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Cy('a읹w$S 7 forcats/man/ 0000755 0001762 0000144 00000000000 14364755572 012510 5 ustar ligges users forcats/man/fct_inorder.Rd 0000644 0001762 0000144 00000002056 14355354616 015272 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/reorder.R
\name{fct_inorder}
\alias{fct_inorder}
\alias{fct_infreq}
\alias{fct_inseq}
\title{Reorder factor levels by first appearance, frequency, or numeric order}
\usage{
fct_inorder(f, ordered = NA)
fct_infreq(f, w = NULL, ordered = NA)
fct_inseq(f, ordered = NA)
}
\arguments{
\item{f}{A factor}
\item{ordered}{A logical which determines the "ordered" status of the
output factor. \code{NA} preserves the existing status of the factor.}
\item{w}{An optional numeric vector giving weights for frequency of
each value (not level) in f.}
}
\description{
This family of functions changes only the order of the levels.
\itemize{
\item \code{fct_inorder()}: by the order in which they first appear.
\item \code{fct_infreq()}: by number of observations with each level (largest first)
\item \code{fct_inseq()}: by numeric value of level.
}
}
\examples{
f <- factor(c("b", "b", "a", "c", "c", "c"))
f
fct_inorder(f)
fct_infreq(f)
f <- factor(1:3, levels = c("3", "2", "1"))
f
fct_inseq(f)
}
forcats/man/lvls.Rd 0000644 0001762 0000144 00000002460 13626051403 013737 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lvls.R
\name{lvls}
\alias{lvls}
\alias{lvls_reorder}
\alias{lvls_revalue}
\alias{lvls_expand}
\title{Low-level functions for manipulating levels}
\usage{
lvls_reorder(f, idx, ordered = NA)
lvls_revalue(f, new_levels)
lvls_expand(f, new_levels)
}
\arguments{
\item{f}{A factor (or character vector).}
\item{idx}{A integer index, with one integer for each existing level.}
\item{ordered}{A logical which determines the "ordered" status of the
output factor. \code{NA} preserves the existing status of the factor.}
\item{new_levels}{A character vector of new levels.}
}
\description{
\code{lvls_reorder} leaves values as they are, but changes the order.
\code{lvls_revalue} changes the values of existing levels; there must
be one new level for each old level.
\code{lvls_expand} expands the set of levels; the new levels must
include the old levels.
}
\details{
These functions are less helpful than the higher-level \code{fct_} functions,
but are safer than the very low-level manipulation of levels directly,
because they are more specific, and hence can more carefully check their
arguments.
}
\examples{
f <- factor(c("a", "b", "c"))
lvls_reorder(f, 3:1)
lvls_revalue(f, c("apple", "banana", "carrot"))
lvls_expand(f, c("a", "b", "c", "d"))
}
forcats/man/fct_relabel.Rd 0000644 0001762 0000144 00000002342 14357100046 015220 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/relabel.R
\name{fct_relabel}
\alias{fct_relabel}
\title{Relabel factor levels with a function, collapsing as necessary}
\usage{
fct_relabel(.f, .fun, ...)
}
\arguments{
\item{.f}{A factor (or character vector).}
\item{.fun}{A function to be applied to each level. Must accept one
character argument and return a character vector of the same length as
its input.
You can also use \code{~} to create as shorthand (in the style of purrr).
\code{~ paste(., "x")} is equivalent to \code{function(.) paste(., "x")}}
\item{...}{Additional arguments to \code{fun}.}
}
\description{
Relabel factor levels with a function, collapsing as necessary
}
\examples{
gss_cat$partyid \%>\% fct_count()
gss_cat$partyid \%>\%
fct_relabel(~ gsub(",", ", ", .x)) \%>\%
fct_count()
convert_income <- function(x) {
regex <- "^(?:Lt |)[$]([0-9]+).*$"
is_range <- grepl(regex, x)
num_income <- as.numeric(gsub(regex, "\\\\1", x[is_range]))
num_income <- trunc(num_income / 5000) * 5000
x[is_range] <- paste0("Gt $", num_income)
x
}
fct_count(gss_cat$rincome)
convert_income(levels(gss_cat$rincome))
rincome2 <- fct_relabel(gss_cat$rincome, convert_income)
fct_count(rincome2)
}
forcats/man/fct_c.Rd 0000644 0001762 0000144 00000001242 13626252103 014032 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/c.R
\name{fct_c}
\alias{fct_c}
\title{Concatenate factors, combining levels}
\usage{
fct_c(...)
}
\arguments{
\item{...}{<\code{\link[rlang:dyn-dots]{dynamic-dots}}> Individual
factors. Uses tidy dots, so you can splice in a list of factors
with \verb{!!!}.}
}
\description{
This is a useful way of patching together factors from multiple sources
that really should have the same levels but don't.
}
\examples{
fa <- factor("a")
fb <- factor("b")
fab <- factor(c("a", "b"))
c(fa, fb, fab)
fct_c(fa, fb, fab)
# You can also pass a list of factors with !!!
fs <- list(fa, fb, fab)
fct_c(!!!fs)
}
forcats/man/fct_collapse.Rd 0000644 0001762 0000144 00000002050 13626261320 015411 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/collapse.R
\name{fct_collapse}
\alias{fct_collapse}
\title{Collapse factor levels into manually defined groups}
\usage{
fct_collapse(.f, ..., other_level = NULL, group_other = "DEPRECATED")
}
\arguments{
\item{.f}{A factor (or character vector).}
\item{...}{<\code{\link[rlang:dyn-dots]{dynamic-dots}}> A series of named character vectors. The levels in
each vector will be replaced with the name.}
\item{other_level}{Value of level used for "other" values. Always
placed at end of levels.}
\item{group_other}{Deprecated. Replace all levels not named in \code{...} with "Other"?}
}
\description{
Collapse factor levels into manually defined groups
}
\examples{
fct_count(gss_cat$partyid)
partyid2 <- fct_collapse(gss_cat$partyid,
missing = c("No answer", "Don't know"),
other = "Other party",
rep = c("Strong republican", "Not str republican"),
ind = c("Ind,near rep", "Independent", "Ind,near dem"),
dem = c("Not str democrat", "Strong democrat")
)
fct_count(partyid2)
}
forcats/man/gss_cat.Rd 0000644 0001762 0000144 00000001450 14241555020 014376 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{gss_cat}
\alias{gss_cat}
\title{A sample of categorical variables from the General Social survey}
\format{
\describe{
\item{year}{year of survey, 2000--2014 (every other year)}
\item{age}{age. Maximum age truncated to 89.}
\item{marital}{marital status}
\item{race}{race}
\item{rincome}{reported income}
\item{partyid}{party affiliation}
\item{relig}{religion}
\item{denom}{denomination}
\item{tvhours}{hours per day watching tv}
}
}
\source{
Downloaded from \url{https://gssdataexplorer.norc.org/}.
}
\usage{
gss_cat
}
\description{
A sample of categorical variables from the General Social survey
}
\examples{
gss_cat
fct_count(gss_cat$relig)
fct_count(fct_lump(gss_cat$relig))
}
\keyword{datasets}
forcats/man/fct_rev.Rd 0000644 0001762 0000144 00000000536 13626051403 014411 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rev.R
\name{fct_rev}
\alias{fct_rev}
\title{Reverse order of factor levels}
\usage{
fct_rev(f)
}
\arguments{
\item{f}{A factor (or character vector).}
}
\description{
This is sometimes useful when plotting a factor.
}
\examples{
f <- factor(c("a", "b", "c"))
fct_rev(f)
}
forcats/man/fct_explicit_na.Rd 0000644 0001762 0000144 00000002065 14357073063 016123 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/explicit_na.R
\name{fct_explicit_na}
\alias{fct_explicit_na}
\title{Make missing values explicit}
\usage{
fct_explicit_na(f, na_level = "(Missing)")
}
\arguments{
\item{f}{A factor (or character vector).}
\item{na_level}{Level to use for missing values: this is what \code{NA}s will be
changed to.}
}
\description{
\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}}
This function is deprecated because the terminology is confusing;
please use \code{\link[=fct_na_value_to_level]{fct_na_value_to_level()}} instead.
This gives missing values an explicit factor level, ensuring that they
appear in summaries and on plots.
}
\examples{
f1 <- factor(c("a", "a", NA, NA, "a", "b", NA, "c", "a", "c", "b"))
fct_count(f1)
table(f1)
sum(is.na(f1))
# previously
f2 <- fct_explicit_na(f1)
# now
f2 <- fct_na_value_to_level(f1)
fct_count(f2)
table(f2)
sum(is.na(f2))
}
\keyword{internal}
forcats/man/fct_match.Rd 0000644 0001762 0000144 00000001607 14360012612 014704 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/match.R
\name{fct_match}
\alias{fct_match}
\title{Test for presence of levels in a factor}
\usage{
fct_match(f, lvls)
}
\arguments{
\item{f}{A factor (or character vector).}
\item{lvls}{A character vector specifying levels to look for.}
}
\value{
A logical vector
}
\description{
Do any of \code{lvls} occur in \code{f}? Compared to \link{\%in\%}, this function validates
\code{lvls} to ensure that they're actually present in \code{f}. In other words,
\code{x \%in\% "not present"} will return \code{FALSE}, but \code{fct_match(x, "not present")}
will throw an error.
}
\examples{
table(fct_match(gss_cat$marital, c("Married", "Divorced")))
# Compare to \%in\%, misspelled levels throw an error
table(gss_cat$marital \%in\% c("Maried", "Davorced"))
\dontrun{
table(fct_match(gss_cat$marital, c("Maried", "Davorced")))
}
}
forcats/man/forcats-package.Rd 0000644 0001762 0000144 00000001750 14241066126 016014 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/forcats-package.R
\docType{package}
\name{forcats-package}
\alias{forcats}
\alias{forcats-package}
\title{forcats: Tools for Working with Categorical Variables (Factors)}
\description{
\if{html}{\figure{logo.png}{options: style='float: right' alt='logo' width='120'}}
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').
}
\seealso{
Useful links:
\itemize{
\item \url{https://forcats.tidyverse.org/}
\item \url{https://github.com/tidyverse/forcats}
\item Report bugs at \url{https://github.com/tidyverse/forcats/issues}
}
}
\author{
\strong{Maintainer}: Hadley Wickham \email{hadley@rstudio.com}
Other contributors:
\itemize{
\item RStudio [copyright holder, funder]
}
}
\keyword{internal}
forcats/man/fct_unify.Rd 0000644 0001762 0000144 00000000750 13626051403 014745 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/c.R
\name{fct_unify}
\alias{fct_unify}
\title{Unify the levels in a list of factors}
\usage{
fct_unify(fs, levels = lvls_union(fs))
}
\arguments{
\item{fs}{A list of factors}
\item{levels}{Set of levels to apply to every factor. Default to
union of all factor levels}
}
\description{
Unify the levels in a list of factors
}
\examples{
fs <- list(factor("a"), factor("b"), factor(c("a", "b")))
fct_unify(fs)
}
forcats/man/fct_other.Rd 0000644 0001762 0000144 00000001671 14357100046 014737 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/other.R
\name{fct_other}
\alias{fct_other}
\title{Manually replace levels with "other"}
\usage{
fct_other(f, keep, drop, other_level = "Other")
}
\arguments{
\item{f}{A factor (or character vector).}
\item{keep, drop}{Pick one of \code{keep} and \code{drop}:
\itemize{
\item \code{keep} will preserve listed levels, replacing all others with
\code{other_level}.
\item \code{drop} will replace listed levels with \code{other_level}, keeping all
as is.
}}
\item{other_level}{Value of level used for "other" values. Always
placed at end of levels.}
}
\description{
Manually replace levels with "other"
}
\examples{
x <- factor(rep(LETTERS[1:9], times = c(40, 10, 5, 27, 1, 1, 1, 1, 1)))
fct_other(x, keep = c("A", "B"))
fct_other(x, drop = c("A", "B"))
}
\seealso{
\code{\link[=fct_lump]{fct_lump()}} to automatically convert the rarest (or most
common) levels to "other".
}
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forcats/man/pipe.Rd 0000644 0001762 0000144 00000000366 13626051403 013717 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{\%>\%}
\alias{\%>\%}
\title{Pipe operator}
\usage{
lhs \%>\% rhs
}
\description{
See \code{\link[magrittr]{\%>\%}} for more details.
}
\keyword{internal}
forcats/man/fct_drop.Rd 0000644 0001762 0000144 00000001401 14360012612 014544 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/drop.R
\name{fct_drop}
\alias{fct_drop}
\title{Drop unused levels}
\usage{
fct_drop(f, only = NULL)
}
\arguments{
\item{f}{A factor (or character vector).}
\item{only}{A character vector restricting the set of levels to be dropped.
If supplied, only levels that have no entries and appear in this vector
will be removed.}
}
\description{
Compared to \code{base::droplevels()}, does not drop \code{NA} levels that have values.
}
\examples{
f <- factor(c("a", "b"), levels = c("a", "b", "c"))
f
fct_drop(f)
# Set only to restrict which levels to drop
fct_drop(f, only = "a")
fct_drop(f, only = "c")
}
\seealso{
\code{\link[=fct_expand]{fct_expand()}} to add additional levels to a factor.
}
forcats/man/fct.Rd 0000644 0001762 0000144 00000002561 14241555020 013533 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fct.R
\name{fct}
\alias{fct}
\title{Create a factor}
\usage{
fct(x = character(), levels = NULL, na = character())
}
\arguments{
\item{x}{A character vector. Values must occur in either \code{levels} or \code{na}.}
\item{levels}{A character vector of known levels. If not supplied, will
be computed from the unique values of \code{x}, in the order in which they
occur.}
\item{na}{A character vector of values that should become missing values.}
}
\value{
A factor.
}
\description{
\code{fct()} is a stricter version of \code{\link[=factor]{factor()}} that errors if your
specification of \code{levels} is inconsistent with the values in \code{x}.
}
\examples{
# Use factors when you know the set of possible values a variable might take
x <- c("A", "O", "O", "AB", "A")
fct(x, levels = c("O", "A", "B", "AB"))
# If you don't specify the levels, fct will create from the data
# in the order that they're seen
fct(x)
# Differences with base R -----------------------------------------------
# factor() silently generates NAs
x <- c("a", "b", "c")
factor(x, levels = c("a", "b"))
# fct() errors
try(fct(x, levels = c("a", "b")))
# Unless you explicitly supply NA:
fct(x, levels = c("a", "b"), na = "c")
# factor() sorts default levels:
factor(c("y", "x"))
# fct() uses in order of appearance:
fct(c("y", "x"))
}
forcats/man/fct_cross.Rd 0000644 0001762 0000144 00000001565 14241066126 014753 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cross.R
\name{fct_cross}
\alias{fct_cross}
\title{Combine levels from two or more factors to create a new factor}
\usage{
fct_cross(..., sep = ":", keep_empty = FALSE)
}
\arguments{
\item{...}{<\code{\link[rlang:dyn-dots]{dynamic-dots}}> Additional factors
or character vectors.}
\item{sep}{A character string to separate the levels}
\item{keep_empty}{If TRUE, keep combinations with no observations as levels}
}
\value{
The new factor
}
\description{
Computes a factor whose levels are all the combinations of the levels of the input factors.
}
\examples{
fruit <- factor(c("apple", "kiwi", "apple", "apple"))
colour <- factor(c("green", "green", "red", "green"))
eaten <- c("yes", "no", "yes", "no")
fct_cross(fruit, colour)
fct_cross(fruit, colour, eaten)
fct_cross(fruit, colour, keep_empty = TRUE)
}
forcats/man/as_factor.Rd 0000644 0001762 0000144 00000002222 13626262603 014722 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/as_factor.R
\name{as_factor}
\alias{as_factor}
\alias{as_factor.factor}
\alias{as_factor.character}
\alias{as_factor.numeric}
\alias{as_factor.logical}
\title{Convert input to a factor}
\usage{
as_factor(x, ...)
\method{as_factor}{factor}(x, ...)
\method{as_factor}{character}(x, ...)
\method{as_factor}{numeric}(x, ...)
\method{as_factor}{logical}(x, ...)
}
\arguments{
\item{x}{Object to coerce to a factor.}
\item{...}{Other arguments passed down to method.}
}
\description{
Compared to base R, when \code{x} is a character, this function creates
levels in the order in which they appear, which will be the same on every
platform. (Base R sorts in the current locale which can vary from place
to place.) When \code{x} is numeric, the ordering is based on the numeric
value and consistent with base R.
}
\details{
This is a generic function.
}
\examples{
# Character object
x <- c("a", "z", "g")
as_factor(x)
as.factor(x)
# Character object containing numbers
y <- c("1.1", "11", "2.2", "22")
as_factor(y)
as.factor(y)
# Numeric object
z <- as.numeric(y)
as_factor(z)
as.factor(z)
}
forcats/man/lvls_union.Rd 0000644 0001762 0000144 00000000567 13626051403 015155 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lvls.R
\name{lvls_union}
\alias{lvls_union}
\title{Find all levels in a list of factors}
\usage{
lvls_union(fs)
}
\arguments{
\item{fs}{A list of factors.}
}
\description{
Find all levels in a list of factors
}
\examples{
fs <- list(factor("a"), factor("b"), factor(c("a", "b")))
lvls_union(fs)
}
forcats/man/fct_lump.Rd 0000644 0001762 0000144 00000006244 14357100046 014574 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lump.R
\name{fct_lump}
\alias{fct_lump}
\alias{fct_lump_min}
\alias{fct_lump_prop}
\alias{fct_lump_n}
\alias{fct_lump_lowfreq}
\title{Lump uncommon factor together levels into "other"}
\usage{
fct_lump(
f,
n,
prop,
w = NULL,
other_level = "Other",
ties.method = c("min", "average", "first", "last", "random", "max")
)
fct_lump_min(f, min, w = NULL, other_level = "Other")
fct_lump_prop(f, prop, w = NULL, other_level = "Other")
fct_lump_n(
f,
n,
w = NULL,
other_level = "Other",
ties.method = c("min", "average", "first", "last", "random", "max")
)
fct_lump_lowfreq(f, w = NULL, other_level = "Other")
}
\arguments{
\item{f}{A factor (or character vector).}
\item{n}{Positive \code{n} preserves the most common \code{n} values.
Negative \code{n} preserves the least common \code{-n} values.
It there are ties, you will get at least \code{abs(n)} values.}
\item{prop}{Positive \code{prop} lumps values which do not appear at least
\code{prop} of the time. Negative \code{prop} lumps values that
do not appear at most \code{-prop} of the time.}
\item{w}{An optional numeric vector giving weights for frequency of
each value (not level) in f.}
\item{other_level}{Value of level used for "other" values. Always
placed at end of levels.}
\item{ties.method}{A character string specifying how ties are
treated. See \code{\link[=rank]{rank()}} for details.}
\item{min}{Preserve levels that appear at least \code{min} number of times.}
}
\description{
A family for lumping together levels that meet some criteria.
