12 Factors and Categorical Data with forcats

12.1 Learning Objectives

By the end of this chapter, you should be able to:

Understand what factors are and why they are used
Reorder factor levels to improve plots
Rename factor levels
Collapse multiple levels into broader categories
Use forcats functions to manipulate categorical variables effectively

12.2 Introduction to Factors

Factors are used to work with categorical data (variables with a fixed set of possible values).
R uses factors to control ordering in plots and summaries.

Example:

x <- factor(c("low", "medium", "high", "medium", "low"))
levels(x)

[1] "high"   "low"    "medium"

12.3 Using forcats

The forcats package provides helper functions for factors.

library(forcats)
library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ ggplot2   3.5.2     ✔ stringr   1.5.1
✔ lubridate 1.9.4     ✔ tibble    3.2.1
✔ purrr     1.0.4     ✔ tidyr     1.3.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

12.4 Reordering Factor Levels

12.4.1 `fct_reorder()`

Reorders factor levels by another variable (e.g., mean of a numeric variable):

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

12.4.2 In-Class Exercise 1 – Reordering

Use fct_reorder() to reorder car classes in the mpg dataset by highway mpg.
Make a boxplot of hwy by class.
Which class has the highest median mpg?

12.5 Changing Factor Labels

12.5.1 `fct_recode()`

Renames levels:

mpg |>
  mutate(drv = fct_recode(drv,
    "front-wheel" = "f",
    "rear-wheel"  = "r",
    "4-wheel"     = "4"
  )) |>
  count(drv)

# A tibble: 3 × 2
  drv             n
  <fct>       <int>
1 4-wheel       103
2 front-wheel   106
3 rear-wheel     25

12.5.2 In-Class Exercise 2 – Recoding

Recode the drv variable to use descriptive names.
Count the number of cars in each drive category.

12.6 Collapsing Levels

12.6.1 `fct_collapse()`

Combines multiple levels into broader categories.

mpg |>
  mutate(class_grouped = fct_collapse(class,
    small = c("2seater", "compact", "subcompact"),
    large = c("suv", "pickup", "minivan")
  )) |>
  count(class_grouped)

# A tibble: 3 × 2
  class_grouped     n
  <fct>         <int>
1 small            87
2 midsize          41
3 large           106

12.6.2 In-Class Exercise 3 – Collapsing Levels

Create a new variable that collapses class into small vs. large.
Make a bar chart of the collapsed variable.

12.7 Reordering Factors for Plots

12.7.1 `fct_infreq()`

Orders factors by frequency:

mpg |>
  mutate(class = fct_infreq(class)) |>
  ggplot(aes(x = class)) +
  geom_bar()

12.7.2 In-Class Challenge – Factor Workflow

Using the mpg dataset:

Reorder the manufacturer variable by number of cars
Collapse classes into fewer categories
Create a bar plot that uses the new ordering and grouping

12.8 Homework Preview

For the next homework, you will:

Choose a dataset with at least one categorical variable
Use forcats functions to:
- Reorder levels
- Recode labels
- Collapse levels where appropriate
Produce at least one visualization that uses your factor manipulations
Render to PDF and submit on Canvas

12.9 Next Steps

Next, we will learn how to work with relational data using dplyr join functions to combine multiple datasets.

--- title: "Factors and Categorical Data with forcats" --- ## Learning Objectives By the end of this chapter, you should be able to: - Understand what factors are and why they are used - Reorder factor levels to improve plots - Rename factor levels - Collapse multiple levels into broader categories - Use `forcats` functions to manipulate categorical variables effectively --- ## Introduction to Factors Factors are used to work with **categorical data** (variables with a fixed set of possible values). R uses factors to control ordering in plots and summaries. Example: ```{r} x <- factor(c("low", "medium", "high", "medium", "low")) levels(x) ``` --- ## Using forcats The `forcats` package provides helper functions for factors. ```{r} library(forcats) library(tidyverse) ``` --- ## Reordering Factor Levels ### `fct_reorder()` Reorders factor levels by another variable (e.g., mean of a numeric variable): ```{r} ggplot(mpg, aes(x = fct_reorder(class, hwy), y = hwy)) + geom_boxplot() ``` --- ### In-Class Exercise 1 – Reordering 1. Use `fct_reorder()` to reorder car classes in the `mpg` dataset by highway mpg. 2. Make a boxplot of `hwy` by class. 3. Which class has the highest median mpg? --- ## Changing Factor Labels ### `fct_recode()` Renames levels: ```{r} mpg |> mutate(drv = fct_recode(drv, "front-wheel" = "f", "rear-wheel" = "r", "4-wheel" = "4" )) |> count(drv) ``` --- ### In-Class Exercise 2 – Recoding 1. Recode the `drv` variable to use descriptive names. 2. Count the number of cars in each drive category. --- ## Collapsing Levels ### `fct_collapse()` Combines multiple levels into broader categories. ```{r} mpg |> mutate(class_grouped = fct_collapse(class, small = c("2seater", "compact", "subcompact"), large = c("suv", "pickup", "minivan") )) |> count(class_grouped) ``` --- ### In-Class Exercise 3 – Collapsing Levels 1. Create a new variable that collapses `class` into `small` vs. `large`. 2. Make a bar chart of the collapsed variable. --- ## Reordering Factors for Plots ### `fct_infreq()` Orders factors by frequency: ```{r} mpg |> mutate(class = fct_infreq(class)) |> ggplot(aes(x = class)) + geom_bar() ``` --- ### In-Class Challenge – Factor Workflow Using the `mpg` dataset: - Reorder the `manufacturer` variable by number of cars - Collapse classes into fewer categories - Create a bar plot that uses the new ordering and grouping --- ## Homework Preview For the next homework, you will: - Choose a dataset with at least one categorical variable - Use `forcats` functions to: - Reorder levels - Recode labels - Collapse levels where appropriate - Produce at least one visualization that uses your factor manipulations - Render to PDF and submit on Canvas --- ## Next Steps Next, we will learn how to work with **relational data** using `dplyr` join functions to combine multiple datasets.