Labels

We’ll 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)

The easiest place to start when turning an exploratory graphic into an expository graphic is with good 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"
  )

The purpose of a plot title is to summarize the main finding. Avoid titles that just describe what the plot is, e.g., “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 and 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.

It’s possible to use mathematical equations instead of text strings. Just switch “” out for quote() and read about the available options in ?plotmath:

df <- tibble(
  x = 1:10,
  y = cumsum(x^2)
)

ggplot(df, aes(x, y)) +
  geom_point() +
  labs(
    x = quote(x[i]),
    y = quote(sum(x[i] ^ 2, i == 1, n))
  )