forcats 0.4.0

We are pleased to announce that forcats 0.4.0 is now on CRAN. The forcats package provides a suite of useful tools that solve common problems with factors in R. This version benefited from the hard work of contributors new and old at our first tidyverse dev day . For a complete set of changes, please see the release notes .

To install the latest version, run:

install.packages("forcats")

As always, attach the package with:

library(forcats)

New functions#

fct_cross() creates a new factor containing the combined levels from two or more input factors, similar to base::interaction().

fruit <- factor(c("apple", "kiwi", "apple", "apple"))
colour <- factor(c("green", "green", "red", "green"))
fct_cross(fruit, colour)
#> [1] apple:green kiwi:green  apple:red   apple:green
#> Levels: apple:green apple:red kiwi:green

fct_lump_min() preserves levels that appear at least min times (can also be used with the w weighted argument).

x <- factor(letters[rpois(50, 3)])
fct_lump_min(x, min = 10)
#>  [1] Other b     Other b     Other Other Other b     Other Other b    
#> [12] Other Other Other b     Other b     Other Other b     b     Other
#> [23] Other Other b     b     Other Other Other Other Other b     Other
#> [34] Other Other b     Other Other Other Other Other Other Other Other
#> [45] Other b     Other b    
#> Levels: b Other

fct_match() tests for the presence of levels in a factor, providing a safer alternative to %in% by throwing an error when there are unexpected levels.

table(fct_match(gss_cat$marital, c("Married", "Divorced")))
#> 
#> FALSE  TRUE 
#>  7983 13500
table(gss_cat$marital %in% c("Maried", "Davorced"))
#> 
#> FALSE 
#> 21483
table(fct_match(gss_cat$marital, c("Maried", "Davorced")))
#> Error: Levels not present in factor: "Maried", "Davorced"

Other improvements#

fct_relevel() can now relevel factors using a function that is passed the current levels.

f <- factor(c("a", "b", "c", "d"), levels = c("b", "c", "d", "a"))
fct_relevel(f, sort)
#> [1] a b c d
#> Levels: a b c d
fct_relevel(f, rev)
#> [1] a b c d
#> Levels: a d c b

as_factor() now has a numeric method which orders factors in numeric order, unlike the other methods which default to order of appearance.

y <- c("1.1", "11", "2.2", "22")
as_factor(y)
#> [1] 1.1 11  2.2 22 
#> Levels: 1.1 11 2.2 22
z <- as.numeric(y)
as_factor(z)
#> [1] 1.1 11  2.2 22 
#> Levels: 1.1 2.2 11 22