Annotations

We’ll focus more on the use of the ggplot2 package and a little dplyr for data manipulation. 3 Three additional packages will be also be used.

library(tidyverse)
library(scales)
library(ggrepel)
library(patchwork)

In addition to labelling 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.

There are two possible sources of labels. First, 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"
    )
  ) |>
  select(displ, hwy, drv, drive_type)

label_info

# A tibble: 3 × 4
# Groups:   drv [3]
  displ   hwy drv   drive_type       
  <dbl> <int> <chr> <chr>            
1   6.5    17 4     4-wheel drive    
2   5.3    25 f     front-wheel drive
3   7      24 r     rear-wheel drive 

Then, we use this new data frame to directly label the three groups to replace the legend with labels placed directly on the plot. Using the fontface and size arguments we can customize the look of the text labels. They’re larger than the rest of the text on the plot and bolded. (theme(legend.position = “none”) turns all the legends off — we’ll talk about it more shortly.)

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 the use of hjust (horizontal justification) and vjust (vertical justification) to control the alignment of the label.

However the annotated plot we made above is hard to read because the labels overlap with each other, and with the points. We can use the geom_label_repel() function from the ggrepel package to address both of these issues. 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() from the ggrepel package. 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")

Remember, in addition to geom_text() and geom_label(), you have many other geoms in ggplot2 available to help annotate your plot. A couple ideas:

• Use geom_hline() and geom_vline() to add reference lines. We often make them thick (linewidth = 2) and white (color = white), and draw them underneath the primary data layer. That makes them easy to see, without drawing attention away from the data.

• Use geom_rect() to draw a rectangle around points of interest. The boundaries of the rectangle are defined by aesthetics xmin, xmax, ymin, ymax. Alternatively, look into the ggforce package, specifically geom_mark_hull(), which allows you to annotate subsets of points with hulls.

• Use geom_segment() with the arrow argument to draw attention to a point with an arrow. Use aesthetics x and y to define the starting location, and xend and yend to define the end location.

Another handy function for adding annotations to plots is annotate(). As a rule of thumb, geoms are generally useful for highlighting a subset of the data while annotate() is useful for adding one or few annotation elements to a plot.

To demonstrate using annotate(), let’s create some text to add to our plot. The text is a bit long, so we’ll use stringr::str_wrap() to automatically add line breaks to it given the number of characters you want per line:

trend_text <- "Larger engine sizes tend to have lower fuel economy." |>
  str_wrap(width = 30)
trend_text

[1] "Larger engine sizes tend to\nhave lower fuel economy."

Then, we add two layers of annotation: one with a label geom and the other with a segment geom. The x and y aesthetics in both define where the annotation should start, and the xend and yend aesthetics in the segment annotation define the end location of the segment. Note also that the segment is styled as 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")
  )

Annotation is a powerful tool for communicating main takeaways and interesting features of your visualizations. The only limit is your imagination (and your patience with positioning annotations to be aesthetically pleasing)!