We start by loading the package ggplot2.

library(ggplot2)

Plotting with ggplot2

ggplot2 is a plotting package that makes it simple to create complex plots from data in a data frame. It provides a more programmatic interface for specifying what variables to plot, how they are displayed, and general visual properties. Therefore, we only need minimal changes if the underlying data change or if we decide to change from a bar plot to a scatter plot. This helps in creating publication quality plots with minimal amounts of adjustments and tweaking.

ggplot2 functions like data in the ‘long’ format, i.e., a column for every dimension, and a row for every observation. Well-structured data will save you lots of time when making figures with ggplot2

ggplot graphics are built step by step by adding new elements. Adding layers in this fashion allows for extensive flexibility and customization of plots.

To build a ggplot, we will use the following basic template that can be used for different types of plots:

ggplot(data = <DATA>, mapping = aes(<MAPPINGS>)) +  <GEOM_FUNCTION>()
ggplot(data = gapminder)
ggplot(data = gapminder, aes(x = gdp_per_cap, y = life_exp))

To add a geom to the plot use the + operator. Because we have two continuous variables, let’s use geom_point() first:

ggplot(data = gapminder, aes(x = gdp_per_cap, y = life_exp)) +
    geom_point()

The + in the ggplot2 package is particularly useful because it allows you to modify existing ggplot objects. This means you can easily set up plot templates and conveniently explore different types of plots, so the above plot can also be generated with code like this:

# Assign plot to a variable
gdp_exp_plot <- ggplot(data = gapminder, aes(x = gdp_per_cap, y = life_exp))

# Draw the plot
gdp_exp_plot +
    geom_point()

Notes

# This is the correct syntax for adding layers
gdp_exp_plot +
  geom_point()

# This will not add the new layer and will return an error message
gdp_exp_plot + geom_point()

Building your plots iteratively

Building plots with ggplot2 is typically an iterative process. We start by defining the dataset we’ll use, lay out the axes, and choose a geom:

ggplot(data = gapminder, aes(x = gdp_per_cap, y = life_exp)) +
  geom_point()

Then, we start modifying this plot to extract more information from it. For instance, we can add transparency (alpha) to avoid overplotting:

ggplot(data = gapminder, aes(x = gdp_per_cap, y = life_exp)) +
    geom_point(alpha = 0.5)

We can also add colors for all the points:

ggplot(data = gapminder, aes(x = gdp_per_cap, y = life_exp)) +
  geom_point(alpha = 0.5, color = "blue")

Or to color each continent in the plot differently, you could use a vector as an input to the argument color. ggplot2 will provide a different color corresponding to different values in the vector. Here is an example where we color with continent:

ggplot(data = gapminder, aes(x = gdp_per_cap, y = life_exp, color = continent)) +
  geom_point(alpha = 0.5)

Notice that we can change the geom layer and colors will be still determined by continent

ggplot(data = gapminder, aes(x = gdp_per_cap, y = life_exp, color = continent)) +
  geom_jitter(alpha = 0.5)

To make our plot more readable, we can add axis labels:

ggplot(data = gapminder, aes(x = gdp_per_cap, y = life_exp, color = continent)) +
  geom_point(alpha = 0.5) +
  labs(x = "GDP per Capita",
       y = "Life Expectancy")

Challenge

Use what you just learned to create a scatter plot of population over life expectancy with the continents showing in different colors. Make sure to give your plot relevant axis labels.

Solution 
ggplot(data = gapminder, aes(x = pop, y = life_exp, color = continent)) +
    geom_point() +
    labs(x = "Population",
         y = "Life Expectancy")


Faceting

ggplot2 has a special technique called faceting that allows the user to split one plot into multiple plots based on a factor included in the dataset. We will use it to split our plot into five panels, one for each continent.

ggplot(data = gapminder, aes(x = gdp_per_cap, y = life_exp, color = continent)) +
    geom_point() +
    labs(x = "GDP per Capita",
         y = "Life Expectancy") +
    facet_grid(. ~ continent)

We can also experiment with stacking the facets vertically, rather than horizontally. The facet_grid geometry allows you to explicitly specify how you want your plots to be arranged via formula notation (rows ~ columns; a . can be used as a placeholder that indicates only one row or column).

ggplot(data = gapminder, aes(x = gdp_per_cap, y = life_exp, color = continent)) +
    geom_point() +
    labs(x = "GDP per Capita",
         y = "Life Expectancy") +
    facet_grid(continent ~ .)

Usually plots with white background look more readable when printed. We can set the background to white using the function theme_bw(). Additionally, you can remove the grid:

ggplot(data = gapminder, aes(x = gdp_per_cap, y = life_exp, color = continent)) +
  geom_point() +
  labs(x = "GDP per Capita",
       y = "Life Expectancy") +
  facet_grid(continent ~ .) +
  theme_bw() +
  theme(panel.grid = element_blank())

Line Plots

Let’s try to plot the life expectancy of each country over each year.

ggplot(data = gapminder, aes(x = year, y = life_exp, color = continent)) +
    geom_line()

This doesn’t look right at all. This is giving us a line per continent, but we really want the lines to be per country, but colored by the continent… there is a parameter of aes() called group.

ggplot(data = gapminder, aes(x = year, y = life_exp, color = continent, group = country)) +
    geom_line()

Bar Plots

We can create barplots using the geom_bar geom. Let’s make a barplot showing the number of data points per continent.

ggplot(data = gapminder, aes(x = continent)) + geom_bar()

Notice, this looks a lot like our plot way back in episode 2!

Box Plots

We can create boxplots using the geom_boxplot geom. Let’s look at the boxplot of population split per continent.

ggplot(data = gapminder, aes(x = continent, y = pop)) + geom_boxplot()

This doesn’t look great because the populations of countries have such a large range. We an fix this by changing the scale of the y-axis to a logarithmic scale using the scale_y_log10() function. We may also want to change the axis labels to make it clear to the reader that this was done:

ggplot(data = gapminder, aes(x = continent, y = pop)) +
    geom_boxplot() +
    scale_y_log10() +
    labs(
        x = 'Continent',
        y = 'Population (log10 scale)'
    )

Challenge

How can we add the individual data points to the boxplot?

Solution 
ggplot(data = gapminder, aes(x = continent, y = pop)) +
    geom_boxplot() +
    scale_y_log10() +
    geom_jitter() +
    labs(
        x = 'Continent',
        y = 'Population (log10 scale)'
    )


ggplot2 themes

In addition to theme_bw(), which changes the plot background to white, ggplot2 comes with several other themes which can be useful to quickly change the look of your visualization. The complete list of themes is available at https://ggplot2.tidyverse.org/reference/ggtheme.html. theme_minimal() and theme_light() are popular, and theme_void() can be useful as a starting point to create a new hand-crafted theme.

The ggthemes package provides a wide variety of options (including an Excel 2003 theme). The ggplot2 extensions website provides a list of packages that extend the capabilities of ggplot2, including additional themes.

Challenge

With all of this information in hand, please take another five minutes to either improve one of the plots generated in this exercise or create a beautiful graph of your own. Use the RStudio ggplot2 cheat sheet for inspiration. Here are some ideas: