countrycode standardizes country names, converts them into ~20 different coding schemes, and assigns region descriptors. Scroll down for more details or visit the countrycode CRAN page

If you use countrycode in your research, we would be very grateful if you could cite our paper:

Arel-Bundock, Vincent, Nils Enevoldsen, and CJ Yetman, (2018). countrycode: An R package to convert country names and country codes. Journal of Open Source Software, 3(28), 848, https://doi.org/10.21105/joss.00848

Want to contribute? Great! Scroll all the way down for details.

Different data sources use different coding schemes to represent countries (e.g. CoW or ISO). This poses two main problems: (1) some of these coding schemes are less than intuitive, and (2) merging these data requires converting from one coding scheme to another, or from long country names to a coding scheme.

The countrycode function can convert to and from 30+ different country coding schemes, and to 600+ variants of country names in different languages and formats. It uses regular expressions to convert long country names (e.g. Sri Lanka) into any of those coding schemes or country names. It can create new variables with various regional groupings.

To get an up-to-date list of supported country codes, install the package and type ?codelist. These include:

• 600+ variants of country names in different languages and formats.
• Correlates of War (numeric and character)
• Gleditsch & Ward (numeric and character)
• ISO (2/3-character and numeric)
• United Nations
• International Monetary Fund
• World Bank
• Polity IV
• Varieties of Democracy
• European Central Bank
• Euro-control (aviation)
• Eurostat
• Food and Agriculture Organization of the United Nations
• International Olympic Committee
• International Civil Aviation Organization
• United Nations Procurement Division
• AR5
• Federal Information Processing Standard (FIPS)
• Global Administrative Unit Layers (GAUL)
• Geopolitical Entities, Names and Codes (GENC)
• Continent and region identifiers.

From the R console, type install.packages("countrycode")

Load library:

> library(countrycode)

Convert single country codes:

# ISO to Correlates of War
countrycode('DZA', origin = 'iso3c', destination = 'cown') 
[1]   615

# English to ISO
countrycode('Albania', origin = 'country.name', destination = 'iso3c') 
[1] "ALB"

# German to Arabic
countrycode(c('Algerien', 'Albanien'), origin = 'country.name.de', destination = 'un.name.ar') 
[1] "الجزائر" "ألبانيا"

The Unicode organisation hosts the CLDR project, which publishes many variants of country names. For each language/culture locale, there is a full set of names, plus possible 'alt-short' or 'alt-variant' variations of specific country names.

> countrycode('United States of America', origin = 'country.name', destination = 'cldr.name.en')
> [1] "United States"
> countrycode('United States of America', origin = 'country.name', destination = 'cldr.short.en')
> [1] "US"

To see a full list of country name variants available, inspect this data.frame:

> head(countrycode::cldr_examples)
             Code                    Example
1    cldr.name.af   Franse Suidelike Gebiede
2   cldr.name.agq                         TF
3    cldr.name.ak                         TF
4    cldr.name.am           የፈረንሳይ ደቡባዊ ግዛቶች
5    cldr.name.ar الأقاليم الجنوبية الفرنسية
6 cldr.name.ar_ly الأقاليم الجنوبية الفرنسية

> cowcodes <- c("ALG", "ALB", "UKG", "CAN", "USA")
> countrycode(cowcodes, origin = "cowc", destination = "iso3c")
[1] "DZA" "ALB" "GBR" "CAN" "USA"

Generate vectors and 2 data frames without a common id (i.e. can't merge the 2 df):

> isocodes <- c(12,8,826,124,840)
> var1     <- sample(1:500,5)
> var2     <- sample(1:500,5)
> df1      <- data.frame(cowcodes,var1)
> df2      <- data.frame(isocodes,var2)

Inspect the data:

> df1
cowcodes var1
1      ALG   71
2      ALB  427
3      UKG  180
4      CAN   21
5      USA  383
> df2
isocodes var2
1       12  238
2        8  329
3      826  463
4      124  437
5      840   26

Create a common variable with the iso3c code in each data frame, merge the data, and create a country identifier:

