Chapter 2 Communication:

Total points: 30

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

Instructions: - This is the complete lessons version with all working examples - Run all the code as you go to see how everything works - Exercise questions are shown but without answer spaces - Use the STUDENT version to practice writing your own code

2.1 Introduction

You’ve learned how to create exploratory plots to understand your data. Now we’ll focus on creating plots for communication - plots you’ll share with others who don’t know your data as well as you do.

To help others quickly understand your plots, you need to make them as self-explanatory as possible. In this chapter, you’ll learn how to:

• Add informative labels (titles, axis labels, captions)
• Use annotations to highlight important features
• Customize scales (axes and legends)
• Apply themes to change the overall appearance
• Combine multiple plots for comparison

These skills are essential for creating publication-quality figures for your research!

2.1.1 Prerequisites

Load the necessary packages:

library(tidyverse)                 # for ggplot2 and mpg
library(ggrepel)                   # for smart label placement
library(patchwork)                 # for combining plots
library(scales)                    # for axis formatting

2.2 Labels

The easiest place to start when turning an exploratory graphic into a publication-ready graphic is with good x and y-axes labels. You add labels with the labs() function:

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) +
  labs(
    x = "Engine displacement (L)",
    y = "Highway fuel economy (mpg)",
    color = "Car type",
    title = "Fuel efficiency generally decreases with engine size",
    subtitle = "Two seaters (sports cars) are an exception because of their light weight",
    caption = "Data from fueleconomy.gov"
  )

Important tips for plot titles:

• Good title: Summarizes the main finding → “Fuel efficiency generally decreases with engine size”
• Bad title: Just describes the plot → “A scatterplot of engine displacement vs. fuel economy”

If you need to add more text, there are two other useful labels:

• subtitle adds additional detail in a smaller font beneath the title
• caption adds text at the bottom right of the plot, often used to describe the source of the data

You can also use labs() to replace the axis and legend titles. It’s usually a good idea to replace short variable names with more detailed descriptions, and to include the units.

Exercises 2.2.1

1. Add labels to make this plot publication-ready. Include a title describing an interesting pattern (not just “boxplot of…”), subtitle, caption, and improved axis/legend labels. +6pts

Start with this plot:

g1 <-  ggplot(
  data = mpg,
  mapping = aes(x = class, y = hwy, fill = class)) +
  geom_boxplot()

g1

2. Recreate the following plot. Add appropriate labels for the axes and legend, AND add a meaningful title describing a pattern you see. Note that both color and shape vary by drive type. +6pts

2.3 Annotations

In addition to labeling major components of your plot, it’s often useful to label individual observations or groups of observations. The first tool you have at your disposal is geom_text(). geom_text() is similar to geom_point(), but it has an additional aesthetic: label. This makes it possible to add textual labels to your plots.

2.3.1 Labeling specific points

You might have a tibble that provides labels. In the following plot we pull out the cars with the highest engine size in each drive type and save their information as a new data frame called label_info.

label_info <- mpg |>
  group_by(drv) |>
  arrange(desc(displ)) |>
  slice_head(n = 1) |>
  mutate(
    drive_type = case_when(
      drv == "f" ~ "front-wheel drive",
      drv == "r" ~ "rear-wheel drive",
      drv == "4" ~ "4-wheel drive"
    )
  ) |> 
  dplyr::select(displ, hwy, drv, drive_type)

label_info

displ	hwy	drv	drive_type
6.5	17	4	4-wheel drive
5.3	25	f	front-wheel drive
7.0	24	r	rear-wheel drive

Note: slice_head(n = 1) selects the first row of each group (after arranging by descending displacement), giving us the car with the largest engine in each drive type.

