Data Visualization using GGPlot2

Key R functions

• Key R function: geom_boxplot() [ggplot2 package]
• Key arguments to customize the plot:
  • width: the width of the box plot
  • notch: logical. If TRUE, creates a notched boxplot. The notch displays a confidence interval around the median which is normally based on the median +/- 1.58*IQR/sqrt(n). Notches are used to compare groups; if the notches of two boxes do not overlap, this is a strong evidence that the medians differ.
  • color, size, linetype: Border line color, size and type
  • fill: box plot areas fill color
  • outlier.colour, outlier.shape, outlier.size: The color, the shape and the size for outlying points.

Data preparation

• Demo dataset: ToothGrowth
  • Continuous variable: len (tooth length). Used on y-axis
  • Grouping variable: dose (dose levels of vitamin C: 0.5, 1, and 2 mg/day). Used on x-axis.

First, convert the variable dose from a numeric to a discrete factor variable:

data("ToothGrowth")
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
head(ToothGrowth, 4)

##    len supp dose
## 1  4.2   VC  0.5
## 2 11.5   VC  0.5
## 3  7.3   VC  0.5
## 4  5.8   VC  0.5

Loading required R package

Load the ggplot2 package and set the default theme to theme_classic() with the legend at the top of the plot:

library(ggplot2)
theme_set(
  theme_classic() +
    theme(legend.position = "top")
  )

Basic boxplots

We start by initiating a plot named e, then we’ll add layers:

# Default plot
e <- ggplot(ToothGrowth, aes(x = dose, y = len))
e + geom_boxplot()

# Notched box plot with mean points
e + geom_boxplot(notch = TRUE, fill = "lightgray")+
  stat_summary(fun.y = mean, geom = "point",
               shape = 18, size = 2.5, color = "#FC4E07")

Note that, it’s possible to use the function scale_x_discrete() for:

• choosing which items to display: for example c(“0.5”, “2”),
• changing the order of items: for example from c(“0.5”, “1”, “2”) to c(“2”, “0.5”, “1”)

For example, type this:

# Choose which items to display: group "0.5" and "2"
e + geom_boxplot() + 
  scale_x_discrete(limits=c("0.5", "2"))

# Change the default order of items
e + geom_boxplot() +
  scale_x_discrete(limits=c("2", "0.5", "1"))

Change boxplot colors by groups:

The following R code will change the boxplot line and fill color. The functions scale_color_manual() and scale_fill_manual() are used to specify custom colors for each group.

# Color by group (dose)
e + geom_boxplot(aes(color = dose))+
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))

# Change fill color by group (dose)
e + geom_boxplot(aes(fill = dose)) +
  scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))

Create a boxplot with multiple groups

Two different grouping variables are used: dose on x-axis and supp as fill color (legend variable).

The space between the grouped box plots is adjusted using the function position_dodge().

e2 <- e + 
  geom_boxplot(aes(fill = supp), position = position_dodge(0.9) ) +
  scale_fill_manual(values = c("#999999", "#E69F00"))
e2

Multiple panel boxplots

You can split the plot into multiple panel using the function facet_wrap():

e2 + facet_wrap(~supp)

Conclusion

This article describes how to create a boxplot using the ggplot2 package.