Dealing with country names
In countries: Deal with Country Data in an Easy Way

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(countries)

Identifying country names

The function is_country() allows to check whether a string is a country name. The argument fuzzy_match can be used to increase tolerance and allow for small typos in the names.

is_country(c("United States","Unated States","dot","DNK",123), fuzzy_match = FALSE) # FALSE is the default and will run faster
is_country(c("United States","Unated States","dot","DNK",123), fuzzy_match = TRUE)

Furthermore, is_country() can also be used to check for a specific subset of countries. In the following example, the function is used to test whether the string relates to India or Sri Lanka, while allowing for different naming conventions and languages.

is_country(x=c("Ceylon","LKA","Indonesia","Inde"), check_for=c("India","Sri Lanka"))

Finally, the package also provides the function find_countrycol(), which can be used to find which columns in a data frame contain country names.

Getting a list of country names

The functions list_countries() and random_countries() allow to get a list of country names. The former will return a list of ALL countries, while the second provides n randomly picked countries.

random_countries(5)
list_countries()[1:5]

The function allows to request country names in different languages and nomenclatures. The list of all possible languages and nomenclatures is available in the next section.

random_countries(5, nomenclature = "ISO3")
random_countries(5, nomenclature = "name_ar")

Converting and translating country names

The function country_name() can be used to convert country names to different naming conventions or to translate them to different languages.

example <- c("United States","DR Congo", "Morocco")

# Getting 3-letters ISO code
country_name(x= example, to="ISO3")

# Translating to Spanish
country_name(x= example, to="name_es")

If multiple arguments are passed to the argument to, the function will output a data.frame object, with one column corresponding to every naming convention.

# Requesting 2-letter ISO codes and translation to Spanish and French
country_name(x= example, to=c("ISO2","name_es","name_fr"))

The to argument supports all the following naming conventions:

tab <- data.frame(CODE=c("**simple**","**ISO3**","**ISO2**","**ISO_code**","**UN_**xx","**WTO_**xx","**name_**xx","**GTAP**", "**all**"),
           DESCRIPTION=c("This is a simple english version of the name containing only ASCII characters. This nomenclature is available for all countries.",
                         "3-letter country codes as defined in ISO standard `3166-1 alpha-3`. This nomenclature is available only for the territories in the standard (currently 249 territories).",
           "2-letter country codes as defined in ISO standard `3166-1 alpha-2`. This nomenclature is available only for the territories in the standard (currently 249 territories).",
           "Numeric country codes as defined in ISO standard `3166-1 numeric`. This country code is the same as the UN's country number (M49 standard). This nomenclature is available for the territories in the ISO standard (currently 249 countries).",
           "Official UN name in 6 official UN languages. Arabic (`UN_ar`), Chinese  (`UN_zh`), English  (`UN_en`), French  (`UN_fr`), Spanish  (`UN_es`), Russian (`UN_ru`). This nomenclature is only available for countries in the M49 standard (currently 249 territories).",
           "Official WTO name in 3 official WTO languages: English (`WTO_en`), French (`WTO_fr`), Spanish (`WTO_es`). This nomenclature is only available for WTO members and observers (currently 189 entities).",
           "Translation of ISO country names in 28 different languages: Arabic (`name_ar`), Bulgarian (`name_bg`), Czech (`name_cs`), Danish (`name_da`), German (`name_de`), Greek (`name_el`), English  (`name_en`), Spanish  (`name_es`), Estonian (`name_et`),  Basque (`name_eu`),  Finnish (`name_fi`), French (`name_fr`), Hungarian (`name_hu`), Italian (`name_it`), Japponease (`name_ja`), Korean (`name_ko`), Lithuanian (`name_lt`), Dutch (`name_nl`), Norwegian (`name_no`), Polish (`name_po`), Portuguese (`name_pt`), Romenian (`name_ro`), Russian (`name_ru`), Slovak (`name_sk`), Swedish (`name_sv`), Thai (`name_th`), Ukranian (`name_uk`), Chinese simplified (`name_zh`), Chinese traditional (`name_zh-tw`)",
           "GTAP country and region codes.", "Converts to all the nomenclatures and languages in this table"))

knitr::kable(tab)

