How to Create a ggplot Jitter Plot in R

Intro

Jitter plots add some variation to a scatter plot so that you can see the individual observations easier. They are commonly used when viewing overlapping points from data that is discrete. In this artilce, we will learn how to create ggplot Jitter plots in R.

If you are in a rush

For those with little time, here is a quick snippet of Jitter 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(mpg)

ggplot(mpg, aes(x = cyl, y = hwy)) +
  geom_jitter()

Loading the Data

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

library(tidyverse)

data(mpg)

glimpse(mpg)

## Rows: 234
## Columns: 11
## $ manufacturer <chr> "audi", "audi", "audi", "audi", "audi", "audi", "audi", "~
## $ model        <chr> "a4", "a4", "a4", "a4", "a4", "a4", "a4", "a4 quattro", "~
## $ displ        <dbl> 1.8, 1.8, 2.0, 2.0, 2.8, 2.8, 3.1, 1.8, 1.8, 2.0, 2.0, 2.~
## $ year         <int> 1999, 1999, 2008, 2008, 1999, 1999, 2008, 1999, 1999, 200~
## $ cyl          <int> 4, 4, 4, 4, 6, 6, 6, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 8, 8, ~
## $ trans        <chr> "auto(l5)", "manual(m5)", "manual(m6)", "auto(av)", "auto~
## $ drv          <chr> "f", "f", "f", "f", "f", "f", "f", "4", "4", "4", "4", "4~
## $ cty          <int> 18, 21, 20, 21, 16, 18, 18, 18, 16, 20, 19, 15, 17, 17, 1~
## $ hwy          <int> 29, 29, 31, 30, 26, 26, 27, 26, 25, 28, 27, 25, 25, 25, 2~
## $ fl           <chr> "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p~
## $ class        <chr> "compact", "compact", "compact", "compact", "compact", "c~

Creating a Basic ggplot Jitter Plot

To create a Jitter plot in ggplot2, we can use the geom_jitter 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(mpg, aes(x = cyl, y = hwy)) +
  geom_jitter()

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

ggplot(mpg, aes(x = cyl, y = hwy)) +
  geom_jitter() +
  coord_flip()

Customizing the ggplot Jitter Plot

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

ggplot(mpg, aes(x = cyl, y = hwy)) +
  geom_jitter(color = 4,
               fill = 4,
               alpha = 0.25)

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 colour to assign colors to each factor.

library(ggplot2)


ggplot(mpg, aes(x = cyl, y = hwy, colour = class)) +
  geom_jitter()

Facets Groups on a ggplot Jitter Plot

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

library(ggplot2)

ggplot(mpg, aes(x = cyl, y = hwy, colour = class)) +
  geom_jitter() +
  facet_grid(~class)

Adding Summary Information to a ggplot Jitter Plot

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

ggplot(mpg, aes(x = cyl, y = hwy)) +
  geom_jitter() +
  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(mpg, aes(x = cyl, y = hwy)) +
  geom_jitter() +
  stat_summary(
    fun.y = mean,
    geom = "point",
    size = 2,
    color = "blue"
   )

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

Adjusting the ggplot Jitter Plot Labels

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

ggplot(mpg, aes(x = cyl, y = hwy)) +
  geom_jitter() +
  labs(
    title = "Comparison of Car by Cycle and Hwy",
    x = "Cycle",
    y = "Hwy"
  )

Limiting X and Y

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

ggplot(mpg, aes(x = cyl, y = hwy)) +
  geom_jitter() +
  xlim(2, 8)

## Warning: Removed 35 rows containing missing values (geom_point).

  ylim(20, 40)

## <ScaleContinuousPosition>
##  Range:  
##  Limits:   20 --   40

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(mpg, aes(x = cyl, y = hwy)) +
  geom_jitter() +
  scale_y_log10()

ggplot(mpg, aes(x = cyl, y = hwy)) +
  geom_jitter() +
  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(mpg, aes(x = cyl, y = hwy, colour = class)) +
  geom_jitter() +
  scale_fill_brewer()

Customizing the Legend of a ggplot Jitter 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(mpg, aes(x = cyl, y = hwy, colour = class)) +
  geom_jitter() +
  theme(legend.position="top")

ggplot(mpg, aes(x = cyl, y = hwy, colour = class)) +
  geom_jitter() +
  theme(legend.position="bottom")

ggplot(mpg, aes(x = cyl, y = hwy, colour = class)) +
  geom_jitter() +
  theme(legend.position="none")

Using Themes with a ggplot Jitter 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(mpg, aes(x = cyl, y = hwy, colour = class)) +
  geom_jitter() +
  theme_classic()