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.
forcats is part of the core tidyverse, so you can load it with library(tidyverse)
or library(forcats)
.
starwars %>%
filter(!is.na(species)) %>%
count(species, sort = TRUE)
#> # A tibble: 37 x 2
#> species n
#> <chr> <int>
#> 1 Human 35
#> 2 Droid 5
#> 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
#> # … with 27 more rows
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 community.rstudio.com.
Please note that the ‘forcats’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.