Elections

d_2018 <- electionsBR::vote_mun_zone_fed(2018)

dput(names(d_2018))

unique(d_2018$SIGLA_UF)

colums <- c("NUM_TURNO", "SIGLA_UF", "CODIGO_MUNICIPIO", "DESCRICAO_CARGO", 
            "NOME_URNA_CANDIDATO", "SIGLA_PARTIDO", "TOTAL_VOTOS")

d2_2018 <- d_2018 %>%
  select(colums) %>% 
  filter(DESCRICAO_CARGO == "Governador",
         NUM_TURNO == 2, 
         SIGLA_UF == "SP")

d2_2018 %>% 
  #select(NOME_URNA_CANDIDATO, TOTAL_VOTOS) %>% #NOME_MUNICIPIO
  group_by(NOME_URNA_CANDIDATO) %>% 
  summarise(total = sum(TOTAL_VOTOS)) %>%
  mutate(perc = round(total/sum(total)*100, 2))

d3_2018 <- d2_2018 %>% 
    #select(CODIGO_MUNICIPIO, NOME_URNA_CANDIDATO, TOTAL_VOTOS) %>% #NOME_MUNICIPIO
    group_by(CODIGO_MUNICIPIO, NOME_URNA_CANDIDATO) %>% 
    summarise(TOTAL_VOTOS = sum(TOTAL_VOTOS)) %>% 
    ungroup() %>% 
    group_by(CODIGO_MUNICIPIO) %>% 
    mutate(perc = round(TOTAL_VOTOS/sum(TOTAL_VOTOS)*100, 2)) %>%
    ungroup() %>% 
    mutate(nominal_perc = if_else(NOME_URNA_CANDIDATO == "MARCIO FRANÇA", perc*-1, perc))

d3_2018 <- d3_2018 %>% 
  janitor::clean_names() %>% 
  mutate(nome_urna_candidato = recode(nome_urna_candidato,
                                      "MARCIO FRANÇA" = "marcio",
                                      "JOÃO DORIA" = "joao"))

d3_2018 <- d3_2018 %>% 
    pivot_wider(
    id_cols=codigo_municipio,
    names_from = nome_urna_candidato, 
    values_from = -c(codigo_municipio, nome_urna_candidato),
    values_fn = list(perc = list))%>% 
    unnest(-codigo_municipio)

d4_2018 <- d3_2018 %>% 
  mutate(plot_perc = if_else(perc_joao >= 50, perc_joao, nominal_perc_marcio))

mun_sp <- read_municipality("SP", tp="simplified", year=2018)

codes <- rio::import("https://github.com/tbrugz/ribge/raw/master/doc/ibge-tse-map.csv")

codes_sp <- codes %>% 
              filter(uf == "SP")

dt_plot_2018 <- mun_sp %>% 
  inner_join(codes_sp, by = c("code_muni" = "cod_municipio_ibge")) %>% 
  select(1:3,5:6,9) %>%
  #mutate(cod_municipio_tse = paste0(0, cod_municipio_tse)) %>% 
  mutate(cod_municipio_tse = as.character(cod_municipio_tse)) %>% 
  inner_join(d4_2018, by = c("cod_municipio_tse" = "codigo_municipio"))

#library(paletteer)
X11(width=10)
ggplot(dt_plot_2018)+
  geom_sf(aes(fill= plot_perc)) + 
  scale_fill_paletteer_c("pals::coolwarm",
                         name= "",
                         limits = c (-85,85),
                         labels=c("Doria - Maior % de votos",
                                  "Menor (50%)",
                                  "Franca  - Maior % de votos"),
                         breaks=c(85,0,-85),
                         direction= -1) +
  #scale_fill_gradientn()
  coord_sf()+
  theme_void()+
  theme(legend.position = "bottom", 
        #legend.justification = c(1, 1),
        legend.direction = "horizontal",
        legend.key.width = unit (1, "in"),
        legend.background = element_blank(),
        legend.text = element_text(size = 12),
        legend.title = element_text(size=12))

dt_plot_2018_factor <-dt_plot_2018 %>% 
  mutate(factor_perc =
           case_when(plot_perc >= 50 & plot_perc < 59.99 ~ "Doria - 50 a 59%",
                     plot_perc >= 60 & plot_perc <= 69.99 ~ "Doria - 60 a 69%",
                     plot_perc >= 70 & plot_perc < 79.99 ~ "Doria - 70 a 79%",
                     plot_perc >= 80 & plot_perc < 89.99 ~ "Doria - 80 a 89%",
                     plot_perc >= 90 & plot_perc < 100 ~ "Doria - 90 a 100%",
                     plot_perc <= -50 & plot_perc > -59.99 ~ "Franca - 50 a 59%",
                     plot_perc <= -60 & plot_perc > -69.99 ~ "Franca - 60 a 69%",
                     plot_perc <= -70 & plot_perc > -79.99 ~ "Franca - 70 a 79%",
                     plot_perc <= -80 & plot_perc > -89.99 ~ "Franca - 80 a 89%",
                     plot_perc <= -90 & plot_perc > -100 ~ "Franca - 90 a 100%"))

levels <- c("Doria - 90 a 100%", "Doria - 80 a 89%", "Doria - 70 a 79%","Doria - 60 a 69%", "Doria - 50 a 59%", 
            "Franca - 50 a 59%", "Franca - 60 a 69%","Franca - 70 a 79%", "Franca - 80 a 89%", "Franca - 90 a 100%")

dt_plot_2018_factor$factor_perc <- ordered(dt_plot_2018_factor$factor_perc, levels = levels)

library(paletteer)

paletteer_d("RColorBrewer::RdYlBu", direction = -1)

colors <- c("#313695FF", "#4575B4FF", "#74ADD1FF", "#ABD9E9FF", 
"#E0F3F8FF", "#FEE090FF", "#FDAE61FF", "#F46D43FF", 
"#D73027FF", "#A50026FF")

legend <- dput(paste(shQuote(levels), shQuote(colors), sep = " = "))

legend <- c("Doria - 90 a 100%" = "#313695FF", 
            "Doria - 80 a 89%" = "#4575B4FF", 
            "Doria - 70 a 79%" = "#74ADD1FF", 
            "Doria - 60 a 69%" = "#ABD9E9FF",
            "Doria - 50 a 59%" = "#E0F3F8FF", 
            "Franca - 50 a 59%" = "#FEE090FF",
            "Franca - 60 a 69%" = "#FDAE61FF", 
            "Franca - 70 a 79%" = "#F46D43FF",
            "Franca - 80 a 89%" = "#D73027FF", 
            "Franca - 90 a 100%" = "#A50026FF")

X11(width=10)
ggplot(dt_plot_2018_factor)+
  geom_sf(aes(fill= factor_perc)) + 
  scale_fill_manual(values = legend, 
                    drop = F,
                    name = "Vitoria") +
  coord_sf()+
  theme_void()+
  theme(
  legend.key.height = unit (0.3, "in"),
        legend.background = element_blank(),
        legend.text = element_text(size = 12),
        legend.title = element_text(size=16))

Elections_SP