Data visualization

Introduction

“The simple graph has brought more information to the data analyst’s mind than any other device.” — John Tukey

R has several systems for making graphs, but ggplot2 is one of the most elegant and most versatile. ggplot2 implements the grammar of graphics, a coherent system for describing and building graphs. With ggplot2, you can do more and faster by learning one system and applying it in many places.

This chapter will teach you how to visualize your data using ggplot2. We will start by creating a simple scatterplot and use that to introduce aesthetic mappings and geometric objects – the fundamental building blocks of ggplot2. We will then walk you through visualizing distributions of single variables as well as visualizing relationships between two or more variables. We’ll finish off with saving your plots and troubleshooting tips.

Prerequisites

This chapter focuses on ggplot2, one of the core packages in the tidyverse. To access the datasets, help pages, and functions used in this chapter, load the tidyverse by running:

library(tidyverse)
#> ── Attaching core tidyverse packages ──────────────── tidyverse 1.3.2.9000 ──
#> ✔ dplyr     1.0.99.9000     ✔ readr     2.1.3      
#> ✔ forcats   0.5.2           ✔ stringr   1.5.0      
#> ✔ ggplot2   3.4.0.9000      ✔ tibble    3.1.8      
#> ✔ lubridate 1.9.0           ✔ tidyr     1.3.0      
#> ✔ purrr     1.0.1           
#> ── Conflicts ─────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

That one line of code loads the core tidyverse; the packages that you will use in almost every data analysis. It also tells you which functions from the tidyverse conflict with functions in base R (or from other packages you might have loaded)You can eliminate that message and force conflict resolution to happen on demand by using the conflicted package, which becomes more important as you load more packages. You can learn more about conflicted at https://conflicted.r-lib.org..

If you run this code and get the error message there is no package called 'tidyverse', you’ll need to first install it, then run library() once again.

install.packages("tidyverse")
library(tidyverse)

You only need to install a package once, but you need to load it every time you start a new session.

In addition to tidyverse, we will also use the palmerpenguins package, which includes the penguins dataset containing body measurements for penguins on three islands in the Palmer Archipelago.

library(palmerpenguins)

First steps

Let’s use our first graph to answer a question: Do penguins with longer flippers weigh more or less than penguins with shorter flippers? You probably already have an answer, but try to make your answer precise. What does the relationship between flipper length and body mass look like? Is it positive? Negative? Linear? Nonlinear? Does the relationship vary by the species of the penguin? And how about by the island where the penguin lives.

The penguins data frame

You can test your answer with the penguins data frame found in palmerpenguins (a.k.a. palmerpenguins::penguins). A data frame is a rectangular collection of variables (in the columns) and observations (in the rows). penguins contains 344 observations collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTERHorst AM, Hill AP, Gorman KB (2020). palmerpenguins: Palmer Archipelago (Antarctica) penguin data. R package version 0.1.0. https://allisonhorst.github.io/palmerpenguins/. doi: 10.5281/zenodo.3960218..

penguins
#> # A tibble: 344 × 8
#>   species island    bill_length_mm bill_depth_mm flipper_length_mm
#>   <fct>   <fct>              <dbl>         <dbl>             <int>
#> 1 Adelie  Torgersen           39.1          18.7               181
#> 2 Adelie  Torgersen           39.5          17.4               186
#> 3 Adelie  Torgersen           40.3          18                 195
#> 4 Adelie  Torgersen           NA            NA                  NA
#> 5 Adelie  Torgersen           36.7          19.3               193
#> 6 Adelie  Torgersen           39.3          20.6               190
#> # … with 338 more rows, and 3 more variables: body_mass_g <int>, sex <fct>,
#> #   year <int>

This data frame contains 8 columns. For an alternative view, where you can see all variables and the first few observations of each variable, use glimpse(). Or, if you’re in RStudio, run View(penguins) to open an interactive data viewer.

glimpse(penguins)
#> Rows: 344
#> Columns: 8
#> $ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, A…
#> $ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torge…
#> $ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.…
#> $ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.…
#> $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, …
#> $ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 347…
#> $ sex               <fct> male, female, female, NA, female, male, female, m…
#> $ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2…

Among the variables in penguins are:

species: a penguin’s species (Adelie, Chinstrap, or Gentoo).
flipper_length_mm: length of a penguin’s flipper, in millimeters.
body_mass_g: body mass of a penguin, in grams.

