Class 13: Working with Categorical Data

Learning Objectives

Today we will explore how to create new variable from old variables and, specifically, how to change the way that categorical variables are presented in plots.

Tea Data

For class today, let’s load a dataset of teas offered by the website Adagio:

tea <- read_csv("")
## # A tibble: 238 x 5
##                   name  type score price num_reviews
##                  <chr> <chr> <int> <int>       <int>
##  1     irish_breakfast black    96    10        3675
##  2     earl_grey_bravo black    95    10        3520
##  3       golden_monkey black    95    27        1125
##  4 black_dragon_pearls black    96    32        1748
##  5         yunnan_noir black    95    17         988
##  6 earl_grey_moonlight black    95    10        2510
##  7   english_breakfast black    93    17        1008
##  8     keemun_concerto black    92    17         499
##  9         yunnan_gold black    95    40        1094
## 10       ceylon_sonata black    94    12        1525
## # ... with 228 more rows

New Variables

In R, we can create new variable from old ones by apply numeric operations or new functions. In plots, simply manipulations can be do in-line; that is, we apply the functions to the variables within the plot. For example, the tea dataset gives prices in cents. We can make a plot of price in dollars against the score as follows:

ggplot(tea, aes(price / 100, score)) +

plot of chunk unnamed-chunk-3

Notice that the expression shows up verbatim in the plot. We can apply other functions such as sqrt or combine two variables similarly (note: this makes no practical sense here):

ggplot(tea, aes(sqrt(price), score / num_reviews)) +

plot of chunk unnamed-chunk-4

If a new variable is particularly useful or complex to construct, it may be useful to create a new variable to store it. The syntax to do this is as follows:

tea$price_dollars <- tea$price / 100

Notice that we need to start every variable name with tea$; otherwise R will not know which dataset we are working with. In ggplot2 commands this is not a problem because we have already stated what the default dataset is.

Making Numeric Data Discrete

Often in plots it will be useful to convert numeric data into categorical data. There are three functions that I typically use to do this, depending on the end-goal:

  • factor: this converts each unique value of the input into its own category
  • cut: breaks the range of the numeric variable into equal parts and combines numbers in the same range together
  • bin: breaks the numeric data into equally sized bins

The second two require an option named n that specifies the number of buckets.

Let’s take a look at how this works for factor:

ggplot(tea, aes(price, num_reviews)) +
  geom_point(aes(color = factor(price)))

plot of chunk unnamed-chunk-6

Cut with 5 bins:

ggplot(tea, aes(price, num_reviews)) +
  geom_point(aes(color = cut(price, 5)))

plot of chunk unnamed-chunk-7

And bin with 5 bins:

ggplot(tea, aes(price, num_reviews)) +
  geom_point(aes(color = bin(price, 5)))

plot of chunk unnamed-chunk-8

You may find these useful, for one thing, when making maps in your second project.

Changing Categorical Variables

The package forcats provides a number of functions for changing the way that categories are displayed. There are a number of functions, but I find that these four are most useful:

  • fct_inorder: order the categories in the order the categories appear
  • fct_infreq: order the categories from the smallest to largest category
  • fct_rec: reverse the order of the categories (useful to apply after fct_infreq)
  • fct_lump: lump together the smallest categories. Set the option n to specify the number of remaining categories

We can see the effect of these most clearly on a bar plot, such as:

ggplot(tea, aes(fct_infreq(type))) +

plot of chunk unnamed-chunk-9


ggplot(tea, aes(fct_lump(type, n = 3))) +

plot of chunk unnamed-chunk-10

They are very useful for when you want to use color but have too many small categories:

ggplot(tea, aes(price, num_reviews)) +
  geom_point(aes(color = fct_lump(type, n = 5)))

plot of chunk unnamed-chunk-11


We will, once again, work on a lab for the remainder of the class: lab13.Rmd Upload your script to GitHub ahead of the next class.