Download democracy data with democracyData package in R

Packages we will need:

library(democracyData)
library(tidyverse)
library(magrittr)       # for pipes
library(ggstream)       # proportion plots
library(ggthemes)       # nice ggplot themes
library(forcats)        # reorder factor variables
library(ggflags)        # add flags
library(peacesciencer)  # more great polisci data
library(countrycode)    # add ISO codes to countries

This blog will highlight some quick datasets that we can download with this nifty package.

To install the democracyData package, it is best to do this via the github of Xavier Marquez:

remotes::install_github("xmarquez/democracyData", force = TRUE)
library(democracyData)

We can download the dataset from the Democracy and Dictatorship Revisited paper by Cheibub Gandhi and Vreeland (2010) with the redownload_pacl() function. It’s all very simple!

pacl <- redownload_pacl()
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This gives us over 80 variables, with information on things such as regime type, geographical data, the name and age of the leaders, and various democracy variables.

We are going to focus on the different regimes across the years.

The six-fold regime classification Cheibub et al (2010) present is rooted in the dichotomous classification of regimes as democracy and dictatorship introduced in Przeworski et al. (2000). They classify according to various metrics, primarily by examining the way in which governments are removed from power and what constitutes the “inner sanctum” of power for a given regime. Dictatorships can be distinguished according to the characteristics of these inner sanctums. Monarchs rely on family and kin networks along with consultative councils; military rulers confine key potential rivals from the armed forces within juntas; and, civilian dictators usually create a smaller body within a regime party—a political bureau—to coopt potential rivals. Democracies highlight their category, depending on how the power of a given leadership ends

We can change the regime variable from numbers to a factor variables, describing the type of regime that the codebook indicates:

pacl %<>% 
  mutate(regime_name = ifelse(regime == 0, "Parliamentary democracies",
       ifelse(regime == 1, "Mixed democracies",
       ifelse(regime == 2, "Presidential democracies",
       ifelse(regime == 3, "Civilian autocracies",
       ifelse(regime == 4, "Military dictatorships",
       ifelse(regime ==  5,"Royal dictatorships", regime))))))) %>%
  mutate(regime = as.factor(regime)) 

Before we make the graph, we can give traffic light hex colours to the types of democracy. This goes from green (full democracy) to more oranges / reds (autocracies):

regime_palette <- c("Military dictatorships" = "#f94144", 
                    "Civilian autocracies" = "#f3722c", 
                    "Royal dictatorships" =  "#f8961e", 
                    "Mixed democracies" = "#f9c74f", 
                    "Presidential democracies" = "#90be6d", 
                    "Parliamentary democracies" = "#43aa8b")

We will use count() to count the number of countries in each regime type and create a variable n

pacl %>% 
  mutate(regime_name = as.factor(regime_name)) %>% 
  mutate(regime_name = fct_relevel(regime_name, 
 levels = c("Parliamentary democracies", 
           "Presidential democracies",
           "Mixed democracies",
           "Royal dictatorships",
           "Civilian autocracies",
           "Military dictatorships"))) %>% 
  group_by(year, un_continent_name) %>% 
  filter(!is.na(regime_name)) %>% 
  count(regime_name) %>% 
  ungroup() %>%  
  filter(un_continent_name != "") %>%
  filter(un_continent_name != "Oceania") -> pacl_count
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We have all the variables we need.

We can now graph the count variables across different regions.

pacl_count %>% 
  ggplot(aes(x = year, y = n, 
             groups = regime_name, 
             fill = regime_name)) +
  ggstream::geom_stream(type = "proportion") + 
  facet_wrap(~un_continent_name) + 
  scale_fill_manual(values = regime_palette) + 
  ggthemes::theme_fivethirtyeight() + 
  theme(legend.title = element_blank(),
        text = element_text(size = 30)) 

I added the title and source header / footer section on canva.com to finish the graph.

Of course, the Cheibub et al (2010) dataset is not the only one that covers types of regimes.

Curtis Bell in 2016 developed the Rulers, Elections, and Irregular Governance Dataset (REIGN) dataset.