To learn more about penguins, open its help page by running ?penguins.

Ultimate goal

Our ultimate goal in this chapter is to recreate the following visualization displaying the relationship between flipper lengths and body masses of these penguins, taking into consideration the species of the penguin.

Creating a ggplot

Let’s recreate this plot layer-by-layer.

With ggplot2, you begin a plot with the function ggplot(), defining a plot object that you then add layers to. The first argument of ggplot() is the dataset to use in the graph and So ggplot(data = penguins) creates an empty graph. This is not a very exciting plot, but you can think of it like an empty canvas you’ll paint the remaining layers of your plot onto.

ggplot(data = penguins)

A blank, gray plot area.

Next, we need to tell ggplot() the variables from this data frame that we want to map to visual properties (aesthetics) of the plot. The mapping argument of the ggplot() function defines how variables in your dataset are mapped to visual properties of your plot. The mapping argument is always paired with the aes() function, and the x and y arguments of aes() specify which variables to map to the x and y axes. For now, we will only map flipper length to the x aesthetic and body mass to the y aesthetic. ggplot2 looks for the mapped variables in the data argument, in this case, penguins.

The following plots show the result of adding these mappings, one at a time.

ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm)
)
ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g)
)

There are two plots. The plot on the left is shows flipper length on the x-axis. The values range from 170 to 230 The plot on the right also shows body mass on the y-axis. The values range from 3000 to 6000.

Our empty canvas now has more structure – it’s clear where flipper lengths will be displayed (on the x-axis) and where body masses will be displayed (on the y-axis). But the penguins themselves are not yet on the plot. This is because we have not yet articulated, in our code, how to represent the observations from our data frame on our plot.

To do so, we need to define a geom: the geometrical object that a plot uses to represent data. These geometric objects are made available in ggplot2 with functions that start with geom_. People often describe plots by the type of geom that the plot uses. For example, bar charts use bar geoms (geom_bar()), line charts use line geoms (geom_line()), boxplots use boxplot geoms (geom_boxplot()), and so on. Scatterplots break the trend; they use the point geom: geom_point().

The function geom_point() adds a layer of points to your plot, which creates a scatterplot. ggplot2 comes with many geom functions that each adds a different type of layer to a plot. You’ll learn a whole bunch of geoms throughout the book, particularly in #chp-layers.

ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g)
) +
  geom_point()
#> Warning: Removed 2 rows containing missing values (`geom_point()`).

A scatterplot of body mass vs. flipper length of penguins. The plot displays a positive, linear, and relatively strong relationship between these two variables.

Now we have something that looks like what we might think of as a “scatter plot”. It doesn’t yet match our “ultimate goal” plot, but using this plot we can start answering the question that motivated our exploration: “What does the relationship between flipper length and body mass look like?” The relationship appears to be positive, fairly linear, and moderately strong. Penguins with longer flippers are generally larger in terms of their body mass.

Before we add more layers to this plot, let’s pause for a moment and review the warning message we got:

Removed 2 rows containing missing values (geom_point()).

We’re seeing this message because there are two penguins in our dataset with missing body mass and flipper length values and ggplot2 has no way of representing them on the plot. You don’t need to worry about understanding the following code yet (you will learn about it in #chp-data-transform), but it’s one way of identifying the observations with NAs for either body mass or flipper length.

penguins |>
  select(species, flipper_length_mm, body_mass_g) |>
  filter(is.na(body_mass_g) | is.na(flipper_length_mm))
#> # A tibble: 2 × 3
#>   species flipper_length_mm body_mass_g
#>   <fct>               <int>       <int>
#> 1 Adelie                 NA          NA
#> 2 Gentoo                 NA          NA

Like R, ggplot2 subscribes to the philosophy that missing values should never silently go missing. This type of warning is probably one of the most common types of warnings you will see when working with real data – missing values are a very common issue and you’ll learn more about them throughout the book, particularly in #chp-missing-values. For the remaining plots in this chapter we will suppress this warning so it’s not printed alongside every single plot we make.

