    library(insee)
    library(ggplot2)
    library(dplyr)
    library(magrittr)
    library(stringr)

    library(raster)
    library(rgdal)
    library(geosphere)
    library(broom)
    library(viridis)
    library(mapproj)

    dataset_list = get_dataset_list()

    list_idbank =
      get_idbank_list("TCRED-ESTIMATIONS-POPULATION") %>%
      filter(AGE == "00-") %>% #all ages
      filter(SEXE == 0) %>% #men and women
      filter(str_detect(REF_AREA, "^D")) %>% #select only departements
      add_insee_title()

    list_idbank_selected = list_idbank %>% pull(idbank)

    # get population data by departement
    pop = get_insee_idbank(list_idbank_selected)

    #get departements' geographical limits
    FranceMap <- raster::getData(name = "GADM", country = "FRA", level = 2)

    # extract the population by departement in 2020
    pop_plot = pop %>%
      group_by(TITLE_EN) %>%
      filter(DATE == "2020-01-01") %>%
      mutate(dptm = gsub("D", "", REF_AREA)) %>%
      filter(dptm %in% FranceMap@data$CC_2) %>%
      mutate(dptm = factor(dptm, levels = FranceMap@data$CC_2)) %>%
      arrange(dptm) %>%
      mutate(id = dptm)

    vec_pop = pop_plot %>% pull(OBS_VALUE)

    # add population data to the departement object map
    FranceMap@data$pop = vec_pop

    get_area = function(long, lat){
      area = areaPolygon(data.frame(long = long, lat = lat)) / 1000000
      return(data.frame(area = area))
    }

    # extract the departements' limits from the spatial object and compute the surface
    FranceMap_tidy_area <-
      broom::tidy(FranceMap) %>%
      group_by(id) %>%
      group_modify(~get_area(long = .x$long, lat = .x$lat))

    FranceMap_tidy <-
      broom::tidy(FranceMap) %>%
      left_join(FranceMap_tidy_area)

    # mapping table
    dptm_df = data.frame(dptm = FranceMap@data$CC_2,
                         dptm_name = FranceMap@data$NAME_2,
                         pop = FranceMap@data$pop,
                         id = rownames(FranceMap@data))

    FranceMap_tidy_final_all =
      FranceMap_tidy %>%
      left_join(dptm_df, by = "id") %>%
      mutate(pop_density = pop/area) %>%
      mutate(density_range = case_when(pop_density < 40 ~ "< 40",
                                       pop_density >= 40 & pop_density < 50 ~ "[40, 50]",
                                       pop_density >= 50 & pop_density < 70 ~ "[50, 70]",
                                       pop_density >= 70 & pop_density < 100 ~ "[70, 100]",
                                       pop_density >= 100 & pop_density < 120 ~ "[100, 120]",
                                       pop_density >= 120 & pop_density < 160 ~ "[120, 160]",
                                       pop_density >= 160 & pop_density < 200 ~ "[160, 200]",
                                       pop_density >= 200 & pop_density < 240 ~ "[200, 240]",
                                       pop_density >= 240 & pop_density < 260 ~ "[240, 260]",
                                       pop_density >= 260 & pop_density < 410 ~ "[260, 410]",
                                       pop_density >= 410 & pop_density < 600 ~ "[410, 600]",
                                       pop_density >= 600 & pop_density < 1000 ~ "[600, 1000]",
                                       pop_density >= 1000 & pop_density < 5000 ~ "[1000, 5000]",
                                       pop_density >= 5000 & pop_density < 10000 ~ "[5000, 10000]",
                                       pop_density >= 20000 ~ ">= 20000"
      )) %>%
      mutate(`people per square kilometer` = factor(density_range,
                                                    levels = c("< 40","[40, 50]", "[50, 70]","[70, 100]",
                                                               "[100, 120]", "[120, 160]", "[160, 200]",
                                                               "[200, 240]", "[240, 260]", "[260, 410]",
                                                               "[410, 600]",  "[600, 1000]", "[1000, 5000]",
                                                               "[5000, 10000]", ">= 20000")))

    ggplot(data = FranceMap_tidy_final_all,
           aes(fill = `people per square kilometer`, x = long, y = lat, group = group) ,
           size = 0, alpha = 0.9) +
      geom_polygon() +
      geom_path(colour = "white") +
      coord_map() +
      theme_void() +
      scale_fill_viridis(discrete = T) +
      ggtitle("Distribution of the population within French territory in 2020") +
      labs(subtitle = "the density displayed here is an approximation, it should not be considered as an official statistics")