Category: data management

Add Correlates of War codes with countrycode package in R

One problem with merging two datasets by country is that the same countries can have different names. Take for example, America. It can be entered into a dataset as any of the following:

  • USA
  • U.S.A.
  • America
  • United States of America
  • United States
  • US
  • U.S.

This can create a big problem because datasets will merge incorrectly if they think that US and America are different countries.

Correlates of War (COW) is a project founded by Peter Singer, and catalogues of all inter-state war since 1963. This project uses a unique code for each country.

For example, America is 2.

When merging two datasets, there is a helpful R package that can convert the various names for a country into the COW code:

install.packages("countrycode")
library(countrycode)

To read more about the countrycode package in the CRAN PDF, click here.

First create a new name for the variable I want to make; I’ll call it COWcode in the dataset.

Then use the countrycode() function. First type in the brackets the name of the original variable that contains the list of countries in the dataset. Then finally add "country.name", "cown". This turns the word name for each country into the numeric COW code. 

dataset$COWcode <- countrycode(dataset$countryname, "country.name", "cown")

If you want to turn into a country name, swap the "country.name" and "cown" 

dataset$countryname <- countrycode(dataset$COWcode, "country.name", "cown")

Now the dataset is ready to merge more easily with my other dataset on the identical country variable type!

There are many other types of codes that you can add to your dataset.

A very popular one is the ISO-2 and ISO-3 codes. For example, if you want to add flags to your graph, you will need a two digit code for each country (for example, Ireland is IE).

To see the list of all the COW codes, click here.

To check out the COW database website, click here.

Alternative codes than the country.name and the cown options include:

• ccTLD: IANA country code top-level domain
• country.name: country name (English)
• country.name.de: country name (German)
• cowc: Correlates of War character
• cown: Correlates of War numeric
• dhs: Demographic and Health Surveys Program
• ecb: European Central Bank
• eurostat: Eurostat
• fao: Food and Agriculture Organization of the United Nations numerical code
• fips: FIPS 10-4 (Federal Information Processing Standard)
• gaul: Global Administrative Unit Layers
• genc2c: GENC 2-letter code
• genc3c: GENC 3-letter code
• genc3n: GENC numeric code
• gwc: Gleditsch & Ward character
• gwn: Gleditsch & Ward numeric
• imf: International Monetary Fund
• ioc: International Olympic Committee
• iso2c: ISO-2 character
• iso3c: ISO-3 character
• iso3n: ISO-3 numeric
• p4n: Polity IV numeric country code
• p4c: Polity IV character country code
• un: United Nations M49 numeric codes
4 codelist
• unicode.symbol: Region subtag (often displayed as emoji flag)
• unpd: United Nations Procurement Division
• vdem: Varieties of Democracy (V-Dem version 8, April 2018)
• wb: World Bank (very similar but not identical to iso3c)
• wvs: World Values Survey numeric code

# Some of my own manual COW code fixes
manual_cow_codes <- tibble::tribble(
  ~country,                 ~cow_code,
  "Palestinian Authority",   999,
  "Micronesia",              987,
  "Serbia"                   345
)

Turn wide to long format with reshape2 package in R

A simple feature to turn wide format into long format in R.

I have a dataset with the annual per capita military budget for 171 countries.

The problem is that it is in completely wrong format to use for panel data (i.e. cross-sectional time-series analysis).

So here is simple way I found to fix this problem and turn this:

WIDE FORMAT : a separate column for each year

into this:

LONG FORMAT : one single “year” column and one single “value” column

It’s like magic.

First install and load the reshape2 package

install.packages("reshape2")
library(reshape2)

I name my new long form dataframe; in this case, the imaginatively named mil_long.

I use the melt() function and first type in the name of the original I want to change; in this case it is mil_wide

id.vars tells R the unique ID for each new variable. Since I am looking at military budgets for each country, I’ll use Country variable as my ID.

variable.name for me is the year variable which, in wide format, is the name of every column. For me, I want to compress all the year columns into this new variable.

value.name is the new variable I make to hold the value that in my dataset is the per capita military budget amount per country per year. I name this new variable … you guessed it, value.

mil_long <- melt(mil_wide, id.vars= "Country", variable.name = "year", value.name = "value"))

So simple, it’s hard to believe.

Looking at my new mil_long dataset, my new long format dataframe has only three columns = “Country”, “year” and “value” and 5,504 rows for each country-year observation across the 32 years.

Now, my dataframe is ready to be transformed into a panel data frame!

reshape2 has two main functions which I think have quite memorable names:  melt and cast.

melt is for wide-format dataframes that you want to “melt” into long-format.

cast for dataframes in long-format data which you figuratively “cast” into a wide-format dataframe.

As a poli-sci person, I have so far only turned my dataframe in long form, for eventual panel data analysis with "plm" package.

Click here to see how to transform dataframes into panel dataframes with the plm package.

Click here to read the full reshape2 package documentation on CRAN