Getting started

The goal of countryatlas is to get country data onto a map with as little friction as possible, using ISO codes as the universal join key. The happy path is a single call.

A map-ready tibble in one call

With a live connection, world_data(year) returns everything you need:

data_2020 <- world_data(2020)

To keep this vignette offline, we use the bundled snapshot and attach geometry ourselves:

data_2020 <- attach_geometry(world_snapshot$countries, geometry = "polygon")

Your first choropleth

No geom_polygon() boilerplate — world_map() does it:

world_map(data_2020, gdp_per_capita, style = "quantile",
          title = "GDP per capita")

Income is an ordered factor, so a categorical fill reads naturally:

world_map(data_2020, income, style = "categorical")

Choosing indicators

You are not limited to GDP. Pass any World Bank indicator code (named, for clean columns), or browse the bundled catalogue:

head(common_indicators)
#> # A tibble: 6 × 3
#>   name                   code           description                       
#>   <chr>                  <chr>          <chr>                             
#> 1 population             SP.POP.TOTL    Population, total                 
#> 2 gdp                    NY.GDP.MKTP.CD GDP (current US$)                 
#> 3 gdp_constant           NY.GDP.MKTP.KD GDP (constant 2015 US$)           
#> 4 gdp_per_capita         NY.GDP.PCAP.KD GDP per capita (constant 2015 US$)
#> 5 gdp_per_capita_current NY.GDP.PCAP.CD GDP per capita (current US$)      
#> 6 gni_per_capita         NY.GNP.PCAP.CD GNI per capita (current US$)

country_data(2020, c(life_exp = "SP.DYN.LE00.IN", pop = "SP.POP.TOTL"))

Next steps

Joining your own data — get a frame keyed on messy names onto a map.
Modern maps with sf & projections — equal-area, projected maps.
Beyond the choropleth — bubbles, cartograms, tiles, flows and more.