dados2012.r

library(rgdal)
library(dplyr)
library(ggmap)
library(RColorBrewer)
library(classInt)
gpclibPermit()

votacao <- read.csv("~/devel/eleicoes2016/raw/eleicao2012/votacao_candidato_munzona_2012_SP.csv", sep=";", quote="\"", header = FALSE, fileEncoding="cp1252")

eleitos <- votacao %>%
  filter(V9 == "SÃO PAULO", V16 == "VEREADOR", V22 %in% c('ELEITO POR MÉDIA', 'ELEITO POR QP')) %>%
  group_by(V15) %>%
  summarise(Amount = sum(V29)) %>%
  order_by(Amount)


cadeiras <- votacao %>%
  filter(V9 == "SÃO PAULO", V16 == "VEREADOR") %>%
  group_by(V28) %>%
  summarise(Amount = sum(V29)) %>%
  mutate(cadeiras = Amount/130000)

shape <- readOGR("/home/markun/devel/eleicoes2016/shape", "zonaseleitorais2")

mapeia <- function(p, votacao, shape) {
  partido <- votacao
  partido <- votacao %>%
    filter(V16=="VEREADOR", V24==p) %>%
    group_by(V10) %>%
    summarise(Amount = sum(V29)) %>%
    mutate(perc = Amount/sum(partido$Amount)) %>%
    mutate(id = as.character(V10))
  
  shape_ft <- fortify(shape, region="Name")
  shape_ft <- left_join(shape_ft, partido, by=c('id'='id'))
  
  #colors <- brewer.pal(9, "YlOrRd") #set breaks for the 9 colors 
  #brks<-classIntervals(shape_ft$Amount, n=9, style="quantile")
  #brks <- brks$brks
  #ggplot(shape_ft)#, col=colors[findInterval(shape_ft$Amount, brks,all.inside=TRUE)])
  #c <- colors[findInterval(shape_ft$Amount, brks, all.inside=TRUE)]
  
  ggplot(data=shape_ft, aes(x=long,y=lat, group=group, fill=shape_ft$Amount)) +
    geom_polygon() +
    geom_path(color='black') + 
    scale_fill_distiller(name="Votos", palette = "YlGn", breaks = pretty_breaks(n = 5), direction = 1) +
    labs(x=NULL,y=NULL,title=p) +
    coord_map()
}

v1 <- mapeia('PSOL', votacao, shape)
v2 <- mapeia('PT', votacao, shape)
v3 <- mapeia('PMDB', votacao, shape)
v4 <- mapeia('PSDB', votacao, shape)
grid.arrange(v1,v2,v3,v4)