Joining your own data

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The headline use case: I have a frame keyed on messy country names — get it on a map. The package exposes the same matching machinery it uses internally.

Standardise any frame

standardize_country() attaches ISO codes and classifications, reconciling spellings automatically:

my_data <- data.frame(
  nation = c("U.S.", "S. Korea", "Czechia", "Kosovo", "Cote d'Ivoire", "UK"),
  score  = c(10, 8, 6, 4, 7, 9)
)
standardize_country(my_data, nation, warn = FALSE)
#> # A tibble: 6 × 6
#>   nation        score iso3c iso2c continent region               
#>   <chr>         <dbl> <chr> <chr> <chr>     <chr>                
#> 1 U.S.             10 USA   US    Americas  North America        
#> 2 S. Korea          8 KOR   KR    Asia      East Asia & Pacific  
#> 3 Czechia           6 CZE   CZ    Europe    Europe & Central Asia
#> 4 Kosovo            4 XKX   XK    Europe    Europe & Central Asia
#> 5 Cote d'Ivoire     7 CIV   CI    Africa    Sub-Saharan Africa   
#> 6 UK                9 GBR   GB    Europe    Europe & Central Asia

One call to a map

join_world() auto-detects the country column, standardises it and attaches geometry:

my_data |>
  join_world(nation, warn = FALSE) |>
  world_map(score, title = "My data on the ISO spine")

Reconcile two messy tables

country_join() joins two frames that each key on country names, by reconciling both sides to iso3c first:

a <- data.frame(country = c("Czechia", "South Korea", "Russia"), gdp = 1:3)
b <- data.frame(nation  = c("Czech Republic", "Korea, Rep.", "Russian Federation"),
                pop = c(10, 51, 144))
country_join(a, b, country, nation)
#> # A tibble: 3 × 5
#>   country       gdp iso3c nation               pop
#>   <chr>       <int> <chr> <chr>              <dbl>
#> 1 Czechia         1 CZE   Czech Republic        10
#> 2 South Korea     2 KOR   Korea, Rep.           51
#> 3 Russia          3 RUS   Russian Federation   144

Check before you trust

Always inspect what failed to match:

check_country_match(my_data$nation)
#> # A tibble: 6 × 4
#>   input         iso3c matched suggestion
#>   <chr>         <chr> <lgl>   <chr>     
#> 1 U.S.          USA   TRUE    <NA>      
#> 2 S. Korea      KOR   TRUE    <NA>      
#> 3 Czechia       CZE   TRUE    <NA>      
#> 4 Kosovo        XKX   TRUE    <NA>      
#> 5 Cote d'Ivoire CIV   TRUE    <NA>      
#> 6 UK            GBR   TRUE    <NA>

If something legitimately cannot be matched, extend the override table:

wdj_overrides(c(Somaliland = "SOM"))[c("Kosovo", "Somaliland")]
#>     Kosovo Somaliland 
#>      "XKX"      "SOM"

Custom origins

If your key is already an ISO-2 or World Bank code, tell standardize_country() via origin:

df <- data.frame(code = c("US", "KR", "BR"))
standardize_country(df, code, origin = "iso2c", warn = FALSE)
#> # A tibble: 3 × 5
#>   code  iso3c iso2c continent region                   
#>   <chr> <chr> <chr> <chr>     <chr>                    
#> 1 US    USA   US    Americas  North America            
#> 2 KR    KOR   KR    Asia      East Asia & Pacific      
#> 3 BR    BRA   BR    Americas  Latin America & Caribbean

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.
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