Adding aesthetics and layers

Scatterplots are useful for displaying the relationship between two variables, but it’s always a good idea to be skeptical of any apparent relationship between two variables and ask if there may be other variables that explain or change the nature of this apparent relationship. Let’s incorporate species into our plot and see if this reveals any additional insights into the apparent relationship between flipper length and body mass. We will do this by representing species with different colored points.

To achieve this, where should species go in the ggplot call from earlier? If you guessed “in the aesthetic mapping, inside of aes()”, you’re already getting the hang of creating data visualizations with ggplot2! And if not, don’t worry. Throughout the book you will make many more ggplots and have many more opportunities to check your intuition as you make them.

ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g, color = species)
) +
  geom_point()

A scatterplot of body mass vs. flipper length of penguins. The plot displays a positive, fairly linear, and relatively strong relationship between these two variables. Species (Adelie, Chinstrap, and Gentoo) are represented with different colors.

When a variable is mapped to an aesthetic, ggplot2 will automatically assign a unique value of the aesthetic (here a unique color) to each unique level of the variable (each of the three species), a process known as scaling. ggplot2 will also add a legend that explains which values correspond to which levels.

Now let’s add one more layer: a smooth curve displaying the relationship between body mass and flipper length. Before you proceed, refer back to the code above, and think about how we can add this to our existing plot.

Since this is a new geometric object representing our data, we will add a new geom: geom_smooth().

ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g, color = species)
) +
  geom_point() +
  geom_smooth()

A scatterplot of body mass vs. flipper length of penguins. Overlaid on the scatterplot are three smooth curves displaying the relationship between these variables for each species (Adelie, Chinstrap, and Gentoo). Different penguin species are plotted in different colors for the points and the smooth curves.

We have successfully added smooth curves, but this plot doesn’t look like the plot from #sec-ultimate-goal, which only has one curve for the entire dataset as opposed to separate curves for each of the penguin species.

When aesthetic mappings are defined in ggplot(), at the global level, they’re inherited by each of the subsequent geom layers of the plot. However, each geom function in ggplot2 can also take a mapping argument, which allows for aesthetic mappings at the local level. Since we want points to be colored based on species but don’t want the smooth curves to be separated out for them, we should specify color = species for geom_point() only.

ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g)
) +
  geom_point(mapping = aes(color = species)) +
  geom_smooth()

A scatterplot of body mass vs. flipper length of penguins. Overlaid on the scatterplot are is a single smooth curve displaying the relationship between these variables for each species (Adelie, Chinstrap, and Gentoo). Different penguin species are plotted in different colors for the points only.

Voila! We have something that looks very much like our ultimate goal, though it’s not yet perfect. We still need to use different shapes for each species of penguins and improve labels.

It’s generally not a good idea to represent information using only colors on a plot, as people perceive colors differently due to color blindness or other color vision differences. Therefore, in addition to color, we can also map species to the shape aesthetic.

ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g)
) +
  geom_point(mapping = aes(color = species, shape = species)) +
  geom_smooth()

Note that the legend is automatically updated to reflect the different shapes of the points as well.

And finally, we can improve the labels of our plot using the labs() function in a new layer. Some of the arguments to labs() might be self explanatory: title adds a title and subtitle adds a subtitle to the plot. Other arguments match the aesthetic mappings, x is the x-axis label, y is the y-axis label, and color and shape define the label for the legend.

ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g)
) +
  geom_point(aes(color = species, shape = species)) +
  geom_smooth() +
  labs(
    title = "Body mass and flipper length",
    subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
    x = "Flipper length (mm)",
    y = "Body mass (g)",
    color = "Species",
    shape = "Species"
  )

We finally have a plot that perfectly matches our “ultimate goal”!

