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forcats

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

https://github.com/tidyverse/forcats

Science Score: 23.0%

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Keywords

factor r tidyverse

Keywords from Contributors

package-creation ropensci rmarkdown pandoc literate-programming tidy-data unconf17 unconf summary-statistics easy-to-use
Last synced: 6 months ago · JSON representation

Repository

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

Basic Info
Statistics
  • Stars: 554
  • Watchers: 22
  • Forks: 128
  • Open Issues: 26
  • Releases: 9
Topics
factor r tidyverse
Created over 9 years ago · Last pushed 6 months ago
Metadata Files
Readme Changelog Contributing License Code of conduct Codeowners Support

README.Rmd 

---
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/graph/badge.svg)](https://app.codecov.io/gh/tidyverse/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](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/).

Owner

  • Name: tidyverse
  • Login: tidyverse
  • Kind: organization

The tidyverse is a collection of R packages that share common principles and are designed to work together seamlessly

GitHub Events

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Last Year
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Last synced: 8 months ago

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  • Development Distribution Score (DDS): 0.243
Past Year
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  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
hadley h****m@g****m 265
Emily Robinson r****s@g****m 11
Mara Averick m****k@g****m 10
Kirill Müller k****r 4
lbouzoubaa l****a@m****u 3
Gareth Maddock 9****9 3
Jenny Bryan j****n@g****m 3
Kelly N. Bodwin k****y@b****s 2
Zhi Yang 7****g 2
Yiming (Paul) Li h****g@g****m 2
Y. Yu 5****e 2
Rafael P. H r****h 2
Joyce Robbins j****3 2
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Jeremy Stanley j****y@i****m 1
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Edward Visel a****e@g****m 1
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David Wilkins d****d@w****g 1
David C Hall d****l 1
Daniel Possenriede p****e@g****m 1
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Chester Ismay i****9@g****m 1
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Bill Denney b****y 1
Abraham Neuwirth Y****e 1
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vincent guyader v****t@g****o 1
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eipi10 j****l@j****m 1
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Issues and Pull Requests

Last synced: 6 months ago

All Time
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  • Average comments per pull request: 0.58
  • Merged pull requests: 25
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
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  • Average time to close issues: 8 months
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Packages

  • Total packages: 2
  • Total downloads:
    • cran 694,363 last-month
  • Total docker downloads: 45,962,769
  • Total dependent packages: 315
    (may contain duplicates)
  • Total dependent repositories: 1,346
    (may contain duplicates)
  • Total versions: 18
  • Total maintainers: 1
cran.r-project.org: forcats

Tools for Working with Categorical Variables (Factors)

  • Versions: 9
  • Dependent Packages: 315
  • Dependent Repositories: 1,346
  • Downloads: 694,363 Last month
  • Docker Downloads: 45,962,769
Rankings
Dependent repos count: 0.3%
Dependent packages count: 0.4%
Downloads: 0.4%
Forks count: 0.5%
Stargazers count: 0.7%
Average: 3.3%
Docker downloads count: 17.3%
Maintainers (1)
hadley@rstudio.com
Last synced: 6 months ago
proxy.golang.org: github.com/tidyverse/forcats
  • Versions: 9
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 5.5%
Average: 5.7%
Dependent repos count: 5.9%
Last synced: 6 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.4 depends
  • cli * imports
  • ellipsis * imports
  • glue * imports
  • lifecycle * imports
  • magrittr * imports
  • rlang >= 1.0.0 imports
  • tibble * imports
  • withr * imports
  • covr * suggests
  • dplyr * suggests
  • ggplot2 * suggests
  • knitr * suggests
  • readr * suggests
  • rmarkdown * suggests
  • testthat >= 3.0.0 suggests
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