How to Create a ggplot Box Plot in R

Intro

Boxplots are used to display distribution data of a continuous variable. There are five statistics included on the plot including mean, quantities and outliers. They are used to get quick visual summaries of continuous variables. In this article, we will learn how to create ggplot box plots in R.

If you are in a rush

For those with little time, here is a quick snippet of box plots. Read on for more details.

library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.1.0     v dplyr   1.0.5
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

data(diamonds)

ggplot(diamonds, aes(x = cut, y = price)) +
  geom_boxplot()

Loading the Data

For our tutorial, we will use the diamonds data set that comes with the ggplot package.

library(tidyverse)

data(diamonds)

glimpse(diamonds)

## Rows: 53,940
## Columns: 10
## $ carat   <dbl> 0.23, 0.21, 0.23, 0.29, 0.31, 0.24, 0.24, 0.26, 0.22, 0.23, 0.~
## $ cut     <ord> Ideal, Premium, Good, Premium, Good, Very Good, Very Good, Ver~
## $ color   <ord> E, E, E, I, J, J, I, H, E, H, J, J, F, J, E, E, I, J, J, J, I,~
## $ clarity <ord> SI2, SI1, VS1, VS2, SI2, VVS2, VVS1, SI1, VS2, VS1, SI1, VS1, ~
## $ depth   <dbl> 61.5, 59.8, 56.9, 62.4, 63.3, 62.8, 62.3, 61.9, 65.1, 59.4, 64~
## $ table   <dbl> 55, 61, 65, 58, 58, 57, 57, 55, 61, 61, 55, 56, 61, 54, 62, 58~
## $ price   <int> 326, 326, 327, 334, 335, 336, 336, 337, 337, 338, 339, 340, 34~
## $ x       <dbl> 3.95, 3.89, 4.05, 4.20, 4.34, 3.94, 3.95, 4.07, 3.87, 4.00, 4.~
## $ y       <dbl> 3.98, 3.84, 4.07, 4.23, 4.35, 3.96, 3.98, 4.11, 3.78, 4.05, 4.~
## $ z       <dbl> 2.43, 2.31, 2.31, 2.63, 2.75, 2.48, 2.47, 2.53, 2.49, 2.39, 2.~

Creating a Basic ggplot Box Plot

To create a box plot in ggplot2, we can use the geom_boxplot method after supplying a continuous variable to the y of our aes, aesthetic. In this example, we will use height from the price data set above.

ggplot(diamonds, aes(y = price)) +
  geom_boxplot()

We can also flip the plot to orient horizontally by using the coord_flip method.

ggplot(diamonds, aes(y = price)) +
  geom_boxplot() +
  coord_flip()

Customizing the ggplot Box Plot

We can customize our box plots using some parameters on the geom_boxplot method. For example, we can change the color using the color named parameter. Here is an example.

ggplot(diamonds, aes(y = price)) +
  geom_boxplot(color = 4,
               fill = 4,
               alpha = 0.25)

Adding Summary Information to a ggplot Box Plot

We can also add summary information to our box plots to visualize in addition to our distributions. For example, we can use the stat_summary method to display the median like so.

ggplot(diamonds, aes(x = cut, y = price)) +
  geom_boxplot() +
  stat_summary(
    fun.y = median,
    geom = "point",
    size = 2,
    color = "red"
   )

## Warning: `fun.y` is deprecated. Use `fun` instead.

Similarly, we can add the mean to each of our plots.

ggplot(diamonds, aes(x = cut, y = price)) +
  geom_boxplot() +
  stat_summary(
    fun.y = mean,
    geom = "point",
    size = 2,
    color = "blue"
   )

## Warning: `fun.y` is deprecated. Use `fun` instead.

Adjusting the ggplot Box Plot Labels

We can adjust the title, x-label, and y-label of our box plot using the labs method. We then pass the title, x and y parameters.

ggplot(diamonds, aes(x = cut, y = price)) +
  geom_boxplot() +
  labs(
    title = "Comparison of Price by Cut",
    x = "Cut",
    y = "Price"
  )

Group by Color

We can color the separate groups of our violin plots by using the fill or colour aesthetic properties. Here is an example of using the fill to assign colors to each factor.

library(ggplot2)


ggplot(diamonds, aes(x = cut, y = price, fill = cut)) +
  geom_boxplot()

Facets Groups on a ggplot Box Plot

If we prefer to have separate plots, we can use the facet_ methods in ggplot. For example, here are plots separated by each cut.

library(ggplot2)

ggplot(diamonds, aes(x = cut, y = price, fill = cut)) +
  geom_boxplot() +
  facet_grid(~cut)

Limiting X and Y

If we would like to limit the y values of our plots, we can use the ylimit function

ggplot(diamonds, aes(x = cut, y = price)) +
  geom_boxplot() +
  ylim(5000, 10000)

## Warning: Removed 44435 rows containing non-finite values (stat_boxplot).

Scaling X and Y

We can also scale the y axis using the scale_ function from ggplot. Here are some example of a log10 and sqrt scale of the y axis.

ggplot(diamonds, aes(x = cut, y = price)) +
  geom_boxplot() +
  scale_y_log10()

ggplot(diamonds, aes(x = cut, y = price)) +
  geom_boxplot() +
  scale_y_sqrt()

Color and Fill Scales

There are many color options in ggplot. We can use scale_ methods like scale_fill_brewer() to have ggplot automatically assign different themes based on our data set.

library(ggplot2)


ggplot(diamonds, aes(x = cut, y = price, fill = cut)) +
  geom_boxplot() +
  scale_fill_brewer()

Customizing the Legend of a ggplot Box Plot

When we have groups, ggplot will add a legend to the plot. We can customize the position of this legend using the theme method and the legend.position parameter. Here are example of moving the legend to the top, bottom, and hiding it.

ggplot(diamonds, aes(x = cut, y = price, fill = cut)) +
  geom_boxplot() +
  theme(legend.position="top")

ggplot(diamonds, aes(x = cut, y = price, fill = cut)) +
  geom_boxplot() +
  theme(legend.position="bottom")

ggplot(diamonds, aes(x = cut, y = price, fill = cut)) +
  geom_boxplot() +
  theme(legend.position="none")

Using Themes with a ggplot Box Plot

If we want to use built in styles for the full plot, ggplot provides themes to add to our plot. Here is an example of adding the theme_classic to our plot.

ggplot(diamonds, aes(x = cut, y = price, fill = cut)) +
  geom_boxplot() +
  theme_classic()