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Lightweight one-row-per-country table

Source: R/world_data.R
country_data.Rd

The analysis counterpart to world_data(): no polygons, one tidy row per country (iso3c, iso2c, country, classifications and the requested indicators). This is what you actually join() / mutate() / summarise() / rank() on; attach geometry only at draw time with attach_geometry().

Usage

country_data(
  year,
  indicator = NULL,
  latest = FALSE,
  panel = FALSE,
  classify = c("income", "continent", "region"),
  cache = TRUE,
  language = "en",
  parallel = TRUE,
  vintage = NULL
)

Arguments

year

A single year or a range (with panel = TRUE).

indicator

A named character vector of WDI codes (or NULL for none).

latest

For a single year: TRUE takes each indicator's most recent non-NA value per country, "common" the most recent year in which every indicator is present. Both add an <indicator>_year column per indicator; see the section below.

panel

Return a panel keyed on iso3c + year (implied when year spans multiple years).

classify

Which classifications to add.

cache

Whether to use the WDI cache.

language

WDI language code.

parallel

Whether to fetch indicators in parallel. Ignored when the cache is memory-only; see world_data().

vintage

The release of the World Development Indicators to read; see world_data().

Value

A tibble, one row per country (or per country-year for a panel).

iso3c is the stable key; country is a label and its spelling depends on where the row came from. A successful fetch carries the World Bank's names ("Korea, Rep.", "Congo, Dem. Rep."), while the country spine used when the fetch returns nothing carries the countrycode names ("South Korea", "Congo - Kinshasa") – as do convert_country(), standardize_country() and the rest of the package. Match on iso3c, and relabel with convert_country(iso3c, to = "country") if you need one consistent set.

The most recent value, and which year it is from

With latest = TRUE each indicator takes its own most recent value, so one row can hold GDP from 2023 beside population from 2021. That is often what is wanted – the freshest number for each – but dividing one by the other mixes years, so every indicator carries an <indicator>_year column, and per_capita(), deflate() and to_ppp() warn (class countryatlas_mixed_years) when the two columns they combine come from different years. latest = "common" instead takes, per country, the most recent year in which every requested indicator is present, so the row is internally consistent; a country with no such year gets NA throughout.

Examples

# \donttest{
country_data(2020, c(co2 = "EN.GHG.CO2.MT.CE.AR5"))
#> # A tibble: 216 × 7
#>    iso3c iso2c country             continent region              income      co2
#>    <chr> <chr> <chr>               <chr>     <chr>               <fct>     <dbl>
#>  1 AFG   AF    Afghanistan         Asia      Middle East, North… Low i…  12.1   
#>  2 ALB   AL    Albania             Europe    Europe & Central A… Upper…   4.57  
#>  3 DZA   DZ    Algeria             Africa    Middle East, North… Upper… 172.    
#>  4 ASM   AS    American Samoa      Oceania   East Asia & Pacific High …   0.0001
#>  5 AND   AD    Andorra             Europe    Europe & Central A… High …  NA     
#>  6 AGO   AO    Angola              Africa    Sub-Saharan Africa  Lower…  20.5   
#>  7 ATG   AG    Antigua and Barbuda Americas  Latin America & Ca… High …   0.326 
#>  8 ARG   AR    Argentina           Americas  Latin America & Ca… Upper… 168.    
#>  9 ARM   AM    Armenia             Asia      Europe & Central A… Upper…   6.91  
#> 10 ABW   AW    Aruba               Americas  Latin America & Ca… High …   0.489 
#> # ℹ 206 more rows
# }