R - Boxplots

R - Modern Boxplots with ggplot2

Boxplots (or box-and-whisker plots) are a powerful way to visualize the distribution of a continuous variable. They concisely display key statistical measures: the minimum, first quartile (Q1), median (Q2), third quartile (Q3), and maximum. They are especially useful for comparing the distributions of a variable across different categories.

Today, ggplot2 is the standard for creating flexible and publication-quality boxplots in R.

Anatomy of a Boxplot

A boxplot shows:

• The median: The line inside the box.
• The interquartile range (IQR): The box itself, stretching from the 25th percentile (Q1) to the 75th percentile (Q3).
• The whiskers: Lines extending from the box to show the range of the data. Typically, they extend to 1.5 times the IQR from the box edges.
• Outliers: Points that fall beyond the whiskers.

Creating a Boxplot

We’ll use the mtcars dataset to compare the distribution of mpg (Miles Per Gallon) across cars with different numbers of cyl (Cylinders).

Setup and Data Preparation

# We will use ggplot2 from the tidyverse
# install.packages("tidyverse")
library(ggplot2)
library(dplyr)

# It's good practice to treat the categorical variable as a factor
# This ensures ggplot2 handles it correctly.
input_data <- mtcars %>%
  mutate(cyl = factor(cyl))

print(head(input_data[, c('mpg', 'cyl')]))

When we execute the above code, it produces the following result:

                     mpg cyl
Mazda RX4         21.0   6
Mazda RX4 Wag     21.0   6
Datsun 710        22.8   4
Hornet 4 Drive    21.4   6
Hornet Sportabout 18.7   8
Valiant           18.1   6

Example: Mileage by Number of Cylinders

We build the plot by specifying the categorical variable (cyl) on the x-axis and the continuous variable (mpg) on the y-axis, then adding the geom_boxplot layer.

# Create the boxplot
boxplot_cyl <- ggplot(data = input_data, aes(x = cyl, y = mpg)) +
  geom_boxplot() +
  labs(
    title = "Distribution of Fuel Efficiency by Number of Cylinders",
    x = "Number of Cylinders",
    y = "Miles Per Gallon (MPG)"
  ) +
  theme_classic()

# Display the plot
print(boxplot_cyl)

# Save the plot to a file
ggsave("boxplot.png", width = 7, height = 5)

This code generates a clear boxplot, boxplot.png, showing that as the number of cylinders increases, the median miles per gallon decreases and its distribution changes.

Customized Boxplots: Notches and Colors

We can easily customize the appearance and add more information. Notches can be added to give a rough guide to the significance of the difference between medians; if the notches of two boxes do not overlap, it’s strong evidence their medians differ. We can also map a variable (or set a fixed color) to the fill aesthetic.

# Create a customized boxplot with notches and colors
boxplot_custom <- ggplot(data = input_data, aes(x = cyl, y = mpg, fill = cyl)) +
  geom_boxplot(
    notch = TRUE,     # Add notches to the boxes
    outlier.color = "red", # Customize the color of outlier points
    outlier.shape = 4
  ) +
  stat_summary(fun = mean, geom = "point", shape = 18, size = 3, color = "white") + # Add mean points
  labs(
    title = "Mileage Data Comparison",
    subtitle = "Notches indicate median confidence intervals. White diamonds are means.",
    x = "Number of Cylinders",
    y = "Miles Per Gallon (MPG)"
  ) +
  scale_fill_brewer(palette = "Pastel1") + # Use a color palette
  theme_gray() +
  guides(fill = "none") # Hide the redundant legend for fill color

# Display the plot
print(boxplot_custom)

# Save the plot to a file
ggsave("boxplot_with_notch.png", width = 8, height = 6)

This advanced example demonstrates several ggplot2 features:

• notch = TRUE adds the confidence interval notches.
• Aesthetics like fill are mapped within aes() for data-driven coloring.
• stat_summary() adds an extra layer showing the mean, which can differ from the median.
• scale_fill_brewer() applies a pre-defined, aesthetically pleasing color palette.