Explanation

To make a map using geom_map() you’ll need two datasets: one that includes the map data (country borders), and another one on how to colour in each area. I tried doing data = NULL or fill = "grey" to make all countries the same colour, but couldn’t get that to work. So I’m creating the necessary second dataset with distinct(world_map, region) which is a list of every country from the map data. This list of countries (=regions) is the first argument to ggplot(), the world_map data goes inside geom_map(). expand_limits() are essential as otherwise you will not see anything.

library(tidyverse)

# Load map data
world_map = map_data("world")

distinct(world_map, region) %>% 
  ggplot(aes(map_id = region)) +
  geom_map(map = world_map) +
  expand_limits(x = world_map$long, y = world_map$lat)

Equal area projection

Global maps that do not use an equal area projection are a huge pet peeve of mine. The above map is not ‘equal-area’ and the closer to the equator an area is the more diminished it is.

Let’s just plot Greenland and Algeria for example:

tibble(region = c("Greenland", "Algeria")) %>% 
  ggplot(aes(map_id = region)) +
  geom_map(map = world_map) +
  expand_limits(x = world_map$long, y = world_map$lat)

In reality, Algeria is bigger than Greenland: 2.4 million km² vs 2.2 million km²!

Therefore, I always use an equal-area projection such as Mollweide:

library(tidyverse)

# Load map data
world_map = map_data("world")

distinct(world_map, region) %>% 
  ggplot(aes(map_id = region)) +
  geom_map(map = world_map) +
  expand_limits(x = world_map$long, y = world_map$lat) +
  coord_map("moll")

But then we get those weird lines in the Northern Hemisphere. That’s because parts of the US and Russia extend across the 180th meridian. And since geom_map() is connecting each area with itself it then draws these lines across the globe.

A simple fix I’ve found is to remove map data that has a longitude greater than 180 degrees:

library(tidyverse)
library(ggthemes)

world_map = map_data("world") %>% 
  filter(! long > 180)

countries = world_map %>% 
  distinct(region) %>% 
  rowid_to_column()

countries %>% 
  ggplot(aes(fill = rowid, map_id = region)) +
  geom_map(map = world_map) +
  expand_limits(x = world_map$long, y = world_map$lat) +
  coord_map("moll") +
  theme_map()

I’ve also thrown in theme_map() from library(ggthemes), put the coutry list which I made with distinct() into a separate object, and added in fill = rowid for some colour. This is useful as I’m generally plotting chloropleths so I want a tibble with values for each country that I can join with whatever data I have for each country (e.g., student numbers, population, etc.).