5. ggplot2 (boxplot)
Masahiko Asano
2026-07-23
R Packageswe use in this section
library(DT)
library(gapminder)
library(gghighlight)
library(ggrepel)
library(stargazer)
library(tidyverse)1. What is a Boxplot?
- A boxplot is one of the methods used to visualize the distribution of continuous variables.
| Variable Type | Visualization Method | Features |
| Continuous | Boxplot | ① Provides instant information about median, quartile range, minimum, and maximum values |
| ② Allows comparison of distributions across groups within a single plot. | ||
- Widely used visualization method alongside histograms.
- Histograms show “where the density is highest,” while boxplots allow
for quick comparison of descriptive statistics for specific
variables.
- With a boxplot, you can see the minimum value, maximum value, first
quartile, second quartile (median), third quartile, and identify
outliers.
- You can view the distribution of variables across groups without the need for color-coding or facet splitting.
How to Read a Boxplot
Source：浅野・矢内『Rによる計量政治学』p.107.
Quartiles
- Create a variable
x
[1] 10 7 9 1 0 2 5 8 3 4 6- Arrange
xin ascending order
[1] 0 1 2 3 4 5 6 7 8 9 10- Buttom end end (First Quartile: Minimum) … 0
- Middle (Second Quartile = Median) … 5
- Upper end (Third Quartile: Maximum) … 10
- Try drawing a boxplot of
xusingBase R
2. Creating a Box Plot
- Donload hr96-26.csv
- Load the data and name it
df
2.1 Simple Boxplot
- Using
df, let’s create a boxplot for the vote share (voteshare).
- The geometric object name is
geom_boxplot().
- Similar to a histogram, only one mapping is required.
- You can map it to both
yandx.
The pipe operator: %>% or
|> ・After the release of R 4.1 in
May 2021, the pipe operator |> was added as an in-built
operator in R.
・The pipe operator can be used as
%>% or |>,
and the results are the same regardless of which one you use.
2.2 Changing the Color of Boxplots
- Bar graphs and histograms represent the distribution of a single
variable.
- Boxplots are often used when showing the distribution of a variable
across different groups.
- Let’s use a boxplot to illustrate the party-wise vote share in the 2021 general election.
df %>%
filter(year == 2021) %>% # Narrowing down to 2021 general election data
ggplot() +
geom_boxplot(aes(y = voteshare, x = seito)) +
labs(x = "Party", y = "Voteshare") +
ggtitle("Vote Share by Party: 2021 General Election") +
theme_bw(base_family = "HiraKakuProN-W3")
How to Change the Color of Boxplot
(1) If you want to change the ‘border color’ of the boxplot:
color = ""
- You can specify
color = "skyblue"withinaes()
df %>%
filter(year == 2021) %>% # Narrowing down to 2021 general election
ggplot() +
geom_boxplot(aes(y = voteshare,
x = seito),
color = "skyblue") +
labs(x = "Party", y = "Voteshare") +
ggtitle("Vote Share by Party: 2021 General Election") +
theme_bw(base_family = "HiraKakuProN-W3")
(2) If you want to change the color inside the box:
fill = ""
- You should specify
fill = "skyblue"outside ofaes().
- When you map
color = "skyblue", it changes the color of the box outline, not the color inside the box.
df %>%
filter(year == 2021) %>% # Narrowing down to 2021 general election
ggplot() +
geom_boxplot(aes(y = voteshare,
x = seito),
fill = "skyblue") +
labs(x = "Party", y = "Voteshare") +
ggtitle("Vote Share by Party: 2021 General Election") +
theme_bw(base_family = "HiraKakuProN-W3")
(3) If you want to change the color for each party:
- To change the color for each party, you can add the
scale_fill_manual()layer.
df %>%
filter(year == 2021) %>% # Narrowing down to 2021 general election
ggplot() +
geom_boxplot(aes(y = voteshare,
x = seito,
fill = seito)) +
labs(x = "Party", y = "Voteshare") +
ggtitle("Vote Share by Party: 2021 General Election") +
theme_bw(base_family = "HiraKakuProN-W3")
(3) If you want to customize colors for each party:
- When you want to customize colors for each party, use
scale_color_manual().
df %>%
filter(year == 2021) %>%# Narrowing down to 2021 general election
ggplot() +
geom_boxplot(aes(y = voteshare,
x = seito,
color = seito)) +
labs(x = "Party", y = "Voteshare") +
ggtitle("Vote Share by Party: 2021 General Election") +
theme_bw(base_family = "HiraKakuProN-W3") +
theme(legend.position = "none") +
scale_color_manual(values = c("N党" = "grey",
"れい" = "grey",
"公明" = "red",
"共産" = "grey",
"国民" = "grey",
"無所" = "grey",
"社民" = "grey",
"立憲" = "grey",
"維新" = "grey",
"自民" = "red",
"諸派" = "grey"))
2.3 Add a dimension to a box plot
- If adding a dimension, do it by color-coding.
