Visualizing Distributions with ggplot2

We wil still use the palmerpenquins library for the penguins dataset and the tidyverse library for the gglot2 package.

library(palmerpenguins)
library(tidyverse)

A categorical variable

A variable is categorical if it can only take one of a small set of values. To examine the distribution of a categorical variable, you can use a bar chart. The height of the bars displays how many observations occurred with each x value.

ggplot(penguins, aes(x = species)) +
  geom_bar()

In bar plots of categorical variables with non-ordered levels, like the penguin species above, it’s often preferable to reorder the bars based on their frequencies. Doing so requires transforming the variable to a factor (how R handles categorical data) and then reordering the levels of that factor.

ggplot(penguins, aes(x = fct_infreq(species))) +
  geom_bar()

You will learn more about factors and functions for dealing with factors (like fct_infreq() shown above) in the later lessons.

A numerical variable

A variable is numerical (or quantitative) if it can take on a wide range of numerical values, and it is sensible to add, subtract, or take averages with those values. Numerical variables can be continuous or discrete.

One commonly used visualization for distributions of continuous variables is a histogram.

ggplot(penguins, aes(x = body_mass_g)) +
  geom_histogram(binwidth = 200)

A histogram divides the x-axis into equally spaced bins and then uses the height of a bar to display the number of observations that fall in each bin. In the graph above, the tallest bar shows that 39 observations have a body_mass_g value between 3,500 and 3,700 grams, which are the left and right edges of the bar.

You can set the width of the intervals in a histogram with the binwidth argument, which is measured in the units of the x variable. You should always explore a variety of binwidths when working with histograms, as different binwidths can reveal different patterns. In the plots below a binwidth of 20 is too narrow, resulting in too many bars, making it difficult to determine the shape of the distribution. Similarly, a binwidth of 2,000 is too high, resulting in all data being binned into only three bars, and also making it difficult to determine the shape of the distribution. A binwidth of 200 provides a sensible balance.

ggplot(penguins, aes(x = body_mass_g)) +
  geom_histogram(binwidth = 20)

ggplot(penguins, aes(x = body_mass_g)) +
  geom_histogram(binwidth = 2000)

An alternative visualization for distributions of numerical variables is a density plot. A density plot is a smoothed-out version of a histogram and a practical alternative, particularly for continuous data that comes from an underlying smooth distribution. We won’t go into how geom_density() estimates the density (you can read more about that in the function documentation), but let’s explain how the density curve is drawn with an analogy. Imagine a histogram made out of wooden blocks. Then, imagine that you drop a cooked spaghetti string over it. The shape the spaghetti will take draped over blocks can be thought of as the shape of the density curve. It shows fewer details than a histogram but can make it easier to quickly glean the shape of the distribution, particularly with respect to modes and skewness.

ggplot(penguins, aes(x = body_mass_g)) +
  geom_density()

#> Warning: Removed 2 rows containing non-finite values (`stat_density()`).