countrycode standardizes country names, converts them into ~40 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

• Why countrycode?
• Installation
• Supported codes
• countrycode
  • Convert of a single name or code
  • Vectors and data.frames
  • Flags
  • Country names in 600+ different languages and formats
  • custom_dict and get_dictionary(): Custom dictionaries and cross-walks
  • destination: Fallback codes
  • nomatch: Fill in missing codes manually
  • custom_match: Override default values
  • warn: Silence warnings
• countryname: Convert country names from any language
• Custom conversion functions and “crosswalks”
• Contributions

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 40+ 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.

From the R console, type:

install.packages("countrycode")

To install the latest development version, you can use the remotes package:

library(remotes)
install_github('vincentarelbundock/countrycode')

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.
• AR5
• Continent and region identifiers.
• Correlates of War (numeric and character)
• European Central Bank
• EUROCONTROL - The European Organisation for the Safety of Air Navigation
• Eurostat
• Federal Information Processing Standard (FIPS)
• Food and Agriculture Organization of the United Nations
• Global Administrative Unit Layers (GAUL)
• Geopolitical Entities, Names and Codes (GENC)
• Gleditsch & Ward (numeric and character)
• International Civil Aviation Organization
• International Monetary Fund
• International Olympic Committee
• ISO (2/3-character and numeric)
• Polity IV
• United Nations
• United Nations Procurement Division
• Varieties of Democracy
• World Bank
• World Values Survey
• Unicode symbols (flags)

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 or Italian to Arabic
countrycode(c('Algerien', 'Albanien'), origin = 'country.name.de', destination = 'un.name.ar') 

## [1] "الجزائر" "ألبانيا"

countrycode(c('Moldavia', 'Stati Uniti'), origin = 'country.name.it', destination = 'un.name.ar') 

## [1] "جمهورية مولدوفا"            "الولايات المتحدة الأمريكية"

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   22
## 2      ALB  159
## 3      UKG  100
## 4      CAN  447
## 5      USA  373

df2

##   isocodes var2
## 1       12   15
## 2        8  207
## 3      826  111
## 4      124  418
## 5      840  331

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  159        8  207        Albania
## 2   CAN      CAN  447      124  418         Canada
## 3   DZA      ALG   22       12   15        Algeria
## 4   GBR      UKG  100      826  111 United Kingdom
## 5   USA      USA  373      840  331  United States

countrycode can convert country names and codes to unicode flags. For example, we can use the gt package to draw a table with countries and their corresponding flags:

library(gt)
library(countrycode)

Countries <- c('Canada', 'Germany', 'Thailand', 'Algeria', 'Eritrea')
Flags <- countrycode(Countries, 'country.name', 'unicode.symbol')
dat <- data.frame(Countries, Flags)
gt(dat)

Note that embedding unicode characters in R graphics is possible, but it can be tricky. If your output looks like \U0001f1e6\U0001f1f6, then you could try feeding it to this function: utf8::utf8_print(). That should cover a lot of cases without dipping into the complexity of graphics devices. As a rule of thumb, if your output looks like □□□□ (boxes), things tend to get more complicated. In that case, you’ll have to think about different output devices, file viewers, and/or file formats (e.g., ‘SVG’ or ‘HTML’).

Since inserting unicode symbols into R graphics is not a countrycode-specific issue, we won’t be able to offer any more support than this. Good luck!

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.agq                         TF
## 2    cldr.name.ak                         TF
## 3    cldr.name.am           የፈረንሳይ ደቡባዊ ግዛቶች
## 4    cldr.name.ar الأقاليم الجنوبية الفرنسية
## 5 cldr.name.ar_ly الأقاليم الجنوبية الفرنسية
## 6 cldr.name.ar_sa الأقاليم الجنوبية الفرنسية

The custom_dict argument accepts data frame which can be used as custom dictionaries to create “crosswalks” between arbitrary entities (non-countries). You can create your own dictionaries (see examples below) or use one of the dictionaries already hosted on the countrycode Github repository. The current list of available dictionaries can be seen by calling:

get_dictionary()

## Available dictionaries: ch_cantons, exiobase3, global_burden_of_disease, gtap10, us_states

You can download a dictionary and see available fields with:

cd <- get_dictionary("us_states")
head(cd)