\itemize{
\item \code{fct_lump_min()}: lumps levels that appear fewer than \code{min} times.
\item \code{fct_lump_prop()}: lumps levels that appear in fewer than (or equal to)
\code{prop * n} times.
\item \code{fct_lump_n()} lumps all levels except for the \code{n} most frequent
(or least frequent if \code{n < 0})
\item \code{fct_lump_lowfreq()} lumps together the least frequent levels, ensuring
that "other" is still the smallest level.
}
\code{fct_lump()} exists primarily for historical reasons, as it automatically
picks between these different methods depending on its arguments.
We no longer recommend that you use it.
}
\examples{
x <- factor(rep(LETTERS[1:9], times = c(40, 10, 5, 27, 1, 1, 1, 1, 1)))
x \%>\% table()
x \%>\%
fct_lump_n(3) \%>\%
table()
x \%>\%
fct_lump_prop(0.10) \%>\%
table()
x \%>\%
fct_lump_min(5) \%>\%
table()
x \%>\%
fct_lump_lowfreq() \%>\%
table()
x <- factor(letters[rpois(100, 5)])
x
table(x)
table(fct_lump_lowfreq(x))
# Use positive values to collapse the rarest
fct_lump_n(x, n = 3)
fct_lump_prop(x, prop = 0.1)
# Use negative values to collapse the most common
fct_lump_n(x, n = -3)
fct_lump_prop(x, prop = -0.1)
# Use weighted frequencies
w <- c(rep(2, 50), rep(1, 50))
fct_lump_n(x, n = 5, w = w)
# Use ties.method to control how tied factors are collapsed
fct_lump_n(x, n = 6)
fct_lump_n(x, n = 6, ties.method = "max")
# Use fct_lump_min() to lump together all levels with fewer than `n` values
table(fct_lump_min(x, min = 10))
table(fct_lump_min(x, min = 15))
}
\seealso{
\code{\link[=fct_other]{fct_other()}} to convert specified levels to other.
}
forcats/man/fct_unique.Rd 0000644 0001762 0000144 00000001505 14355354616 015134 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/unique.R
\name{fct_unique}
\alias{fct_unique}
\title{Unique values of a factor, as a factor}
\usage{
fct_unique(f)
}
\arguments{
\item{f}{A factor.}
}
\value{
A factor.
}
\description{
\code{fct_unique()} extracts the complete set of possible values from the
levels of the factor, rather than looking at the actual values, like
\code{\link[=unique]{unique()}}.
\code{fct_unique()} only uses the values of \code{f} in one way: it looks for
implicit missing values so that they can be included in the result.
}
\examples{
f <- fct(letters[rpois(100, 10)])
unique(f) # in order of appearance
fct_unique(f) # in order of levels
f <- fct(letters[rpois(100, 2)], letters[1:20])
unique(f) # levels that appear in data
fct_unique(f) # all possible levels
}
forcats/man/fct_recode.Rd 0000644 0001762 0000144 00000002234 13764157244 015070 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/recode.R
\name{fct_recode}
\alias{fct_recode}
\title{Change factor levels by hand}
\usage{
fct_recode(.f, ...)
}
\arguments{
\item{.f}{A factor (or character vector).}
\item{...}{<\code{\link[rlang:dyn-dots]{dynamic-dots}}> A sequence of named character
vectors where the name gives the new level, and the value gives the old
level. Levels not otherwise mentioned will be left as is. Levels can
be removed by naming them \code{NULL}.}
}
\description{
Change factor levels by hand
}
\examples{
x <- factor(c("apple", "bear", "banana", "dear"))
fct_recode(x, fruit = "apple", fruit = "banana")
# If you make a mistake you'll get a warning
fct_recode(x, fruit = "apple", fruit = "bananana")
# If you name the level NULL it will be removed
fct_recode(x, NULL = "apple", fruit = "banana")
# Wrap the left hand side in quotes if it contains special variables
fct_recode(x, "an apple" = "apple", "a bear" = "bear")
# When passing a named vector to rename levels use !!! to splice
x <- factor(c("apple", "bear", "banana", "dear"))
levels <- c(fruit = "apple", fruit = "banana")
fct_recode(x, !!!levels)
}
forcats/man/fct_shift.Rd 0000644 0001762 0000144 00000001224 13626051403 014725 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/shift.R
\name{fct_shift}
\alias{fct_shift}
\title{Shift factor levels to left or right, wrapping around at end}
\usage{
fct_shift(f, n = 1L)
}
\arguments{
\item{f}{A factor.}
\item{n}{Positive values shift to the left; negative values shift to
the right.}
}
\description{
This is useful when the levels of an ordered factor are actually cyclical,
with different conventions on the starting point.
}
\examples{
x <- factor(
c("Mon", "Tue", "Wed"),
levels = c("Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"),
ordered = TRUE
)
x
fct_shift(x)
fct_shift(x, 2)
fct_shift(x, -1)
}
forcats/man/fct_reorder.Rd 0000644 0001762 0000144 00000005470 14360012625 015260 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/reorder.R
\name{fct_reorder}
\alias{fct_reorder}
\alias{fct_reorder2}
\alias{last2}
\alias{first2}
\title{Reorder factor levels by sorting along another variable}
\usage{
fct_reorder(
.f,
.x,
.fun = median,
...,
.na_rm = NULL,
.default = Inf,
.desc = FALSE
)
fct_reorder2(
.f,
.x,
.y,
.fun = last2,
...,
.na_rm = NULL,
.default = -Inf,
.desc = TRUE
)
last2(.x, .y)
first2(.x, .y)
}
\arguments{
\item{.f}{A factor (or character vector).}
\item{.x, .y}{The levels of \code{f} are reordered so that the values
of \code{.fun(.x)} (for \code{fct_reorder()}) and \code{fun(.x, .y)} (for \code{fct_reorder2()})
are in ascending order.}
\item{.fun}{n summary function. It should take one vector for
\code{fct_reorder}, and two vectors for \code{fct_reorder2}, and return a single
value.}
\item{...}{Other arguments passed on to \code{.fun}.}
\item{.na_rm}{Should \code{fct_reorder()} remove missing values?
If \code{NULL}, the default, will remove missing values with a warning.
Set to \code{FALSE} to preserve \code{NA}s (if you \code{.fun} already handles them) and
\code{TRUE} to remove silently.}
\item{.default}{What default value should we use for \code{.fun} for
empty levels? Use this to control where empty levels appear in the
output.}
\item{.desc}{Order in descending order? Note the default is different
between \code{fct_reorder} and \code{fct_reorder2}, in order to
match the default ordering of factors in the legend.}
}
\description{
\code{fct_reorder()} is useful for 1d displays where the factor is mapped to
position; \code{fct_reorder2()} for 2d displays where the factor is mapped to
a non-position aesthetic. \code{last2()} and \code{first2()} are helpers for \code{fct_reorder2()};
\code{last2()} finds the last value of \code{y} when sorted by \code{x}; \code{first2()} finds the first value.
}
\examples{
# fct_reorder() -------------------------------------------------------------
# Useful when a categorical variable is mapped to position
boxplot(Sepal.Width ~ Species, data = iris)
boxplot(Sepal.Width ~ fct_reorder(Species, Sepal.Width), data = iris)
# or with
library(ggplot2)
ggplot(iris, aes(fct_reorder(Species, Sepal.Width), Sepal.Width)) +
geom_boxplot()
# fct_reorder2() -------------------------------------------------------------
# Useful when a categorical variable is mapped to color, size, shape etc
chks <- subset(ChickWeight, as.integer(Chick) < 10)
chks <- transform(chks, Chick = fct_shuffle(Chick))
# Without reordering it's hard to match line to legend
ggplot(chks, aes(Time, weight, colour = Chick)) +
geom_point() +
geom_line()
# With reordering it's much easier
ggplot(chks, aes(Time, weight, colour = fct_reorder2(Chick, Time, weight))) +
geom_point() +
geom_line() +
labs(colour = "Chick")
}
forcats/man/fct_na_value_to_level.Rd 0000644 0001762 0000144 00000003240 14357073063 017303 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/na.R
\name{fct_na_value_to_level}
\alias{fct_na_value_to_level}
\alias{fct_na_level_to_value}
\title{Convert between \code{NA} values and \code{NA} levels}
\usage{
fct_na_value_to_level(f, level = NA)
fct_na_level_to_value(f, extra_levels = NULL)
}
\arguments{
\item{f}{A factor (or character vector).}
\item{level}{Optionally, instead of converting the \code{NA} values to an
\code{NA} level, convert it to a level with this value.}
\item{extra_levels}{Optionally, a character vector giving additional levels
that should also be converted to \code{NA} values.}
}
\description{
There are two ways to represent missing values in factors: in the values
and in the levels. \code{NA}s in the values are most useful for data analysis
(since \code{\link[=is.na]{is.na()}} returns what you expect), but because the \code{NA} is not
explicitly recorded in the levels, there's no way to control its position
(it's almost always displayed last or not at all). Putting the \code{NA}s in the levels allows
you to control its display, at the cost of losing accurate \code{is.na()}
reporting.
(It is possible to have a factor with missing values in both the values
and the levels but it requires some explicit gymnastics and we don't
recommend it.)
}
\examples{
# Most factors store NAs in the values:
f1 <- fct(c("a", "b", NA, "c", "b", NA))
levels(f1)
as.integer(f1)
is.na(f1)
# But it's also possible to store them in the levels
f2 <- fct_na_value_to_level(f1)
levels(f2)
as.integer(f2)
is.na(f2)
# If needed, you can convert back to NAs in the values:
f3 <- fct_na_level_to_value(f2)
levels(f3)
as.integer(f3)
is.na(f3)
}
forcats/man/fct_expand.Rd 0000644 0001762 0000144 00000001303 14355354616 015101 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/expand.R
\name{fct_expand}
\alias{fct_expand}
\title{Add additional levels to a factor}
\usage{
fct_expand(f, ..., after = Inf)
}
\arguments{
\item{f}{A factor (or character vector).}
\item{...}{Additional levels to add to the factor. Levels that already
exist will be silently ignored.}
\item{after}{Where should the new values be placed?}
}
\description{
Add additional levels to a factor
}
\examples{
f <- factor(sample(letters[1:3], 20, replace = TRUE))
f
fct_expand(f, "d", "e", "f")
fct_expand(f, letters[1:6])
fct_expand(f, "Z", after = 0)
}
\seealso{
\code{\link[=fct_drop]{fct_drop()}} to drop unused factor levels.
}
forcats/man/fct_relevel.Rd 0000644 0001762 0000144 00000003012 14241066126 015245 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/relevel.R
\name{fct_relevel}
\alias{fct_relevel}
\title{Reorder factor levels by hand}
\usage{
fct_relevel(.f, ..., after = 0L)
}
\arguments{
\item{.f}{A factor (or character vector).}
\item{...}{Either a function (or formula), or character levels.
A function will be called with the current levels as input, and the
return value (which must be a character vector) will be used to relevel
the factor.
Any levels not mentioned will be left in their existing order, by default
after the explicitly mentioned levels. Supports tidy dots.}
\item{after}{Where should the new values be placed?}
}
\description{
This is a generalisation of \code{\link[stats:relevel]{stats::relevel()}} that allows you to move any
number of levels to any location.
}
\examples{
f <- factor(c("a", "b", "c", "d"), levels = c("b", "c", "d", "a"))
fct_relevel(f)
fct_relevel(f, "a")
fct_relevel(f, "b", "a")
# Move to the third position
fct_relevel(f, "a", after = 2)
# Relevel to the end
fct_relevel(f, "a", after = Inf)
fct_relevel(f, "a", after = 3)
# Relevel with a function
fct_relevel(f, sort)
fct_relevel(f, sample)
fct_relevel(f, rev)
# Using 'Inf' allows you to relevel to the end when the number
# of levels is unknown or variable (e.g. vectorised operations)
df <- forcats::gss_cat[, c("rincome", "denom")]
lapply(df, levels)
df2 <- lapply(df, fct_relevel, "Don't know", after = Inf)
lapply(df2, levels)
# You'll get a warning if the levels don't exist
fct_relevel(f, "e")
}
forcats/man/fct_shuffle.Rd 0000644 0001762 0000144 00000000557 13626051403 015254 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/shuffle.R
\name{fct_shuffle}
\alias{fct_shuffle}
\title{Randomly permute factor levels}
\usage{
fct_shuffle(f)
}
\arguments{
\item{f}{A factor (or character vector).}
}
\description{
Randomly permute factor levels
}
\examples{
f <- factor(c("a", "b", "c"))
fct_shuffle(f)
fct_shuffle(f)
}
forcats/man/fct_count.Rd 0000644 0001762 0000144 00000001300 13626051403 014733 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/count.R
\name{fct_count}
\alias{fct_count}
\title{Count entries in a factor}
\usage{
fct_count(f, sort = FALSE, prop = FALSE)
}
\arguments{
\item{f}{A factor (or character vector).}
\item{sort}{If \code{TRUE}, sort the result so that the most common values
float to the top.}
\item{prop}{If \code{TRUE}, compute the fraction of marginal table.}
}
\value{
A tibble with columns \code{f}, \code{n} and \code{p}, if prop is \code{TRUE}.
}
\description{
Count entries in a factor
}
\examples{
f <- factor(sample(letters)[rpois(1000, 10)])
table(f)
fct_count(f)
fct_count(f, sort = TRUE)
fct_count(f, sort = TRUE, prop = TRUE)
}
forcats/DESCRIPTION 0000644 0001762 0000144 00000002402 14365570422 013427 0 ustar ligges users Package: forcats
Title: Tools for Working with Categorical Variables (Factors)
Version: 1.0.0
Authors@R: c(
person("Hadley", "Wickham", , "hadley@rstudio.com", role = c("aut", "cre")),
person("RStudio", role = c("cph", "fnd"))
)
Description: 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').
License: MIT + file LICENSE
URL: https://forcats.tidyverse.org/,
https://github.com/tidyverse/forcats
BugReports: https://github.com/tidyverse/forcats/issues
Depends: R (>= 3.4)
Imports: cli (>= 3.4.0), glue, lifecycle, magrittr, rlang (>= 1.0.0),
tibble
Suggests: covr, dplyr, ggplot2, knitr, readr, rmarkdown, testthat (>=
3.0.0), withr
VignetteBuilder: knitr
Config/Needs/website: tidyverse/tidytemplate
Config/testthat/edition: 3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
NeedsCompilation: no
Packaged: 2023-01-27 14:11:11 UTC; hadleywickham
Author: Hadley Wickham [aut, cre],
RStudio [cph, fnd]
Maintainer: Hadley Wickham
Repository: CRAN
Date/Publication: 2023-01-29 22:20:02 UTC
forcats/build/ 0000755 0001762 0000144 00000000000 14364755577 013041 5 ustar ligges users forcats/build/vignette.rds 0000644 0001762 0000144 00000000323 14364755577 015376 0 ustar ligges users b```b`abd`b2 1# 'N/JN,) MA+)O)M.S(W*ES3!$7Mn:%` `aBRȚn2KjAj^
H vѴpxVaaqIY0AAn0 Ez0?½Ht&${+%$Q/ n ̜/6 forcats/tests/ 0000755 0001762 0000144 00000000000 14364755572 013077 5 ustar ligges users forcats/tests/testthat/ 0000755 0001762 0000144 00000000000 14365570422 014725 5 ustar ligges users forcats/tests/testthat/test-fct.R 0000644 0001762 0000144 00000001663 14241555020 016575 0 ustar ligges users test_that("can create simple example", {
expect_equal(
fct(c("x", "y", "z")),
factor(c("x", "y", "z"))
)
})
test_that("orders by appearance", {
expect_equal(
fct(c("y", "x")),
factor(c("y", "x"), levels = c("y", "x"))
)
})
test_that("checks input types", {
expect_snapshot(error = TRUE, {
fct(1:3)
fct("x", 1:3)
fct("x", "y", na = 1)
})
})
test_that("clear error if levels are incomplete", {
expect_snapshot(error = TRUE,
fct(c("x", "y", "z"), c("x", "y"))
)
})
test_that("can covert values to implicit or explcit NA", {
expect_equal(
fct(c("x", "y", "z"), na = "z"),
factor(c("x", "y", NA), levels = c("x", "y"))
)
expect_equal(
fct(c("x", "y", "z"), c("x", "y"), na = "z"),
factor(c("x", "y", NA), levels = c("x", "y"))
)
expect_equal(
fct(c("x", "y", "z"), c("x", "y", NA), na = "z"),
factor(c("x", "y", NA), levels = c("x", "y", NA), exclude = NULL)
)
})
forcats/tests/testthat/test-match.R 0000644 0001762 0000144 00000000661 14360012612 017107 0 ustar ligges users test_that("equivalent to %in% when levels present", {
f <- factor(c("a", "b", "c", NA))
expect_equal(fct_match(f, "a"), f %in% "a")
expect_equal(fct_match(f, NA), f %in% NA)
})
test_that("error when levels are missing", {
f <- factor(c("a", "b", "c"))
expect_snapshot(fct_match(f, "d"), error = TRUE)
})
test_that("validates its inputs", {
expect_snapshot(error = TRUE, {
fct_match(1)
fct_match("x", 1)
})
})
forcats/tests/testthat/test-relevel.R 0000644 0001762 0000144 00000002127 14355407761 017471 0 ustar ligges users test_that("warns about unknown levels", {
f1 <- factor(c("a", "b"))
expect_snapshot(f2 <- fct_relevel(f1, "d"))
expect_equal(levels(f2), levels(f1))
})
test_that("moves supplied levels to front", {
f1 <- factor(c("a", "b", "c", "d"))
f2 <- fct_relevel(f1, "c", "b")
expect_equal(levels(f2), c("c", "b", "a", "d"))
})
test_that("can moves supplied levels to end", {
f1 <- factor(c("a", "b", "c", "d"))
f2 <- fct_relevel(f1, "a", "b", after = 2)
f3 <- fct_relevel(f1, "a", "b", after = Inf)
expect_equal(levels(f2), c("c", "d", "a", "b"))
expect_equal(levels(f3), c("c", "d", "a", "b"))
})
test_that("can relevel with function ", {
f1 <- fct_rev(factor(c("a", "b")))
f2a <- fct_relevel(f1, rev)
f2b <- fct_relevel(f1, ~ rev(.))
