5. ggplot2 (Histogram)
Masahiko Asano
2022-10-26
R packagesused in this section
library(DT)
library(gapminder)
library(gghighlight)
library(ggrepel)
library(stargazer)
library(tidyverse)1. Histogram
- A histogram is an approximate representation of the distribution of numerical data.
Diffenece between a barchart and a histogram
| Variable types | How to visualize | Feature |
|---|---|---|
| Discrete variable | Bar chart | Lines between bars |
| Continuous variable | Histogram | No Lines between bars |
When x-axis is year of elections
- There is no values between 2017 and 2021 (because there was no
election held between them)
- Years of election look like numeric, but it is not treated as
numeric
2. How to draw a histogram using ggplot2
- You can draw a histogram by using
geom_histogram()
- You need to map a continuous variable on
x-axis
- You don’t have to map on
y-axis
- Let’s draw a histogram of vote share in the lower house elections in Japan between 1996 and 2021
2.1 Draw a simple histogram
- Make a folder, named
datain your R Project folder - Download hr96-21.csv onto the
datafolder in your R Project
- Read the election data,
hr96-21.csvand name itdf
df <- read_csv("data/hr96-21.csv",
na = ".") - Using
if_else(), make a dummy variable:ldp
#####mutate(ldp = if_else(seito == "自民", "LDP", "Non-LDP")- This command means make a dummy variable, namedldp
- If a value in variable
seitois “自民”, then replace it with “LDP” and replace the other values (that is, the other party names) inseitowith “Non-LDP”
df <- df %>%
mutate(ldp = if_else(seito == "自民", "LDP", "Non-LDP"))- Draw a histogram of vote share
df %>%
ggplot() +
geom_histogram(aes(x = voteshare)) 
2.2 What is a good histogram?
- A good histogram clearly address the following questions:
- What is the the number of peaks in distribution?
- If you have peaks, where is most densely (frequently) populated?
- You need to adjust the number of bins to show an appropriate
histogram
- Which one is the best histogram among the following three
histograms?
- Usually, a histogram does not have lines between bars, let me add white lines here.
- The histogram in the left shows two peaks but it has too narrow
bins
- The histogram in the right has too wide bins and we fail to see the
two peaks
- The histogram in the middle has an appropriate width showing two
peaks
- If you have too narrow bins (such as the one in the left), then
you show too much information in the histogram
- If you have too wide bins (such as the one in the right), then
you might lose some important information data
contains
- You need to choose an appripriate size of bins
How to customize the number of bins:
- You can customize the number of
bins using
bins = XX
How to customize the width of bins:
- You can customize the width of
bins using
binwidth = XX
How to customize colors
- By adding lines between bins, you can make your histogram way easier
to see.
- You can customize the color (such as
color = "white") out ofaes()and withingeom_histogram()
bin and binwidth ・You can
customize either binwidth (the width of
bins) or bins (the numbder of
bins)
・You cannot simultaneously customize both
2.3 Customize the scale on x-axis and
y-axis
scale_x_continuous() |
customize x-axis |
scale_y_continuous() |
customize y-axis |
- The following is the histogram generated in the previous section
hist_plot1 <- df %>%
ggplot() +
geom_histogram(aes(x = voteshare),
color = "white",
binwidth = 5)
hist_plot1
- Let’s change the display on
x-axison thie histogram
- Using
scale_x_*(), you can customize the scale onx-axis`
- The range of x-axis is between 1 and 100
- The interval is 10
hist_plot2 <- hist_plot1 +
scale_x_continuous(breaks = seq(0, 100, by = 10),
labels = seq(0, 100, by = 10))
hist_plot2
2.4 Add another dimension
- When you want to add another dimension, you have two ways of getting it done.
(1) facet_wrap(~)
- Let’s add another dimension,
ldp, to the histogram above by usingfacet_wrap(~)
hist_plot2 +
facet_wrap(~ldp) +
theme_bw(base_family = "HiraKakuProN-W3")
(2) fill = ….
- Let’s add another dimension,
ldp, to the histogram above by usingfill = ... - You need to add
position = "identity"
df %>%
mutate(ldp = if_else(seito == "自民", "LDP", "Non-LDP")) %>%
ggplot() +
geom_histogram(aes(x = voteshare,
fill = ldp),
position = "identity",
binwidth = 10,
color = "white") +
labs(x = "Vote Share",
y = "The Number of Candidates ",
fill = "") +
theme_bw(base_family = "HiraKakuProN-W3")
- This histogram has a serious
problem!
- You fail to see the hidden number of non-LDP candidates behind the green bars.
Solution:
- Using
alpha = ..., you can make the bars transparent
・alpha = 0・・・transparent
・alpha = 1・・・not transparent
- Let’s set as
alpha = 0.5here
df %>%
mutate(ldp = if_else(seito == "自民", "LDP", "Non-LDP")) %>%
ggplot() +
geom_histogram(aes(x = voteshare,
fill = ldp),
position = "identity",
binwidth = 10,
color = "white",
alpha = 0.5) +
labs(x = "Vote Share",
y = "The Number of Candidates",
fill = "") +
theme_bw(base_family = "HiraKakuProN-W3")
You had better not do this!!
