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An introduction to ggplot2
Claude Renaux
Seminar for Statistics, ETH Zurich
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 1 / 33
Table of Contents
1 Overview
2 Selection of geoms
3 Multiple Layers and Faceting
4 Aesthetics
5 Tailoring Graphics
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 2 / 33
ggplot2: plots of one variable
In this section we will . . .
. . . get started with ggplot2
. . . look at plots of one variable
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 3 / 33
Must have
Useful cheatsheet: https://www.rstudio.com/resources/cheatsheets/ (pickData Visualisation with ggplot2)
Source: link above. This image is under Creative Commons licence.
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 4 / 33
Why ggplot2?
To mention some advantages:
nice labels
nice colors
small margins
beautiful faceting or multipanel plots
very powerful and flexible: we’ll have a glimpse at the grammar ofgraphics
we can change or update plots
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 5 / 33
Why ggplot2?
To mention some disadvantages:
ggplot2 can only deal with data.frames
default plots of model outputs are normally not possible
ggplot2 is not optimized for the best speed performance
3-D plots are not possible
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 6 / 33
Overview: plots of one variable
One continuous variable
histogram: geom_histogram()
densities: geom_density()
frequency plot: geom_freqpoly()
One discrete variable
barplot: geom_bar()
pie plot: different coordinate system of barplot...
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 7 / 33
Package: ggplot2
There are two important functions.
qplot: similar to base plotting functions
ggplot: look at this function closer
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 8 / 33
Let’s have a look — mpg data set
Let’s look at the mpg data set from ggplot2. It contains 234 observationsabout the fuel efficiency of 38 popular cars in 1999 and 2008.> ?mpg
> str(mpg)
Let’s look at the democode.
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 9 / 33
ggplot2: plots of two variables
In this section we will . . .
. . . glimpse at grammar of graphics underlying ggplot2
. . . create plots with two variables
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 10 / 33
Glimpse at grammar of graphics
The ”gg” in ggplot2 stands for grammar of graphics which is based onWilkinson’s (2005) grammar of graphics.
The layered grammar is useful because . . .
it is a generic way of creating a plot
do not relay on specific or customized graphic for a particular problem
iteratively update or create a plot
add layers
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 11 / 33
Glimpse at grammar of graphics
Idea: all the plots can be build from the same components
data set
geoms (geometric object) — visually representing observations
aes (aesthetics) — aesthetics properties of the geometric objects
scale — defining how the data is plotted
coordinate system
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 12 / 33
Glimpse at grammar of graphics
Source: https://www.rstudio.com/resources/cheatsheets/ of the Data Visualization
Cheat Sheet. This image is under Creative Commons licence.
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 13 / 33
Glimpse at grammar of graphics
Source: https://www.rstudio.com/resources/cheatsheets/ of the Data Visualization
Cheat Sheet. This image is under Creative Commons licence.
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 14 / 33
Overview: plots of two variables
Two continuous variables
scatter plot: geom_point()
scatter plot using jitter: geom_jitter()
smoother: geom_smooth()
Discrete x and continuous y
boxplot: geom_boxplot()
bar plot: geom_bar(stat = "identity")
Continuous function like time series
line plot: geom_line()
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 15 / 33
Let’s have a look — mpg data set
Let’s look at the democode.
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 16 / 33
ggplot2: multiple layers and faceting
In this section we will . . .
. . . look at multiple layers
. . . consider faceting or multipanel conditioning plots
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 17 / 33
Multiple layers
Add multiple layers> # variable cty is the city miles per gallon
> ggplot(data = mpg, aes(x = hwy, y = cty)) +
+ geom_jitter() +
+ geom_smooth() +
+ geom_rug(sides = "bl", position = "jitter")
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Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 18 / 33
Faceting
Let’s look a multi-panel plots> # subset of the mpg data set
> mpg.small <- subset(mpg, manufacturer %in%
+ c("ford", "land rover", "toyota",
+ "chevrolet", "honda", "volkswagen"))
> ggplot(data = mpg.small, aes(x = hwy, y = cty)) +
+ geom_jitter() +
+ facet_wrap( ~ manufacturer)
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land rover toyota volkswagen
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Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 19 / 33
Let’s have a look at the aesthetics
In this section we will have a look at the aesthetics . . .