Exercises

How many rows are in penguins? How many columns?
What does the bill_depth_mm variable in the penguins data frame describe? Read the help for ?penguins to find out.
Make a scatterplot of bill_depth_mm vs. bill_length_mm. Describe the relationship between these two variables.
What happens if you make a scatterplot of species vs bill_depth_mm? Why is the plot not useful?
Why does the following give an error and how would you fix it?
```
ggplot(data = penguins) + 
  geom_point()
```
What does the na.rm argument do in geom_point()? What is the default value of the argument? Create a scatterplot where you successfully use this argument set to TRUE.
Add the following caption to the plot you made in the previous exercise: “Data come from the palmerpenguins package.” Hint: Take a look at the documentation for labs().
Recreate the following visualization. What aesthetic should bill_depth_mm be mapped to? And should it be mapped at the global level or at the geom level?

Run this code in your head and predict what the output will look like. Then, run the code in R and check your predictions.

ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g, color = island)
) +
  geom_point() +
  geom_smooth(se = FALSE)

Will these two graphs look different? Why/why not?

ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g)
) +
  geom_point() +
  geom_smooth()

ggplot() +
  geom_point(
    data = penguins,
    mapping = aes(x = flipper_length_mm, y = body_mass_g)
  ) +
  geom_smooth(
    data = penguins,
    mapping = aes(x = flipper_length_mm, y = body_mass_g)
  )

ggplot2 calls

As we move on from these introductory sections, we’ll transition to a more concise expression of ggplot2 code. So far we’ve been very explicit, which is helpful when you are learning:

ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g)
) +
  geom_point()

Typically, the first one or two arguments to a function are so important that you should know them by heart. The first two arguments to ggplot() are data and mapping, in the remainder of the book, we won’t supply those names. That saves typing, and, by reducing the amount of boilerplate, makes it easier to see what’s different between plots. That’s a really important programming concern that we’ll come back to in #chp-functions.

Rewriting the previous plot more concisely yields:

ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) + 
  geom_point()

In the future, you’ll also learn about the pipe which will allow you to create that plot with:

penguins |> 
  ggplot(aes(x = flipper_length_mm, y = body_mass_g)) + 
  geom_point()

This is the most common syntax you’ll see in the wild.

Visualizing distributions

How you visualize the distribution of a variable depends on the type of variable: categorical or numerical.

A categorical variable

A variable is categorical if it can only take one of a small set of values. To examine the distribution of a categorical variable, you can use a bar chart. The height of the bars displays how many observations occurred with each x value.

ggplot(penguins, aes(x = species)) +
  geom_bar()

A bar chart of frequencies of species of penguins: Adelie (approximately 150), Chinstrap (approximately 90), Gentoo (approximately 125).

In bar plots of categorical variables with non-ordered levels, like the penguin species above, it’s often preferable to reorder the bars based on their frequencies. Doing so requires transforming the variable to a factor (how R handles categorical data) and then reordering the levels of that factor.

ggplot(penguins, aes(x = fct_infreq(species))) +
  geom_bar()

A bar chart of frequencies of species of penguins, where the bars are ordered in decreasing order of their heights (frequencies): Adelie (approximately 150), Gentoo (approximately 125), Chinstrap (approximately 90).

You will learn more about factors and functions for dealing with factors (like fct_infreq() shown above) in #chp-factors.

A numerical variable

A variable is numerical if it can take any of an infinite set of ordered values. Numbers and date-times are two examples of continuous variables. To visualize the distribution of a continuous variable, you can use a histogram or a density plot.

ggplot(penguins, aes(x = body_mass_g)) +
  geom_histogram(binwidth = 200)
ggplot(penguins, aes(x = body_mass_g)) +
  geom_density()

A histogram (on the left) and density plot (on the right) of body masses of penguins. The distribution is unimodal and right skewed, ranging between approximately 2500 to 6500 grams.