expect_equal(levels(f2a), c("a", "b"))
expect_equal(levels(f2b), c("a", "b"))
})
test_that("function must return character vector", {
f <- factor(c("a", "b"))
expect_error(fct_relevel(f, ~1), "character vector")
})
test_that("dots must be unnamed", {
f <- fct(c("a", "b", "c"))
expect_snapshot(fct_relevel(f, a = "b"), error = TRUE)
})
forcats/tests/testthat/test-lump.R 0000644 0001762 0000144 00000013245 14357077664 017022 0 ustar ligges users # common behaviour ---------------------------------------------------------
test_that("can use other_level = NA", {
f <- fct(c("a", "a", "a", "a", "b"))
expect_equal(levels(fct_lump_lowfreq(f, other_level = NA)), c("a", NA))
expect_equal(levels(fct_lump_n(f, n = 1, other_level = NA)), c("a", NA))
expect_equal(levels(fct_lump_prop(f, prop = .2, other_level = NA)), c("a", NA))
expect_equal(levels(fct_lump_min(f, 2, other_level = NA)), c("a", NA))
})
test_that("bad weights generates friendly error messages", {
expect_snapshot(error = TRUE, {
fct_lump(letters, w = letters)
fct_lump(letters, w = 1:10)
fct_lump(letters, w = c(-1, rep(1, 24), -1))
})
})
# fct_lump() ----------------------------------------------------------------
test_that("can only supply one of n and prop", {
f <- c("a", "b", "c")
expect_snapshot(fct_lump(f, n = 1, prop = 0.1), error = TRUE)
})
# fct_lump_min ------------------------------------------------------------
test_that("fct_lump_min works when not weighted", {
f <- c("a", "a", "a", "b", "b", "c", "d", "e", "f", "g")
expect_equal(levels(fct_lump_min(f, min = 3)), c("a", "Other"))
expect_equal(levels(fct_lump_min(f, min = 2)), c("a", "b", "Other"))
})
test_that("fct_lump_min works when weighted", {
f <- c("a", "b", "c", "d", "e")
w <- c( 0.2, 2, 6, 4, 1)
expect_equal(levels(fct_lump_min(f, min = 6, w = w)), c("c", "Other"))
expect_equal(levels(fct_lump_min(f, min = 1.5, w = w)), c("b", "c", "d", "Other"))
})
test_that("checks inputs", {
expect_snapshot(error = TRUE, {
fct_lump_min(1:3)
fct_lump_min(factor(), min = "x")
})
})
# fct_lump_n() ------------------------------------------------------------
test_that("can keep/drop with positive/negative values", {
f <- c("a", "a", "a", "b", "b", "c", "d", "e", "f", "g")
expect_equal(levels(fct_lump_n(f, n = 1)), c("a", "Other"))
expect_equal(levels(fct_lump_n(f, n = 2)), c("a", "b", "Other"))
f <- c("a", "a", "a", "a", "b", "b", "b", "b", "c", "d")
expect_equal(levels(fct_lump_n(f, n = -1)), c("c", "d", "Other"))
})
test_that("ties are respected and can be controled", {
f <- c("a", "a", "a", "b", "b", "c", "d", "e", "f", "g")
expect_equal(levels(fct_lump_n(f, 2)), c("a", "b", "Other"))
expect_equal(
levels(fct_lump_n(f, n = 4, ties.method = "min")),
c("a", "b", "c", "d", "e", "f", "g")
)
expect_equal(
levels(fct_lump_n(f, n = 4, ties.method = "max")),
c("a", "b", "Other")
)
})
test_that("idempotent if all element satisfies condition", {
f <- c("a", "a", "a", "b", "b", "c", "d", "e", "f", "g")
expect_equal(levels(fct_lump_n(f, n = 10)), c("a", "b", "c", "d", "e", "f", "g"))
expect_equal(levels(fct_lump_n(f, n = -10)), c("a", "b", "c", "d", "e", "f", "g"))
})
test_that("can supply weights", {
f <- c("a", "b", "c", "d", "e")
w <- c( 0.2, 2, 6, 4, 1)
expect_equal(levels(fct_lump_n(f, n = 2, w = w)), c("c", "d", "Other"))
expect_equal(levels(fct_lump_n(f, n = 3, w = w)), c("b", "c", "d", "Other"))
})
test_that("checks inputs", {
expect_snapshot(error = TRUE, {
fct_lump_n(1:3)
fct_lump_n(factor(), n = "x")
})
})
# fct_lump_prop -----------------------------------------------------------
test_that("positive/negative prop keeps/drops most commmon", {
f <- c("a", "a", "a", "b", "b", "c", "d", "e", "f", "g")
expect_equal(levels(fct_lump_prop(f, prop = 0.25)), c("a", "Other"))
expect_equal(levels(fct_lump_prop(f, prop = 0.15)), c("a", "b", "Other"))
f <- c("a", "a", "a", "a", "b", "b", "b", "b", "c", "d")
expect_equal(levels(fct_lump(f, n = -1)), c("c", "d", "Other"))
expect_equal(levels(fct_lump(f, prop = -0.2)), c("c", "d", "Other"))
})
test_that("can use weights", {
f <- c("a", "b", "c", "d", "e")
w <- c( 0.2, 2, 6, 4, 1)
expect_equal(levels(fct_lump_prop(f, prop = 0.3, w = w)), c("c", "d", "Other"))
expect_equal(levels(fct_lump_prop(f, prop = 0.2, w = w)), c("c", "d", "Other"))
})
test_that("can use weights with empty levels", {
f <- factor(c("a", "a", "b", "c"), levels = c("a", "b", "c", "d"))
expect_equal(
fct_lump_prop(f, prop = 0.25, w = rep(1, 4)),
fct(c("a", "a", "Other", "Other"))
)
})
test_that("NAs included in total", {
f <- factor(c("a", "a", "b", "c", rep(NA, 7)))
o1 <- fct_lump_prop(f, prop = 0.10)
expect_equal(levels(o1), c("a", "Other"))
o2 <- fct_lump_prop(f, w = rep(1, 11), prop = 0.10)
expect_equal(levels(o2), c("a", "Other"))
})
test_that("idempotent if element satisfy n condition", {
f <- c("a", "a", "a", "b", "b", "c", "d", "e", "f", "g")
expect_equal(levels(fct_lump_prop(f, prop = 0.01)), c("a", "b", "c", "d", "e", "f", "g"))
expect_equal(levels(fct_lump_prop(f, prop = -1)), c("a", "b", "c", "d", "e", "f", "g"))
})
test_that("checks inputs", {
expect_snapshot(error = TRUE, {
fct_lump_prop(1:3)
fct_lump_prop(factor(), prop = "x")
})
})
# fct_lump_lowfreq() -----------------------------------------------------------
test_that("only have one small other level", {
f <- c("a", "a", "a", "a", "b", "b", "b", "c", "c", "d")
expect_equal(levels(fct_lump(f)), c("a", "b", "c", "Other"))
})
test_that("lumps smallest", {
lump_test <- function(x) {
paste(ifelse(in_smallest(x), "X", letters[seq_along(x)]), collapse = "")
}
# smallest
expect_equal(lump_test(c(1, 2, 3, 6)), "Xbcd")
expect_equal(lump_test(c(1, 2, 3, 7)), "XXXd")
expect_equal(lump_test(c(1, 2, 3, 7, 13)), "XXXde")
expect_equal(lump_test(c(1, 2, 3, 7, 14)), "XXXXe")
# doesn't lump if none small enough
expect_equal(lump_test(c(2, 2, 4)), "abc")
# order doesn't matter
expect_equal(lump_test(c(2, 2, 5)), "XXc")
expect_equal(lump_test(c(2, 5, 2)), "XbX")
expect_equal(lump_test(c(5, 2, 2)), "aXX")
})
forcats/tests/testthat/test-anon.R 0000644 0001762 0000144 00000000626 14360012612 016747 0 ustar ligges users test_that("new levels are padded numerics", {
f1 <- factor(letters[1:10])
f2 <- fct_anon(f1)
expect_equal(levels(f2), sprintf("%02d", 1:10))
})
test_that("prefix added to start of level", {
f1 <- factor("x")
f2 <- fct_anon(f1, prefix = "X")
expect_equal(levels(f2), "X1")
})
test_that("validates its inputs", {
expect_snapshot(error = TRUE, {
fct_anon(1)
fct_anon("x", 1)
})
})
forcats/tests/testthat/test-collapse.R 0000644 0001762 0000144 00000003717 14360012612 017622 0 ustar ligges users test_that("can collapse multiple values", {
f1 <- factor(letters[1:3])
f2 <- fct_collapse(f1, x = c("a", "b"), y = "c")
expect_equal(f2, factor(c("x", "x", "y")))
})
test_that("empty dots yields unchanged factor", {
f1 <- factor(letters[1:3])
f2 <- fct_collapse(f1)
expect_identical(f1, f2)
})
test_that("can collapse missing levels", {
f1 <- factor(c("x", NA), exclude = NULL)
f2 <- fct_collapse(f1, y = NA_character_)
expect_equal(f2, factor(c("x", "y")))
})
test_that("can collapse un-named levels", {
f <- factor(letters[1:3])
expect_equal(
fct_collapse(f, xy = c("a", "b"), other_level = "Other"),
fct(c("xy", "xy", "Other"), levels = c("xy", "Other"))
)
expect_equal(
fct_collapse(f, xy = c("a", "b"), other_level = NA),
fct(c("xy", "xy", NA), levels = c("xy", NA))
)
})
test_that("collapses levels correctly when group_other is TRUE but no other variables to group", {
f1 <- factor(letters[1:4])
f2 <- fct_collapse(f1, x1 = c("a", "b", "d"), x2 = "c", other_level = "Other")
expect_equal(f2, factor(c("x1", "x1", "x2", "x1"), levels = c("x1", "x2")))
})
test_that("collapses levels correctly when group_other is TRUE and some Other variables to group", {
f1 <- factor(letters[1:4])
f2 <- fct_collapse(f1, x1 = c("a", "d"), x2 = "c", other_level = "Other")
expect_equal(f2, factor(c("x1", "Other", "x2", "x1"), levels = c("x1", "x2", "Other")))
})
test_that("does not automatically collapse unnamed levels to Other", {
f1 <- factor(letters[1:3])
f2 <- fct_collapse(f1, xy = c("a", "b"))
expect_equal(f2, factor(c("xy", "xy", "c"), levels = c("xy", "c")))
})
test_that("group_other is deprecated", {
f1 <- factor(letters[1:4])
expect_snapshot(
f2 <- fct_collapse(f1, x1 = c("a", "d"), x2 = "c", group_other = TRUE)
)
expect_equal(levels(f2), c("x1", "x2", "Other"))
})
test_that("valdiates inputs", {
expect_snapshot(error = TRUE, {
fct_collapse(1)
fct_collapse("x", other_level = 1)
})
})
forcats/tests/testthat/test-drop.R 0000644 0001762 0000144 00000001330 14360012612 016751 0 ustar ligges users test_that("doesn't add NA level", {
f1 <- factor(c("a", NA), levels = c("a", "b"))
f2 <- fct_drop(f1)
expect_equal(levels(f2), "a")
})
test_that("can optionally restrict levels to drop", {
f1 <- factor("a", levels = c("a", "b", "c"))
expect_equal(levels(fct_drop(f1, only = "b")), c("a", "c"))
expect_equal(levels(fct_drop(f1, only = "a")), c("a", "b", "c"))
})
test_that("preserves ordered class and attributes", {
f1 <- ordered(letters[1:2], letters[1:3])
attr(f1, "x") <- "test"
f2 <- fct_drop(f1)
expect_s3_class(f2, "ordered")
expect_equal(attr(f2, "x"), attr(f1, "x"))
})
test_that("validates its inputs", {
expect_snapshot(error = TRUE, {
fct_drop(1)
fct_drop("x", only = 1)
})
})
forcats/tests/testthat/test-relabel.R 0000644 0001762 0000144 00000001673 14241066126 017434 0 ustar ligges users test_that("identity", {
f1 <- factor(c("a", "b"))
expect_identical(fct_relabel(f1, identity), f1)
})
test_that("gives useful errors", {
f1 <- factor("a")
expect_snapshot(error = TRUE, {
fct_relabel(f1, 1)
fct_relabel(f1, function(x) 1)
fct_relabel(f1, function(x) x[-1])
})
})
test_that("total collapse", {
f1 <- factor(letters)
new_levels <- function(x) rep("1", length(x))
expect_identical(fct_relabel(f1, new_levels), factor(new_levels(letters)))
})
test_that("additional arguments", {
f1 <- factor(letters)
expect_identical(fct_relabel(f1, paste0, "."), factor(paste0(letters, ".")))
})
test_that("formulas are coerced to functions", {
f1 <- factor(letters)
expect_identical(
fct_relabel(f1, ~ paste0(.x, ".")),
factor(paste0(letters, "."))
)
})
test_that("string input is coerced to a factor", {
expect_identical(
fct_relabel(LETTERS[1:2], function(x) x),
factor(LETTERS[1:2])
)
})
forcats/tests/testthat/test-count.R 0000644 0001762 0000144 00000002112 14360012612 017134 0 ustar ligges users test_that("0 count for empty levels", {
f <- factor(levels = c("a", "b"))
expect_equal(fct_count(f)$n, c(0, 0))
f <- factor("a", levels = c("a", "b", "c"))
expect_equal(fct_count(f)$n, c(1, 0, 0))
})
test_that("counts NA values and levels", {
f1 <- factor(c("a", "a", NA))
expect_equal(
fct_count(f1),
tibble::tibble(
f = fct(c("a", NA)),
n = c(2, 1)
)
)
f2 <- factor(c("a", "a", NA), exclude = NULL)
expect_equal(
fct_count(f2),
tibble::tibble(
f = fct(c("a", NA), c("a", NA)),
n = c(2, 1)
)
)
})
test_that("returns marginal table", {
f <- factor(c("a", "a", "b"))
out <- fct_count(f, prop = TRUE)
expect_equal(out$n, c(2, 1))
expect_equal(out$p, c(2, 1) / 3)
})
test_that("sort = TRUE brings most frequent values to top", {
f <- factor(c("a", "b", "b"))
out <- fct_count(f, sort = TRUE)
expect_equal(out$f, factor(c("b", "a"), levels = c("a", "b")))
})
test_that("validates its inputs", {
expect_snapshot(error = TRUE, {
fct_count(1)
fct_count("x", sort = 1)
fct_count("x", prop = 1)
})
})
forcats/tests/testthat/test-c.R 0000644 0001762 0000144 00000001000 14360012612 016221 0 ustar ligges users test_that("uses tidy_dots", {
fs <- list(factor("a"), factor("b"))
fab <- factor(c("a", "b"))
expect_equal(fct_c(!!!fs), fab)
expect_equal(fct_c(fs[[1]], fs[[2]]), fab)
})
test_that("all inputs must be factors", {
expect_error(fct_c("a"), "must be factors")
})
test_that("empty input yields empty factor", {
expect_equal(fct_c(), factor())
})
test_that("validates inputs", {
expect_snapshot(error = TRUE, {
fct_c(1)
fct_unify(list(1))
fct_unify(list(factor()), levels = 1)
})
})
forcats/tests/testthat/test-other.R 0000644 0001762 0000144 00000002004 14360012612 017125 0 ustar ligges users test_that("keeps levels in keep", {
x1 <- factor(c("a", "b"))
x2 <- fct_other(x1, keep = "a")
expect_equal(levels(x2), c("a", "Other"))
})
test_that("drops levels in drop", {
x1 <- factor(c("a", "b"))
x2 <- fct_other(x1, drop = "a")
# other always placed at end
expect_equal(levels(x2), c("b", "Other"))
})
test_that("works without warning if no levels replaced", {
x <- factor("a")
expect_no_warning(fct_other(x, keep = "a"))
expect_no_warning(fct_other(x, drop = "b"))
})
test_that("can use NA as other level", {
f <- fct(c("a", "b"))
expect_equal(
fct_other(f, keep = "a", other_level = NA),
fct(c("a", NA), levels = c("a", NA))
)
expect_equal(
levels(lvls_other(f, c(TRUE, FALSE), other_level = NA)),
c("a", NA)
)
})
test_that("validates its inputs", {
f <- factor(c("a", "b"))
expect_snapshot(error = TRUE, {
fct_other(1)
fct_other(f)
fct_other(f, keep = "a", drop = "a")
fct_other(f, keep = 1)
fct_other(f, keep = "a", other_level = 1)
})
})
forcats/tests/testthat/test-reorder.R 0000644 0001762 0000144 00000014364 14360012623 017464 0 ustar ligges users # fct_reorder -------------------------------------------------------------
test_that("can reorder can control ordering", {
f <- c("a", "a", "b", "b", "b")
x <- c(3, 3, 2, 2, 1)
f1 <- fct_reorder(f, x)
expect_equal(levels(f1), c("b", "a"))
f2 <- fct_reorder(f, x, .desc = TRUE)
expect_equal(levels(f2), c("a", "b"))
})
test_that("automatically removes missing values with a warning", {
f1 <- fct(c("a", "b", "c", "c"))
x <- c(3, 2, 1, NA)
expect_snapshot(f2 <- fct_reorder(f1, x))
expect_equal(levels(f2), c("c", "b", "a"))
expect_no_warning(fct_reorder(f1, x, .na_rm = TRUE))
expect_no_warning(f3 <- fct_reorder(f1, x, .na_rm = FALSE))
expect_equal(levels(f3), c("b", "a", "c"))
})
test_that("can control the placement of empty levels", {
f1 <- fct(c("a", "b", "c"), letters[1:4])
x <- c(1, 2, 3)
f2 <- fct_reorder(f1, x, .default = -Inf)
expect_equal(levels(f2), c("d", "a", "b", "c"))
})
test_that("can control the placement of levels with all missing data", {
f1 <- fct(c("a", "b", "c"))
x <- c(1, 2, NA)
f2 <- fct_reorder(f1, x, .na_rm = TRUE, .default = -Inf)
expect_equal(levels(f2), c("c", "a", "b"))
})
test_that("fct_reorder() complains if summary doesn't return single value", {
expect_snapshot(error = TRUE, {
fct_reorder("a", 1, function(x) c(1, 2))
})
})
test_that("fct_reorder() validates its inputs", {
expect_snapshot(error = TRUE, {
fct_reorder(1)
fct_reorder("x", 1, 1)
fct_reorder("x", 1, .na_rm = 1)
fct_reorder("x", 1, .desc = 1)
})
})
# fct_reorder2 ------------------------------------------------------------
test_that("can reorder by 2d summary", {
df <- tibble::tribble(
~g, ~x, ~y,
"a", 1, 10,
"a", 2, 5,
"b", 1, 5,
"b", 2, 10
)
f1 <- fct_reorder2(df$g, df$x, df$y)
expect_equal(levels(f1), c("b", "a"))
f2 <- fct_reorder(df$g, df$x, .desc = TRUE)
expect_equal(levels(f2), c("a", "b"))
})
test_that("fct_reorder2() automatically removes missing values with a warning", {
f1 <- fct(c("a", "b", "c", "c"))
x <- c(1, 1, 1, 2)
y <- c(1, 2, 3, NA)
expect_snapshot(f2 <- fct_reorder2(f1, x, y))
expect_equal(levels(f2), c("c", "b", "a"))