Then, we use this new data frame to directly label the three groups. Using the fontface and size arguments we can customize the look of the text labels:

ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  # 
  geom_point(alpha = 0.3) + 
  geom_smooth(se = FALSE) +
  geom_text(
    data = label_info,
    aes(x = displ, y = hwy, label = drive_type),
    fontface = "bold",
    size = 5,
    hjust = "right",
    vjust = "bottom"
  ) +
  theme(legend.position = "none")

Note:

1. The use of hjust (horizontal justification) and vjust (vertical justification) to control the alignment of the label. We’ve seen this before, but it is slightly more complex here.

2. alpha is fixed, yet we see different transparencies in the points.

The annotated plot we made above is hard to read because the labels overlap with points. We can use the geom_label_repel() function from the ggrepel package to fix this. This useful package will automatically adjust labels so that they don’t overlap:

ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point(alpha = 0.3) +
  geom_smooth(se = FALSE) +
  geom_label_repel(
    data = label_info,
    aes(x = displ, y = hwy, label = drive_type),
    fontface = "bold",
    size = 5,
    nudge_y = 2
  ) +
  theme(legend.position = "none")

You can also use the same idea to highlight certain points on a plot with geom_text_repel(). Note another handy technique used here: we added a second layer of large, hollow points to further highlight the labelled points.

potential_outliers <- mpg |>
  filter(hwy > 40 | (hwy > 20 & displ > 5))

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  geom_text_repel(data = potential_outliers, aes(label = model)) +
  geom_point(data = potential_outliers, color = "red") +
  geom_point(
    data = potential_outliers,
    color = "red", size = 3, shape = "circle open"
  )

2.3.2 Using annotate() for single annotations

Another handy function for adding annotations to plots is annotate(). While geom_text() is useful for labeling many points from your data, annotate() is useful for adding one or a few annotation elements to a plot.

To demonstrate using annotate(), let’s create some text to add to our plot:

trend_text <- "Larger engine sizes tend to have lower fuel economy."

Then, we add two layers of annotation: one with a label geom and the other with a segment geom (an arrow):

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  annotate(
    geom = "label",
    x = 3.5,
    y = 38,
    label = trend_text,
    hjust = "left",
    color = "red"
  ) +
  annotate(
    geom = "segment",
    x = 3,
    y = 35,
    xend = 5,
    yend = 25,
    color = "red",
    arrow = arrow(type = "closed")
  )

Other useful annotation geoms:

• Use geom_hline() and geom_vline() to add reference lines
• Use geom_rect() to draw a rectangle around points of interest
• Use geom_segment() with the arrow argument to draw attention to a point

Annotation is a powerful tool for communicating main takeaways and interesting features of your visualizations!

Exercises 2.3.1

1. Use annotate() to add a point geom in the middle of the following plot without having to create a tibble. Customize the shape, size, or color of the point. +2pts

ggplot(mpg, aes(displ, hwy)) +
  geom_point()

2. How do labels with geom_text() interact with faceting? How can you add a label to a single facet? How can you put a different label in each facet? +4pts

3. What arguments to geom_label() control the appearance of the background box? +2pts

2.4 Scales

The third way you can make your plot better for communication is to adjust the scales. Scales control how the aesthetic mappings manifest visually.

2.4.1 Default scales

Normally, ggplot2 automatically adds scales for you. For example, when you type:

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(color = class))

ggplot2 automatically adds default scales behind the scenes:

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(color = class)) +
  scale_x_continuous() +
  scale_y_continuous() +
  scale_color_discrete()

Note: The naming scheme for scales: scale_ followed by the name of the aesthetic, then _, then the name of the scale. The default scales are named according to the type of variable they align with: continuous, discrete, datetime, or date.

There are lots of non-default scales which you’ll learn about below. The default scales have been carefully chosen to do a good job for a wide range of inputs. Nevertheless, you might want to override the defaults for two reasons:

• You might want to tweak some of the parameters of the default scale (e.g., change the breaks on the axes, or the key labels on the legend)

• You might want to replace the scale altogether and use a completely different algorithm (e.g., use a log scale)

2.4.2 Axis ticks and legend keys

Collectively, axes and legends are called guides. Axes are used for x and y aesthetics; legends are used for everything else.