A histogram divides the x-axis into equally spaced bins and then uses the height of a bar to display the number of observations that fall in each bin. In the graph above, the tallest bar shows that 39 observations have a body_mass_g value between 3,500 and 3,700 grams, which are the left and right edges of the bar.

penguins |>
  count(cut_width(body_mass_g, 200))
#> # A tibble: 19 × 2
#>   `cut_width(body_mass_g, 200)`     n
#>   <fct>                         <int>
#> 1 [2.7e+03,2.9e+03]                 7
#> 2 (2.9e+03,3.1e+03]                10
#> 3 (3.1e+03,3.3e+03]                23
#> 4 (3.3e+03,3.5e+03]                38
#> 5 (3.5e+03,3.7e+03]                39
#> 6 (3.7e+03,3.9e+03]                37
#> # … with 13 more rows

You can set the width of the intervals in a histogram with the binwidth argument, which is measured in the units of the x variable. You should always explore a variety of binwidths when working with histograms, as different binwidths can reveal different patterns. In the plots below a binwidth of 20 is too narrow, resulting in too many bars, making it difficult to determine the shape of the distribution. Similarly, a binwidth of 2,000 is too high, resulting in all data being binned into only three bars, and also making it difficult to determine the shape of the distribution.

ggplot(penguins, aes(x = body_mass_g)) +
  geom_histogram(binwidth = 20)
ggplot(penguins, aes(x = body_mass_g)) +
  geom_histogram(binwidth = 200)
ggplot(penguins, aes(x = body_mass_g)) +
  geom_histogram(binwidth = 2000)

Three histograms of body masses of penguins, one with binwidth of 20 (right), one with binwidth of 200 (center), and one with binwidth of 2000 (left). The histogram with binwidth of 20 shows lots of ups and downs in the heights of the bins, creating a jagged outline. The histogram with binwidth of 2000 shows only three bins.

Exercises

Make a bar plot of species of penguins, where you assign species to the y aesthetic. How is this plot different?

How are the following two plots different? Which aesthetic, color or fill, is more useful for changing the color of bars?

ggplot(penguins, aes(x = species)) +
  geom_bar(color = "red")

ggplot(penguins, aes(x = species)) +
  geom_bar(fill = "red")

What does the bins argument in geom_histogram() do?
Make a histogram of the carat variable in the diamonds dataset. Experiment with different binwidths. What binwidth reveals the most interesting patterns?

Visualizing relationships

To visualize a relationship we need to have at least two variables mapped to aesthetics of a plot. In the following sections you will learn about commonly used plots for visualizing relationships between two or more variables and the geoms used for creating them.

A numerical and a categorical variable

To visualize the relationship between a numerical and a categorical variable we can use side-by-side box plots. A boxplot is a type of visual shorthand for a distribution of values that is popular among statisticians. As shown in #fig-eda-boxplot, each boxplot consists of:

A box that stretches from the 25th percentile of the distribution to the 75th percentile, a distance known as the interquartile range (IQR). In the middle of the box is a line that displays the median, i.e. 50th percentile, of the distribution. These three lines give you a sense of the spread of the distribution and whether or not the distribution is symmetric about the median or skewed to one side.
Visual points that display observations that fall more than 1.5 times the IQR from either edge of the box. These outlying points are unusual so are plotted individually.
A line (or whisker) that extends from each end of the box and goes to the farthest non-outlier point in the distribution.

A diagram depicting how a boxplot is created following the steps outlined above. — Diagram depicting how a boxplot is created.

Let’s take a look at the distribution of body mass by species using geom_boxplot():

ggplot(penguins, aes(x = species, y = body_mass_g)) +
  geom_boxplot()

Side-by-side box plots of distributions of body masses of Adelie, Chinstrap, and Gentoo penguins. The distribution of Adelie and Chinstrap penguins' body masses appear to be symmetric with medians around 3750 grams. The median body mass of Gentoo penguins is much higher, around 5000 grams, and the distribution of the body masses of these penguins appears to be somewhat right skewed.

Alternatively, we can make frequency polygons with geom_freqpoly(). geom_freqpoly() performs the same calculation as geom_histogram(), but instead of displaying the counts with bars, it uses lines instead. It’s much easier to understand overlapping lines than bars of geom_histogram(). There are a few challenges with this type of plot, which we will come back to in #sec-cat-num on exploring the covariation between a categorical and a numerical variable.

ggplot(penguins, aes(x = body_mass_g, color = species)) +
  geom_freqpoly(binwidth = 200, linewidth = 0.75)

A frequency polygon of body masses of penguins by species of penguins. Each species (Adelie, Chinstrap, and Gentoo) is represented with different colored outlines for the polygons.

