I. Standardize

If data has non-standardized names, standardize them to an ISO 3166-1 code or other standardized code or name with parse_country:

gap <- gapminder %>% 
    # standardize to ISO 3166 Alpha-2 code
    mutate(country_code = parse_country(country))

gap %>%
    select(country, country_code, year, lifeExp) %>%
    sample_n(10)
#> # A tibble: 10 x 4
#>    country               country_code  year lifeExp
#>    <fct>                 <fct>        <int>   <dbl>
#>  1 West Bank and Gaza    PS            1992    69.7
#>  2 Haiti                 HT            1952    37.6
#>  3 Sao Tome and Principe ST            1952    46.5
#>  4 Somalia               SO            1987    44.5
#>  5 Mauritius             MU            1967    61.6
#>  6 Pakistan              PK            2002    63.6
#>  7 Hong Kong, China      HK            1952    61.0
#>  8 Japan                 JP            1967    71.4
#>  9 Madagascar            MG            1997    55.0
#> 10 Tunisia               TN            1987    66.9

If country names are particularly irregular, in unsupported languages, or are even just unique location names, parse_country can use Google Maps or Data Science Toolkit geocoding APIs to parse instead of regex:

parse_country(c("somewhere in Japan", "日本", "Japon", "जापान"), how = "google")
#> [1] "JP" "JP" "JP" "JP"

parse_country(c("1600 Pennsylvania Ave, DC", "Eiffel Tower"), how = "google")
#> [1] "US" "FR"

II. Convert

If data comes with countries already coded, convert them with as_country_code():

# 2016 Olympic gold medal data
olympics <- read_tsv("https://raw.githubusercontent.com/nbremer/olympicfeathers/gh-pages/data/raw%20medal%20data/Rio%202016%20gold%20medal%20winners.txt")

olympics %>% count(country = as_country_code(NOC, from = "ioc"), sort = TRUE)
#> # A tibble: 59 x 2
#>    country     n
#>    <chr>   <int>
#>  1 US         46
#>  2 GB         28
#>  3 CN         26
#>  4 RU         19
#>  5 DE         18
#>  6 JP         12
#>  7 FR         11
#>  8 KR          9
#>  9 AU          8
#> 10 HU          8
#> # ... with 49 more rows

or to convert to country names, use as_country_name():

olympics %>% 
    count(country = as_country_name(NOC, from = "ioc"), 
          Event_gender) %>% 
    spread(Event_gender, n) %>% 
    arrange(desc(W))
#> # A tibble: 59 x 4
#>    country         M     W     X
#>    <chr>       <int> <int> <int>
#>  1 US             17    27     2
#>  2 China          12    14    NA
#>  3 Russia          9    10    NA
#>  4 Hungary         1     7    NA
#>  5 Japan           5     7    NA
#>  6 UK             19     7     2
#>  7 Netherlands     2     6    NA
#>  8 Australia       3     5    NA
#>  9 Germany        10     5     3
#> 10 South Korea     4     5    NA
#> # ... with 49 more rows

or translate to another language:

olympics$NOC %>% 
    unique() %>% 
    as_country_name(from = "ioc", to = "ta-my") %>% 
    head(10)
#>  [1] "சீனா"        "யூகே"       "யூஎஸ்"       "ஹங்கேரி"     "ஸ்வீடன்"      
#>  [6] "கனடா"       "நெதர்லாந்து"  "ஜப்பான்"      "ஸ்பெயின்"     "ஆஸ்திரேலியா"

Language formats largely follow IETF language tag BCP 47 format. For all available formats, run DT::datatable(codes) for an interactive widget of format names and further information.

III. Format

A particularly common hangup with country data is presentation. While “Yemen, Rep.” may be fine for exploratory work, to create a plot to share, such names need to be changed to something more palatable either by editing the data or manually overriding the labels directly on the plot.

If the existing format is already standardized, passport offers another option: use a formatter function created with country_format, just like for thousands separators or currency formatting. Reorder simply with order_countries:

living_longer <- gap %>% 
    group_by(country_code) %>% 
    summarise(start_life_exp = lifeExp[which.min(year)], 
              stop_life_exp = lifeExp[which.max(year)], 
              diff_life_exp = stop_life_exp - start_life_exp) %>% 
    top_n(10, diff_life_exp) 

# Plot country codes...
ggplot(living_longer, aes(x = country_code, y = stop_life_exp - 3.3,
                          ymin = start_life_exp, 
                          ymax = stop_life_exp - 3.3, 
                          colour = factor(diff_life_exp))) + 
    geom_point(pch = 17, size = 15) + 
    geom_linerange(size = 10) + 
                     # ...just pass `labels` a formatter function!
    scale_x_discrete(labels = country_format(),
                     # Easily change order
                     limits = order_countries(living_longer$country_code, 
                                              living_longer$diff_life_exp)) + 
    scale_y_continuous(limits = c(30, 80)) + 
    labs(title = "Life gets better",
         subtitle = "Largest increase in life expectancy",
         x = NULL, y = "Life expectancy") + 
    theme(axis.text.x = element_text(angle = 30, hjust = 1), 
          legend.position = "none")

By default country_format will use Unicode CLDR (see below) English names, which are intelligible and suitable for most purposes. If desired, other languages or formats can be specified just like in as_country_name.