expect_no_warning(fct_reorder2(f1, x, y, .na_rm = TRUE))
# unlike fct_reorder() the default summary function can handle missing values
expect_no_warning(f3 <- fct_reorder2(f1, x, y, .na_rm = FALSE))
expect_equal(levels(f2), c("c", "b", "a"))
})
test_that("can control the placement of empty levels", {
f1 <- fct(c("a", "b", "c"), letters[1:4])
x <- c(1, 2, 3)
f2 <- fct_reorder(f1, x, .default = -Inf)
expect_equal(levels(f2), c("d", "a", "b", "c"))
})
test_that("missing groups appear at end by default", {
df <- tibble::tribble(
~g, ~x, ~y,
"a", NA, NA,
"b", 1, 10,
)
f1 <- fct_reorder2(df$g, df$x, df$y, .na_rm = TRUE)
expect_equal(levels(f1), c("b", "a"))
f2 <- fct_reorder2(df$g, df$x, df$y, .default = Inf, .na_rm = TRUE)
expect_equal(levels(f2), c("a", "b"))
})
test_that("fct_reorder2() complains if summary doesn't return single value", {
expect_snapshot(error = TRUE, {
fct_reorder2("a", 1, 1, function(x, y) c(1, 2))
})
})
test_that("first2/last2 return expected values", {
expect_equal(first2(4:1, 1:4), 4)
expect_equal(last2(4:1, 1:4), 1)
})
test_that("last2 ignores points where either value is missing", {
expect_equal(last2(1:4, c(1:3, NA)), 3)
expect_equal(last2(c(1:3, NA), 1:4), 3)
})
test_that("last2 returns NA if no non-missing pairs", {
expect_equal(last2(c(NA, 1), c(1, NA)), NA_real_)
expect_equal(last2(c(NA, 1), c("x", NA)), NA_character_)
})
# fct_infreq --------------------------------------------------------------
test_that("fct_infreq() preserves explicit NA", {
x <- c("a", "b", "b", NA, NA, NA)
f <- factor(x, exclude = FALSE)
expect_equal(fct_infreq(f), fct(x, c(NA, "b", "a")))
})
test_that("fct_infreq() ignores implict NA", {
x <- c("a", "b", "b", NA, NA, NA)
f <- factor(x)
expect_equal(fct_infreq(f), fct(x, c("b", "a")))
})
test_that("fct_infreq() respects weights", {
x <- c("a", "b", "b", "c")
f <- factor(x)
w <- c(1, 1, 3, 3)
expect_equal(fct_infreq(f, w), fct(x, c("b", "c", "a")))
})
test_that("fct_infreq() validates its inputs", {
f <- fct(c("a", "b", "c"))
expect_snapshot(error = TRUE, {
fct_infreq(1)
fct_infreq(f, 1:4)
fct_infreq(f, "x")
fct_infreq(f, ordered = 1)
})
})
test_that("fct_infreq() preserves empty levels", {
# at end
x <- c("b", "b", "a")
f <- fct(x, letters[1:3])
expect_equal(fct_infreq(f), fct(x, c("b", "a", "c")))
# at beginning
x <- c("b", "b", "c")
f <- fct(x, letters[1:3])
expect_equal(fct_infreq(f), fct(x, c("b", "c", "a")))
})
# fct_inorder -------------------------------------------------------------
test_that("fct_inorder() preserves explicit NA", {
x <- c(NA, "a", "b", "b", NA, NA)
f <- factor(x, exclude = FALSE)
expect_equal(fct_inorder(f), fct(x, c(NA, "a", "b")))
})
test_that("fct_inorder() ignores implict NA", {
x <- c(NA, "a", "b", "b", NA, NA)
f <- factor(x)
expect_equal(fct_inorder(f), fct(x, c("a", "b")))
})
test_that("fct_inorder() preserves empty levels", {
# at beginning
x <- c("b", "b", "a")
f <- fct(x, letters[1:3])
expect_equal(fct_inorder(f), fct(x, c("b", "a", "c")))
# at end
x <- c("b", "b", "c")
f <- fct(x, letters[1:3])
expect_equal(fct_inorder(f), fct(x, c("b", "c", "a")))
})
test_that("fct_inorder() validates its inputs", {
f <- fct(c("a", "b", "c"))
expect_snapshot(error = TRUE, {
fct_inorder(1)
fct_inorder(f, 1:4)
fct_inorder(f, "x")
fct_inorder(f, ordered = 1)
})
})
# fct_inseq ---------------------------------------------------------------
test_that("fct_inseq sorts in numeric order", {
x <- c("1", "2", "3")
expect_equal(
fct_inseq(factor(x, levels = c("3", "1", "2"))),
factor(x, levels = c("1", "2", "3"))
)
# non-numeric go to end
x <- c("1", "2", "3", "a")
expect_equal(
fct_inseq(factor(x, levels = c("a", "3", "1", "2"))),
factor(x, levels = c("1", "2", "3", "a"))
)
})
test_that("fct_inseq gives error for non-numeric levels", {
expect_snapshot(error = TRUE, {
fct_inseq("x")
})
})
test_that("fct_inorder() validates its inputs", {
expect_snapshot(error = TRUE, {
fct_inseq(1)
fct_inseq("1", ordered = 1)
})
})
forcats/tests/testthat/test-shift.R 0000644 0001762 0000144 00000000741 14360012612 017127 0 ustar ligges users test_that("can shift in either direction", {
f1 <- factor(c("a", "b", "c"))
f2_l <- fct_shift(f1, 1)
expect_equal(levels(f2_l), c("b", "c", "a"))
f2_r <- fct_shift(f1, -1)
expect_equal(levels(f2_r), c("c", "a", "b"))
})
test_that("0 shift leaves unchanged", {
f1 <- factor(c("a", "b", "c"))
f2 <- fct_shift(f1, 0)
expect_identical(f1, f2)
})
test_that("validates its inputs", {
expect_snapshot(error = TRUE, {
fct_shift(1)
fct_shift("x", NA)
})
})
forcats/tests/testthat/test-cross.R 0000644 0001762 0000144 00000004346 14360012612 017150 0 ustar ligges users test_that("empty input returns empty factor", {
expect_equal(fct_cross(), factor())
})
test_that("gives correct levels", {
fruit <- as_factor(c("apple", "kiwi", "apple", "apple"))
colour <- as_factor(c("green", "green", "red", "green"))
f2 <- fct_cross(fruit, colour)
expect_setequal(levels(f2), c("apple:green", "kiwi:green", "apple:red"))
})
test_that("recycle inputs", {
expect_length(fct_cross("a", c("a", "b", "c"), "d"), 3)
expect_error(fct_cross(c("a", "b", "c"), c("a", "b")), "recycle", class = "error")
})
test_that("keeps empty levels when requested", {
fruit <- as_factor(c("apple", "kiwi", "apple", "apple"))
colour <- as_factor(c("green", "green", "red", "green"))
f2 <- fct_cross(fruit, colour, keep_empty = TRUE)
expect_setequal(levels(f2), c("apple:green", "kiwi:green", "apple:red", "kiwi:red"))
})
test_that("order of levels is preserved", {
fruit <- as_factor(c("apple", "kiwi", "apple", "apple"))
colour <- as_factor(c("green", "green", "red", "green"))
fruit <- fct_relevel(fruit, c("kiwi", "apple"))
colour <- fct_relevel(colour, c("red", "green"))
f2 <- fct_cross(fruit, colour)
expect_setequal(levels(f2), c("kiwi:green", "apple:red", "apple:green"))
})
test_that("gives NA output on NA input", {
fruit <- as_factor(c("apple", "kiwi", "apple", "apple"))
colour <- as_factor(c("green", "green", "red", "green"))
fruit[1] <- NA
f2 <- fct_cross(fruit, colour)
expect_true(is.na(f2[1]))
})
test_that("gives NA output on NA input, when keeping empty levels", {
fruit <- as_factor(c("apple", "kiwi", "apple", "apple"))
colour <- as_factor(c("green", "green", "red", "green"))
fruit[1] <- NA
f2 <- fct_cross(fruit, colour, keep_empty = TRUE)
expect_true(is.na(f2[1]))
})
test_that("can combine more than two factors", {
fruit <- as_factor(c("apple", "kiwi", "apple", "apple"))
colour <- as_factor(c("green", "green", "red", "green"))
eaten <- c("yes", "no", "yes", "no")
f2 <- fct_cross(fruit, colour, eaten)
expect_setequal(levels(f2), c("apple:green:no", "apple:green:yes", "apple:red:yes", "kiwi:green:no"))
})
test_that("validates its inputs", {
expect_snapshot(error = TRUE, {
fct_cross(x = "x")
fct_cross("x", sep = 1)
fct_cross("x", keep_empty = 1)
})
})
forcats/tests/testthat/test-recode.R 0000644 0001762 0000144 00000002002 14241066126 017252 0 ustar ligges users test_that("warns about unknown levels", {
f1 <- factor(c("a", "b"))
expect_warning(f2 <- fct_recode(f1, d = "e"), "Unknown levels")
expect_equal(levels(f2), levels(f1))
})
test_that("can collapse levels", {
f1 <- factor(c("a1", "a2", "b1", "b2"))
f2 <- factor(c("a", "a", "b", "b"))
expect_equal(fct_recode(f1, a = "a1", a = "a2", b = "b1", b = "b2"), f2)
})
test_that("can recode multiple levels to NA", {
f1 <- factor(c("a1", "empty", "a2", "b", "missing"))
f2 <- factor(c("a", NA, "a", "b", NA))
expect_equal(fct_recode(f1, NULL = "missing", NULL = "empty", a = "a1", a = "a2"), f2)
})
test_that("can just remove levels", {
f1 <- factor(c("a", "missing"))
f2 <- factor(c("a", NA))
expect_equal(fct_recode(f1, NULL = "missing"), f2)
})
# check_recode_levels -----------------------------------------------------
test_that("new levels must be character", {
f <- factor(c("a", "b"))
expect_snapshot(error = TRUE, {
fct_recode(f, "a")
fct_recode(f, x = 1, y = c("a", "b"))
})
})
forcats/tests/testthat/test-explicit_na.R 0000644 0001762 0000144 00000001331 14357073063 020322 0 ustar ligges users test_that("factor unchanged if no missing levels", {
withr::local_options(lifecycle_verbosity = "quiet")
f1 <- factor(letters[1:3])
f2 <- fct_explicit_na(f1)
expect_identical(f1, f2)
})
test_that("converts implicit NA", {
withr::local_options(lifecycle_verbosity = "quiet")
f1 <- factor(c("a", NA))
f2 <- fct_explicit_na(f1)
expect_equal(f2, fct_inorder(c("a", "(Missing)")))
})
test_that("converts explicit NA", {
withr::local_options(lifecycle_verbosity = "quiet")
f1 <- factor(c("a", NA), exclude = NULL)
f2 <- fct_explicit_na(f1)
expect_equal(f2, fct_inorder(c("a", "(Missing)")))
})
test_that("fct_explicit_na is deprecated", {
expect_snapshot({
. <- fct_explicit_na(factor())
})
})
forcats/tests/testthat/test-unique.R 0000644 0001762 0000144 00000001242 14355354616 017336 0 ustar ligges users test_that("includes implicit missing values", {
x <- factor(c(NA, "x"))
expect_equal(fct_unique(x), fct(c("x", NA)))
})
test_that("preserve position of explicit NA levels", {
x <- fct(c(NA, "x"), levels = c(NA, "x"))
expect_equal(fct_unique(x), x)
})
test_that("preserve mix of implicit and explicit NA (#328)", {
# Possible to create with factor(exclude = NULL) + is.na(), but directly
# specifying makes it a little easier to see what's happening.
f <- structure(
c(2L, 1L, NA, 1L, 2L, NA),
levels = c("x", NA),
class = "factor"
)
expect_equal(
fct_unique(f),
structure(c(1L, 2L, NA), levels = c("x", NA), class = "factor")
)
})
forcats/tests/testthat/test-expand.R 0000644 0001762 0000144 00000000737 14360012612 017276 0 ustar ligges users test_that("can add levels at any location", {
x <- c("a", "b", "c")
expect_equal(fct_expand(x, "d"), fct(x, c("a", "b", "c", "d")))
expect_equal(fct_expand(x, "d", after = 0), fct(x, c("d", "a", "b", "c")))
})
test_that("doesn't repeat existing levels", {
x <- c("a", "b", "c")
expect_equal(fct_expand(x, "a", "b"), fct(x))
})
test_that("validates its inputs", {
expect_snapshot(error = TRUE, {
fct_expand("x", d = "d")
fct_expand("x", after = "x")
})
})
forcats/tests/testthat/_snaps/ 0000755 0001762 0000144 00000000000 14360020241 016170 5 ustar ligges users forcats/tests/testthat/_snaps/cross.md 0000644 0001762 0000144 00000000776 14360012612 017660 0 ustar ligges users # validates its inputs
Code
fct_cross(x = "x")
Condition
Error in `fct_cross()`:
! Arguments in `...` must be passed by position, not name.
x Problematic argument:
* x = "x"
Code
fct_cross("x", sep = 1)
Condition
Error in `fct_cross()`:
! `sep` must be a single string, not the number 1.
Code
fct_cross("x", keep_empty = 1)
Condition
Error in `fct_cross()`:
! `keep_empty` must be `TRUE` or `FALSE`, not the number 1.
forcats/tests/testthat/_snaps/na.md 0000644 0001762 0000144 00000001306 14360012612 017113 0 ustar ligges users # checks input types
Code
fct_na_value_to_level(1)
Condition
Error in `fct_na_value_to_level()`:
! `f` must be a factor or character vector, not a number.
Code
fct_na_value_to_level(f, level = 1)
Condition
Error in `fct_na_value_to_level()`:
! `level` must be a single string or `NA`, not the number 1.
Code
fct_na_level_to_value(1)
Condition
Error in `fct_na_level_to_value()`:
! `f` must be a factor or character vector, not a number.
Code
fct_na_level_to_value(f, extra_levels = 1)
Condition
Error in `fct_na_level_to_value()`:
! `extra_levels` must be a character vector or `NULL`, not the number 1.
forcats/tests/testthat/_snaps/explicit_na.md 0000644 0001762 0000144 00000000336 14360012553 021022 0 ustar ligges users # fct_explicit_na is deprecated
Code
. <- fct_explicit_na(factor())
Condition
Warning:
`fct_explicit_na()` was deprecated in forcats 1.0.0.
i Please use `fct_na_value_to_level()` instead.
forcats/tests/testthat/_snaps/other.md 0000644 0001762 0000144 00000001362 14360012612 017640 0 ustar ligges users # validates its inputs
Code
fct_other(1)
Condition
Error in `fct_other()`:
! `f` must be a factor or character vector, not a number.
Code
fct_other(f)
Condition
Error in `fct_other()`:
! One of `keep` or `drop` must be supplied.
Code
fct_other(f, keep = "a", drop = "a")
Condition
Error in `fct_other()`:
! Exactly one of `keep` or `drop` must be supplied.
Code
fct_other(f, keep = 1)
Condition
Error in `fct_other()`:
! `keep` must be a character vector, not the number 1.
Code
fct_other(f, keep = "a", other_level = 1)
Condition
Error in `fct_other()`:
! `other_level` must be a single string or `NA`, not the number 1.
forcats/tests/testthat/_snaps/count.md 0000644 0001762 0000144 00000000701 14360012612 017643 0 ustar ligges users # validates its inputs
Code
fct_count(1)
Condition
Error in `fct_count()`:
! `f` must be a factor or character vector, not a number.
Code
fct_count("x", sort = 1)
Condition
Error in `fct_count()`:
! `sort` must be `TRUE` or `FALSE`, not the number 1.
Code
fct_count("x", prop = 1)
Condition
Error in `fct_count()`:
! `prop` must be `TRUE` or `FALSE`, not the number 1.
forcats/tests/testthat/_snaps/expand.md 0000644 0001762 0000144 00000000554 14360012612 020000 0 ustar ligges users # validates its inputs
Code
fct_expand("x", d = "d")
Condition
Error in `fct_expand()`:
! Arguments in `...` must be passed by position, not name.
x Problematic argument:
* d = "d"
Code
fct_expand("x", after = "x")
Condition
Error in `fct_expand()`:
! `after` must be a number, not the string "x".
forcats/tests/testthat/_snaps/lump.md 0000644 0001762 0000144 00000003200 14360020236 017466 0 ustar ligges users # bad weights generates friendly error messages
Code
fct_lump(letters, w = letters)
Condition
Error in `fct_lump_lowfreq()`:
! `w` must be a numeric vector, not a string.
Code
fct_lump(letters, w = 1:10)
Condition
Error in `fct_lump_lowfreq()`:
! `w` must be the same length as `f` (26), not length 10.
Code
fct_lump(letters, w = c(-1, rep(1, 24), -1))
Condition
Error in `fct_lump_lowfreq()`:
! All `w` must be non-negative and non-missing.
2 problems at positions 1 and 26.
# can only supply one of n and prop
Code
fct_lump(f, n = 1, prop = 0.1)
Condition
Error in `fct_lump()`:
! Must supply only one of `n` and `prop`.
# checks inputs
Code
fct_lump_min(1:3)
Condition
Error in `fct_lump_min()`:
! `f` must be a factor or character vector, not an integer vector.
Code
fct_lump_min(factor(), min = "x")
Condition
Error in `fct_lump_min()`:
! `min` must be a number, not the string "x".
---
Code
fct_lump_n(1:3)
Condition
Error in `fct_lump_n()`:
! `f` must be a factor or character vector, not an integer vector.
Code
fct_lump_n(factor(), n = "x")
Condition
Error in `fct_lump_n()`:
! `n` must be a number, not the string "x".
---
Code
fct_lump_prop(1:3)
Condition
Error in `fct_lump_prop()`:
! `f` must be a factor or character vector, not an integer vector.
Code
fct_lump_prop(factor(), prop = "x")
Condition
Error in `fct_lump_prop()`:
! `prop` must be a number, not the string "x".
forcats/tests/testthat/_snaps/reorder.md 0000644 0001762 0000144 00000006477 14360012623 020177 0 ustar ligges users # automatically removes missing values with a warning
Code
f2 <- fct_reorder(f1, x)
Condition
Warning:
`fct_reorder()` removing 1 missing value.
i Use `.na_rm = TRUE` to silence this message.
i Use `.na_rm = FALSE` to preserve NAs.