We’ve also customized the thickness of the lines using the linewidth argument in order to make them stand out a bit more against the background.

We can also use overlaid density plots, with species mapped to both color and fill aesthetics and using the alpha aesthetic to add transparency to the filled density curves. This aesthetic takes values between 0 (completely transparent) and 1 (completely opaque). In the following plot it’s set to 0.5.

ggplot(penguins, aes(x = body_mass_g, color = species, fill = species)) +
  geom_density(alpha = 0.5)

A frequency polygon of body masses of penguins (on the left) and density plot (on the right). Each species of penguins (Adelie, Chinstrap, and Gentoo) are represented in different colored outlines for the frequency polygons and the density curves. The density curves are also filled with the same colors, with some transparency added.

Note the terminology we have used here:

We map variables to aesthetics if we want the visual attribute represented by that aesthetic to vary based on the values of that variable.
Otherwise, we set the value of an aesthetic.

Two categorical variables

We can use segmented bar plots to visualize the distribution between two categorical variables. In creating this bar chart, we map the variable we want to divide the data into first to the x aesthetic and the variable we then further want to divide each group into to the fill aesthetic.

Below are two segmented bar plots, both displaying the relationship between island and species, or specifically, visualizing the distribution of species within each island. The plot on the left shows the frequencies of each species of penguins on each island and the plot on the right shows the relative frequencies (proportions) of each species within each island (despite the incorrectly labeled y-axis that says “count”). The relative frequency plot, created by setting position = "fill" in the geom is more useful for comparing species distributions across islands since it’s not affected by the unequal numbers of penguins across the islands. Based on the plot on the left, we can see that Gentoo penguins all live on Biscoe island and make up roughly 75% of the penguins on that island, Chinstrap all live on Dream island and make up roughly 50% of the penguins on that island, and Adelie live on all three islands and make up all of the penguins on Torgersen.

ggplot(penguins, aes(x = island, fill = species)) +
  geom_bar()
ggplot(penguins, aes(x = island, fill = species)) +
  geom_bar(position = "fill")

Bar plots of penguin species by island (Biscoe, Dream, and Torgersen). On the right, frequencies of species are shown. On the left, relative frequencies of species are shown.

Two numerical variables

So far you’ve learned about scatterplots (created with geom_point()) and smooth curves (created with geom_smooth()) for visualizing the relationship between two numerical variables. A scatterplot is probably the most commonly used plot for visualizing the relationship between two variables.

ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point()

A scatterplot of body mass vs. flipper length of penguins. The plot displays a positive, linear, relatively strong relationship between these two variables.

Three or more variables

One way to add additional variables to a plot is by mapping them to an aesthetic. For example, in the following scatterplot the colors of points represent species and the shapes of points represent islands.

ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point(aes(color = species, shape = island))

However adding too many aesthetic mappings to a plot makes it cluttered and difficult to make sense of. Another way, which is particularly useful for categorical variables, is to split your plot into facets, subplots that each display one subset of the data.

To facet your plot by a single variable, use facet_wrap(). The first argument of facet_wrap() is a formulaHere “formula” is the name of the type of thing created by ~, not a synonym for “equation”., which you create with ~ followed by a variable name. The variable that you pass to facet_wrap() should be categorical.

ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point(aes(color = species, shape = species)) +
  facet_wrap(~island)

A scatterplot of body mass vs. flipper length of penguins. The shapes and colors of points represent species. Penguins from each island are on a separate facet. Within each facet, the relationship between body mass and flipper length is positive, linear, relatively strong.

You will learn about many other geoms for visualizing distributions of variables and relationships between them in #chp-layers.

