Boxplots can be created for individual variables or for variables by group. The format is boxplot(x, data=), where x is a formula and data= denotes the data frame providing the data. An example of a formula is y~group where a separate boxplot for numeric variable y is generated for each value of group. Add varwidth=TRUE to make boxplot widths proportional to the square root of the samples sizes. Add horizontal=TRUE to reverse the axis orientation.
# Boxplot of MPG by Car Cylinders
boxplot(mpg~cyl,data=mtcars, main="Car Milage Data",
xlab="Number of Cylinders", ylab="Miles Per Gallon")
# Notched Boxplot of Tooth Growth Against 2 Crossed Factors
# boxes colored for ease of interpretation
boxplot(len~supp*dose, data=ToothGrowth, notch=TRUE,
main="Tooth Growth", xlab="Suppliment and Dose")
In the notched boxplot, if two boxes' notches do not overlap this is ‘strong evidence’ their medians differ (Chambers et al., 1983, p. 62).
Colors recycle. In the example above, if I had listed 6 colors, each box would have its own color. Earl F. Glynn has created an easy to use list of colors is PDF format.
The boxplot.matrix( ) function in the sfsmisc package draws a boxplot for each column (row) in a matrix. The boxplot.n( ) function in the gplots package annotates each boxplot with its sample size. The bplot( ) function in the Rlab package offers many more options controlling the positioning and labeling of boxes in the output.
# Violin Plots
x1 <- mtcars$mpg[mtcars$cyl==4]
x2 <- mtcars$mpg[mtcars$cyl==6]
x3 <- mtcars$mpg[mtcars$cyl==8]
vioplot(x1, x2, x3, names=c("4 cyl", "6 cyl", "8 cyl"),
title("Violin Plots of Miles Per Gallon")
Bagplot - A 2D Boxplot Extension
The bagplot(x, y) function in the aplpack package provides a bivariate version of the univariate boxplot. The bag contains 50% of all points. The bivariate median is approximated. The fence separates points in the fence from points outside. Outliers are displayed.
# Example of a Bagplot
bagplot(wt,mpg, xlab="Car Weight", ylab="Miles Per Gallon",
Try the boxplot exercises in this course on plotting and data visualization in R.