ggplot2 Workshop
To start learning ggplot2, build a chart incrementally. Let’s begin by reviewing the basic RGraphics. In this illustration, the Gapminder package is used. It comes with six variables including country, continent, year, life expectancy, population and GDP per capita.
Let’s install the package, load the data and examine the dataset:
install.packages("gapminder", repos = "https://cran.r-project.org")##
## The downloaded binary packages are in
## /var/folders/qp/s6y46pq11y13t0gpnf4_v9vm0000gp/T//RtmpTJttwU/downloaded_packageslibrary(gapminder)
summary(gapminder) # Summary of Gapminder dataset## country continent year lifeExp
## Afghanistan: 12 Africa :624 Min. :1952 Min. :23.60
## Albania : 12 Americas:300 1st Qu.:1966 1st Qu.:48.20
## Algeria : 12 Asia :396 Median :1980 Median :60.71
## Angola : 12 Europe :360 Mean :1980 Mean :59.47
## Argentina : 12 Oceania : 24 3rd Qu.:1993 3rd Qu.:70.85
## Australia : 12 Max. :2007 Max. :82.60
## (Other) :1632
## pop gdpPercap
## Min. :6.001e+04 Min. : 241.2
## 1st Qu.:2.794e+06 1st Qu.: 1202.1
## Median :7.024e+06 Median : 3531.8
## Mean :2.960e+07 Mean : 7215.3
## 3rd Qu.:1.959e+07 3rd Qu.: 9325.5
## Max. :1.319e+09 Max. :113523.1
## str(gapminder) # Structure of dataset## Classes 'tbl_df', 'tbl' and 'data.frame': 1704 obs. of 6 variables:
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num 28.8 30.3 32 34 36.1 ...
## $ pop : int 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num 779 821 853 836 740 ...gm=gapminder
head(gm)## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.summary(gm)## country continent year lifeExp
## Afghanistan: 12 Africa :624 Min. :1952 Min. :23.60
## Albania : 12 Americas:300 1st Qu.:1966 1st Qu.:48.20
## Algeria : 12 Asia :396 Median :1980 Median :60.71
## Angola : 12 Europe :360 Mean :1980 Mean :59.47
## Argentina : 12 Oceania : 24 3rd Qu.:1993 3rd Qu.:70.85
## Australia : 12 Max. :2007 Max. :82.60
## (Other) :1632
## pop gdpPercap
## Min. :6.001e+04 Min. : 241.2
## 1st Qu.:2.794e+06 1st Qu.: 1202.1
## Median :7.024e+06 Median : 3531.8
## Mean :2.960e+07 Mean : 7215.3
## 3rd Qu.:1.959e+07 3rd Qu.: 9325.5
## Max. :1.319e+09 Max. :113523.1
## RGraphics come with simple plotting functions such as hist() and plot():
# Plot one variable
hist(gm$lifeExp) # Histogram of variable lifeExp (Life expectancy)
The plot() function can do multiple variables:
# Plot two variables with logged version of x
plot(lifeExp ~ gdpPercap, gm, subset = year == 2007, log = "x", pch=16)
# Plot two variables with selected country
plot(lifeExp ~ year, gm, subset = country == "Cambodia", type = "p")
# Try different plot types
plot(lifeExp ~ year, gm, subset = country == "Cambodia", type = "l")
# Different symbols
plot(lifeExp ~ year, gm, subset = country == "Cambodia", type = "b", pch=18) 
# Add labels to axes
plot(lifeExp ~ year, gm, subset = country == "Cambodia", type = "b", pch=18, xlab="Year", ylab="Life Expectancy") 
# Specify font
plot(lifeExp ~ year, gm, subset = country == "Cambodia", type = "b", pch=18, xlab="Year", ylab="Life Expectancy",family="Palatino") 
With ggplot2, we can apply the Grammer of Graphics methods and modify the chart with more finetuning and detail attention:
Let’s install the package, load it and try it step by step: ## install.packages(“ggplot2”, repos =“https://cran.r-project.org”) ## library(ggplot2)
# More layered plots using ggplot2, with regression line
p <- ggplot2::ggplot(data = gm) Why there is no chart?
Add layer?
