Distributions I

Categorical Variables

February 7, 2025

Thinking About Data

What are some ways we can classify data? 😎 💭

  • anecdotal vs. representative
  • census vs. sample
  • observational vs. experimental
  • categorical vs. numerical
  • discrete vs. continuous
  • cross-sectional vs. time series
  • longitudinal vs. panel
  • unstructured vs. structured

What are some ways we can classify data? 😎 💭

  • anecdotal vs. representative
  • census vs. sample
  • observational vs. experimental
  • categorical vs. numerical
  • discrete vs. continuous
  • cross-sectional vs. time series
  • longitudinal vs. panel
  • unstructured vs. structured

Variable Types

  • Categorical
    • Binary - two categories
    • Nominal - multiple unordered categories
    • Ordinal - multiple ordered categories
  • Numerical
    • Continuous - fractional values (measurement data)
    • Discrete - non-negative whole numbers (count data)

What types of variables are these? 🤔

  • Is a country a democracy? (yes/no)
  • Polity (-10 to 10 in 1 unit increments)
  • V-Dem Polyarchy (0-1 in 0.01 increments)
  • V-Dem Regimes of the World Measure
    • closed autocracy | electoral autocracy | electoral democracy | democracy
  • Number of protest events
  • Protest types (sit in, march, strike, etc.)

Categorical Data

V-Dem Regimes of the World Measure


  • Closed Autocracy
  • Electoral Autocracy
  • Electoral Democracy
  • Liberal Democracy

Data Setup

library(tidyverse)
library(vdemlite)

vdem2022 <- fetchdem(indicators = c("v2x_regime", "e_regionpol_6C"), 
                     start_year = 2022, end_year = 2022) |>
  rename(
    country = country_name, 
    regime = v2x_regime, 
    region = e_regionpol_6C
    ) |>
  mutate(
    region = case_match(region, 
                     1 ~ "Eastern Europe", 
                     2 ~ "Latin America",  
                     3 ~ "Middle East",   
                     4 ~ "Africa", 
                     5 ~ "The West", 
                     6 ~ "Asia"),
    regime = case_match(regime,
                    0 ~ "Closed Autocracy",
                    1 ~ "Electoral Autocracy",
                    2 ~  "Electoral Democracy",
                    3 ~  "Liberal Democracy")
  )

Examine the Data


glimpse(vdem2022)
Rows: 179
Columns: 6
$ country         <chr> "Mexico", "Suriname", "Sweden", "Switzerland", "Ghana"…
$ country_text_id <chr> "MEX", "SUR", "SWE", "CHE", "GHA", "ZAF", "JPN", "MMR"…
$ country_id      <dbl> 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 19, 2…
$ year            <dbl> 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, …
$ regime          <chr> "Electoral Democracy", "Electoral Democracy", "Liberal…
$ region          <chr> "Latin America", "Latin America", "The West", "The Wes…


Let’s count the number of regimes by type…


vdem2022 |>
  count(regime)
               regime  n
1    Closed Autocracy 33
2 Electoral Autocracy 54
3 Electoral Democracy 58
4   Liberal Democracy 34

Now let’s visualize the distribution of regimes with a bar plot…

Now let’s visualize the distribution of regimes with a bar plot…


vdem2022 |>
  ggplot(aes(x = regime)) +
  geom_bar(fill = "steelblue") +
  labs(
    x = "Regime",
    y = "Frequency",
    title = "Regimes of the World in 2022",
    caption = "Source: V-Dem Institute"
  ) +
  theme_minimal()

Now let’s visualize the distribution of regimes with a bar plot…


vdem2022 |>
  ggplot(aes(x = regime)) +
  geom_bar(fill = "steelblue") +
  labs(
    x = "Regime",
    y = "Frequency",
    title = "Regimes of the World in 2022",
    caption = "Source: V-Dem Institute"
  ) +
  theme_minimal()

More about geom_bar()


  • geom_bar() is different from geom_col()
  • Used to create bar plots where the height of the bar represents counts or frequencies of categorical variable
  • By default, geom_bar() counts the number of occurrences of each category or group and plots it as the height of the bar
  • Like geom_histogram(), geom_bar() only requires the x aesthetic (y is automatically calcualted for you)

Your Turn!

  • Expore the distribution of regimes for a different year
  • Preprocess your data to include only the year you are interested in
  • Visualize the distribution of regimes using geom_bar()
  • Use the labs() function to change title
  • What is different about the year that you chose relative to 2022?
05:00

How do Regimes Vary by Region?


Code 
vdem2022 |>
  ggplot(aes(x = region, fill = regime)) +
      geom_bar() +
  theme_minimal() +
  labs(
    x = "Region",
    y = "Frequency",
    title = "Regimes of the World by World Region in 2022",
    caption = "Source: V-Dem Institute",
    fill = "Regime"
  ) +
  scale_fill_viridis_d(option = "inferno", end = .8)

How do Regimes Vary by Region?


vdem2022 |>
  ggplot(aes(x = region, fill = regime)) +
      geom_bar() +
  theme_minimal() +
  labs(
    x = "Region",
    y = "Frequency",
    title = "Regimes of the World by World Region in 2022",
    caption = "Source: V-Dem Institute",
    fill = "Regime"
  ) +
  scale_fill_viridis_d(option = "inferno", end = .8)

How do Regimes Vary by Region?


vdem2022 |>
  ggplot(aes(x = region, fill = regime)) +
      geom_bar() +
  theme_minimal() +
  labs(
    x = "Region",
    y = "Frequency",
    title = "Regimes of the World by World Region in 2022",
    caption = "Source: V-Dem Institute",
    fill = "Regime"
  ) +
  scale_fill_viridis_d(option = "inferno", end = .8)

What Did We Just Do?


  • We used geom_bar() to visualize the distribution of regimes by region
  • Essentially, we used two categorical variables to create a bar plot
  • Regime type is…
    • …an ordered categorical variable
  • Region is…
    • …a nominal categorical variable

Some regions have more countries than others. Why does this create an issue for telling a story with our data here?

Show Proportions Instead


Code 
vdem2022 %>%
  ggplot(., aes(x = region, fill = regime)) +
      geom_bar(position = "fill") +
  theme_minimal() +
  labs(
    x = "Region",
    y = "Proportion",
    title = "Regimes of the World by World Region in 2022",
    caption = "Source: V-Dem Institute",
    fill = "Regime"
  ) +
  scale_fill_viridis_d(option = "inferno", end = .8)

We use position = "fill" to normalize the data and make the plot more interpretable…


vdem2022 %>%
  ggplot(., aes(x = region, fill = regime)) +
      geom_bar(position = "fill") +
  theme_minimal() +
  labs(
    x = "Region",
    y = "Proportion",
    title = "Regimes of the World by World Region in 2022",
    caption = "Source: V-Dem Institute",
    fill = "Regime"
  ) +
  scale_fill_viridis_d(option = "inferno", end = .8)

Your Turn!

  • Explore the distribution of regimes by region for a different year
  • Preprocess your data to include only the year you are interested in
  • Visualize the distribution of regimes using geom_bar() and position = "fill"
  • Use the labs() function to change title
  • What is different about the year that you chose relative to 2022?
05:00