3.9 Exploring Variation in Categorical Variables

So far we have focused on examining the distributions of quantitative variables. Our methods for examining distributions differ, however, depending on whether a variable is quantitative or categorical.

Although we are tempted to call this a roughly normal or bell-shaped distribution, isn’t it a little strange to say the center of this distribution is “Asian”? What is the range of this distribution? Is it “Other” minus “African American”? Something about these descriptions of this distribution seems off!

We have thus far used histograms to examine the distribution of a variable. But histograms aren’t appropriate if the variable is categorical. And if R knows it’s categorical (if, for example, you have specified it as a factor), it won’t even run the histogram, and will give you an error message instead.

Bar Graphs

When a variable is categorical (also called a factor) you can visualize the distribution with a bar graph. It looks like a histogram, but it’s not. There is no such thing as bins, for example, in a bar graph. The number of bars in a bar graph will always equal the number of categories (also called levels) in your variable.

So let’s take a look at factor variables like Sex and RaceEthnic from the data frame Fingers. Because the variable Sex has two levels in this data frame (male and female), we expect to see two bars.

Here’s the code for making a bar graph in R:

gf_bar(~ Sex, data = Fingers)

Note that the y-axis depicts counts, the frequency of that particular level in the data frame. In this case, the count is the frequency of female and male.

Use the code window below to create a bar graph of RaceEthnic. (Again, use the gf_bar() command.)

You can change the width of these bars by adding the argument width and setting it to some number between 0 and 1.

You learned about the arguments color and fill for gf_histogram(). It turns out that these arguments work the same way for gf_bar(). Try playing with the colors here.

The function gf_bar() will have frequency counts on the y-axis but you can also make bar graphs that depict percents with gf_percents() or proportions with gf_props().

Shape, Center, and Spread

Visualizing the distributions of categorical variables is just as important as visualizing the distributions of quantitative variables. However, the features to look at need to be adjusted a little.

Shape of the distribution of a categorical variable doesn’t really make sense. Just by reordering the bars, you can alter the shape. So, we don’t want to pay too much attention to the shape of the distribution of a categorical variable.

Both center and spread are still worth noting. In some ways, center is easier to determine in a categorical variable than it is in a quantitative variable. The category with the most cases is the center; it’s where most observations are. This is also called the mode of the distribution—the most frequent score.

Spread is a way of characterizing how well distributed the cases are across the categories. Do most observations fall in one category, or is there an even distribution across all the categories?

Frequency Tables

Categorical variables can also be summarized with frequency tables (also called tallies). We’ve used the tally() command before. Use tally() to look at the distributions of Sex and RaceEthnic from the data frame Fingers.

Sex
female   male
   112     45

RaceEthnic
           White African American            Asian           Latino
              50               11               56               28
           Other
              12

Sometimes you may also want the total across all levels of the variable. Because this total is presented outside the main breakdown in the tally table, we say it is in the “margin.” You can get totals by adding margins as an argument to tally().

tally(~ Sex, data = Fingers, margins = TRUE)

Sex
female   male  Total
   112     45    157

More often than not, it may be more useful to look at the relative frequencies (proportions) than these counts. We can add an argument to tally() to get these same data in that format.

tally(~ Sex, data = Fingers, format = "proportion")

Sex
   female      male
0.7133758 0.2866242

Try including both format and margin as arguments for a tally of RaceEthnic. What do you predict the total proportion (in the margin) will be?

RaceEthnic
           White African American            Asian           Latino
      0.31847134       0.07006369       0.35668790       0.17834395
           Other            Total
      0.07643312       1.00000000

Do you think we could also use tally() for quantitative variables like Thumb? Let’s try it here. Write code for creating a frequency table of Thumb.

Summary: Visualizations to Help You Explore Variation

Let’s recap the different types of visualizations that go with different types of variables. Distributions of categorical variables are best explored with frequency tables (tallies) and bar graphs; distributions of quantitative variables are better explored with histograms and box plots. For both kinds of variables, one might choose to use raw frequencies, or one might choose relative frequencies (such as density or proportion), depending on the purpose. It is important to have a comprehensive toolbox for examining all kinds of variables.

Visualizations with One Variable

Variable	Visualization Type	R Code
Categorical	Frequency Table Bar Graph	tally gf_bar
Quantitative	Histogram Box Plot	gf_histogram gf_boxplot