There are two primary arguments that affect the appearance of the ticks on the axes and the keys on the legend: breaks and labels. Breaks controls the position of the ticks, or the values associated with the keys. Labels controls the text label associated with each tick/key.

The most common use of breaks is to override the default choice:

ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point() +
  scale_y_continuous(breaks = seq(15, 40, by = 5))

You can use labels in the same way (a character vector the same length as breaks), but you can also set it to NULL to suppress the labels altogether.

# Remove axis labels
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point() +
  scale_x_continuous(labels = NULL) +
  scale_y_continuous(labels = NULL)

You can also use breaks and labels to control the appearance of legends. For example, here we remove the legend labels for the color aesthetic:

ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point() +
  scale_color_discrete(labels = NULL)

2.4.3 Legend layout

You will most often use breaks and labels to tweak the axes. While they both also work for legends, there are a few other techniques you are more likely to use.

To control the overall position of the legend, you need to use a theme() setting. We’ll come back to themes at the end of the chapter, but in brief, they control the non-data parts of the plot. The theme setting legend.position controls where the legend is drawn:

base <- ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(color = class))

base + theme(legend.position = "right")  # the default

base + theme(legend.position = "left")

base + theme(legend.position = "top")

base + theme(legend.position = "bottom")

base + theme(legend.position = "none")  # remove legend

2.4.4 Replacing a scale

Instead of just tweaking the details a little, you can instead replace the scale altogether. There are two types of scales you’re mostly likely to want to switch out: continuous position scales and color scales.

It’s very useful to plot transformations of your data. For example, it’s easier to see the precise relationship between carat and price if we log transform them:

# Left: original scale
ggplot(diamonds, aes(x = carat, y = price)) +
  geom_bin2d()

# Right: log-transformed scale
ggplot(diamonds, aes(x = carat, y = price)) +
  geom_bin2d() +
  scale_x_log10() +
  scale_y_log10()

Another scale that is frequently customized is color. The default categorical scale picks colors that are evenly spaced around the color wheel. A popular alternative is the ColorBrewer scales which have been hand-tuned to work better for people with common types of color blindness. The two plots below don’t look that different, but there is enough difference in the shades of red and green that they can still be distinguished even by people with red-green color blindness.

# Default colors
ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(color = drv))

# ColorBrewer colors
ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(color = drv)) +
  scale_color_brewer(palette = "Set1")

Don’t forget simpler techniques. If there are just a few colors, you can add a redundant shape mapping. This will also help ensure your plot is interpretable in black and white.

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(color = drv, shape = drv)) +
  scale_color_brewer(palette = "Set1")

The ColorBrewer scales are documented at https://colorbrewer2.org/ and made available in R via the RColorBrewer package, by Erich Neuwirth. You can see all the available palettes with RColorBrewer::display.brewer.all().

When you have a predefined mapping between values and colors, use scale_color_manual(). For example, if we map presidential party to color (see ?presidential), we want to use the standard mapping of red for Republicans and blue for Democrats. One approach is to use scale_color_manual():

presidential |>
  # to assign an ID to each president
  mutate(id = 33 + row_number()) |>
  ggplot(aes(x = start, y = id, color = party)) +
  geom_point() +
  geom_segment(aes(xend = end, yend = id)) +
  scale_color_manual(values = c(Republican = "#E81B23", Democratic = "#00AEF3"))

For continuous color, you can use the built-in scale_color_gradient() or scale_fill_gradient() that you have seen before. If you have a diverging scale, you can use scale_color_gradient2(). That allows you to give a different color for the midpoint (usually 0).