> df1$iso3c   <- countrycode(df1$cowcodes, origin = "cowc", destination = "iso3c")
> df2$iso3c   <- countrycode(df2$isocodes, origin = "iso3n", destination = "iso3c")
> df3         <- merge(df1,df2,id="iso3c")
> df3$country <- countrycode(df3$iso3c, origin = "iso3c", destination = "country.name")
> df3
iso3c cowcodes var1 isocodes var2        country
1   ALB      ALB  113        8  245        ALBANIA
2   CAN      CAN  373      124  197         CANADA
3   DZA      ALG  254       12  295        ALGERIA
4   GBR      UKG  351      826   57 UNITED KINGDOM
5   USA      USA  241      840   85  UNITED STATES

Since version 0.19, countrycode accepts user supplied dictionaries via the custom_dict argument. For example, the countrycode Github repository includes a dictionary of regexes and abbreviations to work with US state names.

Load the library and download the custom dictionary data.frame:

library(countrycode)
url = "https://raw.githubusercontent.com/vincentarelbundock/countrycode/master/data/extra/us_states.csv"
state_dict = read.csv(url, stringsAsFactors=FALSE)

Convert:

countrycode('State of Alabama', 
            origin = 'state', 
            destination = 'abbreviation', 
            custom_dict = state_dict,
            origin_regex = TRUE)
[1] "AL"
countrycode(c('MI', 'OH', 'Bad'), 'abbreviation', 'state', custom_dict=state_dict)
[1] "Michigan" "Ohio"     NA        

Note that if you use a custom dictionary with country codes, you could easily merge it into the countrycode::codelist or countrycode::codelist_panel to gain access to all other codes.

Since version 0.19, countrycode accepts a user supplied named vector of custom matches via the custom_match argument. Any match pairs in the custom_match vector will supercede the default results of the command. This allows the user to convert to an available country code and make minor post-edits all at once. The names of the named vector are used as the origin code, and the values of the named vector are used as the destination code.

For example, Eurostat uses a modified version of iso2c, with Greece (EL instead of GR) and the UK (UK instead of GB) being the only differences. Getting a proper result converting to Eurostat is easy to achieve using the iso2c destination and the new custom_match argument. (Note: since version 0.19, countrycode also includes a eurostat origin/destination code, so while this is a good example, doing so for Eurostat is not necessary)

example: convert from country name to Eurostat code

library(countrycode)
country_names <- c('Greece', 'United Kingdom', 'Germany', 'France')
custom_match <- c(Greece = 'EL', `United Kingdom` = 'UK')
countrycode(country_names, 
            origin = 'country.name', 
            destination = 'iso2c', 
            custom_match = custom_match)

example: convert from Eurostat code to country name

library(eurostat)
library(countrycode)
df <- eurostat::get_eurostat("nama_10_lp_ulc")
custom_match <- c(EL = 'Greece', UK = 'United Kingdom')
countrycode(df$geo, origin = 'iso2c', destination = 'country.name', custom_match = custom_match)

Use warn = TRUE to print out a list of source elements for which no match was found. When the source vector are long country names that need to be matched using regular expressions, there is always a risk that multiple regex will match a given string. When this is the case, countrycode assigns a value arbitrarily, but the warn argument allows the user to print a list of all strings that were matched many times.

Use the nomatch argument to specify the value that countrycode inserts where no match was found:

> countrycode(c('DZA', 'USA', '???'), origin = 'iso3c', destination = 'country.name', nomatch = 'BAD CODE')
> [1] "Algeria"       "United States" "BAD CODE"  
> countrycode(c('Canada', 'Fake country'), origin = 'country.name', destination = 'iso3c', nomatch = 'BAD')
> [1] "CAN" "BAD"

The best way to contribute is to add a get_* function that downloads country codes from an official sources and formats the data.

Each data source must be associated with a script called get_source.R in the dictionary folder. For instance, get_world_bank.R or get_fao.R.

Each get_source.R file must include a single function also named get_source. That function downloads the original data and cleans it. It returns:

A data.frame or tibble

• One column must be called regex and use countrycode regexes as unique identifiers.
  • Please use the CountryToRegex function from utilities.R to produce that column.
• No duplicate entries for a single country (if data is cross-sectional) or country-year (if data is panel).

If you feel a bit lazier (or don't have access to a solid online source), you can also merge your new country code manual in the dictionary_static.csv file located in the data folder.