We will use the following libraries in this post:
All the data sets used in this post can be found here and code can be downloaded from here.
Let us continue with the scatter plot examining the relationship between displacement and miles per gallon but let us make one additional change. We now want 3 sub plots for each type of cylinder. How can we do this? We can split or group the data by cylinder type and plot the subset of data which means dealing with 3 different data sets, plotting 3 plots and arranging them for comparison. ggplot2 offers the following 2 functions which allow us to plot subset of data with a simple formula based interface:
facet_grid()
facet_wrap()
Faceting allows us to create multiple sub plots. It partitions a plot into a matrix of panels with each panel showing a different subset of data.
facet_grid() allows us to split up the data by one or two discrete variables and create sub plots. The sub plots can be arranged horizontally or vertically using a formula of the form vertical ~ horizontal. In the below example, 3 sub plots are created, one each for the levels of the cyl variable and the sub plots are arranged vertically
ggplot(mtcars, aes(disp, mpg)) +
geom_point() +
facet_grid(cyl ~ .)
Below we reproduce the previous example but arrange the sub plots horizontally.
ggplot(mtcars, aes(disp, mpg)) +
geom_point() +
facet_grid(. ~ cyl)
In certain cases, we might want different discrete variables to represent the horizontal and vertical direction. In the below example, we examine the relationship between displacement and miles per gallon for different combinations of cyl and gear variables.
ggplot(mtcars, aes(disp, mpg)) +
geom_point() +
facet_grid(cyl ~ gear)
Below, we switch the variables representing the vertical and horizontal directions.
ggplot(mtcars, aes(disp, mpg)) +
geom_point() +
facet_grid(gear ~ cyl)
If you carefully observe the second example, the range of X axis is same for all the 3 sub plots i.e. it is a fixed range. You can allow each of the sub plots to have different range using the scales argument and supplying it the value ‘free’.
ggplot(mtcars, aes(disp, mpg, color = factor(cyl))) +
geom_point() +
facet_grid(. ~ cyl, scales = "free")
Now, each of the sub plot has a different range.
In the third example, the labels are displayed at the bottom for X axis and at the right for the Y axis. It can be changed using the switch argument and supplying the value ‘both’. The labels will now be displayed at the top for the X axis and at left for the Y axis. If you just want to change the labels for a particular axis, use the values x and y for the X and Y axis respectively.
ggplot(mtcars, aes(disp, mpg)) +
geom_point() +
facet_grid(cyl ~ gear, switch = "both")
facet_wrap() allows us to arrange sub plots in a certain number of rows and columns. In the below example, we will use facet_wrap() to arrange the sub plots in a single row.
ggplot(mtcars, aes(disp, mpg)) +
geom_point() +
facet_wrap(~cyl)
To arrange the sub plots in a specific number of rows, use the nrow argument. In the below example, we arrange the sub plots in 2 rows.
ggplot(mtcars, aes(disp, mpg)) +
geom_point() +
facet_wrap(~cyl, nrow = 2)
Here, we arrange the sub plots in 3 columns instead of rows using the ncol argument.
ggplot(mtcars, aes(disp, mpg)) +
geom_point() +
facet_wrap(~cyl, ncol = 3)
You can allow each of the sub plots to have different range using the scales argument and supplying it the value ‘free’.
ggplot(mtcars, aes(disp, mpg)) +
geom_point() +
facet_wrap(~cyl, scales = "free")
If 2 discrete variables are used to create the sub plots, we can either use the formula interface to specify the variables as shown below
ggplot(mtcars, aes(disp, mpg)) +
geom_point() +
facet_wrap(~cyl + gear, nrow = 2)
or use a character vector of variable names.
ggplot(mtcars, aes(disp, mpg)) +
geom_point() +
facet_wrap(c("cyl", "gear"), ncol = 2)
In this post, we learnt to create sub plots using:
facet_grid()
facet_wrap()
In the next post, we will learn to modify the theme of a plot.