passport

passport smooths the process of working with country names and codes via powerful parsing, standardization, and conversion utilities arranged in a simple, consistent API. Country name formats include multiple sources including the Unicode CLDR common-sense standardizations in hundreds of languages.

Working with country data can be frustrating. Even with well-curated data like gapminder, there are some oddities:

passport offers a framework for working with country names and codes without manually editing data or scraping codes from Wikipedia.

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 France                   FR            2002    79.6
#>  2 Ireland                  IE            1997    76.1
#>  3 Honduras                 HN            1982    60.9
#>  4 Iran                     IR            1967    52.5
#>  5 Central African Republic CF            1972    43.5
#>  6 Madagascar               MG            1997    55.0
#>  7 Albania                  AL            1952    55.2
#>  8 Jamaica                  JM            2002    72.0
#>  9 Philippines              PH            1997    68.6
#> 10 Libya                    LY            1972    52.8

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 to ISO or other codes with as_country_code()
convert them to country names with as_country_name()
convert them to other languages with as_country_name()

# NATO member defense expenditure data; see `?nato`
data("nato", package = "passport")

nato %>% 
    select(country_stanag) %>% 
    distinct() %>%
    mutate(
        country_iso = as_country_code(country_stanag, from = "stanag"),
        country_name = as_country_name(country_stanag, from = "stanag", short = FALSE),
        country_name_thai = as_country_name(country_stanag, from = "stanag", to = "ta-my")
    )
#> # A tibble: 29 x 4
#>    country_stanag country_iso country_name country_name_thai
#>    <chr>          <chr>       <chr>        <chr>            
#>  1 ALB            AL          Albania      அல்பேனியா         
#>  2 BEL            BE          Belgium      பெல்ஜியம்          
#>  3 BGR            BG          Bulgaria     பல்கேரியா         
#>  4 CAN            CA          Canada       கனடா             
#>  5 CZE            CZ          Czechia      செசியா           
#>  6 DEU            DE          Germany      ஜெர்மனி           
#>  7 DNK            DK          Denmark      டென்மார்க்          
#>  8 ESP            ES          Spain        ஸ்பெயின்           
#>  9 EST            EE          Estonia      எஸ்டோனியா         
#> 10 FRA            FR          France       பிரான்ஸ்           
#> # … with 19 more rows

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:

library(ggplot2)

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) 
#> `summarise()` ungrouping output (override with `.groups` argument)

# Plot country codes...
ggplot(living_longer, aes(x = country_code, y = stop_life_exp - 4.5,
                          ymin = start_life_exp, 
                          ymax = stop_life_exp - 4.5, 
                          colour = factor(diff_life_exp))) + 
    geom_point(pch = 17, size = 7) + 
    geom_linerange(size = 5) + 
                     # ...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.

Data

The data underlying passport comes from a number of sources, including

The Unicode Common Locale Data Repository (CLDR) Project supplies country names in many, many languages, from Afrikaans to Zulu. Even better, CLDR aspires to use the most customary name instead of formal or official ones, e.g. “Switzerland” instead of “Swiss Confederation”.
The United Nations Statistics Division maintains and publishes the M.49 region code and the UN geoscheme region codes and names.
The CIA World Factbook supplies a standardized set of names and codes.
The National Geospatial-Intelligence Agency (NGA) is the organization responsible for standardizing US government use of country codes. It inherited the now-deprecated FIPS 10-4 from NIST, which it turned into the GEC, which is now also deprecated in favor of GENC, a US government profile of ISO 3166.
Wikipedia offers a rich set of country codes, some of which are aggregated here.
Open Knowledge International’s Frictionless Data supplies a set of codes collated from a number of sources on datahub.io.
The regex powering parse_country() are from countrycode. If you would like to improve both packages, please contribute regex there!

passport

Travel smoothly between country name and code formats

Edward Visel

07 November 2020

I. Standardize

II. Convert

III. Format

Data

Licensing