# Choropleth maps
library(sf)
library(chilemapas)
library(ggplot2)
library("readxl")
library(RColorBrewer)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
help(package='chilemapas')
#comunas_rm <- mapa_comunas[mapa_comunas$codigo_region==13,]
gd <- generar_distritos()
## Joining, by = "codigo_distrito"
# gd
# gd$codigo_distrito
# gd$codigo_distrito
# elecciones_2017 <- read_xlsx('DISTRITO-01-2017.xlsx')
# saveRDS(elecciones_2017, "POBLAC.rds")
elec_2017 <- readRDS("diputados_2017_rm.rds")
# elec_2017
x <- unique(elec_2017$Distrito)
y <- unique(elec_2017$Pacto)
# y
names(elec_2017)[17] <- "votos"
# elec_2017
# elec_2017 <- data.frame(elec_2017, stringsAsFactors = TRUE)
# elec_2017[,'votos']<-factor(elec_2017[,'votos'])
# elec_2017[,'Distrito']<-factor(elec_2017[,'Distrito'])
# elec_2017[,'Pacto']<-factor(elec_2017[,'Pacto'])
# elec_2017
a <- aggregate(votos~Distrito+Pacto, data=elec_2017, FUN=sum)
library(sjmisc)
## Warning: package 'sjmisc' was built under R version 4.0.3
for (i in 1:1000){
if (str_contains(a$Distrito[i], "8")){
a$Distrito[i] <- '08'
}
if (str_contains(a$Distrito[i], "9")){
a$Distrito[i] <- '09'
}
}
# a$Distrito
#write.csv(a, file = "grupos.csv")
comunas_rm<-merge(gd,a,by.x="codigo_distrito",by.y="Distrito",all.y=TRUE,sort=F)
# munas_rm
comunas_rm <- comunas_rm[ which(comunas_rm$Pacto == 'CHILE VAMOS'),]
# comunas_rm
# comunas_rm
comunas_rm[7,2] <- '13'
# comunas_rm
library(RColorBrewer)
paleta <- rev(brewer.pal(n = 5,name = "Reds"))
p_cont<-ggplot(comunas_rm) +
geom_sf(aes(fill = `votos`, geometry = geometry)) +
scale_fill_gradientn(colours = rev(paleta), name = "Cantidad bruta de votos") +
labs(title = "Elecciones para diputados 2017", subtitle = "Distritos de la RM - 2017 - Chile Vamos") +
theme_minimal(base_size = 10)
p_cont
