Using infoelectoral to make electoral maps

Install and load the packages

Install infoelectoral and load the packages needed.

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

Download the results

Download some results. In this case we download the election for Congress of December 2015.

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

Import the geometries

Import the geometry shapes for the municipalities using mapSpain.

library(mapSpain)
shp <- esp_get_munic(year = "2016") %>% select(LAU_CODE)
shp_ccaa <- mapSpain::esp_get_ccaa()

Recode party names

Since most parties have different names throughout the country, you will need to recode them to group their results. You can use the column codigo_partido_nacional included in the resulting data.frame that indicates the grouping party code at the national level. After that you’ll have to create the complete municipality code (LAU_CODE) for the merge with the sf object and transform the data from long to wide format.

First, let’s group the parties by codigo_partido_nacional and siglas and sum the votes to see which party codes correspond to the main parties.

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

## # A tibble: 56 × 3
##    codigo_partido_nacional siglas_r      votos
##    <chr>                   <chr>         <dbl>
##  1 903316                  PP          7216024
##  2 903484                  PSOE        5530428
##  3 901079                  C´s         3500063
##  4 903736                  PODEMOS     3182256
##  5 905033                  EN COMÚ      927053
##  6 904850                  IULV-CA,UPe  923377
##  7 905008                  PODEMOS-COM  671077
##  8 905063                  ERC-CATSÍ    599375
##  9 904991                  DL           565742
## 10 905041                  PODEMOS-En   408417
## # ℹ 46 more rows

Then, we’ll have to recode the party names and calculate the percentage of votes.

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)

Join data and sf object

With the LAU_CODE column merge the data with the geometries of the municipalities.

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

Visualize

At last, we may use ggplot2 to visualize the data. In this case we use purrr::map to create a list of plots each of them with their own color gradient scale and patchwork to show them together.

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)