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Data Visualization with R & ggplot2
Karthik Ram
April 11, 2013
Data Visualization with R & ggplot2 Karthik Ram
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Download this PDF
github.com/karthikram/ggplot-lecture
https://speakerdeck.com/karthik/
Data Visualization with R & ggplot2 Karthik Ram
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Some housekeeping
Install some packages (make sure you also have recent copies of
reshape2 and plyr)
install.packages("ggplot2", dependencies = TRUE)
Data Visualization with R & ggplot2 Karthik Ram
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Base graphics
• Ugly, laborious, and verbose
• There are better ways to describe statistical visualizations.
Data Visualization with R & ggplot2 Karthik Ram
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Why ggplot2?
• Follows a grammar, just like any language.
• It defines basic components that make up a sentence. In this
case, the grammar defines components in a plot.
• Grammar of graphics originally coined by Lee Wilkinson
Data Visualization with R & ggplot2 Karthik Ram
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Why ggplot2?
• Supports a continuum of expertise.
• Get started right away but with practice you can effortless
build complex, publication quality figures.
Data Visualization with R & ggplot2 Karthik Ram
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Section 1
Basics
Data Visualization with R & ggplot2 Karthik Ram
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Some terminology
• ggplot - The main function where you specify the dataset and
variables to plot
• geoms - geometric objects
• geom point(), geom bar(), geom density(), geom line(),
geom area()
• aes - aesthetics
• shape, transparency (alpha), color, fill, linetype.
• scales Define how your data will be plotted
• continuous, discrete, log
Data Visualization with R & ggplot2 Karthik Ram
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Section 2
Assembling your first ggplot
Data Visualization with R & ggplot2 Karthik Ram
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The iris dataset
head(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 .2 setosa
## 2 4.9 3. 1.4 .2 setosa
## 3 4.7 3.2 1.3 .2 setosa
## 4 4.6 3.1 1.5 .2 setosa
## 5 5. 3.6 1.4 .2 setosa
## 6 5.4 3.9 1.7 .4 setosa
Data Visualization with R & ggplot2 Karthik Ram
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Let’s try an example
ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width)) +
geom_point()
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Data Visualization with R & ggplot2 Karthik Ram
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Basic structure
ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width))
+ geom_point()
myplot <- ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width))
myplot + geom_point()
• Specify the data and variables inside the ggplot function.
• Anything else that goes in here becomes a global setting.
• Then add layers of geometric objects, statistical models, and
panels.
Data Visualization with R & ggplot2 Karthik Ram
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Quick note
• Never use qplot - short for quick plot.
• You’ll end up unlearning and relearning a good bit.
Data Visualization with R & ggplot2 Karthik Ram
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Increase the size of points
ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width)) +
geom_point(size = 3)
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Data Visualization with R & ggplot2 Karthik Ram
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Add some color
ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
geom_point(size = 3)
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virginica
Data Visualization with R & ggplot2 Karthik Ram
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Differentiate points by shape
ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
geom_point(aes(shape = Species), size = 3)
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Data Visualization with R & ggplot2 Karthik Ram
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Exercise 1
# Make a small sample of the diamonds dataset
d2 <- diamonds[sample(1:dim(diamonds)[1], 1 ), ]
Then generate this plot below.
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0.5 1.0 1.5 2.0 2.5
carat
price
color
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H
I
J
Data Visualization with R & ggplot2 Karthik Ram
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Section 3
Box plots
Data Visualization with R & ggplot2 Karthik Ram
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See ?geom boxplot for list of options
library(MASS)
ggplot(birthwt, aes(factor(race), bwt)) + geom_boxplot()
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factor(race)
bwt
Data Visualization with R & ggplot2 Karthik Ram
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Section 4
Histograms
Data Visualization with R & ggplot2 Karthik Ram
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See ?geom histogram for list of options
h <- ggplot(faithful, aes(x = waiting))
h + geom_histogram(binwidth = 3 , colour = "black")
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0 50 100 150
waiting
count
Data Visualization with R & ggplot2 Karthik Ram
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h <- ggplot(faithful, aes(x = waiting))
h + geom_histogram(binwidth = 8, fill = "steelblue",
colour = "black")
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60
30 50 70 90
waiting
count
Data Visualization with R & ggplot2 Karthik Ram
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Section 5
Line plots
Data Visualization with R & ggplot2 Karthik Ram
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climate <- read.csv("climate.csv", header = T)
ggplot(climate, aes(Year, Anomaly1 y)) +
geom_line()
0.0
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1920 1950 1980
Year
Anomaly10y
climate <- read.csv(text =
RCurl::getURL(https://raw.github.com/karthikram/ggplot-lecture/master/climate.csv))
Data Visualization with R & ggplot2 Karthik Ram
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We can also plot confidence regions
ggplot(climate, aes(Year, Anomaly1 y)) +
geom_ribbon(aes(ymin = Anomaly1 y - Unc1 y,
ymax = Anomaly1 y + Unc1 y),
fill = "blue", alpha = .1) +
geom_line(color = "steelblue")
0.0
0.5
1920 1950 1980
Year
Anomaly10y
Data Visualization with R & ggplot2 Karthik Ram
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Exercise 2
• Modify the previous plot and change it such that there are
three lines instead of one with a confidence band.