# fct_reorder() complains if summary doesn't return single value
Code
fct_reorder("a", 1, function(x) c(1, 2))
Condition
Error in `fct_reorder()`:
! `.fun` must return a single value per group
# fct_reorder() validates its inputs
Code
fct_reorder(1)
Condition
Error in `fct_reorder()`:
! `.f` must be a factor or character vector, not a number.
Code
fct_reorder("x", 1, 1)
Condition
Error in `fct_reorder()`:
! Can't convert `.fun`, a number, to a function.
Code
fct_reorder("x", 1, .na_rm = 1)
Condition
Error in `fct_reorder()`:
! `.na_rm` must be `TRUE`, `FALSE`, or `NULL`, not the number 1.
Code
fct_reorder("x", 1, .desc = 1)
Condition
Error in `fct_reorder()`:
! `.desc` must be `TRUE` or `FALSE`, not the number 1.
# fct_reorder2() automatically removes missing values with a warning
Code
f2 <- fct_reorder2(f1, x, y)
Condition
Warning:
`fct_reorder2()` removing 1 missing value.
i Use `.na_rm = TRUE` to silence this message.
i Use `.na_rm = FALSE` to preserve NAs.
# fct_reorder2() complains if summary doesn't return single value
Code
fct_reorder2("a", 1, 1, function(x, y) c(1, 2))
Condition
Error in `fct_reorder2()`:
! `.fun` must return a single value per group
# fct_infreq() validates its inputs
Code
fct_infreq(1)
Condition
Error in `fct_infreq()`:
! `f` must be a factor or character vector, not a number.
Code
fct_infreq(f, 1:4)
Condition
Error in `fct_infreq()`:
! `w` must be the same length as `f` (3), not length 4.
Code
fct_infreq(f, "x")
Condition
Error in `fct_infreq()`:
! `w` must be a numeric vector, not a string.
Code
fct_infreq(f, ordered = 1)
Condition
Error in `fct_infreq()`:
! `ordered` must be `TRUE`, `FALSE`, or `NA`, not the number 1.
# fct_inorder() validates its inputs
Code
fct_inorder(1)
Condition
Error in `fct_inorder()`:
! `f` must be a factor or character vector, not a number.
Code
fct_inorder(f, 1:4)
Condition
Error in `fct_inorder()`:
! `ordered` must be `TRUE`, `FALSE`, or `NA`, not an integer vector.
Code
fct_inorder(f, "x")
Condition
Error in `fct_inorder()`:
! `ordered` must be `TRUE`, `FALSE`, or `NA`, not the string "x".
Code
fct_inorder(f, ordered = 1)
Condition
Error in `fct_inorder()`:
! `ordered` must be `TRUE`, `FALSE`, or `NA`, not the number 1.
---
Code
fct_inseq(1)
Condition
Error in `fct_inseq()`:
! `f` must be a factor or character vector, not a number.
Code
fct_inseq("1", ordered = 1)
Condition
Error in `fct_inseq()`:
! `ordered` must be `TRUE`, `FALSE`, or `NA`, not the number 1.
# fct_inseq gives error for non-numeric levels
Code
fct_inseq("x")
Condition
Error in `fct_inseq()`:
! At least one existing level must be coercible to numeric.
forcats/tests/testthat/_snaps/c.md 0000644 0001762 0000144 00000000640 14360012612 016737 0 ustar ligges users # validates inputs
Code
fct_c(1)
Condition
Error in `fct_c()`:
! All elements of `...` must be factors.
Code
fct_unify(list(1))
Condition
Error in `fct_unify()`:
! All elements of `fs` must be factors.
Code
fct_unify(list(factor()), levels = 1)
Condition
Error in `fct_unify()`:
! `levels` must be a character vector, not the number 1.
forcats/tests/testthat/_snaps/anon.md 0000644 0001762 0000144 00000000446 14360012612 017454 0 ustar ligges users # validates its inputs
Code
fct_anon(1)
Condition
Error in `fct_anon()`:
! `f` must be a factor or character vector, not a number.
Code
fct_anon("x", 1)
Condition
Error in `fct_anon()`:
! `prefix` must be a single string, not the number 1.
forcats/tests/testthat/_snaps/relabel.md 0000644 0001762 0000144 00000000745 14360012612 020131 0 ustar ligges users # gives useful errors
Code
fct_relabel(f1, 1)
Condition
Error in `fct_relabel()`:
! Can't convert `.fun`, a number, to a function.
Code
fct_relabel(f1, function(x) 1)
Condition
Error in `lvls_revalue()`:
! `new_levels` must be a character vector, not the number 1.
Code
fct_relabel(f1, function(x) x[-1])
Condition
Error in `lvls_revalue()`:
! `new_levels` must be the same length (0) as `levels(f)` (1).
forcats/tests/testthat/_snaps/collapse.md 0000644 0001762 0000144 00000001167 14360012612 020324 0 ustar ligges users # group_other is deprecated
Code
f2 <- fct_collapse(f1, x1 = c("a", "d"), x2 = "c", group_other = TRUE)
Condition
Warning:
The `group_other` argument of `fct_collapse()` is deprecated as of forcats 0.5.0.
i Please use the `other_level` argument instead.
# valdiates inputs
Code
fct_collapse(1)
Condition
Error in `fct_collapse()`:
! `.f` must be a factor or character vector, not a number.
Code
fct_collapse("x", other_level = 1)
Condition
Error in `fct_collapse()`:
! `other_level` must be a single string, `NA`, or `NULL`, not the number 1.
forcats/tests/testthat/_snaps/lvls.md 0000644 0001762 0000144 00000001071 14360012612 017474 0 ustar ligges users # must include all existing levels
Code
lvls_expand(f1, c("a", "c"))
Condition
Error in `lvls_expand()`:
! `new_levels` must include all levels in `f`.
i Missing 1 level: b
# `new_levels` must checks its inputs
Code
lvls_revalue(f1, 1:5)
Condition
Error in `lvls_revalue()`:
! `new_levels` must be a character vector, not an integer vector.
Code
lvls_revalue(f1, c("a", "b", "c"))
Condition
Error in `lvls_revalue()`:
! `new_levels` must be the same length (3) as `levels(f)` (2).
forcats/tests/testthat/_snaps/relevel.md 0000644 0001762 0000144 00000000543 14360012554 020162 0 ustar ligges users # warns about unknown levels
Code
f2 <- fct_relevel(f1, "d")
Condition
Warning:
1 unknown level in `f`: d
# dots must be unnamed
Code
fct_relevel(f, a = "b")
Condition
Error in `fct_relevel()`:
! Arguments in `...` must be passed by position, not name.
x Problematic argument:
* a = "b"
forcats/tests/testthat/_snaps/fct.md 0000644 0001762 0000144 00000001201 14360012612 017263 0 ustar ligges users # checks input types
Code
fct(1:3)
Condition
Error in `fct()`:
! `x` must be a character vector, not an integer vector.
Code
fct("x", 1:3)
Condition
Error in `fct()`:
! `levels` must be a character vector or `NULL`, not an integer vector.
Code
fct("x", "y", na = 1)
Condition
Error in `fct()`:
! `na` must be a character vector, not the number 1.
# clear error if levels are incomplete
Code
fct(c("x", "y", "z"), c("x", "y"))
Condition
Error in `fct()`:
! All values of `x` must appear in `levels` or `na`
i Missing level: "z"
forcats/tests/testthat/_snaps/recode.md 0000644 0001762 0000144 00000000615 14360012554 017765 0 ustar ligges users # new levels must be character
Code
fct_recode(f, "a")
Condition
Error in `fct_recode()`:
! Each element of `...` must be a named string.
i Problems with 1 argument: 1
Code
fct_recode(f, x = 1, y = c("a", "b"))
Condition
Error in `fct_recode()`:
! Each element of `...` must be a named string.
i Problems with 2 arguments: x and y
forcats/tests/testthat/_snaps/drop.md 0000644 0001762 0000144 00000000470 14360012612 017462 0 ustar ligges users # validates its inputs
Code
fct_drop(1)
Condition
Error in `fct_drop()`:
! `f` must be a factor or character vector, not a number.
Code
fct_drop("x", only = 1)
Condition
Error in `fct_drop()`:
! `only` must be a character vector or `NULL`, not the number 1.
forcats/tests/testthat/_snaps/match.md 0000644 0001762 0000144 00000000741 14360012612 017613 0 ustar ligges users # error when levels are missing
Code
fct_match(f, "d")
Condition
Error in `fct_match()`:
! All `lvls` must be present in `f`.
i Missing levels: "d"
# validates its inputs
Code
fct_match(1)
Condition
Error in `fct_match()`:
! `f` must be a factor or character vector, not a number.
Code
fct_match("x", 1)
Condition
Error in `fct_match()`:
! `lvls` must be a character vector, not the number 1.
forcats/tests/testthat/_snaps/utils.md 0000644 0001762 0000144 00000000650 14360012555 017664 0 ustar ligges users # check_factor() fails when needed
Code
check_factor(NA)
Condition
Error:
! `NA` must be a factor or character vector, not `NA`.
# check_factor_list() checks its inputs
Code
check_factor_list(1)
Condition
Error:
! `1` must be a list, not a number.
Code
check_factor_list(list(1))
Condition
Error:
! All elements of `list(1)` must be factors.
forcats/tests/testthat/_snaps/shift.md 0000644 0001762 0000144 00000000435 14360012612 017634 0 ustar ligges users # validates its inputs
Code
fct_shift(1)
Condition
Error in `fct_shift()`:
! `f` must be a factor or character vector, not a number.
Code
fct_shift("x", NA)
Condition
Error in `fct_shift()`:
! `n` must be a whole number, not `NA`.
forcats/tests/testthat/test-utils.R 0000644 0001762 0000144 00000000556 14241066126 017165 0 ustar ligges users test_that("check_factor() fails when needed", {
expect_equal(check_factor("x"), factor("x"))
expect_equal(check_factor(factor("x")), factor("x"))
expect_snapshot(error = TRUE, {
check_factor(NA)
})
})
test_that("check_factor_list() checks its inputs", {
expect_snapshot(error = TRUE, {
check_factor_list(1)
check_factor_list(list(1))
})
})
forcats/tests/testthat/test-shuffle.R 0000644 0001762 0000144 00000000232 14241066126 017450 0 ustar ligges users test_that("reproducibility shuffles", {
set.seed(1014)
f1 <- factor(c("a", "b"))
f2 <- fct_shuffle(f1)
expect_equal(levels(f2), c("a", "b"))
})
forcats/tests/testthat/test-rev.R 0000644 0001762 0000144 00000000200 14241066126 016603 0 ustar ligges users test_that("reverses levels", {
f1 <- factor(c("a", "b", "a"))
f2 <- fct_rev(f1)
expect_equal(levels(f2), c("b", "a"))
})
forcats/tests/testthat/test-as_factor.R 0000644 0001762 0000144 00000000703 14241066126 017760 0 ustar ligges users test_that("equivalent to fct_inorder", {
x <- c("a", "z", "g")
expect_equal(as_factor(x), fct_inorder(x))
})
test_that("leaves factors as is", {
f1 <- factor(letters)
f2 <- as_factor(f1)
expect_identical(f1, f2)
})
test_that("logical has fixed levels", {
f <- as_factor(FALSE)
expect_equal(levels(f), c("FALSE", "TRUE"))
})
test_that("supports NA (#89)", {
x <- c("a", "z", "g", NA)
expect_equal(as_factor(x), fct_inorder(x))
})
forcats/tests/testthat/test-na.R 0000644 0001762 0000144 00000001623 14357073063 016425 0 ustar ligges users test_that("can turn a NA value into a NA level", {
x <- c("a", "b", NA)
f <- fct(x)
expect_equal(fct_na_value_to_level(f), fct(x, x))
})
test_that("can turn a NA value into a custom level", {
x <- c("a", "b", NA)
f <- fct(x)
expect_equal(
fct_na_value_to_level(f, "MISSING"),
fct(c("a", "b", "MISSING"))
)
})
test_that("can turn a NA level into an NA value", {
x <- c("a", "b", NA)
f <- fct(x, x)
expect_equal(fct_na_level_to_value(f), fct(x))
})
test_that("can turn custom levels into an NA value", {
x <- c("a", "b", NA)
f <- fct(x, x)
expect_equal(
fct_na_level_to_value(f, extra_levels = "a"),
fct(c(NA, "b", NA), "b")
)
})
test_that("checks input types", {
f <- fct("a")
expect_snapshot(error = TRUE, {
fct_na_value_to_level(1)
fct_na_value_to_level(f, level = 1)
fct_na_level_to_value(1)
fct_na_level_to_value(f, extra_levels = 1)
})
})
forcats/tests/testthat/test-lvls.R 0000644 0001762 0000144 00000004420 14355410031 016771 0 ustar ligges users # lvls_expand -------------------------------------------------------------
test_that("changes levels, not values", {
f1 <- factor(c("a"))
f2 <- factor(c("a"), levels = c("a", "b"))
expect_equal(lvls_expand(f1, c("a", "b")), f2)
})
test_that("must include all existing levels", {
f1 <- factor(c("a", "b"))
expect_snapshot(error = TRUE, {
lvls_expand(f1, c("a", "c"))
})
})
# lvls_revalue ------------------------------------------------------------
test_that("changes values and levels", {
f1 <- factor(c("a", "b"))
f2 <- factor(c("b", "a"), levels = c("b", "a"))
expect_equal(lvls_revalue(f1, c("b", "a")), f2)
})
test_that("can collapse values", {
f1 <- factor(c("a", "b"))
f2 <- factor(c("a", "a"))
expect_equal(lvls_revalue(f1, c("a", "a")), f2)
})
test_that("preserves missing values", {
f1 <- factor(c("a", NA), exclude = NULL)
f2 <- lvls_revalue(f1, levels(f1))
expect_equal(levels(f2), levels(f1))
})
test_that("`new_levels` must checks its inputs", {
f1 <- factor(c("a", "b"))
expect_snapshot(error = TRUE, {
lvls_revalue(f1, 1:5)
lvls_revalue(f1, c("a", "b", "c"))
})
})
# lvls_reorder ------------------------------------------------------------
test_that("changes levels, not values", {
f1 <- factor(c("a", "b"))
f2 <- factor(c("a", "b"), levels = c("b", "a"))
expect_equal(lvls_reorder(f1, 2:1), f2)
})
test_that("idx must be numeric", {
f <- factor(c("a", "b"))
expect_error(lvls_reorder(f, "a"), "must be numeric")
})
test_that("must have one integer per level", {
f <- factor(c("a", "b", "c"))
expect_error(lvls_reorder(f, c(1, 2)), "one integer for each level")
expect_error(lvls_reorder(f, c(1, 2, 2)), "one integer for each level")
expect_error(lvls_reorder(f, c(1, 2.5)), "one integer for each level")
})
test_that("can change ordered status of output", {
f1 <- factor(letters[1:3])
f2 <- ordered(f1)
expect_equal(is.ordered(lvls_reorder(f1, 1:3)), FALSE)
expect_equal(is.ordered(lvls_reorder(f1, 1:3, ordered = FALSE)), FALSE)
expect_equal(is.ordered(lvls_reorder(f1, 1:3, ordered = TRUE)), TRUE)
expect_equal(is.ordered(lvls_reorder(f2, 1:3)), TRUE)
expect_equal(is.ordered(lvls_reorder(f2, 1:3, ordered = FALSE)), FALSE)
expect_equal(is.ordered(lvls_reorder(f2, 1:3, ordered = TRUE)), TRUE)
})
forcats/tests/testthat.R 0000644 0001762 0000144 00000000072 12752645041 015045 0 ustar ligges users library(testthat)
library(forcats)
test_check("forcats")
forcats/vignettes/ 0000755 0001762 0000144 00000000000 14364755577 013752 5 ustar ligges users forcats/vignettes/forcats.Rmd 0000644 0001762 0000144 00000016710 14360103324 016031 0 ustar ligges users ---
title: "Introduction to forcats"
author: "Emily Robinson"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Introduction to forcats}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
The goal of the **forcats** package is to provide a suite of useful tools that solve common problems with factors.
Factors are useful when you have categorical data, variables that have a fixed and known set of values, and when you want to display character vectors in non-alphabetical order.
If you want to learn more, the best place to start is the [chapter on factors](https://r4ds.had.co.nz/factors.html) in R for Data Science.
## Ordering by frequency
```{r message = FALSE}
library(dplyr)
library(ggplot2)
library(forcats)
```
Let's try answering the question, "what are the most common hair colors of star wars characters?" Let's start off by making a bar plot:
```{r initial-plot}
#| fig.alt: >
#| A bar chart of hair color of starwars characters. The bars are
#| alphabetically ordered, making it hard to see general patterns.
ggplot(starwars, aes(y = hair_color)) +
geom_bar()
```
That's okay, but it would be more helpful the graph was ordered by count.
This is a case of an **unordered** categorical variable where we want it ordered by its frequency.
To do so, we can use the function `fct_infreq()`:
```{r fct-infreq-hair}
#| fig.alt: >
#| The bar chart of hair color, now ordered so that the least
#| frequent colours come first and the most frequent colors come last.
#| This makes it easy to see that the most common hair color is none
#| (~35), followed by brown (~18), then black (~12). Surprisingly,
#| NAs are at the top of the graph, even though there are ~5 NAs and
#| other colors have smaller values.
ggplot(starwars, aes(y = fct_infreq(hair_color))) +
geom_bar()
```
Note that `fct_infreq()` it automatically puts NA at the top, even though that doesn't have the smallest number of entries.
It's a little surprising that the `NA` bar isn't ordered by frequency.
To understand why we need to make a brief digression to discuss `NA`s in values vs. `NA`s in levels
## `NAs`s in levels and values
There are two ways to represent a missing value in a factor:
- You can include it in the values of the factor; it does not appear in the levels and `is.na()` reports it as missing.
This is how missing values are encoded in a factor by default:
```{r}
f <- factor(c("x", "y", NA))
levels(f)
is.na(f)
```
- You can include it in the levels of the factor and `is.na()` does not report it as missing.
This requires a little more work to create, because, by default, `factor()` uses `exclude = NA`, meaning that missing values are not included in the levels.
You can force `NA` to be included by setting `exclude = NULL`:
```{r}
f <- factor(c("x", "y", NA), exclude = NULL)
levels(f)
is.na(f)
```
`NA`s in the values tend to be best for data analysis, since `is.na()` works as you'd expect.