- For example, try adding the gender of candidates as a new dimension
by specifying the gender (gender) of the candidates.
- Specify it inside
aes()likefill = "gender".
df %>%
filter(year == 2021) %>% # 2021年総選挙データだけに絞る
ggplot() +
geom_boxplot(aes(y = voteshare,
x = seito,
fill = gender)) +
labs(x = "Party", y = "Voteshare") +
ggtitle("Vote Share by Gender: 2021 General Election") +
theme_bw(base_family = "HiraKakuProN-W3") +
theme(legend.position = "bottom")
2.4 Customizing the Presentation of Box Plots
2.4.1 Narrowing or Widening the Width of the Box
- To narrow the width of the box, specify width outside of
aes().
- The default width of the box is 0.75 if not specified Here, we will try setting the box width to 0.25
df %>%
filter(year == 2021) %>% # Focus only on the 2021 general election data
ggplot() +
geom_boxplot(aes(y = voteshare,
x = seito),
width = 0.25) + # Set width to 0.25
labs(x = "Party", y = "Vote Share") +
ggtitle("Vote Share by Party: 2021 General Election") +
theme_bw(base_family = "HiraKakuProN-W3")
2.4.2 Vertical Display ↔︎ Horizontal Display
- When there are too many boxes, the figure becomes horizontally elongated.
df %>%
filter(year == 2021) %>% # Focus only on the 2021 general election data
ggplot() +
geom_boxplot(aes(y = voteshare,
x = seito)) +
labs(x = "Party", y = "Vote Share") +
ggtitle("Vote Share by Party: 2021 General Election") +
theme_bw(base_family = "HiraKakuProN-W3")
Solution:
- Swapping x and y will make it vertically oriented.
df %>%
filter(year == 2021) %>% # Focus only on the 2021 general election data
ggplot() +
geom_boxplot(aes(y = seito,
x = voteshare)) +
labs(x = "Party", y = "Vote Share") +
ggtitle("Vote Share by Party: 2021 General Election") +
theme_bw(base_family = "HiraKakuProN-W3")
2.4.3 Reverse the order of party names display
- Use
scale_y_discrete(limits = rev)
df %>%
filter(year == 2021) %>% # Focus only on the 2021 general election data
ggplot() +
geom_boxplot(aes(y = seito,
x = voteshare)) +
labs(x = "Party", y = "Vote Share") +
theme_bw(base_family = "HiraKakuProN-W3") +
ggtitle("Vote Share by Party: 2021 General Election") +
scale_y_discrete(limits = rev)
2.4.4 Customizing the Order of Party Names Display
- Suppose you want to display in order of highest vote share.
- For example, you want to draw box plots for each party with the highest average vote share in the 2021 general election.
Check the average vote share for each party.
df |>
filter(year == 2021) |>
group_by(seito) |>
summarize(ave_vs_2021 = mean(voteshare)) |>
DT::datatable()- Before passing to
ggplot(), convert seito into a factor and set the order.
- Try drawing the plot after converting seito to a factor
df %>%
filter(year == 2021) %>% # Focus only on the 2021 general election data
mutate(seito = factor(seito,
levels = c("公明",
"自民",
"立憲",
"国民",
"維新",
"社民",
"無所",
"共産",
"れい",
"諸派",
"Ｎ党"))) %>% # 'N' of "N Party" is a full-width character
ggplot() +
geom_boxplot(aes(y = seito,
x = voteshare)) +
labs(x = "Party", y = "Vote Share") +
ggtitle("Vote Share by Party: 2021 General Election") +
theme_bw(base_family = "HiraKakuProN-W3") 
mean and median
・The thick vertical line inside the box plot represents the
median.
・Here, the positions of the parties are arranged based on the
mean.
→ The mean and median do not necessarily always coincide.
- Box plots allow for quick comparison of the distributions of
variables across groups.
- In the 2021 House of Representatives election, the party with the
highest average vote share was Komeito（公明党）.
- Visually, the party with the largest variance is Democratic Party
for the People（国民民主党）.
- Compared to the Liberal Democratic Party（自民党）,
Komeito（公明党） has less variance.
- N Party（N党）, Other Factions（諸派）, Reiwa（れい）, and Komeito（公明党）have smaller variances.
3 Exercise
Q3.1:
- Using the analysis method from
2.2 Changing the Color of Box Plots, draw a box plot for the 2009 general election vote share for each political party.
- Customize the colors for each party, with Democratic Party candidates in blue, Liberal Democratic Party candidates in red, and candidates from other parties in grey.
Q3.2:
- Using the analysis method from
2.4.4 Customizing the Order of Party Names Display, draw a box plot for the 2009 general election vote share for each political party. Display the box plots in black and white, in order of highest vote share from the top.
Q3.3:
- Using the analysis method from
2.4.4 Customizing the Order of Party Names Display, display a list of the names, electoral districts, ages, vote shares, and vote counts of the Democratic Party candidates with the highest vote share in the 2009 general election.