Exercises

Which variables in mpg are categorical? Which variables are continuous? (Hint: type ?mpg to read the documentation for the dataset). How can you see this information when you run mpg?
Make a scatterplot of hwy vs. displ using the mpg data frame. Next, map a third, numerical variable to color, then size, then both color and size, then shape. How do these aesthetics behave differently for categorical vs. numerical variables?
In the scatterplot of hwy vs. displ, what happens if you map a third variable to linewidth?
What happens if you map the same variable to multiple aesthetics?
Make a scatterplot of bill_depth_mm vs. bill_length_mm and color the points by species. What does adding coloring by species reveal about the relationship between these two variables?

Why does the following yield two separate legends? How would you fix it to combine the two legends?

ggplot(
  data = penguins,
  mapping = aes(
    x = bill_length_mm, y = bill_depth_mm, 
    color = species, shape = species
  )
) +
  geom_point() +
  labs(color = "Species")

Scatterplot of bill depth vs. bill length where different color and shape pairings represent each species. The plot has two legends, one labelled "species" which shows the shape scale and the other that shows the color scale.

Saving your plots

Once you’ve made a plot, you might want to get it out of R by saving it as an image that you can use elsewhere. That’s the job of ggsave(), which will save the most recent plot to disk:

ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point()
ggsave(filename = "my-plot.png")

This will save your plot to your working directory, a concept you’ll learn more about in #chp-workflow-scripts.

If you don’t specify the width and height they will be taken from the dimensions of the current plotting device. For reproducible code, you’ll want to specify them. You can learn more about ggsave() in the documentation.

Generally, however, we recommend that you assemble your final reports using Quarto, a reproducible authoring system that allows you to interleave your code and your prose and automatically include your plots in your write-ups. You will learn more about Quarto in #chp-quarto.

Exercises

Run the following lines of code. Which of the two plots is saved as mpg-plot.png? Why?

ggplot(mpg, aes(x = class)) +
  geom_bar()
ggplot(mpg, aes(x = cty, y = hwy)) +
  geom_point()
ggsave("mpg-plot.png")

What do you need to change in the code above to save the plot as a PDF instead of a PNG?

Common problems

As you start to run R code, you’re likely to run into problems. Don’t worry — it happens to everyone. We have all been writing R code for years, but every day we still write code that doesn’t work!

Start by carefully comparing the code that you’re running to the code in the book. R is extremely picky, and a misplaced character can make all the difference. Make sure that every ( is matched with a ) and every " is paired with another ". Sometimes you’ll run the code and nothing happens. Check the left-hand of your console: if it’s a +, it means that R doesn’t think you’ve typed a complete expression and it’s waiting for you to finish it. In this case, it’s usually easy to start from scratch again by pressing ESCAPE to abort processing the current command.

One common problem when creating ggplot2 graphics is to put the + in the wrong place: it has to come at the end of the line, not the start. In other words, make sure you haven’t accidentally written code like this:

ggplot(data = mpg) 
+ geom_point(mapping = aes(x = displ, y = hwy))

If you’re still stuck, try the help. You can get help about any R function by running ?function_name in the console, or selecting the function name and pressing F1 in RStudio. Don’t worry if the help doesn’t seem that helpful - instead skip down to the examples and look for code that matches what you’re trying to do.

If that doesn’t help, carefully read the error message. Sometimes the answer will be buried there! But when you’re new to R, even if the answer is in the error message, you might not yet know how to understand it. Another great tool is Google: try googling the error message, as it’s likely someone else has had the same problem, and has gotten help online.

Summary

In this chapter, you’ve learned the basics of data visualization with ggplot2. We started with the basic idea that underpins ggplot2: a visualization is a mapping from variables in your data to aesthetic properties like position, color, size and shape. You then learned about increasing the complexity and improving the presentation of your plots layer-by-layer. You also learned about commonly used plots for visualizing the distribution of a single variable as well as for visualizing relationships between two or more variables, by levering additional aesthetic mappings and/or splitting your plot into small multiples using faceting.

We’ll use visualizations again and again through out this book, introducing new techniques as we need them as well as do a deeper dive into creating visualizations with ggplot2 in #chp-layers through #chp-EDA.

With the basics of visualization under your belt, in the next chapter we’re going to switch gears a little and give you some practical workflow advice. We intersperse workflow advice with data science tools throughout this part of the book because it’ll help you stay organized as you write increasing amounts of R code.