How about now?
library(ggplot2)
p <- ggplot(data = gm,
mapping = aes(x = gdpPercap,
y = lifeExp)) # Shows nothing. Why?
p + geom_point()
First, we need to start the data component, then add the aesthetic mapping defining the basics (i.e. variables), followed by the geometric objects. Here is an alternative:
# Alternative
p <- ggplot(data=gm, aes(x=gdpPercap, y=lifeExp))What is still missing?
p + geom_point()
Now, we can add more features to the chart.
# Add some color grouping
p <- ggplot(data = gm,
mapping = aes(x = gdpPercap,
y = lifeExp, color=continent))
p + geom_point()
You may try different markers using the pch option:
p <- ggplot(data = gm,
mapping = aes(x = gdpPercap,
y = lifeExp, color=continent))
p + geom_point(pch=6)
Add a regression line, dropped the color grouping
p <- ggplot(data = gm,
mapping = aes(x = gdpPercap,
y = lifeExp))
p + geom_point(pch=16) + geom_smooth(method="lm") 
There are a series of methods for plotting the regression line.
## geom_smooth methods:
# "auto", "lm", "glm", "gam", "loess"
# Add a line, dropped the color grouping, try other method
p <- ggplot(data = gm,
mapping = aes(x = gdpPercap,
y = lifeExp))
p + geom_point(pch=16) + geom_smooth(method="loess") 
Let’s put the data in perspective. Some variables have high variance. Apply log to make the more plottable:
# Add a regression line with logged x, dropped the color grouping
p <- ggplot(data = gm,
mapping = aes(x = gdpPercap,
y = lifeExp))
p + geom_point(pch=16) + geom_smooth(method="lm") +
scale_x_log10()
Focus on color now!
p <- ggplot(data = gm,
mapping = aes(x = gdpPercap,
y = lifeExp,
color = "purple"))
p + geom_point() +
geom_smooth(method = "loess") +
scale_x_log10() # Why it is not purple?
Make sure the color option is applied in the right place.
# How about now?
p <- ggplot(data = gm,
mapping = aes(x = gdpPercap,
y = lifeExp))
p + geom_point(color = "purple") +
geom_smooth(method = "loess") + scale_x_log10()
Add a better theme than the default gray background with grid lines.
# Add theme
p <- ggplot(data = gm,
mapping = aes(x = gdpPercap,
y = lifeExp))
p + geom_point(color = "purple", pch=20) +
theme_bw() +
geom_smooth(method = "loess") + scale_x_log10()
Here comes the title.
# Add title
p <- ggplot(data = gm,
mapping = aes(x = gdpPercap,
y = lifeExp))
p + geom_point(color = "purple") +
geom_smooth(method = "loess") + scale_x_log10() +
theme_bw() +
ggtitle("Life Expectacy and GDP Per Capita (logged)") 
There is a better way to do all.
# Add title, labels and caption (located at bottom)
p <- ggplot(data = gm,
mapping = aes(x = gdpPercap,
y = lifeExp))
p + geom_point(color = "purple") +
geom_smooth(method = "loess") + scale_x_log10() +
theme_bw() +
labs(title="Life Expectacy and GDP Per Capita (logged)",
x="GDP Per Capita",y="Life Expectancy",caption="") 
Alignment of title and caption takes a bit more work. Use the theme function to specify alignment.
# Why the title is not centered?
p <- ggplot(data = gm,
mapping = aes(x = gdpPercap,
y = lifeExp))
p + geom_point(color = "purple") +
geom_smooth(method = "loess") + scale_x_log10() +
theme_bw() +
labs(title="Life Expectacy and GDP Per Capita (logged)",
x="GDP Per Capita",y="Life Expectancy") +
theme(plot.title = element_text(hjust = 0.5))
And font.
# Set font
p <- ggplot(data = gm,
mapping = aes(x = gdpPercap,
y = lifeExp))
p + geom_point(color = "purple") +
geom_smooth(method = "loess") + scale_x_log10() +
theme_bw() +
labs(title="Life Expectacy and GDP Per Capita (logged)",
x="GDP Per Capita",y="Life Expectancy") +
theme(plot.title = element_text(hjust = 0.5),
text=element_text(size=16,family="Palatino"))
Revised: 3/13/2019