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.
Installation
# 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")
Cheatsheet
Getting started
forcats is part of the core tidyverse, so you can load it with
library(tidyverse) or library(forcats).
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/.
forcats
Overview
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.Installation
Cheatsheet
Getting started
forcats is part of the core tidyverse, so you can load it with
library(tidyverse)orlibrary(forcats).More resources
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.
Getting help
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/.