This describes political conditions in every country (including tenures and personal characteristics of world leaders, the types of political institutions and political regimes in effect, election outcomes and election announcements, and irregular events like coups)

Again, to download this dataset with the democracyData package, it is very simple:

reign <- download_reign()
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I want to compare North and South Korea since their independence from Japan and see the changes in regimes and democracy scores over the years.

Next, we can easily download Freedom House or Polity 5 scores.

The Freedom House Scores default dataset ranges from 1972 to 2020, covering around 195 countries (depending on the year)

fh <- download_fh()

Alternatively, we can look at Polity Scores. This default dataset countains around 190 ish countries (again depending on the year and the number of countries in existance at that time) and covers a far longer range of years; from 1880 to 2018.

polityiv <- redownload_polityIV()

Alternatively, to download democracy scores, we can also use the peacesciencer dataset. Click here to read more about this package:

democracy_scores <- peacesciencer::create_stateyears() %>% 
  add_gwcode_to_cow() %>%
  add_democracy() 

With inner_join() we can merge these two datasets together:

reign %>% 
  select(ccode = cown, everything()) %>% 
  inner_join(democracy_scores, by = c("year", "ccode")) -> reign_demo

We next choose the years and countries for our plot.

Also, for the geom_flag() we will need the country name to be lower case ISO code. Click here to read more about the ggflags package.

reign_demo %>% 
    filter(year > 1945) %>% 
    mutate(gwf_regimetype = str_to_title(gwf_regimetype)) %>% 
    mutate(iso2c_lower = tolower(countrycode::countrycode(reign_country, "country.name", "iso2c"))) %>% 
filter(reign_country == "Korea North" | reign_country == "Korea South") -> korea_reign

We may to use specific hex colours for our graphs. I always prefer these deeper colours, rather than the pastel defaults that ggplot uses. I take them from coolors.co website!

korea_palette <- c("Military" = "#5f0f40",
                   "Party-Personal" = "#9a031e",
                   "Personal" = "#fb8b24",
                   "Presidential" = "#2a9d8f",
                   "Parliamentary" = "#1e6091")

We will add a flag to the start of the graph, so we create a mini dataset that only has the democracy scores for the first year in the dataset.

  korea_start <- korea_reign %>%
    group_by(reign_country) %>% 
    slice(which.min(year)) %>% 
    ungroup() 

Next we plot the graph

korea_reign %>% 
 ggplot(aes(x = year, y = v2x_polyarchy, groups = reign_country))  +
    geom_line(aes(color = gwf_regimetype), 
         size = 7, alpha = 0.7, show.legend = FALSE) +
    geom_point(aes(color = gwf_regimetype), size = 7, alpha = 0.7) +
    ggflags::geom_flag(data = korea_start, 
       aes(y = v2x_polyarchy, x = 1945, country = iso2c_lower), 
           size = 20) -> korea_plot

And then work on the aesthetics of the plot:

korea_plot + ggthemes::theme_fivethirtyeight() + 
    ggtitle("Electoral democracy on Korean Peninsula") +
    labs(subtitle = "Sources: Teorell et al. (2019) and Curtis (2016)") +
    xlab("Year") + 
    ylab("Democracy Scores") + 
    theme(plot.title = element_text(face = "bold"),
      axis.ticks = element_blank(),
      legend.box.background = element_blank(),
      legend.title = element_blank(),
      legend.text = element_text(size = 40),
      text = element_text(size = 30)) +
    scale_color_manual(values = korea_palette) + 
    scale_x_continuous(breaks = round(seq(min(korea_reign$year), max(korea_reign$year), by = 5),1))

While North Korea has been consistently ruled by the Kim dynasty, South Korea has gone through various types of government and varying levels of democracy!

References

Cheibub, J. A., Gandhi, J., & Vreeland, J. R. (2010). . Public choice143(1), 67-101.

Przeworski, A., Alvarez, R. M., Alvarez, M. E., Cheibub, J. A., Limongi, F., & Neto, F. P. L. (2000). Democracy and development: Political institutions and well-being in the world, 1950-1990 (No. 3). Cambridge University Press.

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