Further options and warning messages

country_name() can identify countries even when they are provided in mixed formats or in different languages. It is robust to small misspellings and recognises alternative name formulations and old nomenclatures.

fuzzy_example <- c("US","C@ète d^Ivoire","Zaire","FYROM","Estados Unidos","ITA","blablabla")

country_name(x= fuzzy_example, to=c("UN_en"))

More information on the country matching process can be obtained by setting verbose=TRUE. The function will print information on:

The number of unique values provided by the user. In the example below 7 distinct strings have been provided.
How many country names correspond exactly to the ones in the function's reference list and how many have been processed with fuzzy matching. In the example below, "C@ète d^Ivoire" and "blablabla" are the only names processed with fuzzy matching. The function's reference table can be accessed with the command data(country_reference_list). Finally, if any match is poor, the function will print the number of country names which are probably mismatched (in this example only "blablabla").

country_name(x= fuzzy_example, to=c("UN_en"), verbose=TRUE)

In addition, setting verbose=TRUE will also print additional informations relating to specific warnings that are normally given by the function:

Multiple country IDs have been matched to the same country name: This warning is issued if multiple strings have been matched to the same country. In verbose mode, the strings and corresponding countries will be listed. In the example above, both "US" and "Estados Unidos" are matched to the same country. If the vector of country names is a unique identifier, this could indicate that some country name was not recognised correctly. The user might consider using custom tables (refer to the next section).
Unable to find an EXACT match for all country names: indicates that it is impossible to find an exact match for one or more country names with fuzzy_match=FALSE. The user might consider using fuzzy_match=TRUE or custom tables (refer to the next section).
There is low confidence on the matching of some country names: This warning indicates that some strings have been matched poorly. Thus indicating that the country might have been misidentified. In verbose mode the function will provide a list of problematic strings (see the example below). If poor_matches is set to FALSE (the default), the function will return NA for these uncertain string. On the other hand, if poor_matches=TRUE the function will always return the closest match, even if poor. The user might consider using custom tables to solve issues with misidentification of country names (refer to the next section). Alternatively, the user can set na_fill=TRUE to replace the resulting NAs with the original name provided in x.
Some country IDs have no match in one or more country naming conventions: Conversion is requested to a nomenclature for which there is no information on the country. For instance, in the example below "Taiwan" has no correspondence in the UN M49 standard. In verbose mode, the function will print all the country names affected by this problem. The user might consider using custom tables to solve this type of issues (refer to the next section). Alternatively, the user can set na_fill=TRUE to replace the resulting NAs with the original name provided in x.

country_name(x= c("Taiwan","lsajdèd"), to=c("UN_en"), verbose=FALSE)

country_name(x= c("Taiwan","lsajdèd"), to=c("UN_en"), verbose=FALSE, na_fill = TRUE)

All the information from verbose mode can be accessed by setting ´simplify=FALSE´. This will return a list object containing:

converted_data: the normal output of the function
match_table: the conversion table with information on the closest match for each country name and distance metrics.
summary: summary values for the distance metrics
warning: logical value indicating whether a warning is issued by the function
call: the arguments passed by the user

Using custom conversion tables

In some cases, the user might be unhappy with the naming conversion or no valid conversion might exist for the provided territory. In these cases, it might be useful to tweak the conversion table. The package contains a utility function called match_table(), which can be used to generate conversion tables for small adjustments.

example_custom <- c("Siam","Burma","H#@°)Koe2")

#suppose we are unhappy with how "H#@°)Koe2" is interpreted by the function
country_name(x = example_custom, to = "name_en")

#match_table can be used to generate a table for small adjustments
tab <- match_table(x = example_custom, to = "name_en")
tab$name_en[2] <- "Hong Kong"

#which can then be used for conversion
country_name(x = example_custom, to = "name_en", custom_table = tab)