1,3)], by= "comuna")
df <- merge(x=df, y= df6[,c(1,3)], by= "comuna")
df <- merge(x=df, y= df7[,c(1,3)], by= "comuna")
df <- merge(x=df, y= df8[,c(1,3)], by= "comuna")
df <- merge(x=df, y= df9[,c(1,3)], by= "comuna")
df <- merge(x=df, y= df10[,c(1,3)], by= "comuna")
# df <- merge(x=df, y= df11[,c(1,3)], by= "comuna")
df <- df[,c(1,3:12)]
df <- mutate_if(df, is.factor, as.character)
df_prueba <- df
receptaculo2 <- data.frame()
for (i in unique(df_prueba$comuna)) {
filtro <- filter(df_prueba, comuna == i)
filtro <- filtro[,-1]
rp <- length(filtro[filtro[,c(1:10)]>= 1])
filtro$PO_representados <- rp
receptaculo2 <- rbind(receptaculo2,filtro)
}
receptaculo2<- cbind(receptaculo2,unique(df_prueba$comuna))
names(receptaculo2)[12] <- "comuna"
receptaculo2$PO_poblacion <- sum_row(receptaculo2[,c(2:11)])
tabla_matp <-xtabs(pob_tot$expc~comuna, data = pob_tot)
tabla_matp <- as.data.frame(tabla_matp)
tabla_matp <- mutate_if(tabla_matp, is.factor, as.character)
receptaculo2 <- merge(x=receptaculo2,y=tabla_matp, by="comuna")
names(receptaculo2)[14] <- "TOTAL_POB2020"5 Porcentaje de población de publos originarios en la comuna
receptaculo2$`PO_poblacion(%)` <- round((receptaculo2$PO_poblacion*100)/receptaculo2$TOTAL_POB2020,4)
pueblosr <- data.frame()
for (i in 1:324) {
lista <- row.names(as.data.frame(sort(apply(receptaculo2[i,c(2:11)],2,max), decreasing=TRUE) ))[1]
pueblosr <- rbind(pueblosr,lista)
}
names(pueblosr)[1] <- "pueblo_principal"
receptaculo2 <- cbind(receptaculo2,pueblosr)
datatable(receptaculo2, extensions = 'Buttons', escape = FALSE, rownames = FALSE,
options = list(dom = 'Bfrtip',
buttons = list('colvis', list(extend = 'collection',
buttons = list(
list(extend='copy'),
list(extend='excel',
filename = 'tabla_etnia_pobreza'),
list(extend='pdf',
filename= 'tabla_etnia_pobreza')),
text = 'Download')), scrollX = TRUE))