Helpers for reordering factor levels (including moving specified levels to front, ordering by first appearance, reversing, and randomly shuffling), and tools for modifying factor levels (including collapsing rare levels into other, 'anonymising', and manually 'recoding').
R uses factors to handle categorical variables, variables that have a fixed and known set of possible values. Historically, factors were much easier to work with than character vectors, so many base R functions automatically convert character vectors to factors. (For historical context, 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.) These days, making factors automatically is no longer so helpful, so packages in the tidyverse never create them automatically.
However, factors are still useful when you have true categorical data, and when you want to override the ordering of character vectors to improve display. The goal of the forcats package is to provide a suite of useful tools that solve common problems with factors. If you’re not familiar with strings, 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("devtools")devtools::install_github("tidyverse/forcats")
forcats is now part of the core tidyverse, so you do not need to load it explicitly:
Factors are used to describe categorical variables with a fixed and
known set of levels. You can create factors with the base
x1 <- c("Dec", "Apr", "Jan", "Mar")month_levels <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun","Jul", "Aug", "Sep", "Oct", "Nov", "Dec")factor(x1, month_levels)#>  Dec Apr Jan Mar#> Levels: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Decparse_factor(x1, month_levels)#>  Dec Apr Jan Mar#> Levels: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
The advantage of
parse_factor() is that it will generate a warning if
x are not valid levels:
x2 <- c("Dec", "Apr", "Jam", "Mar")factor(x2, month_levels)#>  Dec Apr <NA> Mar#> Levels: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Decparse_factor(x2, month_levels)#> Warning: 1 parsing failure.#> row # A tibble: 1 x 4 col row col expected actual expected <int> <int> <chr> <chr> actual 1 3 NA value in level set Jam#>  Dec Apr <NA> Mar#> attr(,"problems")#> # A tibble: 1 x 4#> row col expected actual#> <int> <int> <chr> <chr>#> 1 3 NA value in level set Jam#> Levels: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Once you have the factor, forcats provides helpers for solving common problems.
fct_c() now requires explicit splicing with
!!! if you have a
list of factors that you want to combine. This is consistent with an emerging
standards for handling
... throughout the tidyverse.
fct_reorder2() have renamed
avoid spurious matching of named arguments.
All functions that take
... use "tidy" dots: this means that you use can
!!! to splice in a list of values, and trailing empty arguments are
automatically removed. Additionally, all other arguments gain a
in order to avoid unhelpful matching of named arguments (#110).
w argument (#70, @wilkox) to weight value
frequencies before lumping them together (#68).
fct_inorder() accept NA levels (#98).
fct_explicit_na() also replaces NAs encoded in levels.
fct_lump() correctly acccounts for
NA values in input (#41)
lvls_revalue() preserves NA levels.
Test coverage increased from 80% to 99%.
fct_drop() now preserves attributes (#83).
lvls_expand() now also take character vectors (#99).
fct_relabel() now accepts objects coercible to functions
rlang::as_function (#91, @alistaire47)
as_factor() which works like
as.factor() but orders levels by
appearance to avoid differences between locales (#39).
fct_other() makes it easier to convert selected levels to "other" (#40)
fct_relabel() allows programmatic relabeling of levels (#50, @krlmlr).
fct_c() can take either a list of factors or individual factors (#42).
only argument to restrict which levels are dropped (#69)
and no longer adds
NA level if not present (#52).
fct_recode() is now checks that each new value is of length 1 (#56).
after argument so you can also move levels
to the end (or any other position you like) (#29).
fct_infreq() gain an
argument, allowing you to override the existing "ordered" status (#54).
Minor fixes for R CMD check
Add package docs