- Let’s generate a histogram of vote share for each party in the lower house elections between 1996 and 2021
df %>%
ggplot() +
geom_histogram(aes(x = voteshare,
fill = seito),
position = "identity",
binwidth = 10,
color = "white",
alpha = 0.5) +
labs(x = "Vote Share",
y = "The Number of Candidates",
fill = "") +
theme_bw(base_family = "HiraKakuProN-W3")
- You have too much information to figure it out.
Solution:
- Reduce the number of parties, say 3 parties.
df %>%
filter(seito == "自民"|seito == "共産"|seito == "民主") %>%
mutate(party = case_when(seito == "自民" ~ "LDP",
seito == "共産" ~ "JCP",
seito == "民主" ~ "CDP")) |>
ggplot() +
geom_histogram(aes(x = voteshare,
fill = party),
position = "identity",
binwidth = 10,
color = "white",
alpha = 0.5) +
labs(x = "Vote Share",
y = "The Number of Candidates",
fill = "") +
theme_bw(base_family = "HiraKakuProN-W3")
- Delete
position = "identity", then you can add up each distribution in one bar
df %>%
filter(seito == "自民"|seito == "共産"|seito == "民主") %>%
mutate(party = case_when(seito == "自民" ~ "LDP",
seito == "共産" ~ "JCP",
seito == "民主" ~ "CDP")) |>
ggplot() +
geom_histogram(aes(x = voteshare,
fill = party),
binwidth = 10,
color = "white",
alpha = 0.5) +
labs(x = "Vote Share",
y = "The Number of Candidates",
fill = "") +
theme_bw(base_family = "HiraKakuProN-W3")
- If you want to overlup the 3 distribution in one facet, then you
should use
geom_density()instead ofgeom_histogram()
df %>%
filter(seito == "自民"|seito == "共産"|seito == "民主") %>%
mutate(party = case_when(seito == "自民" ~ "LDP",
seito == "共産" ~ "JCP",
seito == "民主" ~ "CDP")) |>
ggplot() +
geom_density(aes(x = voteshare,
fill = party),
color = "white",
alpha = 0.5,
adjust = 0.8) +
labs(x = "Vote Share",
y = "Density",
fill = "") +
theme_bw(base_family = "HiraKakuProN-W3")
3 Exercise
Q3.1:
In reference to2.4 Add another dimension, draw a histogram of vote share (voteshare) for the LDP (Liberal Democratic Party) and CDP (Constitutional Democratic Party) candidates in the 2021 lower house election.
・You see the party name in variable, seito inhr96_21.csv
・You need to usegeom_histogram()to draw a histogram.
・You need to pay attention to the hidden values behind the bars in drawing a histogram.Q3.2:
In reference to2.4 Add another dimension, draw a histogram of vote share (voteshare) for the LDP (Liberal Democratic Party) and CDP (Constitutional Democratic Party) and JCP (Japan Communist Party) candidates in the 2021 lower house election.
・You see the party name in variable, seito inhr96_21.csv
・You need to usegeom_histogram()to draw a histogram.
・You need to pay attention to the hidden values behind the bars in drawing a histogram.
In answering Q3.1 and Q3.2
questions, use hr96-21.csv
・hr96_21.csv is a collection of Japanese lower house
election data covering 9 national elections (1996, 2000, 2003, 2005,
2009, 2012, 2014, 2017, 2021)
・You need the following three variables which are included in
hr96_21.csv to draw histograms:
| variable | detail |
|---|---|
| year | Election year (1996-2021) |
| voteshare | Vote share (%) |
| seito | Candidate’s affiliated party (in Japanese) |
・hr96_21.csv contains the following 23 variables:
| variable | detail |
|---|---|
| year | Election year (1996-2021) |
| pref | Prefecture |
| ku | Electoral district name |
| kun | Number of electoral district |
| rank | Ascending order of votes |
| wl | 0 = loser / 1 = single-member district (smd) winner / 2 = zombie winner |
| nocand | Number of candidates in each district |
| seito | Candidate’s affiliated party (in Japanese) |
| j_name | Candidate’s name (Japanese) |
| name | Candidate’s name (English) |
| previous | Previous wins |
| gender | Candidate’s gender:“male”, “female” |
| age | Candidate’s age |
| exp | Election expenditure (yen) spent by each candidate |
| status | 0 = challenger / 1 = incumbent / 2 = former incumbent |
| vote | votes each candidate garnered |
| voteshare | Vote share (%) |
| eligible | Eligible voters in each district |
| turnout | Turnout in each district (%) |
| castvote | Total votes cast in each district |
| seshu_dummy | 0 = Not-hereditary candidates, 1 = hereditary candidate |
| jiban_seshu | Relationship between candidate and his predecessor |
| nojiban_seshu | Relationship between candidate and his predecessor |