. . . size
. . . shape
. . . color
. . . and combine them
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 20 / 33
size
> ggplot(data = mpg, aes(x = hwy, y = cty, size = displ)) +
+ geom_jitter()
> # displ. engine displacement, in litres
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Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 21 / 33
shape
> ggplot(data = mpg, aes(x = hwy, y = cty, shape = factor(cyl))) +
+ geom_jitter(size = 3)
> # one can set a fixed size for all the points
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Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 22 / 33
color
> ggplot(data = mpg, aes(x = hwy, y = cty, color = factor(cyl))) +
+ geom_jitter(size = 3)
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Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 23 / 33
Combine them
> ggplot(data = mpg, aes(x = hwy, y = cty, color = factor(cyl),
+ shape = factor(cyl), size = displ)) +
+ geom_jitter()
> # nice, there is only one combined legend for shape and color
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Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 24 / 33
The logic . . .
In this section we will look at. . .
. . . where to put what?
. . . the order of the layers
. . . how to save a plot
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 25 / 33
Where to put what?
All the arguments specified in the function ggplot() are passed to allthe layers.
Holds unless specified otherwise in a layer.
Arguments specified in a layer effect only the corresponding layer.
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 26 / 33
Where to put what?
Basic plot to start with> ggplot(data = mpg, aes(x = hwy, y = cty)) + geom_jitter() + geom_smooth()
Color both points and smoothers per group of drv⇒ three smoothers are fitted> ggplot(data = mpg, aes(x = hwy, y = cty, color = drv)) + geom_jitter() +
+ geom_smooth()
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Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 27 / 33
Where to put what?
Color the points per group of drv⇒ one smoother is fitted> ggplot(data = mpg, aes(x = hwy, y = cty)) + geom_jitter(aes(color = drv)) +
+ geom_smooth()
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Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 28 / 33
Where to put what?
Color the smoothers per groug drv
⇒ three smoothers are fitted> ggplot(data = mpg, aes(x = hwy, y = cty)) + geom_jitter() +
+ geom_smooth(aes(color = drv))
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Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 29 / 33
Does the order of the layers matter?
Plot the points first as a layer and then add the layer with boxplots:> ggplot(data = mpg, aes(x = class, y = cty)) +
+ geom_jitter(alpha = 0.4) +
+ geom_boxplot(aes(fill = class))
Plot the boxplot first and afterwards add the layer of points:> ggplot(data = mpg, aes(x = class, y = cty)) +
+ geom_boxplot(aes(fill = class)) +
+ geom_jitter(alpha = 0.4)
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 30 / 33
How to save a plot?
The plot to be saved> v <- ggplot(data = mpg, aes(x = class, y = cty)) + geom_boxplot() +
+ geom_jitter(alpha = 0.3)
Plots which are saved in a object can be saved by ggsave().> ggsave(filename = "cool-boxplot-II.png", plot = v)
> # Cool, `ggsave()` recognizes the output format!
> # (pdf, png, jpg, eps, svg)
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 31 / 33
How to save a plot?
Control the width & height and change the path> # the save of the figure can be controlled by
> ggsave(filename = "cool-boxplot-III.jpg", plot = v, width = 5, height = 4,
+ path = "/path/of/figures/")
Alternatively don’t forget print the plot> pdf("cool-boxplot-IV.pdf")
> print(v)
> dev.off()
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 32 / 33
Book Recommendations: R graphics & ggplot
R Graphics CookbookWinston Chang, O’Reilly Media, 2012
and its online companion:http://www.cookbook-r.com/Graphs/
ggplot2: Elegant Graphics for Data Analysis (Use R!)Hadley Wickham, Springer, 2009
and its online companion:http://ggplot2.org/book/
Seminar for Statistics, ETH Zurich (SfS) An introduction to ggplot2 33 / 33
Recommended