forcats: working with categorical data

The R package forcats is designed to make working with categorical variables easier and more efficient. It provides a set of functions that allow you to manipulate and analyze categorical data with ease. In this lesson, we’ll cover the basics of the forcats package and some of its most useful functions.

Categorical Variables

Let’s review what categorical data is. Categorical data is a type of data that consists of categories or labels.

Examples of categorical data include:

• Colors (red, blue, green, etc.)
• Types of vehicles (sedan, SUV, truck)
• Educational degrees (high school, college, graduate school)

Categorical data can be further divided into two types: nominal and ordinal. Nominal data consists of categories that have no inherent order, while ordinal data consists of categories that have a natural order. For example, educational degrees are ordinal data because they can be ordered from least to most advanced.

mpg Data

We will play with different functions in the forcats packages using the mpg dataset from earlier in the semester.

library(forcats)
library(tidyverse)
data("mpg")

Recall our side-by-side boxplot:

mpg %>% 
  ggplot(aes(x=class, y=hwy)) +
  geom_boxplot()

Reordering Factor Levels

One of the most useful functions is fct_relevel(), which allows you to reorder the levels of a factor. This can be useful when you want to change the default ordering of the levels or when you want to group certain levels together.

Is class a factor?

mpg$class %>% is.factor()

[1] FALSE

Let’s make it a factor!

mpg <- mpg %>% 
  mutate(class = class %>%  as.factor())

Let’s check the levels and their current ordering!

mpg$class %>%  
  levels()

[1] "2seater"    "compact"    "midsize"    "minivan"    "pickup"    
[6] "subcompact" "suv"       

To reorder the levels with fct_relevel()

mpg <- mpg %>% 
  mutate(class = class %>%  fct_relevel( "compact","subcompact","midsize","2seater","minivan","suv","pickup"))

mpg$class %>% 
  levels()

[1] "compact"    "subcompact" "midsize"    "2seater"    "minivan"   
[6] "suv"        "pickup"    

Let’s recreate our side-by-side boxplot now:

mpg %>% 
  ggplot(aes(x=class, y=hwy)) +
  geom_boxplot()

Rather than reordering them manually by typing the order, you could also re-level by some numeric criteria using fct_reorder(). For example:

mpg <- mpg %>% 
  mutate(class = class %>% fct_reorder(hwy, median))

mpg$class %>% 
  levels()

[1] "pickup"     "suv"        "minivan"    "2seater"    "subcompact"
[6] "compact"    "midsize"   

Renaming Factor levels with fct_recode

Sometimes you might not like the way the levels are named.

mpg <- mpg %>% 
  mutate(class = class %>%  fct_recode("two-seater" = "2seater"))

## NEW NAME = OLD NAME

mpg$class %>% 
  levels()

[1] "pickup"     "suv"        "minivan"    "two-seater" "subcompact"
[6] "compact"    "midsize"   

Factor Collapsing with fct_collapse()

Let’s say we wanted to create only two categories – cars and larger vehicles.

mpg <- mpg %>% 
  mutate(class_two = class %>%  fct_collapse( cars = c("compact", "subcompact", "midsize", "two-seater"), big = c("pickup", "suv", "minivan")))

mpg$class_two %>% 
  levels()

[1] "big"  "cars"

Lumping into an other category

• fct_lump_min(): lumps levels that appear fewer than min times.

• fct_lump_prop(): lumps levels that appear in fewer than (or equal to) prop * n times.

• fct_lump_n() lumps all levels except for the n most frequent (or least frequent if n < 0)

mpg %>% 
  count(manufacturer)

# A tibble: 15 × 2
   manufacturer     n
   <chr>        <int>
 1 audi            18
 2 chevrolet       19
 3 dodge           37
 4 ford            25
 5 honda            9
 6 hyundai         14
 7 jeep             8
 8 land rover       4
 9 lincoln          3
10 mercury          4
11 nissan          13
12 pontiac          5
13 subaru          14
14 toyota          34
15 volkswagen      27

Let’s say we wanted only the manufacturers with at least 15 cars produced. Everything else we want to just be other:

mpg <- mpg %>% 
  mutate(class_lumped = class %>% fct_lump_min(15))

mpg$manufacturer %>% 
  levels()

NULL

Create a table using kableExtra:

library(kableExtra)

mpg %>% 
  count(manufacturer) %>% 
  kbl() %>% 
  kable_styling()

manufacturer	n
audi	18
chevrolet	19
dodge	37
ford	25
honda	9
hyundai	14
jeep	8
land rover	4
lincoln	3
mercury	4
nissan	13
pontiac	5
subaru	14
toyota	34
volkswagen	27