`NA`s in the levels can be useful for display if you need to control where they appear in a table or a plot.
To fix the issue above, we can use `fct_na_value_to_level()` to convert the `NA` in the value to an NA in the levels.
Then they appear where you'd expect:
```{r}
#| fig.alt: >
#| The bar chart of hair color, now ordered so that NAs are
#| ordered where you'd expect: in between white (4) and black (12).
ggplot(starwars, aes(y = fct_infreq(fct_na_value_to_level(hair_color)))) +
geom_bar() +
labs(y = "Hair color")
```
(If you need the opposite operation, you can use `fct_na_level_to_value()`.)
## Combining levels
Let's take a look at skin color now:
```{r}
starwars %>%
count(skin_color, sort = TRUE)
```
We see that there's 31 different skin colors - if we want to make a plot this would be way too many to display!
Let's reduce it to only be the top 5.
We can use `fct_lump()` to "lump" all the infrequent colors into one factor, "other." The argument `n` is the number of levels we want to keep.
```{r}
starwars %>%
mutate(skin_color = fct_lump(skin_color, n = 5)) %>%
count(skin_color, sort = TRUE)
```
We could also have used `prop` instead, which keeps all the levels that appear at least `prop` of the time.
For example, let's keep skin colors that at least 10% of the characters have:
```{r}
starwars %>%
mutate(skin_color = fct_lump(skin_color, prop = .1)) %>%
count(skin_color, sort = TRUE)
```
Only light and fair remain; everything else is other.
If you wanted to call it something than "other", you can change it with the argument `other_level`:
```{r}
starwars %>%
mutate(skin_color = fct_lump(skin_color, prop = .1, other_level = "extra")) %>%
count(skin_color, sort = TRUE)
```
What if we wanted to see if the average mass differed by eye color?
We'll only look at the 6 most popular eye colors and remove `NA`s.
```{r fct-lump-mean}
avg_mass_eye_color <- starwars %>%
mutate(eye_color = fct_lump(eye_color, n = 6)) %>%
group_by(eye_color) %>%
summarise(mean_mass = mean(mass, na.rm = TRUE))
avg_mass_eye_color
```
## Ordering by another variable
It looks like people (or at least one person) with orange eyes are definitely heavier!
If we wanted to make a graph, it would be nice if it was ordered by `mean_mass`.
We can do this with `fct_reorder()`, which reorders one variable by another.
```{r fct-reorder}
#| fig-alt: >
#| A column chart with eye color on the x-axis and mean mass on the
#| y-axis. The bars are ordered by mean_mass, so that the tallest bar
#| (orange eye color with mean mass of ~275) is at the far right.
avg_mass_eye_color %>%
mutate(eye_color = fct_reorder(eye_color, mean_mass)) %>%
ggplot(aes(x = eye_color, y = mean_mass)) +
geom_col()
```
## Manually reordering
Let's switch to using another dataset, `gss_cat`, the general social survey.
What is the income distribution among the respondents?
```{r}
gss_cat %>%
count(rincome)
```
Notice that the income levels are in the correct order - they start with the non-answers and then go from highest to lowest.
This is the same order you'd see if you plotted it as a bar chart.
This is not a coincidence.
When you're working with ordinal data, where there is an order, you can have an ordered factor.
You can examine them with the base function `levels()`, which prints them in order:
```{r}
levels(gss_cat$rincome)
```
But what if your factor came in the wrong order?
Let's simulate that by reordering the levels of `rincome` randomly with `fct_shuffle()`:
```{r}
reshuffled_income <- gss_cat$rincome %>%
fct_shuffle()
levels(reshuffled_income)
```
Now if we plotted it, it would show in this order, which is all over the place!
How can we fix this and put it in the right order?
We can use the function `fct_relevel()` when we need to manually reorder our factor levels.
In addition to the factor, you give it a character vector of level names, and specify where you want to move them.
It defaults to moving them to the front, but you can move them after another level with the argument `after`.
If you want to move it to the end, you set `after` equal to `Inf`.
For example, let's say we wanted to move `Lt $1000` and `$1000 to 2999` to the front.
We would write:
```{r}
fct_relevel(reshuffled_income, c("Lt $1000", "$1000 to 2999")) %>%
levels()
```
What if we want to move them to the second and third place?
```{r}
fct_relevel(reshuffled_income, c("Lt $1000", "$1000 to 2999"), after = 1) %>%
levels()
```
forcats/R/ 0000755 0001762 0000144 00000000000 14360012623 012111 5 ustar ligges users forcats/R/count.R 0000644 0001762 0000144 00000001534 14360012612 013365 0 ustar ligges users #' Count entries in a factor
#'
#' @param f A factor (or character vector).
#' @param sort If `TRUE`, sort the result so that the most common values
#' float to the top.
#' @param prop If `TRUE`, compute the fraction of marginal table.
#' @return A tibble with columns `f`, `n` and `p`, if prop is `TRUE`.
#' @export
#' @examples
#' f <- factor(sample(letters)[rpois(1000, 10)])
#' table(f)
#' fct_count(f)
#' fct_count(f, sort = TRUE)
#' fct_count(f, sort = TRUE, prop = TRUE)
fct_count <- function(f, sort = FALSE, prop = FALSE) {
f <- check_factor(f)
check_bool(sort)
check_bool(prop)
n_na <- sum(is.na(f))
n <- c(tabulate(f, nlevels(f)), if (n_na > 0) n_na)
df <- tibble::tibble(
f = fct_unique(f),
n = n
)
if (sort) {
df <- df[order(df$n, decreasing = TRUE), ]
}
if (prop) {
df$p <- prop.table(df$n)
}
df
}
forcats/R/rev.R 0000644 0001762 0000144 00000000446 13626051403 013037 0 ustar ligges users #' Reverse order of factor levels
#'
#' This is sometimes useful when plotting a factor.
#'
#' @param f A factor (or character vector).
#' @export
#' @examples
#' f <- factor(c("a", "b", "c"))
#' fct_rev(f)
fct_rev <- function(f) {
f <- check_factor(f)
lvls_reorder(f, rev(lvls_seq(f)))
}
forcats/R/utils.R 0000644 0001762 0000144 00000001577 14360012612 013404 0 ustar ligges users #' Pipe operator
#'
#' See \code{\link[magrittr]{\%>\%}} for more details.
#'
#' @name %>%
#' @rdname pipe
#' @keywords internal
#' @export
#' @importFrom magrittr %>%
#' @usage lhs \%>\% rhs
NULL
check_factor <- function(x, arg = caller_arg(x), call = caller_env()) {
if (is.character(x)) {
factor(x)
} else if (is.factor(x)) {
x
} else {
cli::cli_abort(
"{.arg {arg}} must be a factor or character vector, not {.obj_type_friendly {x}}.",
call = call
)
}
}
check_factor_list <- function(x, arg = caller_arg(x), call = caller_env()) {
if (!is.list(x)) {
cli::cli_abort(
"{.arg {arg}} must be a list, not {.obj_type_friendly {x}}.",
call = call
)
}
is_factor <- vapply(x, is.factor, logical(1))
if (any(!is_factor)) {
cli::cli_abort(
"All elements of {.arg {arg}} must be factors.",
call = call
)
}
x
}
forcats/R/anon.R 0000644 0001762 0000144 00000001423 14360012612 013165 0 ustar ligges users #' Anonymise factor levels
#'
#' Replaces factor levels with arbitrary numeric identifiers. Neither
#' the values nor the order of the levels are preserved.
#'
#' @param f A factor.
#' @param prefix A character prefix to insert in front of the random labels.
#' @export
#' @examples
#' gss_cat$relig %>% fct_count()
#' gss_cat$relig %>%
#' fct_anon() %>%
#' fct_count()
#' gss_cat$relig %>%
#' fct_anon("X") %>%
#' fct_count()
fct_anon <- function(f, prefix = "") {
f <- check_factor(f)
check_string(prefix)
levels <- paste0(prefix, zero_pad(seq_len(nlevels(f))))
f <- lvls_revalue(f, sample(levels))
lvls_reorder(f, match(levels, levels(f)))
}
digits <- function(x) nchar(max(x, na.rm = TRUE))
zero_pad <- function(x) {
sprintf(paste0("%0", digits(x), "d"), x)
}
forcats/R/shuffle.R 0000644 0001762 0000144 00000000414 13626051403 013672 0 ustar ligges users #' Randomly permute factor levels
#'
#' @param f A factor (or character vector).
#' @export
#' @examples
#' f <- factor(c("a", "b", "c"))
#' fct_shuffle(f)
#' fct_shuffle(f)
fct_shuffle <- function(f) {
f <- check_factor(f)
lvls_reorder(f, sample(lvls_seq(f)))
}
forcats/R/lump.R 0000644 0001762 0000144 00000014345 14360020235 013216 0 ustar ligges users #' Lump uncommon factor together levels into "other"
#'
#' @description
#' A family for lumping together levels that meet some criteria.
#' * `fct_lump_min()`: lumps levels that appear fewer than `min` times.
#' * `fct_lump_prop()`: lumps levels that appear in fewer than (or equal to)
#' `prop * n` times.
#' * `fct_lump_n()` lumps all levels except for the `n` most frequent
#' (or least frequent if `n < 0`)
#' * `fct_lump_lowfreq()` lumps together the least frequent levels, ensuring
#' that "other" is still the smallest level.
#'
#' `fct_lump()` exists primarily for historical reasons, as it automatically
#' picks between these different methods depending on its arguments.
#' We no longer recommend that you use it.
#'
#' @param f A factor (or character vector).
#' @param n Positive `n` preserves the most common `n` values.
#' Negative `n` preserves the least common `-n` values.
#' It there are ties, you will get at least `abs(n)` values.
#' @param prop Positive `prop` lumps values which do not appear at least
#' `prop` of the time. Negative `prop` lumps values that
#' do not appear at most `-prop` of the time.
#' @param min Preserve levels that appear at least `min` number of times.
#' @param w An optional numeric vector giving weights for frequency of
#' each value (not level) in f.
#' @param other_level Value of level used for "other" values. Always
#' placed at end of levels.
#' @param ties.method A character string specifying how ties are
#' treated. See [rank()] for details.
#' @export
#' @seealso [fct_other()] to convert specified levels to other.
#' @examples
#' x <- factor(rep(LETTERS[1:9], times = c(40, 10, 5, 27, 1, 1, 1, 1, 1)))
#' x %>% table()
#' x %>%
#' fct_lump_n(3) %>%
#' table()
#' x %>%
#' fct_lump_prop(0.10) %>%
#' table()
#' x %>%
#' fct_lump_min(5) %>%
#' table()
#' x %>%
#' fct_lump_lowfreq() %>%
#' table()
#'
#' x <- factor(letters[rpois(100, 5)])
#' x
#' table(x)
#' table(fct_lump_lowfreq(x))
#'
#' # Use positive values to collapse the rarest
#' fct_lump_n(x, n = 3)
#' fct_lump_prop(x, prop = 0.1)
#'
#' # Use negative values to collapse the most common
#' fct_lump_n(x, n = -3)
#' fct_lump_prop(x, prop = -0.1)
#'
#' # Use weighted frequencies
#' w <- c(rep(2, 50), rep(1, 50))
#' fct_lump_n(x, n = 5, w = w)
#'
#' # Use ties.method to control how tied factors are collapsed
#' fct_lump_n(x, n = 6)
#' fct_lump_n(x, n = 6, ties.method = "max")
#'
#' # Use fct_lump_min() to lump together all levels with fewer than `n` values
#' table(fct_lump_min(x, min = 10))
#' table(fct_lump_min(x, min = 15))
fct_lump <- function(f, n, prop, w = NULL, other_level = "Other",
ties.method = c("min", "average", "first", "last", "random", "max")) {
if (missing(n) && missing(prop)) {
fct_lump_lowfreq(f, w = w, other_level = other_level)
} else if (missing(prop)) {
fct_lump_n(f, n, w = w, other_level = other_level, ties.method = ties.method)
} else if (missing(n)) {
fct_lump_prop(f, prop, w = w, other_level = other_level)
} else {
cli::cli_abort("Must supply only one of {.arg n} and {.arg prop}.")
}
}
#' @export
#' @rdname fct_lump
fct_lump_min <- function(f, min, w = NULL, other_level = "Other") {
f <- check_factor(f)
check_number_decimal(min, min = 0)
check_string(other_level, allow_na = TRUE)
level_w <- compute_weights(f, w)
lvls_other(f, level_w >= min, other_level)
}
#' @export
#' @rdname fct_lump
fct_lump_prop <- function(f, prop, w = NULL, other_level = "Other") {
f <- check_factor(f)
check_number_decimal(prop)
check_string(other_level, allow_na = TRUE)
level_w <- compute_weights(f, w)
# Compute proportion of total, including NAs
if (is.null(w)) {
prop_n <- level_w / length(f)
} else {
prop_n <- level_w / sum(w)
}
if (prop < 0) {
lvls_other(f, prop_n <= -prop, other_level)
} else {
lvls_other(f, prop_n > prop, other_level)
}
}
#' @export
#' @rdname fct_lump
fct_lump_n <- function(f, n, w = NULL, other_level = "Other",
ties.method = c("min", "average", "first", "last", "random", "max")) {
f <- check_factor(f)
check_number_decimal(n)
check_string(other_level, allow_na = TRUE)
ties.method <- arg_match(ties.method)
level_w <- compute_weights(f, w)
if (n < 0) {
rank <- rank(level_w, ties.method = ties.method)
n <- -n
} else {
rank <- rank(-level_w, ties.method = ties.method)
}
lvls_other(f, rank <= n, other_level)
}
#' @export
#' @rdname fct_lump
fct_lump_lowfreq <- function(f, w = NULL, other_level = "Other") {
f <- check_factor(f)
check_string(other_level, allow_na = TRUE)
level_w <- compute_weights(f, w)
lvls_other(f, !in_smallest(level_w), other_level)
}
# helpers -----------------------------------------------------------------
compute_weights <- function(f, w = NULL, call = caller_env()) {
w <- check_weights(w, length(f), call = call)
w <- w %||% rep(1L, length(f))
n <- as.vector(tapply(w, f, sum))
# fill in counts for empty levels
n[is.na(n)] <- 0
n
}
# Lump together smallest groups, ensuring that the collective
# "other" is still the smallest group. Assumes x is vector
# of counts in descending order
lump_cutoff <- function(x) {
left <- sum(x)
for (i in seq_along(x)) {
# After group, there are this many left
left <- left - x[i]
if (x[i] > left) {
return(i + 1)
}
}
length(x) + 1
}
# Given vector of counts, returns logical vector if in
# smallest groups
in_smallest <- function(x) {
ord_x <- order(x, decreasing = TRUE)
idx <- lump_cutoff(x[ord_x])
to_lump <- seq_along(x) >= idx
# Undo initial ordering
to_lump[order(ord_x)]
}
check_weights <- function(w, n = length(w), call = caller_env()) {
if (is.null(w)) {
return(w)
}
if (!is.numeric(w)) {
cli::cli_abort(
"{.arg w} must be a numeric vector, not {.obj_type_friendly w}.",
call = call
)
}
if (length(w) != n) {
cli::cli_abort(
"{.arg w} must be the same length as {.arg f} ({n}), not length {length(w)}.",
call = call
)
}
bad <- w < 0 | is.na(w)
if (any(bad)) {
probs <- which(bad)
cli::cli_abort(
c(
"All {.arg w} must be non-negative and non-missing.",
"{length(probs)} problem{?s} at positions {probs}."
),
call = call
)
}
w
}
forcats/R/drop.R 0000644 0001762 0000144 00000001610 14360012612 013174 0 ustar ligges users #' Drop unused levels
#'
#' Compared to `base::droplevels()`, does not drop `NA` levels that have values.
#'
#' @param f A factor (or character vector).
#' @param only A character vector restricting the set of levels to be dropped.
#' If supplied, only levels that have no entries and appear in this vector
#' will be removed.
#' @export
#' @seealso [fct_expand()] to add additional levels to a factor.
#' @examples
#' f <- factor(c("a", "b"), levels = c("a", "b", "c"))
#' f
#' fct_drop(f)
#'
#' # Set only to restrict which levels to drop
#' fct_drop(f, only = "a")
#' fct_drop(f, only = "c")
fct_drop <- function(f, only = NULL) {
f <- check_factor(f)
check_character(only, allow_null = TRUE)
levels <- levels(f)
count <- table(f)
to_drop <- levels[count == 0]
if (!is.null(only)) {
to_drop <- intersect(to_drop, only)
}
refactor(f, new_levels = setdiff(levels, to_drop))
}
forcats/R/shift.R 0000644 0001762 0000144 00000001443 14360012612 013351 0 ustar ligges users #' Shift factor levels to left or right, wrapping around at end
#'
#' This is useful when the levels of an ordered factor are actually cyclical,
#' with different conventions on the starting point.
#'
#' @param f A factor.
#' @param n Positive values shift to the left; negative values shift to
#' the right.
#' @export
#' @examples
#' x <- factor(
#' c("Mon", "Tue", "Wed"),
#' levels = c("Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"),
#' ordered = TRUE
#' )
#' x
#' fct_shift(x)
#' fct_shift(x, 2)
#' fct_shift(x, -1)
fct_shift <- function(f, n = 1L) {
check_factor(f)
check_number_whole(n)
lvls_reorder(f, shift(nlevels(f), n))
}
shift <- function(m, n) {
stopifnot(is.numeric(m), length(m) == 1L)
stopifnot(is.numeric(n), length(n) == 1L)
((seq_len(m) - 1) + n) %% m + 1
}
forcats/R/data.R 0000644 0001762 0000144 00000001154 14241555020 013147 0 ustar ligges users #' A sample of categorical variables from the General Social survey
#'
#' @source Downloaded from \url{https://gssdataexplorer.norc.org/}.
