Choropleth Maps

October 1, 2024

Choropleth Map

Simple Features

Mercator Projection

Source: Wikipedia

Natural Earth Projection

Source: Wikipedia

Disputed Territory

Source: Wikimedia Commons

Development

Map Code


Grab country shapes with ne_countries()


library(rnaturalearth)
library(dplyr)

world_map_df <- ne_countries(scale = "medium", returnclass = "sf") |>
    filter(name != "Antarctica") # remove Antarctica

#world_map_df |>
#glimpse()

# view contents of geometry column
world_map_df |>
  select(geometry)

Map Code


Make a map using geom_sf() from ggplot2.


library(ggplot2)

ggplot(data = world_map_df) +
  geom_sf(aes(fill = income_grp)) + 
  labs(title = "World Bank country income categories")

Map Code


Add a color scale and a theme.


library(ggthemes)

ggplot(data = world_map_df) +
  geom_sf(aes(fill = income_grp)) + 
  labs(
    title = "World Bank country income categories",
    fill = "Category"
    ) +
    scale_fill_viridis_d() +
    theme_map()

Your Turn!


  • Make a map of WB income categories
  • Start by grabbing the country shapes
  • Store data frame in an object
  • Use geom_sf() to make the map
  • Style the map
05:00

Map Other Data

Map Other Data


Grab data from the WB, join with country shapes…


library(wbstats)

oil_rents_df <- wb_data(c(oil_rents_gdp = "NY.GDP.PETR.RT.ZS"), mrnev = 1) 

rents_map_df <- left_join(world_map_df, oil_rents_df, join_by(iso_a3 == iso3c))

rents_map_df |>
  select(last_col(5):last_col()) |> #select last 5 columns of df
  glimpse()

Map Other Data

ggplot(data = rents_map_df) +
  geom_sf(aes(fill = oil_rents_gdp)) + # shade based on oil rents
  labs(
    title = "Oil rents (% of GDP)",
    subtitle = "(Most recent available data)", # add subtitle
    fill = "Percent", 
    caption = "Source: World Bank Development Indicators"
    ) +
  theme_map() +
  theme(
    legend.position = "right", 
    #legend.title = element_text(size = 8),
    #legend.text = element_text(size = 6)
    plot.title = element_text(face = "bold"), # move legend
    ) +
  scale_fill_viridis_c( # chg from discrete (_d) to continuous (_c)
      option = "magma", #  chg to magma theme
      labels = scales::label_percent(scale = 1) # add % label for legend
      )

Your Turn!


  • Using the code from module 3.1 as a template…
  • Try mapping a favorite variable from the World Bank
  • First, download the relevant data using wbstats
  • Then merge it with your country shapes
  • Map using geom_sf()
  • Beautify your map!
10:00

Map Some V-Dem Data


  • Now try mapping some V-Dem data
  • Remind yourself of how to download data from V-Dem
  • You will have to convert country codes to iso3c
  • Then merge with country shapes
  • Then map your V-Dem indicator!
10:00

Coding With AI


  • Sometimes coding with AI can helpful
  • Sometimes not!
  • Try prompting ChatGPT to code up a choropleth map
  • Tell it what packages to use
  • Note differences from our code
  • Debug if necessary
10:00