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https://github.com/tidyverse/forcats

🐈🐈🐈🐈: tools for working with categorical variables (factors)
https://github.com/tidyverse/forcats

factor r tidyverse

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🐈🐈🐈🐈: tools for working with categorical variables (factors)

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README 

        
---

output: github_document

---



```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-"

)

```



# forcats 



[![CRAN status](https://www.r-pkg.org/badges/version/forcats)](https://cran.r-project.org/package=forcats)

[![R-CMD-check](https://github.com/tidyverse/forcats/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/tidyverse/forcats/actions/workflows/R-CMD-check.yaml)

[![Codecov test coverage](https://codecov.io/gh/tidyverse/forcats/branch/main/graph/badge.svg)](https://app.codecov.io/gh/tidyverse/forcats?branch=main)



## 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](https://r4ds.hadley.nz/factors.html) 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)`.



```{r setup, message = FALSE}

library(forcats)

library(dplyr)

library(ggplot2)

```



```{r}

starwars %>% 

  filter(!is.na(species)) %>%

  count(species, sort = TRUE)

```



```{r}

starwars %>%

  filter(!is.na(species)) %>%

  mutate(species = fct_lump(species, n = 3)) %>%

  count(species)

```



```{r unordered-plot}

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

  geom_bar() + 

  coord_flip()

```



```{r ordered-plot}

starwars %>%

  mutate(eye_color = fct_infreq(eye_color)) %>%

  ggplot(aes(x = eye_color)) + 

  geom_bar() + 

  coord_flip()

```



## More resources 



For a history of factors, I recommend [_stringsAsFactors: An unauthorized biography_](https://simplystats.github.io/2015/07/24/stringsasfactors-an-unauthorized-biography/) by Roger Peng and [_stringsAsFactors = \_](https://notstatschat.tumblr.com/post/124987394001/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_](https://peerj.com/preprints/3163/), by Amelia McNamara and Nicholas Horton. 



## Getting help



If you encounter a clear bug, please file a minimal reproducible example on [Github](https://github.com/tidyverse/forcats/issues). For questions and other discussion, please use [community.rstudio.com](https://community.rstudio.com/).