#' @format
#' \describe{
#' \item{year}{year of survey, 2000--2014 (every other year)}
#' \item{age}{age. Maximum age truncated to 89.}
#' \item{marital}{marital status}
#' \item{race}{race}
#' \item{rincome}{reported income}
#' \item{partyid}{party affiliation}
#' \item{relig}{religion}
#' \item{denom}{denomination}
#' \item{tvhours}{hours per day watching tv}
#' }
#' @examples
#' gss_cat
#'
#' fct_count(gss_cat$relig)
#' fct_count(fct_lump(gss_cat$relig))
"gss_cat"
forcats/R/compat-obj-type.R 0000644 0001762 0000144 00000017642 14360012612 015256 0 ustar ligges users # nocov start --- r-lib/rlang compat-obj-type
#
# Changelog
# =========
#
# 2022-10-04:
# - `obj_type_friendly(value = TRUE)` now shows numeric scalars
# literally.
# - `stop_friendly_type()` now takes `show_value`, passed to
# `obj_type_friendly()` as the `value` argument.
#
# 2022-10-03:
# - Added `allow_na` and `allow_null` arguments.
# - `NULL` is now backticked.
# - Better friendly type for infinities and `NaN`.
#
# 2022-09-16:
# - Unprefixed usage of rlang functions with `rlang::` to
# avoid onLoad issues when called from rlang (#1482).
#
# 2022-08-11:
# - Prefixed usage of rlang functions with `rlang::`.
#
# 2022-06-22:
# - `friendly_type_of()` is now `obj_type_friendly()`.
# - Added `obj_type_oo()`.
#
# 2021-12-20:
# - Added support for scalar values and empty vectors.
# - Added `stop_input_type()`
#
# 2021-06-30:
# - Added support for missing arguments.
#
# 2021-04-19:
# - Added support for matrices and arrays (#141).
# - Added documentation.
# - Added changelog.
#' Return English-friendly type
#' @param x Any R object.
#' @param value Whether to describe the value of `x`. Special values
#' like `NA` or `""` are always described.
#' @param length Whether to mention the length of vectors and lists.
#' @return A string describing the type. Starts with an indefinite
#' article, e.g. "an integer vector".
#' @noRd
obj_type_friendly <- function(x, value = TRUE) {
if (is_missing(x)) {
return("absent")
}
if (is.object(x)) {
if (inherits(x, "quosure")) {
type <- "quosure"
} else {
type <- paste(class(x), collapse = "/")
}
return(sprintf("a <%s> object", type))
}
if (!is_vector(x)) {
return(.rlang_as_friendly_type(typeof(x)))
}
n_dim <- length(dim(x))
if (!n_dim) {
if (!is_list(x) && length(x) == 1) {
if (is_na(x)) {
return(switch(
typeof(x),
logical = "`NA`",
integer = "an integer `NA`",
double =
if (is.nan(x)) {
"`NaN`"
} else {
"a numeric `NA`"
},
complex = "a complex `NA`",
character = "a character `NA`",
.rlang_stop_unexpected_typeof(x)
))
}
show_infinites <- function(x) {
if (x > 0) {
"`Inf`"
} else {
"`-Inf`"
}
}
str_encode <- function(x, width = 30, ...) {
if (nchar(x) > width) {
x <- substr(x, 1, width - 3)
x <- paste0(x, "...")
}
encodeString(x, ...)
}
if (value) {
if (is.numeric(x) && is.infinite(x)) {
return(show_infinites(x))
}
if (is.numeric(x) || is.complex(x)) {
number <- as.character(round(x, 2))
what <- if (is.complex(x)) "the complex number" else "the number"
return(paste(what, number))
}
return(switch(
typeof(x),
logical = if (x) "`TRUE`" else "`FALSE`",
character = {
what <- if (nzchar(x)) "the string" else "the empty string"
paste(what, str_encode(x, quote = "\""))
},
raw = paste("the raw value", as.character(x)),
.rlang_stop_unexpected_typeof(x)
))
}
return(switch(
typeof(x),
logical = "a logical value",
integer = "an integer",
double = if (is.infinite(x)) show_infinites(x) else "a number",
complex = "a complex number",
character = if (nzchar(x)) "a string" else "\"\"",
raw = "a raw value",
.rlang_stop_unexpected_typeof(x)
))
}
if (length(x) == 0) {
return(switch(
typeof(x),
logical = "an empty logical vector",
integer = "an empty integer vector",
double = "an empty numeric vector",
complex = "an empty complex vector",
character = "an empty character vector",
raw = "an empty raw vector",
list = "an empty list",
.rlang_stop_unexpected_typeof(x)
))
}
}
vec_type_friendly(x)
}
vec_type_friendly <- function(x, length = FALSE) {
if (!is_vector(x)) {
abort("`x` must be a vector.")
}
type <- typeof(x)
n_dim <- length(dim(x))
add_length <- function(type) {
if (length && !n_dim) {
paste0(type, sprintf(" of length %s", length(x)))
} else {
type
}
}
if (type == "list") {
if (n_dim < 2) {
return(add_length("a list"))
} else if (is.data.frame(x)) {
return("a data frame")
} else if (n_dim == 2) {
return("a list matrix")
} else {
return("a list array")
}
}
type <- switch(
type,
logical = "a logical %s",
integer = "an integer %s",
numeric = ,
double = "a double %s",
complex = "a complex %s",
character = "a character %s",
raw = "a raw %s",
type = paste0("a ", type, " %s")
)
if (n_dim < 2) {
kind <- "vector"
} else if (n_dim == 2) {
kind <- "matrix"
} else {
kind <- "array"
}
out <- sprintf(type, kind)
if (n_dim >= 2) {
out
} else {
add_length(out)
}
}
.rlang_as_friendly_type <- function(type) {
switch(
type,
list = "a list",
NULL = "`NULL`",
environment = "an environment",
externalptr = "a pointer",
weakref = "a weak reference",
S4 = "an S4 object",
name = ,
symbol = "a symbol",
language = "a call",
pairlist = "a pairlist node",
expression = "an expression vector",
char = "an internal string",
promise = "an internal promise",
... = "an internal dots object",
any = "an internal `any` object",
bytecode = "an internal bytecode object",
primitive = ,
builtin = ,
special = "a primitive function",
closure = "a function",
type
)
}
.rlang_stop_unexpected_typeof <- function(x, call = caller_env()) {
abort(
sprintf("Unexpected type <%s>.", typeof(x)),
call = call
)
}
#' Return OO type
#' @param x Any R object.
#' @return One of `"bare"` (for non-OO objects), `"S3"`, `"S4"`,
#' `"R6"`, or `"R7"`.
#' @noRd
obj_type_oo <- function(x) {
if (!is.object(x)) {
return("bare")
}
class <- inherits(x, c("R6", "R7_object"), which = TRUE)
if (class[[1]]) {
"R6"
} else if (class[[2]]) {
"R7"
} else if (isS4(x)) {
"S4"
} else {
"S3"
}
}
#' @param x The object type which does not conform to `what`. Its
#' `obj_type_friendly()` is taken and mentioned in the error message.
#' @param what The friendly expected type as a string. Can be a
#' character vector of expected types, in which case the error
#' message mentions all of them in an "or" enumeration.
#' @param show_value Passed to `value` argument of `obj_type_friendly()`.
#' @param ... Arguments passed to [abort()].
#' @inheritParams args_error_context
#' @noRd
stop_input_type <- function(x,
what,
...,
allow_na = FALSE,
allow_null = FALSE,
show_value = TRUE,
arg = caller_arg(x),
call = caller_env()) {
# From compat-cli.R
cli <- env_get_list(
nms = c("format_arg", "format_code"),
last = topenv(),
default = function(x) sprintf("`%s`", x),
inherit = TRUE
)
if (allow_na) {
what <- c(what, cli$format_code("NA"))
}
if (allow_null) {
what <- c(what, cli$format_code("NULL"))
}
if (length(what)) {
what <- oxford_comma(what)
}
message <- sprintf(
"%s must be %s, not %s.",
cli$format_arg(arg),
what,
obj_type_friendly(x, value = show_value)
)
abort(message, ..., call = call, arg = arg)
}
oxford_comma <- function(chr, sep = ", ", final = "or") {
n <- length(chr)
if (n < 2) {
return(chr)
}
head <- chr[seq_len(n - 1)]
last <- chr[n]
head <- paste(head, collapse = sep)
# Write a or b. But a, b, or c.
if (n > 2) {
paste0(head, sep, final, " ", last)
} else {
paste0(head, " ", final, " ", last)
}
}
# nocov end
forcats/R/lvls.R 0000644 0001762 0000144 00000006026 14360012612 013216 0 ustar ligges users #' Low-level functions for manipulating levels
#'
#' `lvls_reorder` leaves values as they are, but changes the order.
#' `lvls_revalue` changes the values of existing levels; there must
#' be one new level for each old level.
#' `lvls_expand` expands the set of levels; the new levels must
#' include the old levels.
#'
#' These functions are less helpful than the higher-level `fct_` functions,
#' but are safer than the very low-level manipulation of levels directly,
#' because they are more specific, and hence can more carefully check their
#' arguments.
#'
#' @param f A factor (or character vector).
#' @param idx A integer index, with one integer for each existing level.
#' @param new_levels A character vector of new levels.
#' @param ordered A logical which determines the "ordered" status of the
#' output factor. `NA` preserves the existing status of the factor.
#' @name lvls
#' @examples
#' f <- factor(c("a", "b", "c"))
#' lvls_reorder(f, 3:1)
#' lvls_revalue(f, c("apple", "banana", "carrot"))
#' lvls_expand(f, c("a", "b", "c", "d"))
NULL
#' @export
#' @rdname lvls
lvls_reorder <- function(f, idx, ordered = NA) {
f <- check_factor(f)
if (!is.numeric(idx)) {
cli::cli_abort("{.arg idx} must be numeric")
}
if (!setequal(idx, lvls_seq(f)) || length(idx) != nlevels(f)) {
cli::cli_abort("{.arg idx} must contain one integer for each level of {.arg f}")
}
refactor(f, levels(f)[idx], ordered = ordered)
}
#' @export
#' @rdname lvls
lvls_revalue <- function(f, new_levels) {
f <- check_factor(f)
check_character(new_levels)
if (length(new_levels) != nlevels(f)) {
n_new <- length(new_levels)
n_old <- nlevels(f)
cli::cli_abort(
"{.arg new_levels} must be the same length ({n_new}) as {.code levels(f)} ({n_old})."
)
}
if (anyDuplicated(new_levels)) {
# Collapse levels, creating a new factor
u_levels <- unique(new_levels)
index <- match(new_levels, u_levels)
out <- index[f]
attributes(out) <- attributes(f)
attr(out, "levels") <- u_levels
out
} else {
attr(f, "levels") <- new_levels
f
}
}
#' @export
#' @rdname lvls
lvls_expand <- function(f, new_levels) {
f <- check_factor(f)
missing <- setdiff(levels(f), new_levels)
if (length(missing) > 0) {
cli::cli_abort(c(
"{.arg new_levels} must include all levels in {.arg f}.",
i = "Missing {length(missing)} level{?s}: {missing}"
))
}
refactor(f, new_levels)
}
lvls_seq <- function(f) {
seq_along(levels(f))
}
refactor <- function(f, new_levels, ordered = NA) {
if (is.na(ordered)) {
ordered <- is.ordered(f)
}
new_f <- factor(f, levels = new_levels, exclude = NULL, ordered = ordered)
attributes(new_f) <- utils::modifyList(attributes(f), attributes(new_f))
new_f
}
#' Find all levels in a list of factors
#'
#' @param fs A list of factors.
#' @export
#' @examples
#' fs <- list(factor("a"), factor("b"), factor(c("a", "b")))
#' lvls_union(fs)
lvls_union <- function(fs) {
fs <- check_factor_list(fs)
Reduce(function(x, y) union(x, levels(y)), fs, init = character())
}
forcats/R/unique.R 0000644 0001762 0000144 00000001744 14355354616 013567 0 ustar ligges users #' Unique values of a factor, as a factor
#'
#' @description
#' `fct_unique()` extracts the complete set of possible values from the
#' levels of the factor, rather than looking at the actual values, like
#' [unique()].
#'
#' `fct_unique()` only uses the values of `f` in one way: it looks for
#' implicit missing values so that they can be included in the result.
#'
#' @param f A factor.
#' @return A factor.
#' @export
#' @examples
#' f <- fct(letters[rpois(100, 10)])
#' unique(f) # in order of appearance
#' fct_unique(f) # in order of levels
#'
#' f <- fct(letters[rpois(100, 2)], letters[1:20])
#' unique(f) # levels that appear in data
#' fct_unique(f) # all possible levels
fct_unique <- function(f) {
f <- check_factor(f)
levels <- levels(f)
out <- seq_along(levels)
# Ensure out includes any implicit missings
if (anyNA(f)) {
out <- c(out, NA_integer_)
}
structure(
out,
levels = levels,
class = c(if (is.ordered(f)) "ordered", "factor")
)
}
forcats/R/fct.R 0000644 0001762 0000144 00000003362 14360012612 013012 0 ustar ligges users #' Create a factor
#'
#' `fct()` is a stricter version of [factor()] that errors if your
#' specification of `levels` is inconsistent with the values in `x`.
#'
#' @param x A character vector. Values must occur in either `levels` or `na`.
#' @param levels A character vector of known levels. If not supplied, will
#' be computed from the unique values of `x`, in the order in which they
#' occur.
#' @param na A character vector of values that should become missing values.
#' @return A factor.
#' @export
#' @examples
#' # Use factors when you know the set of possible values a variable might take
#' x <- c("A", "O", "O", "AB", "A")
#' fct(x, levels = c("O", "A", "B", "AB"))
#'
#' # If you don't specify the levels, fct will create from the data
#' # in the order that they're seen
#' fct(x)
#'
#'
#' # Differences with base R -----------------------------------------------
#' # factor() silently generates NAs
#' x <- c("a", "b", "c")
#' factor(x, levels = c("a", "b"))
#' # fct() errors
#' try(fct(x, levels = c("a", "b")))
#' # Unless you explicitly supply NA:
#' fct(x, levels = c("a", "b"), na = "c")
#'
#' # factor() sorts default levels:
#' factor(c("y", "x"))
#' # fct() uses in order of appearance:
#' fct(c("y", "x"))
fct <- function(x = character(), levels = NULL, na = character()) {
check_character(x)
check_character(levels, allow_null = TRUE)
check_character(na)
x[x %in% na] <- NA
if (is.null(levels)) {
levels <- unique(x)
levels <- levels[!is.na(levels)]
}
invalid <- setdiff(x, c(levels, NA))
if (length(invalid) > 0 ) {
cli::cli_abort(c(
"All values of {.arg x} must appear in {.arg levels} or {.arg na}",
i = "Missing level{?s}: {.str {invalid}}"
))
}
factor(x, levels = levels, exclude = NULL)
}
forcats/R/relevel.R 0000644 0001762 0000144 00000004245 14357357714 013722 0 ustar ligges users #' Reorder factor levels by hand
#'
#' This is a generalisation of [stats::relevel()] that allows you to move any
#' number of levels to any location.
#'
#' @param .f A factor (or character vector).
#' @param ... Either a function (or formula), or character levels.
#'
#' A function will be called with the current levels as input, and the
#' return value (which must be a character vector) will be used to relevel
#' the factor.
#'
#' Any levels not mentioned will be left in their existing order, by default
#' after the explicitly mentioned levels. Supports tidy dots.
#' @param after Where should the new values be placed?
#' @export
#' @examples
#' f <- factor(c("a", "b", "c", "d"), levels = c("b", "c", "d", "a"))
#' fct_relevel(f)
#' fct_relevel(f, "a")
#' fct_relevel(f, "b", "a")
#'
#' # Move to the third position
#' fct_relevel(f, "a", after = 2)
#'
#' # Relevel to the end
#' fct_relevel(f, "a", after = Inf)
#' fct_relevel(f, "a", after = 3)
#'
#' # Relevel with a function
#' fct_relevel(f, sort)
#' fct_relevel(f, sample)
#' fct_relevel(f, rev)
#'
#' # Using 'Inf' allows you to relevel to the end when the number
#' # of levels is unknown or variable (e.g. vectorised operations)
#' df <- forcats::gss_cat[, c("rincome", "denom")]
#' lapply(df, levels)
#'
#' df2 <- lapply(df, fct_relevel, "Don't know", after = Inf)
#' lapply(df2, levels)
#'
#' # You'll get a warning if the levels don't exist
#' fct_relevel(f, "e")
fct_relevel <- function(.f, ..., after = 0L) {
f <- check_factor(.f)
check_dots_unnamed()
old_levels <- levels(f)
if (dots_n(...) == 1L && (is.function(..1) || is_formula(..1))) {
fun <- as_function(..1)
first_levels <- fun(old_levels)
if (!is.character(first_levels)) {
cli::cli_abort("Re-leveling function must return character vector")
}
} else {
first_levels <- chr(...)
}
unknown <- setdiff(first_levels, old_levels)
if (length(unknown) > 0) {
cli::cli_warn("{length(unknown)} unknown level{?s} in `f`: {unknown}")
first_levels <- intersect(first_levels, old_levels)
}
new_levels <- append(setdiff(old_levels, first_levels), first_levels, after = after)
lvls_reorder(f, match(new_levels, old_levels))
}
forcats/R/relabel.R 0000644 0001762 0000144 00000002331 14357077752 013665 0 ustar ligges users #' Relabel factor levels with a function, collapsing as necessary
#'
#' @param .f A factor (or character vector).
#' @param .fun A function to be applied to each level. Must accept one
#' character argument and return a character vector of the same length as
#' its input.
#'
#' You can also use `~` to create as shorthand (in the style of purrr).
#' `~ paste(., "x")` is equivalent to `function(.) paste(., "x")`
#' @param ... Additional arguments to `fun`.