0.0
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1920 1950 1980
Year
Anomaly10y
Data Visualization with R & ggplot2 Karthik Ram
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Section 6
Bar plots
Data Visualization with R & ggplot2 Karthik Ram
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ggplot(iris, aes(Species, Sepal.Length)) +
geom_bar(stat = "identity")
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setosa versicolor virginica
Species
Sepal.Length
Data Visualization with R & ggplot2 Karthik Ram
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df <- melt(iris, id.vars = "Species")
ggplot(df, aes(Species, value, fill = variable)) +
geom_bar(stat = "identity")
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750
setosa versicolor virginica
Species
value
variable
Sepal.Length
Sepal.Width
Petal.Length
Petal.Width
Data Visualization with R & ggplot2 Karthik Ram
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Section 7
plyr and reshape are key for using R
Data Visualization with R & ggplot2 Karthik Ram
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plyr and reshape
These two packages are the swiss army knives of R.
• plyr
1 ddply
2 llply
3 join
• reshape.
1 melt
2 dcast
3 acast
Data Visualization with R & ggplot2 Karthik Ram
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iris[1:2, ]
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 .2 setosa
## 2 4.9 3. 1.4 .2 setosa
df <- melt(iris, id.vars = "Species")
df[1:2, ]
## Species variable value
## 1 setosa Sepal.Length 5.1
## 2 setosa Sepal.Length 4.9
Data Visualization with R & ggplot2 Karthik Ram
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ggplot(df, aes(Species, value, fill = variable)) +
geom_bar(stat = "identity", position = "dodge")
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8
setosa versicolor virginica
Species
value
variable
Sepal.Length
Sepal.Width
Petal.Length
Petal.Width
Data Visualization with R & ggplot2 Karthik Ram
Slide 34
Slide 34 text
Exercise 3
Using the d2 dataset you created earlier, generate this plot below.
Take a quick look at the data first to see if it needs to be binned.
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I1 SI2 SI1 VS2 VS1 VVS2 VVS1 IF
clarity
count
cut
Fair
Good
Very Good
Premium
Ideal
Data Visualization with R & ggplot2 Karthik Ram
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Exercise 4
• Using the climate dataset, create a new variable called sign.
Make it logical (true/false) based on the sign of Anomaly10y.
• Plot a bar plot and use sign variable as the fill.
0.0
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1920 1950 1980
Year
Anomaly10y
sign
FALSE
TRUE
Data Visualization with R & ggplot2 Karthik Ram
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Slide 36 text
Section 8
Density Plots
Data Visualization with R & ggplot2 Karthik Ram
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Slide 37 text
Density plots
ggplot(faithful, aes(waiting)) + geom_density()
0.00
0.01
0.02
0.03
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waiting
density
Data Visualization with R & ggplot2 Karthik Ram
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Density plots
ggplot(faithful, aes(waiting)) +
geom_density(fill = "blue", alpha = .1)
0.00
0.01
0.02
0.03
50 60 70 80 90
waiting
density
Data Visualization with R & ggplot2 Karthik Ram
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Slide 39 text
ggplot(faithful, aes(waiting)) +
geom_line(stat = "density")
0.01
0.02
0.03
50 60 70 80 90
waiting
density
Data Visualization with R & ggplot2 Karthik Ram
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Slide 40 text
Section 9
Mapping Variables to colors
Data Visualization with R & ggplot2 Karthik Ram
Slide 41
Slide 41 text
Colors
# Map all points to one color
aes(color = "black")
# Or map the points to a variavble
aes(color = variable)
# Then add a scale for the colors. Below we manually
# define colors but there are other ways (see next slide)
scale_fill_manual(values = c("color1", "color2"))
Data Visualization with R & ggplot2 Karthik Ram
Slide 42
Slide 42 text
The RColorBrewer package
library(RColorBrewer)
display.brewer.all()
Data Visualization with R & ggplot2 Karthik Ram
Slide 43
Slide 43 text
Using a color brewer palette
df <- melt(iris, id.vars = "Species")
ggplot(df, aes(Species, value, fill = variable)) +
geom_bar(stat = "identity", position = "dodge") +
scale_fill_brewer(palette = "Set1")
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setosa versicolor virginica
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Data Visualization with R & ggplot2 Karthik Ram
Slide 44
Slide 44 text
Manual color scale
ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
geom_point() +
facet_grid(Species ˜ .) +
scale_color_manual(values = c("red", "green", "blue"))
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Data Visualization with R & ggplot2 Karthik Ram
Slide 45
Slide 45 text
Refer to a color chart for beautiful visualizations
http://tools.medialab.sciences-po.fr/iwanthue/
Data Visualization with R & ggplot2 Karthik Ram
Slide 46
Slide 46 text
Section 10
Faceting
Data Visualization with R & ggplot2 Karthik Ram
Slide 47
Slide 47 text
Faceting along columns
ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
geom_point() +
facet_grid(Species ˜ .)
