## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE, results = "hide", include = TRUE, warning = FALSE, message = FALSE, eval = FALSE)

## -----------------------------------------------------------------------------
# # install_packages("infoelectoral")
# library(infoelectoral)
# # Cargo el resto de librerías
# library(dplyr)
# library(tidyr)

## -----------------------------------------------------------------------------
# results <- municipios("congreso", "2015", "12") # Descargo los datos

## -----------------------------------------------------------------------------
# library(mapSpain)
# shp <- esp_get_munic_siane(year = "2016") %>% select(LAU_CODE)
# shp_ccaa <- mapSpain::esp_get_ccaa_siane()

## -----------------------------------------------------------------------------
# results %>%
#   group_by(codigo_partido_nacional) %>%
#   summarise(
#     siglas_r = paste(unique(siglas)[1], collapse = ", "),
#     votos = sum(votos)
#   ) %>%
#   arrange(-votos)

## -----------------------------------------------------------------------------
# results <-
#   results %>%
#   mutate(
#     siglas_r = case_when(
#       codigo_partido_nacional == "903316" ~ "PP",
#       codigo_partido_nacional == "903484" ~ "PSOE",
#       codigo_partido_nacional == "901079" ~ "Cs",
#       codigo_partido_nacional %in% c("903736", "905033", "905008", "905041") ~ "Podemos",
#       codigo_partido_nacional == "904850" ~ "IU"
#     ),
#     # Construyo la columna que identifica al municipio (LAU_CODE)
#     LAU_CODE = paste0(codigo_provincia, codigo_municipio),
#     # Calculo el % sobre censo
#     pct = round((votos / censo_ine) * 100, 2)
#   ) %>%
#   filter(!is.na(siglas_r)) %>%
#   # Selecciono las columnas necesarias
#   select(codigo_ccaa, LAU_CODE, siglas_r, censo_ine, votos_candidaturas, pct)

## -----------------------------------------------------------------------------
# shp <- left_join(shp, results, by = "LAU_CODE")

## ----fig.align="center", fig.height = 12, fig.width=8-------------------------
# library(ggplot2)
# library(purrr)
# library(patchwork)
# 
# colores <- c("#0cb2ff", "#E01021", "#612d62", "#E85B2D", "#E01021")
# names(colores) <- c("PP", "PSOE", "Podemos", "Cs", "IU")
# 
# # Creo una lista de plots
# maps <-
#   map(names(colores), function(p) {
#     shp %>%
#       filter(siglas_r == p) %>%
#       ggplot() +
#       geom_sf(
#         aes(fill = pct, color = pct),
#         linewidth = 0, show.legend = F
#       ) +
#       geom_sf(
#         data = shp_ccaa, fill = NA, color = "black",
#         linewidth = 0.1
#       ) +
#       facet_wrap(~siglas_r) +
#       scale_fill_gradient(
#         low = "white", high = colores[p],
#         na.value = "grey90", aesthetics = c("fill", "color")
#       ) +
#       theme_void()
#   })
# 
# 
# # Uso patchworks para mostrar los plots
# wrap_plots(maps, ncol = 2)