Bar Charts

January 29, 2025

Bar Charts

When do we want to use bar charts?

• Bar charts are very common in data viz

• Usually, when we want to compare the value of a numerical variable (GDP per capita, etc) across different values of a categorical variable

When do we want to use bar charts?

• Examples:

  • How does GDP per capita vary across world regions? [GDP per capita is numerical and world region is categorical]

  • Voteshare for Trump in different US states?

• Example for today:

  • Level of democracy (measured continuously) by world region

The Grammar of Graphics

• Data viz has a language with its own grammar
• Basic components include:
  • Data we are trying to visualize
  • Aesthetics (dimensions)
  • Geom (e.g. bar, line, scatter plot)
  • Color scales
  • Themes
  • Annotations

Get the data

library(tidyverse)
#library(vdemlite)
library(vdemdata)
myData <- vdem %>% 
   filter(year == 2022)  %>%  # filter to only include the year 2020
  mutate(region = e_regionpol_6C) %>%   ## make a better region variable
  mutate(region = case_match(region,    # new variable called region
                        1 ~ "Eastern Europe", 
                        2 ~ "Latin America",  
                        3 ~ "Middle East",   
                        4 ~ "Africa", 
                        5 ~ "The West", 
                        6 ~ "Asia")) %>%
    select(country_name, year, v2x_egaldem, v2x_polyarchy, v2x_delibdem, region, v2x_regime, e_peaveduc)

Calculate mean of Egalitarian Democracy by Region

# create summary data object
dem_summary <- myData%>% 
  group_by(region) %>% 
  summarize(mean_egal_dem = mean(v2x_egaldem, na.rm=TRUE))
dem_summary

# A tibble: 6 × 2
  region         mean_egal_dem
  <chr>                  <dbl>
1 Africa                 0.269
2 Asia                   0.304
3 Eastern Europe         0.419
4 Latin America          0.399
5 Middle East            0.189
6 The West               0.753

The Grammar of Graphics

• Data viz has a language with its own grammar
• Basic components include:
  • Data we are trying to visualize
  • Aesthetics (dimensions)
  • Geom (e.g. bar, line, scatter plot)
  • Color scales
  • Themes
  • Annotations

Let’s start with the first two, the data and the aesthetic…

ggplot(dem_summary, aes(x = region, y = mean_egal_dem)) 

This gives us the axes without any visualization:

ggplot(dem_summary, aes(x = region, y = mean_egal_dem)) 

You can also pipe the data into ggplot…

dem_summary %>% 
  ggplot(., aes(x = region, y = mean_egal_dem)) 

Now let’s add a geom. In this case we want a bar chart so we add geom_col(). Note that with ggplot we add, we do NOT pipe

ggplot(dem_summary, aes(x = region, y = mean_egal_dem)) + 
  geom_col()

That gets the idea across but doesnt look great…

ggplot(dem_summary, aes(x = region, y = mean_egal_dem)) + 
  geom_col()

…let’s change the color of the bars by specifying fill = "dodgerblue".

ggplot(dem_summary, aes(x = region, y = mean_egal_dem)) + 
  geom_col(fill = "dodgerblue")

Note how color of original bars is simply overwritten:

ggplot(dem_summary, aes(x = region, y = mean_egal_dem)) + 
  geom_col(fill = "dodgerblue")

Now let’s add some labels with the labs() function:

ggplot(dem_summary, aes(x = region, y = mean_egal_dem)) + 
  geom_col(fill = "dodgerblue") +
  labs(
    x = "Region", 
    y = "Avg. Egal Democracy Score", 
    title = "Democracy by region, 1990 - present", 
    caption = "Source: V-Dem Institute"
    )

And that gives us…

ggplot(dem_summary, aes(x = region, y = mean_egal_dem)) + 
  geom_col(fill = "dodgerblue") +
  labs(
    x = "Region", 
    y = "Avg. Egal Democracy Score", 
    title = "Democracy by region, 1990 - present", 
    caption = "Source: V-Dem Institute"
    )

Next, we reorder the bars with fct_reorder() from the forcats package.

library(forcats)
ggplot(dem_summary, aes(x = fct_reorder(region, -mean_egal_dem), y = mean_egal_dem)) +
  geom_col(fill = "dodgerblue") + 
  labs(
    x = "Region", 
    y = "Avg. Egal Democracy Score", 
    title = "Democracy by region, 1990 - present", 
    caption = "Source: V-Dem Institute"
    )

This way, we get a nice ordering of the bars according to levels of democracy, which in this case is helpful for interpretation…

library(forcats)
ggplot(dem_summary, aes(x = fct_reorder(region, -mean_egal_dem), y = mean_egal_dem)) +
  geom_col(fill = "dodgerblue") + 
  labs(
    x = "Region", 
    y = "Avg. Egal Democracy Score", 
    title = "Democracy by region, 1990 - present", 
    caption = "Source: V-Dem Institute"
    )

Now let’s change the theme to theme_minimal().

ggplot(dem_summary, aes(x = reorder(region, -mean_egal_dem), y = mean_egal_dem)) +
  geom_col(fill = "dodgerblue") + 
  labs(
    x = "Region", 
    y = "Avg. Egal Democracy Score", 
    title = "Democracy by region, 1990 - present", 
    caption = "Source: V-Dem Institute"
    ) +
  theme_minimal()

Better…

ggplot(dem_summary, aes(x = reorder(region, -mean_egal_dem), y = mean_egal_dem)) +
  geom_col(fill = "dodgerblue") + 
  labs(
    x = "Region", 
    y = "Avg. Egal Democracy Score", 
    title = "Democracy by region, 1990 - present", 
    caption = "Source: V-Dem Institute"
    ) +
  theme_minimal()

You can also save your plot as an object to modify or use later.

dem_bar_chart <- ggplot(dem_summary, aes(x = reorder(region, -mean_egal_dem), y = mean_egal_dem)) +
  geom_col(fill = "dodgerblue") + 
  labs(
    x = "Region", 
    y = "Avg. Egal Democracy Score", 
    title = "Democracy by region, 1990 - present", 
    caption = "Source: V-Dem Institute"
    ) +
  theme_minimal()

Which gives us…

dem_bar_chart

You can also build up sequentially

chart1 <- ggplot(dem_summary, aes(x = reorder(region, -mean_egal_dem), y = mean_egal_dem)) +
  geom_col(fill = "coral3") 
chart1

You can also build up sequentially

chart2 <- chart1 +   
  labs(
    x = "Region", 
    y = "Avg. Egal Democracy Score", 
    title = "Democracy by region, 1990 - present", 
    caption = "Source: V-Dem Institute"
    )
chart2

Add a theme

chart3 <- chart2 +   
  theme_minimal()
chart3

Posit Cloud