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Data Visualization with R & ggplot2 Karthik Ram
Slide 48
Slide 48 text
and along rows
ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
geom_point() +
facet_grid(. ˜ Species)
setosa versicolor virginica
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Data Visualization with R & ggplot2 Karthik Ram
Slide 49
Slide 49 text
or just wrap your panels
ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
geom_point() +
facet_wrap( ˜ Species)
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Data Visualization with R & ggplot2 Karthik Ram
Slide 50
Slide 50 text
Section 11
Adding smoothers
Data Visualization with R & ggplot2 Karthik Ram
Slide 51
Slide 51 text
ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
geom_point(aes(shape = Species), size = 3) +
geom_smooth(method = "lm")
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Data Visualization with R & ggplot2 Karthik Ram
Slide 52
Slide 52 text
ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
geom_point(aes(shape = Species), size = 3) +
geom_smooth(method = "lm") +
facet_grid(. ˜ Species)
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Data Visualization with R & ggplot2 Karthik Ram
Slide 53
Slide 53 text
Section 12
Themes
Data Visualization with R & ggplot2 Karthik Ram
Slide 54
Slide 54 text
Adding themes
Themes are a great way to define custom plots.
+theme()
# see ?theme() for more options
Data Visualization with R & ggplot2 Karthik Ram
Slide 55
Slide 55 text
A themed plot
ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
geom_point(size = 1.2, shape = 16) +
facet_wrap( ˜ Species) +
theme(legend.key = element_rect(fill = NA),
legend.position = "bottom",
strip.background = element_rect(fill = NA),
axis.title.y = element_text(angle = ))
Data Visualization with R & ggplot2 Karthik Ram
Slide 56
Slide 56 text
Adding themes
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Data Visualization with R & ggplot2 Karthik Ram
Slide 57
Slide 57 text
ggthemes library
install.packages("ggthemes")
library(ggthemes)
# Then add one of these themes to your plot
+theme_stata()
+theme_excel()
+theme_wsj()
+theme_solarized()
Data Visualization with R & ggplot2 Karthik Ram
Slide 58
Slide 58 text
Section 13
Create functions to automate your plotting
Data Visualization with R & ggplot2 Karthik Ram
Slide 59
Slide 59 text
Write functions for day to day plots
my_custom_plot <- function(df, title = "", ...) {
ggplot(df, ...) +
ggtitle(title) +
whatever geoms() +
theme(...)
}
Then just call your function to generate a plot. It’s a lot easier to
fix one function that do it over and over for many plots
plot1 <- my_custom_plot(dataset1, title = "Figure 1")
Data Visualization with R & ggplot2 Karthik Ram
Slide 60
Slide 60 text
Section 14
Scales
Data Visualization with R & ggplot2 Karthik Ram
Slide 61
Slide 61 text
Commonly used scales
scale_fill_discrete(), scale_colour_discrete()
scale_fill_hue(), scale_color_hue()
scale_fill_manual(), scale_color_manual()
scale_fill_brewer(), scale_color_brewer()
scale_linetype(), scale_shape_manual()
Data Visualization with R & ggplot2 Karthik Ram
Slide 62
Slide 62 text
Adding a continuous scale
library(MASS)
ggplot(birthwt, aes(factor(race), bwt)) +
geom_boxplot(width = .2) +
scale_y_continuous(labels = (paste (1:4, " Kg")),
breaks = seq(1 , 4 , by = 1 ))
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Data Visualization with R & ggplot2 Karthik Ram
Slide 63
Slide 63 text
Another continuous scale with custom labels
# Assign the plot to an object
dd <- ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
geom_point(size = 4, shape = 16) +
facet_grid(. ˜Species)
# Now add a scale
dd +
scale_y_continuous(breaks = seq(2, 8, by = 1),
labels = paste (2:8, " cm"))
Data Visualization with R & ggplot2 Karthik Ram
Slide 64
Slide 64 text
gradients
h + geom_histogram( aes(fill = ..count..), color="black") +
scale_fill_gradient(low="green", high="red")
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Data Visualization with R & ggplot2 Karthik Ram
Slide 65
Slide 65 text
Section 15
Publication quality figures
Data Visualization with R & ggplot2 Karthik Ram
Slide 66
Slide 66 text
• If the plot is on your screen
ggsave("˜/path/to/figure/filename.png")
• If your plot is assigned to an object
ggsave(plot1, file = "˜/path/to/figure/filename.png")
• Specify a size
ggsave(file = "/path/to/figure/filename.png", width = 6,
height =4)
• or any format (pdf, png, eps, svg, jpg)
ggsave(file = "/path/to/figure/filename.eps")
ggsave(file = "/path/to/figure/filename.jpg")
ggsave(file = "/path/to/figure/filename.pdf")
Data Visualization with R & ggplot2 Karthik Ram
Slide 67
Slide 67 text
Further help
• You’ve just scratched the surface with ggplot2.
• Practice
• Read the docs (either locally in R or at
http://docs.ggplot2.org/current/)
• Work together
Data Visualization with R & ggplot2 Karthik Ram