Building a dataset for political science analysis in R, PART 2

Packages we will need

library(tidyverse)
library(peacesciencer)
library(countrycode)
library(bbplot)

The main workhorse of this blog is the peacesciencer package by Stephen Miller!

The package will create both dyad datasets and state datasets with all sovereign countries.

Thank you Mr Miller!

There are heaps of options and variables to add.

Go to the page to read about them all in detail.

Here is a short list from the package description of all the key variables that can be quickly added:

We create the dyad dataset with the create_dyadyears() function. A dyad-year dataset focuses on information about the relationship between two countries (such as whether the two countries are at war, how much they trade together, whether they are geographically contiguous et cetera).

In the literature, the study of interstate conflict has adopted a heavy focus on dyads as a unit of analysis.

Alternatively, if we want just state-year data like in the previous blog post, we use the function create_stateyears()

We can add the variables with type D to the create_dyadyears() function and we can add the variables with type S to the create_stateyears() !

Focusing on the create_dyadyears() function, the arguments we can include are directed and mry.

The directed argument indicates whether we want directed or non-directed dyad relationship.

In a directed analysis, data include two observations (i.e. two rows) per dyad per year (such as one for USA – Russia and another row for Russia – USA), but in a nondirected analysis, we include only one observation (one row) per dyad per year.

The mry argument indicates whether they want to extend the data to the most recently concluded calendar year – i.e. 2020 – or not (i.e. until the data was last available).

dyad_df <- create_dyadyears(directed = FALSE, mry = TRUE) %>%
  add_atop_alliance() %>%  
  add_nmc() %>%
  add_cow_trade() %>% 
  add_creg_fractionalization() 

I added dyadic variables for the

You can follow these links to check out the codebooks if you want more information about descriptions about each variable and how the data were collected!

The code comes with the COW code but I like adding the actual names also!

dyad_df$country_1 <- countrycode(dyad_df$ccode1, "cown", "country.name")

With this dataframe, we can plot the CINC data of the top three superpowers, just looking at any variable that has a 1 at the end and only looking at the corresponding country_1!

According to our pals over at le Wikipedia, the Composite Index of National Capability (CINC) is a statistical measure of national power created by J. David Singer for the Correlates of War project in 1963. It uses an average of percentages of world totals in six different components (such as coal consumption, military expenditure and population). The components represent demographic, economic, and military strength

First, let’s choose some nice hex colors

pal <- c("China" = "#DE2910",
         "United States" = "#3C3B6E", 
         "Russia" = "#FFD900")

And then create the plot

dyad_df %>% 
 filter(country_1 == "Russia" | 
          country_1 == "United States" | 
          country_1 == "China") %>% 
  ggplot(aes(x = year, y = cinc1, group = as.factor(country_1))) +
  geom_line(aes(color = country_1)) +
  geom_line(aes(color = country_1), size = 2, alpha = 0.8) + 
  scale_color_manual(values =  pal) +
  bbplot::bbc_style()

In PART 3, we will merge together our data with our variables from PART 1, look at some descriptive statistics and run some panel data regression analysis with our different variables!

Summarise data with skimr package in R

A nice way to summarise all the variables in a dataset.

install.packages("skimr")
library(skimr)

The data we’ll look at is from the Correlates of War . It provides dyadic records of militarized interstate disputes (MIDs) over the period of 1816-2010.

skim(mid)

n_missing : tells which variables have missing values

complete_rate : the percentage of the variables which are missing

Column 4 – 7 gives the mean, standard deviation, min, 25th percentile, median, 75th percentile and max values.

The last column is a histogram of each variables, so you can easily scan and see if variables are normally distributed, skewed or binary.

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