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Install and load the packages

Install infoelectoral and load the packages needed.

# devtools::install_github("ropenspain/elecciones") # <--- Instala la librería elecciones

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

Download the results

Download some results. In this case we download the election for Congress of decembre 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_munic.sf %>% select(LAU_CODE)

Recode the party names

Since most parties have different names throughout the country, you need to recode them to group their results. You can use the column code.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.

siglas_r <- results %>% 
  group_by(codigo_partido_nacional) %>% 
  summarise(siglas_r = siglas[1]) %>% 
  filter(siglas_r %in% c("PP", "PSE-EE (PSO", "PODEMOS-AHA",
                         "PODEMOS-COM", "PODEMOS-En", "C's", "EN COMÚ",
                         "IU-UPeC")) %>% 
  mutate(siglas_r = case_when(
    siglas_r %in% c("PODEMOS-COM", "PODEMOS-En", "EN COMÚ", "PODEMOS-AHA") ~ "Podemos",
    siglas_r == "PSE-EE (PSO" ~ "PSOE",
    siglas_r == "C's" ~ "Cs",
    siglas_r == "IU-UPeC" ~ "IU",
    TRUE ~ siglas_r
    
  ))

results <- merge(results, siglas_r, by = "codigo_partido_nacional")

results <- results %>% 
  mutate(
    # 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)
    ) %>% 
  # Selecciono las columnas necesarias
  select(codigo_ccaa, LAU_CODE, siglas_r, censo_ine, votos_candidaturas, pct) %>% 
  # Transformo los datos de formato long a wide
  pivot_wider(names_from = "siglas_r", values_from = "pct")

Merge the data and the geometries

With the LAU_CODE column merge the data with the geomtries of the municipalities. Since the Canary Island are too far away you are going to need to get them closer to the Iberian Peninsula.

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

# Acercamos las Canarias a la peninsula
shp$geometry[shp$codigo_ccaa == "05"] <- shp$geometry[shp$codigo_ccaa == "05"] + c(5, 5)

Visualize

At last, we may use tmap or even ggplot to visualize the maps. In this case we use the formar because of it’s easier to create facets.

colores5 <- list(c("#ededed", "#0cb2ff"), # PP
                 c("#ededed", "#E01021"), # PSOE
                 c("#ededed", "#612d62"), # Podemos
                 c("#ededed", "#E85B2D"), # Cs
                 c("#ededed", "#E01021")) # IU

breaks <- c(0,10,30,50,70,100)

library(tmap)
mapa <- tm_shape(shp) + 
  tm_polygons(col = c("PP", "PSOE", "Podemos", "Cs", "IU"), style = "fixed", 
              palette = colores5, breaks = breaks,
              title = "% sobre censo", 
              border.alpha = 0, lwd = 0, legend.show = T, legend.outside = T) +
  tm_layout(between.margin = 5, frame = FALSE,
            title = c("Partido Popular", "PSOE", "Podemos", "Ciudadanos", "Izquierda Unida"),
            title.fontface = "bold") +
  tm_legend(legend.text.size = 1, 
            legend.title.size = 1) +
  tm_facets(sync = TRUE, ncol = 2)
mapa