Exercises 2.4.1

1. Why doesn’t the following code override the default scale? Fix the plot so that it colors the points with a gradient. +2pts

df <- tibble(x = rnorm(10000), y = rnorm(10000))

ggplot(df) +
  geom_point(aes(x = x, y = y, fill = x)) +
  scale_fill_gradient(low = "yellow", high = "red")

2. What is the first argument to every scale? How does it compare to labs()? +2pts

2.5 Themes

You can customize the overall appearance of your plot with built-in themes:

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) +
  theme_classic()

ggplot2 includes eight built-in themes, with theme_gray() as the default:

• theme_gray() - The default (gray background, white gridlines)
• theme_bw() - White background with gridlines
  
• theme_classic() - No gridlines (classic look for publications)
• theme_minimal() - Minimal theme
• theme_light() - Light background
• theme_dark() - Dark background
• theme_linedraw() - Black lines
• theme_void() - Empty theme

Figure 2.1: The eight themes built-in to ggplot2.

For publication figures, theme_bw(), theme_classic(), and theme_minimal() are popular choices.

You can also customize individual theme elements using theme(). For example, to move the legend position:

ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point() +
  theme_bw() +
  theme(legend.position = "bottom")

Common theme customizations include legend.position, plot.title, and axis.text. For a complete list, see ?theme.

Exercises 2.5.1

1. Apply a different theme to the following plot. Try theme_bw(), theme_classic(), or theme_minimal(). Which do you think looks best for a publication? +2pts

ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point() +
  labs(
    title = "Larger engine sizes tend to have lower fuel economy",
    caption = "Source: https://fueleconomy.gov."
  )

2.6 Layout

So far we talked about how to create and modify a single plot. What if you have multiple plots you want to lay out in a certain way? The patchwork package allows you to combine separate plots into the same graphic.

To place two plots next to each other, you can simply add them to each other. Note that you first need to create the plots and save them as objects:

p1 <- ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point() + 
  labs(title = "Plot 1")

p2 <- ggplot(mpg, aes(x = drv, y = hwy)) + 
  geom_boxplot() + 
  labs(title = "Plot 2")

# Place side-by-side with +
p1 + p2

You can also stack plots vertically using /:

# Stack vertically with /
p1 / p2

You can create more complex layouts using both + and /. Use parentheses to control the order of operations:

p3 <- ggplot(mpg, aes(x = cty, y = hwy)) +
  geom_point() +
  labs(title = "Plot 3")

# p1 and p3 side-by-side, then p2 below
(p1 + p3) / p2

Additionally, patchwork allows you to add a common title, subtitle, and caption to your combined plots using plot_annotation():

(p1 + p2) / p3 +
  plot_annotation(
    title = "Fuel economy analysis",
    subtitle = "Exploring the mpg dataset",
    caption = "Data from fueleconomy.gov"
  )

If you’d like to learn more about patchwork, see the package website: https://patchwork.data-imaginist.com.

Exercises 2.6.1

1. What happens if you omit the parentheses in the following plot layout? Try it. Can you explain why this happens? +2pts

p1 <- ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  labs(title = "Plot 1")

p2 <- ggplot(mpg, aes(x = drv, y = hwy)) +
  geom_boxplot() +
  labs(title = "Plot 2")

p3 <- ggplot(mpg, aes(x = cty, y = hwy)) +
  geom_point() +
  labs(title = "Plot 3")

(p1 + p3) / p2

2. Using the three plots from the previous exercise, recreate the following patchwork with p1 on top and p2 and p3 side-by-side on the bottom: +2pts

2.7 Summary

In this chapter you’ve learned about:

• Labels: Adding informative titles, subtitles, captions, and axis labels with labs()

• Annotations: Using geom_text(), geom_label_repel(), and annotate() to highlight specific features

• Scales: Customizing axes and legends with scale_*() functions

• Themes: Changing the overall appearance with built-in themes and theme() customization

• Layout: Combining multiple plots with the patchwork package using + and /

These skills are essential for creating publication-quality figures for your research!

While you’ve learned how to make many different types of plots and customize them, we’ve barely scratched the surface of what you can do with ggplot2. If you want to get a comprehensive understanding, we recommend:

• ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham
• R Graphics Cookbook by Winston Chang
• Fundamentals of Data Visualization by Claus Wilke