Categorical Data

April 23, 2024

Today’s Classwork

• Download classwork folder for today’s class from here

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(vdemdata)

vdem2022 <- vdem |>
  filter(year == 2022) |>
  select(
    country = country_name, 
    regime = v2x_regime, 
    region = e_regionpol_6C
    ) |>
  mutate(
    region = case_match(region, # replace the values in region with country names
                     1 ~ "Eastern Europe", 
                     2 ~ "Latin America",  
                     3 ~ "Middle East",   
                     4 ~ "Africa", 
                     5 ~ "The West", 
                     6 ~ "Asia")
  ) |>
  mutate(regime = case_match(regime,
                    0 ~ "Closed Autocracy",
                    1 ~ "Electoral Autocracy",
                    2 ~  "Electoral Democracy",
                    3 ~  "Liberal Democracy")
  )

Examine the Data

glimpse(vdem2022)

Rows: 179
Columns: 3
$ country <chr> "Mexico", "Suriname", "Sweden", "Switzerland", "Ghana", "South…
$ regime  <chr> "Electoral Democracy", "Electoral Democracy", "Liberal Democra…
$ region  <chr> "Latin America", "Latin America", "The West", "The West", "Afr…

Let’s count the number of regimes by type…

vdem2022 |>
  count(regime)

               regime  n
1    Closed Autocracy 33
2 Electoral Autocracy 56
3 Electoral Democracy 58
4   Liberal Democracy 32

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?

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

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