R uses factors to handle categorical variables, variables that have a fixed and known set of possible values. Factors are also helpful for reordering character vectors to improve display. The goal of the forcats package is to provide a suite of tools that solve common problems with factors, including changing the order of levels or the values. Some examples include:

• fct_reorder(): Reordering a factor by another variable.
• fct_infreq(): Reordering a factor by the frequency of values.
• fct_relevel(): Changing the order of a factor by hand.
• fct_lump(): Collapsing the least/most frequent values of a factor into “other”.

You can learn more about each of these in vignette("forcats"). If you’re new to factors, the best place to start is the chapter on factors in R for Data Science.

# The easiest way to get forcats is to install the whole tidyverse:
install.packages("tidyverse")

# Alternatively, install just forcats:
install.packages("forcats")

# Or the the development version from GitHub:
# install.packages("pak")
pak::pak("tidyverse/forcats")

forcats is part of the core tidyverse, so you can load it with library(tidyverse) or library(forcats).

library(forcats)
library(dplyr)
library(ggplot2)

starwars |> 
  filter(!is.na(species)) |>
  count(species, sort = TRUE)
#> # A tibble: 37 × 2
#>    species      n
#>    <chr>    <int>
#>  1 Human       35
#>  2 Droid        6
#>  3 Gungan       3
#>  4 Kaminoan     2
#>  5 Mirialan     2
#>  6 Twi'lek      2
#>  7 Wookiee      2
#>  8 Zabrak       2
#>  9 Aleena       1
#> 10 Besalisk     1
#> # ℹ 27 more rows

starwars |>
  filter(!is.na(species)) |>
  mutate(species = fct_lump(species, n = 3)) |>
  count(species)
#> # A tibble: 4 × 2
#>   species     n
#>   <fct>   <int>
#> 1 Droid       6
#> 2 Gungan      3
#> 3 Human      35
#> 4 Other      39

ggplot(starwars, aes(x = eye_color)) + 
  geom_bar() + 
  coord_flip()

starwars |>
  mutate(eye_color = fct_infreq(eye_color)) |>
  ggplot(aes(x = eye_color)) + 
  geom_bar() + 
  coord_flip()

For a history of factors, I recommend stringsAsFactors: An unauthorized biography by Roger Peng and stringsAsFactors = <sigh> by Thomas Lumley. If you want to learn more about other approaches to working with factors and categorical data, I recommend Wrangling categorical data in R, by Amelia McNamara and Nicholas Horton.

If you encounter a clear bug, please file a minimal reproducible example on Github. For questions and other discussion, please use https://forum.posit.co/.