Introduction to ggplot2 - Solutions

Exercise A - (2 min)

1. Using the preceding slide as a template, make a scatterplot with pop on the x-axis and lifeExp on the y-axis, based on gapminder_2007.
2. Repeat the preceding but with gdpPercap on the y-axis.

Solution

# Prep
library(gapminder)
library(tidyverse)

gapminder_2007 <- gapminder %>% 
  filter(year == 2007)

# Part 1
gapminder_2007 |> 
ggplot(aes(x = gdpPercap, y = lifeExp)) + 
  geom_point()

# Part 2
gapminder_2007 |> 
ggplot(aes(x = pop, y = gdpPercap)) + 
  geom_point()

Exercise B - (5 min)

Label your axes and give each plot a title!

1. Make a scatterplot with the log base 10 of pop on the x-axis and lifeExp on the y-axis using gapminder_2007.
2. Figure out how to make a plot with the y-axis on the log scale. Then repeat my plot from the previous slide with gdpPercap in levels and lifeExp in logs.
3. Repeat 2 but with both axes on the log scale.

Solution

# Part 1
gapminder_2007 |> 
  ggplot(aes(x = pop, y = lifeExp)) +
  geom_point() +
  scale_x_log10()

# Part 2
gapminder_2007 |> 
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point() +
  scale_y_log10()

# Part 3
gapminder_2007 |> 
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point() + 
  scale_x_log10() + 
  scale_y_log10()

Exercise C - (2 min)

1. Would it make sense to set size = continent? What about setting col = pop?
2. Using gapminder data from 1952, plot life expectancy on the y-axis and log population on the x-axis. Color the points by continent.

Solution

1. Neither of these makes sense since continent is categorical and pop is continuous: color is useful for categorical variables and size for continuous ones.
2. Run the following:

gapminder |> 
  filter(year == 1952) |>
  ggplot(aes(x = pop, y = lifeExp, color = continent)) +
  geom_point() +
  scale_x_log10()

Exercise - D (3 min)

1. Make a scatterplot of gapminder data from 1997. Facet by continent and put GDP/capita on the log scale on the x-axis and life expectancy on the y-axis. Indicate population by the size of each point.
2. What do you think would happen if we had tried to facet by pop rather than year? Why?

Solution

1. Run the following:

gapminder %>% 
  filter(year == 1977) |> 
  ggplot(aes(x = gdpPercap, y = lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() +
  facet_wrap(~ continent)

2. You’ll get something crazy if you try this. Population is continuous rather than categorical so every country has a different value for this variable. You’ll end up with one plot for every country, containing a single point.

Exercise E - (3 min)

1. Try appending expand_limits(y = 0) to the previous plot. What happens? Why and when might this be helpful?
2. Make a scatterplot with average GPD/capita across all countries contained in gapminder on the y-axis and year on the x-axis.
3. Repeat the preceding, broken down by continent, using color to distinguish the points. Put mean GPD/capita on the log scale.
4. Modify the last plot to include both points and lines.

Solution

1. The function expand_limits() lets us tweak the limits of our x or y-axis in a ggplot. In this particular example expand_limits(y = 0) ensures that the y-axis begins at zero. Without using this command, ggplot will choose the y-axis on its own so that there is no “empty space” in the plot. Sometimes we may want to override this behavior.
2. Run the following:

gapminder |> 
  group_by(year) |> 
  summarize(meanGDPc = mean(gdpPercap)) |> 
  ggplot(aes(x = year, y = meanGDPc)) +
  geom_point() 

3. Run the following:

gapminder |> 
  group_by(year, continent) |> 
  summarize(meanGDPc = mean(gdpPercap)) |>  
  ggplot(aes(x = year, y = meanGDPc, color = continent)) +
  geom_point() +
  scale_y_log10()

4. Run the following:

gapminder |> 
  group_by(year, continent) |> 
  summarize(meanGDPc = mean(gdpPercap)) |>  
  ggplot(aes(x = year, y = meanGDPc, color = continent)) +
  geom_point() +
  geom_line() +
  scale_y_log10()

Exercise F - (3 min)

1. What happens if you don’t specify a binwidth? Try it and find out!
2. Make a histogram of GDP/capita across countries in 1977. Play around with different binwidths until you find one that gives a good summary of the data.
3. Repeat the preceding but with GDP/capita on the log scale. Compare and contrast.

Solution

1. If you don’t specify a bin width, ggplot2 will pick one for you and give you a warning suggesting that you pick a better bin width manually.
2. There’s no obvious right answer for bin width, but here’s one possibility:

gapminder |>  
  filter(year == 1977) |> 
  ggplot(aes(x = gdpPercap)) +
  geom_histogram(binwidth = 5000)

3. There’s no right answer here: it’s a discussion question! But a key feature worth noticing is that taking logs mainly eliminates the huge positive skewness in GDP/capita:

gapminder |>  
  filter(year == 1977) |> 
  ggplot(aes(x = gdpPercap)) +
  scale_x_log10() +
  geom_histogram(binwidth = 0.2)

Exercise G - (2 min)

Use faceting to construct a collection of boxplots, each of which compares log GDP/capita across continents in a given year.

Solution

gapminder |> 
  ggplot(aes(x = continent, y = gdpPercap)) +
  geom_boxplot() +
  facet_wrap(~ year) +
  scale_y_log10() +
  ggtitle('GDP per Capita by Continent: 1952-2007')

Exercise H - (3 min)

1. Go back and turn your boxplots from the last exercise sideways to make it easier to read the continent labels.
2. Make a collection of bar plots faceted by year that compare mean GDP per capita across countries in a given year. Orient your plots so it’s easy to read the continent labels.

Solution

# Part 1
gapminder |> 
  ggplot(aes(x = continent, y = gdpPercap)) +
  geom_boxplot() +
  facet_wrap(~ year) +
  scale_y_log10() +
  coord_flip() +
  ggtitle('GDP per Capita by Continent: 1952-2007')

# Part 2
gapminder |>  
  group_by(year, continent) |> 
  summarize(meanGDPc = mean(gdpPercap)) |>
  ggplot(aes(x = continent, y = meanGDPc)) +
  geom_col() +
  facet_wrap(~ year) +
  coord_flip()

Exercise I - (3 min)

Make a dot chart of GDP per capita in all European countries in the year 2007. Sort the dots so that the country with the highest GDP per capita appears a the top and the country with the lowest appears at the bottom.

Solution

gapminder %>%
  filter(continent == 'Europe', year == 2007) %>%
  mutate(country = fct_reorder(country, gdpPercap)) %>%
  ggplot(aes(x = gdpPercap, y = country)) +
  geom_point()