##   state.name state.abb    state.regex
## 1    Alabama        AL    .*alabama.*
## 2     Alaska        AK     .*alaska.*
## 3    Arizona        AZ    .*arizona.*
## 4   Arkansas        AR   .*arkansas.*
## 5 California        CA .*california.*
## 6   Colorado        CO   .*colorado.*

Now we can use the dictionary for conversions:

st <- c("Arkansas", "Quebec", "Tennessee")
countrycode(st, "state.regex", "state.abb", custom_dict = cd)

## Warning: Some values were not matched unambiguously: Quebec

## [1] "AR" NA   "TN"

countrycode(c("MN", "MA", "MO"), "state.abb", "state.name", custom_dict = cd)

## [1] "Minnesota"     "Massachusetts" "Missouri"

Here’s an example with the GTAP dataset:

cd <- get_dictionary("gtap10")
countrycode("Christmas Island", "country.name.en.regex", "gtap.cha", custom_dict = cd)

## [1] "AUS"

countrycode already supports ISO4217 (currencies) and ISO3166 (country codes). The ISOcodes package supplies other codes, including ISO15924 (language writing systems), ISO639 (languages), and ISO8859 (computer character encodings). Users can convert those codes using countrycode’s custom_dict argument.

For example, the ISOcodes::ISO_639_2 dataframe includes 4 columns: Alpha_3_B, Alpha_3_T, Alpha_2, and Name. We can convert language names like this:

countrycode('abk', 'Alpha_3_B', 'Name', custom_dict = ISOcodes::ISO_639_2)

## [1] "Abkhazian"

The ISOcodes::ISO_8859 dataset is a 3-dimensional array where the second dimension represents the character encoding. We take the subset of ISO_8859_1 codes and convert the dict to a dataframe for use in countrycode’s custom_dict argument:

library(ISOcodes)
dict <- ISOcodes::ISO_8859[, 'ISO_8859_1', ]
dict <- data.frame(dict)

The resulting dataframe has 3 columns: Code, Name, Character. We convert the code 0x00fd like this:

countrycode("0x00fd", "Code", "Name", custom_dict = dict)

## [1] "LATIN SMALL LETTER Y WITH ACUTE"

countrycode("0x00fd", "Code", "Character", custom_dict = dict)

## [1] "ý"

Some destination codes not cover all the relevant countries. For example, “SRB” is included in the iso3c code but not in the cowc code. Some users may want to use cowc but to fill in missing entries with iso3c codes. We can do this by feeding a vector of code names to the destination argument. countrycode will then try one after the other.

For example,

x <- c("Algeria", "Serbia")

countrycode(x, "country.name", "cowc")

## Warning: Some values were not matched unambiguously: Serbia

## [1] "ALG" NA

countrycode(x, "country.name", "iso3c")

## [1] "DZA" "SRB"

countrycode(x, "country.name", c("cowc", "iso3c"))

## Warning: Some values were not matched unambiguously: Serbia

## [1] "ALG" "SRB"

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"

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)

## [1] "EL" "UK" "DE" "FR"

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) |>
    head()

## Warning: Some values were not matched unambiguously: EA, EA12, EA19, EA20, EU15, EU27_2020, EU28, XK

## [1] "Austria"  "Belgium"  "Bulgaria" "Cyprus"   "Czechia"  "Germany"

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.

The function countryname tries to convert country names from any language. For example:

library(countrycode)
x <- c('ジンバブエ', 'Afeganistãu',  'Barbadas', 'Sverige', 'UK',  
       'il-Georgia tan-Nofsinhar u l-Gżejjer Sandwich tan-Nofsinhar')

countryname(x)

## [1] "Zimbabwe"                              
## [2] "Afghanistan"                           
## [3] "Barbados"                              
## [4] "Sweden"                                
## [5] "UK"                                    
## [6] "South Georgia & South Sandwich Islands"

countryname(x, 'iso3c')

## [1] "ZWE" "AFG" "BRB" "SWE" "GBR" "SGS"

It is easy to to create alternative functions with different default arguments and/or dictionaries. For example, we can create:

• name_to_iso3c function that sets new defaults for the origin and destination arguments, and automatically converts country names to iso3c
• statecode function to convert US state codes using a custom dictionary by default, that we download from the internet.

