Combining ggplo2 and dplyr

Overview

Teaching: 15 min
Exercises: 15 min

Questions

• How can I use ggplot2 and dplyr together?

Objectives

• Use pipes (%>%) to pass data to ggplot()

Combining dplyr and ggplot2

First install and load ggplot2:

install.packages('ggplot2')

library("ggplot2")

In the plotting lesson we looked at how to make a multi-panel figure by adding a layer of facet panels using ggplot2. Here is the code we used (with some extra comments):

# Filter countries located in the Americas
americas <- gapminder[gapminder$continent == "Americas", ]
# Make the plot
ggplot(data = americas, mapping = aes(x = year, y = lifeExp)) +
  geom_line() +
  facet_wrap( ~ country) +
  theme(axis.text.x = element_text(angle = 45))

This code makes the right plot but it also creates an intermediate variable (americas) that we might not have any other uses for. Just as we used %>% to pipe data along a chain of dplyr functions we can use it to pass data to ggplot(). Because %>% replaces the first argument in a function we don’t need to specify the data = argument in the ggplot() function. By combining dplyr and ggplot2 functions we can make the same figure without creating any new variables or modifying the data.

gapminder %>%
  # Filter countries located in the Americas
  filter(continent == "Americas") %>%
  # Make the plot
  ggplot(mapping = aes(x = year, y = lifeExp)) +
  geom_line() +
  facet_wrap( ~ country) +
  theme(axis.text.x = element_text(angle = 45))

More examples of using the function mutate() and the ggplot2 package.

gapminder %>%
  # extract first letter of country name into new column
  mutate(startsWith = substr(country, 1, 1)) %>%
  # only keep countries starting with A or Z
  filter(startsWith %in% c("A", "Z")) %>%
  # plot lifeExp into facets
  ggplot(aes(x = year, y = lifeExp, colour = continent)) +
  geom_line() +
  facet_wrap(vars(country)) +
  theme_minimal()

Advanced Challenge

Calculate the average life expectancy in 2002 of 2 randomly selected countries for each continent. Then arrange the continent names in reverse order. Hint: Use the dplyr functions arrange() and sample_n(), they have similar syntax to other dplyr functions.

Solution to Advanced Challenge

lifeExp_2countries_bycontinents <- gapminder %>%
   filter(year==2002) %>%
   group_by(continent) %>%
   sample_n(2) %>%
   summarize(mean_lifeExp=mean(lifeExp)) %>%
   arrange(desc(mean_lifeExp))

Other great resources

• R for Data Science
• Data Wrangling Cheat sheet
• Introduction to dplyr
• Data wrangling with R and RStudio

Key Points

• Use ggplot2 to create plots.

• Think about graphics in layers: aesthetics, geometry, statistics, scale transformation, and grouping.