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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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.