#################################
#  new function: name_to_iso3c  #
#################################

# Custom defaults
name_to_iso3c <- function(sourcevar,
                          origin = "country.name",
                          destination = "iso3c",
                          ...) {
  countrycode(sourcevar, origin = origin, destination = destination, ...)
}

name_to_iso3c(c("Algeria", "Canada"))

## [1] "DZA" "CAN"

#############################
#  new function: statecode  #
#############################

# Download dictionary
state_dict <- "https://raw.githubusercontent.com/vincentarelbundock/countrycode/main/data/custom_dictionaries/data_us_states.csv"
state_dict <- read.csv(state_dict)

# Identify regular expression origin codes
attr(state_dict, "origin_regex") <- "state.regex"

# Define a custom conversion function
statecode <- function(sourcevar,
                      origin = "state.regex",
                      destination = "abbreviation",
                      custom_dict = state_dict,
                      ...) {
    countrycode(sourcevar,
                origin = origin,
                destination = destination,
                custom_dict = custom_dict,
                ...)
}

# Voilà!
x <- c("Alabama", "New Mexico")
statecode(x, "state.regex", "abbreviation")

## [1] "AL" "NM"

x <- c("AL", "NM", "VT")
statecode(x, "abbreviation", "state")

## [1] "Alabama"    "New Mexico" "Vermont"

New country codes are created by two files:

1. dictionary/get_*.R is an R script which can scrape the code from an original online source (e.g., get_world_bank.R). This scripts only side effect is that it writes a CSV file to the dictionary folder.
2. dictionary/data_*.csv is a CSV file with 1 column called country, which includes the English country name, and 1 or more columns named after the codes you want to add (e.g., iso3c, un.name.en, continent).

After creating those two files, you should:

• Run dictionary/build.R
• If the code is a valid origin code (i.e., no two countries share the same code), add it to the valid_origin vector in R/countrycode.R
• Add the new code name to the documentation in R/codelist.R
• Build the documentation using the devtools package: devtools::document()
• Add a bullet point to NEWS.md file.

If you need help with any of these steps, or if you just want to submit a CSV file, feel free to open an issue on Github or write an email to Vincent. I’ll be happy to help you out!

The countrycode repository holds several custom dictionaries: https://github.com/vincentarelbundock/countrycode/tree/master/data/custom_dictionaries

To add your own custom dictionary, please make sure that:

1. You save a comma-separated CSV file that looks something like data/custom_dictionaries/us_states.csv
2. The custom dictionary has a unique purpose (not overlapping with existing custom dictionaries)
3. It uses UTF-8 encoding and conforms to RFC 4180 CSV standard (e.g. comma-delimited, etc.).
   • R commands to produce such a file are shown below.
4. /blank fields are blank, not the string ‘NA’ (not RFC 4180, but important here because of Namibia)
5. It has concise, sensible, valid (in the R data frame sense) column header names

Using base write.csv:

write.csv(custom_dict, 'custom_dict.csv', quote = TRUE, na = '', 
          row.names = FALSE, qmethod = 'double', fileEncoding = 'UTF-8')

Using readr:

readr::write_csv(custom_dict, 'custom_dict.csv', na = '')

When using custom dictionaries, it is often useful to give “meta” information to countrycode so that it knows how to use certain codes. To do this, we can set attributes of the dictionary. In this example, we download a dictionary of US state codes. Then, we identify a column of regular expressions using the origin_regex attribute, and we identify the valid origin codes using the origin_valid attribute.

state_dict <- "https://raw.githubusercontent.com/vincentarelbundock/countrycode/main/data/custom_dictionaries/data_us_states.csv"
state_dict <- read.csv(state_dict)

attr(state_dict, "origin_regex") <- "state.regex"
attr(state_dict, "origin_valid") <- c("state.regex", "abbreviation")

countrycode("Alabama", "state.regex", "abbreviation", custom_dict = state_dict)

## [1] "AL"

countrycode("AL", "abbreviation", "state", custom_dict = state_dict)

## [1] "Alabama"

countrycode("Alabama", "state", "abbreviation", custom_dict = state_dict)

## Error in countrycode("Alabama", "state", "abbreviation", custom_dict = state_dict): The `origin` argument must be a string of length 1 equal to one of these values: state.regex, abbreviation.