#' @export
#' @examples
#' gss_cat$partyid %>% fct_count()
#' gss_cat$partyid %>%
#' fct_relabel(~ gsub(",", ", ", .x)) %>%
#' fct_count()
#'
#' convert_income <- function(x) {
#' regex <- "^(?:Lt |)[$]([0-9]+).*$"
#' is_range <- grepl(regex, x)
#' num_income <- as.numeric(gsub(regex, "\\1", x[is_range]))
#' num_income <- trunc(num_income / 5000) * 5000
#' x[is_range] <- paste0("Gt $", num_income)
#' x
#' }
#' fct_count(gss_cat$rincome)
#' convert_income(levels(gss_cat$rincome))
#' rincome2 <- fct_relabel(gss_cat$rincome, convert_income)
#' fct_count(rincome2)
fct_relabel <- function(.f, .fun, ...) {
f <- check_factor(.f)
.fun <- as_function(.fun)
old_levels <- levels(f)
new_levels <- .fun(old_levels, ...)
lvls_revalue(f, new_levels)
}
forcats/R/compat-types-check.R 0000644 0001762 0000144 00000024351 14360012612 015737 0 ustar ligges users # nocov start --- r-lib/rlang compat-types-check
#
# Dependencies
# ============
#
# - compat-obj-type.R
#
# Changelog
# =========
#
# 2022-10-07:
# - `check_number_whole()` and `_decimal()` no longer treat
# non-numeric types such as factors or dates as numbers. Numeric
# types are detected with `is.numeric()`.
#
# 2022-10-04:
# - Added `check_name()` that forbids the empty string.
# `check_string()` allows the empty string by default.
#
# 2022-09-28:
# - Removed `what` arguments.
# - Added `allow_na` and `allow_null` arguments.
# - Added `allow_decimal` and `allow_infinite` arguments.
# - Improved errors with absent arguments.
#
#
# 2022-09-16:
# - Unprefixed usage of rlang functions with `rlang::` to
# avoid onLoad issues when called from rlang (#1482).
#
# 2022-08-11:
# - Added changelog.
# Scalars -----------------------------------------------------------------
check_bool <- function(x,
...,
allow_na = FALSE,
allow_null = FALSE,
arg = caller_arg(x),
call = caller_env()) {
if (!missing(x)) {
if (is_bool(x)) {
return(invisible(NULL))
}
if (allow_null && is_null(x)) {
return(invisible(NULL))
}
if (allow_na && identical(x, NA)) {
return(invisible(NULL))
}
}
stop_input_type(
x,
c("`TRUE`", "`FALSE`"),
...,
allow_na = allow_na,
allow_null = allow_null,
arg = arg,
call = call
)
}
check_string <- function(x,
...,
allow_empty = TRUE,
allow_na = FALSE,
allow_null = FALSE,
arg = caller_arg(x),
call = caller_env()) {
if (!missing(x)) {
is_string <- .rlang_check_is_string(
x,
allow_empty = allow_empty,
allow_na = allow_na,
allow_null = allow_null
)
if (is_string) {
return(invisible(NULL))
}
}
stop_input_type(
x,
"a single string",
...,
allow_na = allow_na,
allow_null = allow_null,
arg = arg,
call = call
)
}
.rlang_check_is_string <- function(x,
allow_empty,
allow_na,
allow_null) {
if (is_string(x)) {
if (allow_empty || !is_string(x, "")) {
return(TRUE)
}
}
if (allow_null && is_null(x)) {
return(TRUE)
}
if (allow_na && (identical(x, NA) || identical(x, na_chr))) {
return(TRUE)
}
FALSE
}
check_name <- function(x,
...,
allow_null = FALSE,
arg = caller_arg(x),
call = caller_env()) {
if (!missing(x)) {
is_string <- .rlang_check_is_string(
x,
allow_empty = FALSE,
allow_na = FALSE,
allow_null = allow_null
)
if (is_string) {
return(invisible(NULL))
}
}
stop_input_type(
x,
"a valid name",
...,
allow_na = FALSE,
allow_null = allow_null,
arg = arg,
call = call
)
}
check_number_decimal <- function(x,
...,
min = -Inf,
max = Inf,
allow_infinite = TRUE,
allow_na = FALSE,
allow_null = FALSE,
arg = caller_arg(x),
call = caller_env()) {
.rlang_types_check_number(
x,
...,
min = min,
max = max,
allow_decimal = TRUE,
allow_infinite = allow_infinite,
allow_na = allow_na,
allow_null = allow_null,
arg = arg,
call = call
)
}
check_number_whole <- function(x,
...,
min = -Inf,
max = Inf,
allow_na = FALSE,
allow_null = FALSE,
arg = caller_arg(x),
call = caller_env()) {
.rlang_types_check_number(
x,
...,
min = min,
max = max,
allow_decimal = FALSE,
allow_infinite = FALSE,
allow_na = allow_na,
allow_null = allow_null,
arg = arg,
call = call
)
}
.rlang_types_check_number <- function(x,
...,
min = -Inf,
max = Inf,
allow_decimal = FALSE,
allow_infinite = FALSE,
allow_na = FALSE,
allow_null = FALSE,
arg = caller_arg(x),
call = caller_env()) {
if (allow_decimal) {
what <- "a number"
} else {
what <- "a whole number"
}
.stop <- function(x, what, ...) stop_input_type(
x,
what,
...,
allow_na = allow_na,
allow_null = allow_null,
arg = arg,
call = call
)
if (!missing(x)) {
is_number <- is_number(
x,
allow_decimal = allow_decimal,
allow_infinite = allow_infinite
)
if (is_number) {
if (min > -Inf && max < Inf) {
what <- sprintf("a number between %s and %s", min, max)
} else {
what <- NULL
}
if (x < min) {
what <- what %||% sprintf("a number larger than %s", min)
.stop(x, what, ...)
}
if (x > max) {
what <- what %||% sprintf("a number smaller than %s", max)
.stop(x, what, ...)
}
return(invisible(NULL))
}
if (allow_null && is_null(x)) {
return(invisible(NULL))
}
if (allow_na && (identical(x, NA) ||
identical(x, na_dbl) ||
identical(x, na_int))) {
return(invisible(NULL))
}
}
.stop(x, what, ...)
}
is_number <- function(x,
allow_decimal = FALSE,
allow_infinite = FALSE) {
if (!typeof(x) %in% c("integer", "double")) {
return(FALSE)
}
if (!is.numeric(x)) {
return(FALSE)
}
if (length(x) != 1) {
return(FALSE)
}
if (is.na(x)) {
return(FALSE)
}
if (!allow_decimal && !is_integerish(x)) {
return(FALSE)
}
if (!allow_infinite && is.infinite(x)) {
return(FALSE)
}
TRUE
}
check_symbol <- function(x,
...,
allow_null = FALSE,
arg = caller_arg(x),
call = caller_env()) {
if (!missing(x)) {
if (is_symbol(x)) {
return(invisible(NULL))
}
if (allow_null && is_null(x)) {
return(invisible(NULL))
}
}
stop_input_type(
x,
"a symbol",
...,
allow_null = allow_null,
arg = arg,
call = call
)
}
check_arg <- function(x,
...,
allow_null = FALSE,
arg = caller_arg(x),
call = caller_env()) {
if (!missing(x)) {
if (is_symbol(x)) {
return(invisible(NULL))
}
if (allow_null && is_null(x)) {
return(invisible(NULL))
}
}
stop_input_type(
x,
"an argument name",
...,
allow_null = allow_null,
arg = arg,
call = call
)
}
check_call <- function(x,
...,
allow_null = FALSE,
arg = caller_arg(x),
call = caller_env()) {
if (!missing(x)) {
if (is_call(x)) {
return(invisible(NULL))
}
if (allow_null && is_null(x)) {
return(invisible(NULL))
}
}
stop_input_type(
x,
"a defused call",
...,
allow_null = allow_null,
arg = arg,
call = call
)
}
check_environment <- function(x,
...,
allow_null = FALSE,
arg = caller_arg(x),
call = caller_env()) {
if (!missing(x)) {
if (is_environment(x)) {
return(invisible(NULL))
}
if (allow_null && is_null(x)) {
return(invisible(NULL))
}
}
stop_input_type(
x,
"an environment",
...,
allow_null = allow_null,
arg = arg,
call = call
)
}
check_function <- function(x,
...,
allow_null = FALSE,
arg = caller_arg(x),
call = caller_env()) {
if (!missing(x)) {
if (is_function(x)) {
return(invisible(NULL))
}
if (allow_null && is_null(x)) {
return(invisible(NULL))
}
}
stop_input_type(
x,
"a function",
...,
allow_null = allow_null,
arg = arg,
call = call
)
}
check_closure <- function(x,
...,
allow_null = FALSE,
arg = caller_arg(x),
call = caller_env()) {
if (!missing(x)) {
if (is_closure(x)) {
return(invisible(NULL))
}
if (allow_null && is_null(x)) {
return(invisible(NULL))
}
}
stop_input_type(
x,
"an R function",
...,
allow_null = allow_null,
arg = arg,
call = call
)
}
check_formula <- function(x,
...,
allow_null = FALSE,
arg = caller_arg(x),
call = caller_env()) {
if (!missing(x)) {
if (is_formula(x)) {
return(invisible(NULL))
}
if (allow_null && is_null(x)) {
return(invisible(NULL))
}
}
stop_input_type(
x,
"a formula",
...,
allow_null = allow_null,
arg = arg,
call = call
)
}
# Vectors -----------------------------------------------------------------
check_character <- function(x,
...,
allow_null = FALSE,
arg = caller_arg(x),
call = caller_env()) {
if (!missing(x)) {
if (is_character(x)) {
return(invisible(NULL))
}
if (allow_null && is_null(x)) {
return(invisible(NULL))
}
}
stop_input_type(
x,
"a character vector",
...,
allow_null = allow_null,
arg = arg,
call = call
)
}
# nocov end
forcats/R/explicit_na.R 0000644 0001762 0000144 00000002403 14357073063 014545 0 ustar ligges users #' Make missing values explicit
#'
#' @description
#' `r lifecycle::badge("deprecated")`
#'
#' This function is deprecated because the terminology is confusing;
#' please use [fct_na_value_to_level()] instead.
#'
#' This gives missing values an explicit factor level, ensuring that they
#' appear in summaries and on plots.
#'
#' @param f A factor (or character vector).
#' @param na_level Level to use for missing values: this is what `NA`s will be
#' changed to.
#' @export
#' @keywords internal
#' @examples
#' f1 <- factor(c("a", "a", NA, NA, "a", "b", NA, "c", "a", "c", "b"))
#' fct_count(f1)
#' table(f1)
#' sum(is.na(f1))
#'
#' # previously
#' f2 <- fct_explicit_na(f1)
#' # now
#' f2 <- fct_na_value_to_level(f1)
#'
#' fct_count(f2)
#' table(f2)
#' sum(is.na(f2))
fct_explicit_na <- function(f, na_level = "(Missing)") {
lifecycle::deprecate_warn(
when = "1.0.0",
what = "fct_explicit_na()",
with = "fct_na_value_to_level()"
)
f <- check_factor(f)
is_missing <- is.na(f)
is_missing_level <- is.na(levels(f))
if (any(is_missing)) {
f <- fct_expand(f, na_level)
f[is_missing] <- na_level
f
} else if (any(is_missing_level)) {
levs <- levels(f)
levs[is.na(levs)] <- na_level
lvls_revalue(f, levs)
} else {
f
}
}
forcats/R/match.R 0000644 0001762 0000144 00000002243 14360012612 013327 0 ustar ligges users #' Test for presence of levels in a factor
#'
#' Do any of `lvls` occur in `f`? Compared to [%in%], this function validates
#' `lvls` to ensure that they're actually present in `f`. In other words,
#' `x %in% "not present"` will return `FALSE`, but `fct_match(x, "not present")`
#' will throw an error.
#'
#' @rdname fct_match
#' @param f A factor (or character vector).
#' @param lvls A character vector specifying levels to look for.
#' @return A logical vector
#' @export
#' @examples
#' table(fct_match(gss_cat$marital, c("Married", "Divorced")))
#'
#' # Compare to %in%, misspelled levels throw an error
#' table(gss_cat$marital %in% c("Maried", "Davorced"))
#' \dontrun{
#' table(fct_match(gss_cat$marital, c("Maried", "Davorced")))
#' }
fct_match <- function(f, lvls) {
f <- check_factor(f)
if (identical(lvls, NA)) {
lvls <- NA_character_
}
check_character(lvls)
bad_lvls <- setdiff(lvls, levels(f))
bad_lvls <- bad_lvls[!is.na(bad_lvls)]
if (length(bad_lvls) > 0) {
missing <- encodeString(bad_lvls, quote = '"')
cli::cli_abort(c(
"All {.arg lvls} must be present in {.arg f}.",
i = "Missing levels: {missing}"
))
}
f %in% lvls
}
forcats/R/forcats-package.R 0000644 0001762 0000144 00000000307 14241066126 015273 0 ustar ligges users #' @keywords internal
"_PACKAGE"
## usethis namespace: start
#' @import rlang
#' @importFrom glue glue
#' @importFrom lifecycle deprecated
#' @importFrom stats median
## usethis namespace: end
NULL
forcats/R/as_factor.R 0000644 0001762 0000144 00000002512 14355407043 014205 0 ustar ligges users #' Convert input to a factor
#'
#' Compared to base R, when `x` is a character, this function creates
#' levels in the order in which they appear, which will be the same on every
#' platform. (Base R sorts in the current locale which can vary from place
#' to place.) When `x` is numeric, the ordering is based on the numeric
#' value and consistent with base R.
#'
#' This is a generic function.
#'
#' @param x Object to coerce to a factor.
#' @param ... Other arguments passed down to method.
#' @export
#' @examples
#' # Character object
#' x <- c("a", "z", "g")
#' as_factor(x)
#' as.factor(x)
#'
#' # Character object containing numbers
#' y <- c("1.1", "11", "2.2", "22")
#' as_factor(y)
#' as.factor(y)
#'
#' # Numeric object
#' z <- as.numeric(y)
#' as_factor(z)
#' as.factor(z)
as_factor <- function(x, ...) {
check_dots_used()
UseMethod("as_factor")
}
#' @rdname as_factor
#' @export
as_factor.factor <- function(x, ...) {
x
}
#' @rdname as_factor
#' @export
as_factor.character <- function(x, ...) {
# Preserve label for future haven compatibility
structure(
fct_inorder(x),
label = attr(x, "label", exact = TRUE)
)
}
#' @rdname as_factor
#' @export
as_factor.numeric <- function(x, ...) {
factor(x)
}
#' @rdname as_factor
#' @export
as_factor.logical <- function(x, ...) {
factor(x, levels = c("FALSE", "TRUE"))
}
forcats/R/recode.R 0000644 0001762 0000144 00000004505 14357073063 013514 0 ustar ligges users #' Change factor levels by hand
#'
#' @param .f A factor (or character vector).
#' @param ... <[`dynamic-dots`][rlang::dyn-dots]> A sequence of named character
#' vectors where the name gives the new level, and the value gives the old
#' level. Levels not otherwise mentioned will be left as is. Levels can
#' be removed by naming them `NULL`.
#' @export
#' @examples
#' x <- factor(c("apple", "bear", "banana", "dear"))
#' fct_recode(x, fruit = "apple", fruit = "banana")
#'
#' # If you make a mistake you'll get a warning
#' fct_recode(x, fruit = "apple", fruit = "bananana")
#'
#' # If you name the level NULL it will be removed
#' fct_recode(x, NULL = "apple", fruit = "banana")
#'
#' # Wrap the left hand side in quotes if it contains special variables
#' fct_recode(x, "an apple" = "apple", "a bear" = "bear")
#'
#' # When passing a named vector to rename levels use !!! to splice
#' x <- factor(c("apple", "bear", "banana", "dear"))
#' levels <- c(fruit = "apple", fruit = "banana")
#' fct_recode(x, !!!levels)
fct_recode <- function(.f, ...) {
f <- check_factor(.f)
new_levels <- check_recode_levels(...)
# Remove any named NULL and finish if all NULLs
nulls <- names(new_levels) == "NULL"
if (any(nulls)) {
f <- factor(f, levels = setdiff(levels(f), new_levels[nulls]))
new_levels <- new_levels[!nulls]
}
lvls_revalue(f, lvls_rename(f, new_levels))
}
lvls_rename <- function(f, new_levels) {
# Match old levels with new levels
old_levels <- levels(f)
idx <- match(new_levels, old_levels)
# Handle levels that don't exist
if (any(is.na(idx))) {
bad <- new_levels[is.na(idx)]
warning("Unknown levels in `f`: ", paste(bad, collapse = ", "), call. = FALSE)
new_levels <- new_levels[!is.na(idx)]
idx <- idx[!is.na(idx)]
}
old_levels[idx] <- names(new_levels)
old_levels
}
check_recode_levels <- function(..., call = caller_env()) {
levels <- list2(...)
is_ok <- function(x) is.character(x) && length(x) == 1
ok <- vapply(levels, is_ok, logical(1)) & names2(levels) != ""
if (!all(ok)) {
indx <- names2(levels)
indx[indx == ""] <- seq_along(levels)[indx == ""]
problems <- indx[!ok]
cli::cli_abort(c(
"Each element of {.arg ...} must be a named string.",
i = "Problems with {length(problems)} argument{?s}: {problems}"
), call = call)
}
unlist(levels)
}
forcats/R/na.R 0000644 0001762 0000144 00000004043 14360012612 012631 0 ustar ligges users #' Convert between `NA` values and `NA` levels
#'
#' @description
#' There are two ways to represent missing values in factors: in the values
#' and in the levels. `NA`s in the values are most useful for data analysis
#' (since [is.na()] returns what you expect), but because the `NA` is not
#' explicitly recorded in the levels, there's no way to control its position
#' (it's almost always displayed last or not at all). Putting the `NA`s in the levels allows
#' you to control its display, at the cost of losing accurate `is.na()`
#' reporting.
#'
#' (It is possible to have a factor with missing values in both the values
#' and the levels but it requires some explicit gymnastics and we don't
#' recommend it.)
#'
#' @param f A factor (or character vector).
#' @param level Optionally, instead of converting the `NA` values to an
#' `NA` level, convert it to a level with this value.
#' @export
#' @examples
#' # Most factors store NAs in the values:
#' f1 <- fct(c("a", "b", NA, "c", "b", NA))
#' levels(f1)
#' as.integer(f1)
#' is.na(f1)
#'
#' # But it's also possible to store them in the levels
#' f2 <- fct_na_value_to_level(f1)
#' levels(f2)
#' as.integer(f2)
#' is.na(f2)
#'
#' # If needed, you can convert back to NAs in the values:
#' f3 <- fct_na_level_to_value(f2)
#' levels(f3)
#' as.integer(f3)
#' is.na(f3)
fct_na_value_to_level <- function(f, level = NA) {
f <- check_factor(f)
check_string(level, allow_na = TRUE)
f <- fct_expand(f, NA)
new_levels <- levels(f)
new_levels[is.na(new_levels)] <- level
lvls_revalue(f, new_levels)
}
#' @export
#' @rdname fct_na_value_to_level
#' @param extra_levels Optionally, a character vector giving additional levels
#' that should also be converted to `NA` values.
fct_na_level_to_value <- function(f, extra_levels = NULL) {
f <- check_factor(f)
check_character(extra_levels, allow_null = TRUE)
new_levels <- setdiff(levels(f), union(NA, extra_levels))
idx <- match(levels(f), new_levels)
out <- idx[as.integer(f)]
attributes(out) <- attributes(f)
attr(out, "levels") <- new_levels
out
}
forcats/R/collapse.R 0000644 0001762 0000144 00000002733 14360012612 014041 0 ustar ligges users #' Collapse factor levels into manually defined groups
#'
#' @param .f A factor (or character vector).
#' @param ... <[`dynamic-dots`][rlang::dyn-dots]> A series of named character vectors. The levels in
#' each vector will be replaced with the name.
#' @inheritParams fct_other
#' @param group_other Deprecated. Replace all levels not named in `...` with "Other"?
#' @export
#' @examples
#' fct_count(gss_cat$partyid)
#'
#' partyid2 <- fct_collapse(gss_cat$partyid,
#' missing = c("No answer", "Don't know"),
#' other = "Other party",
#' rep = c("Strong republican", "Not str republican"),
#' ind = c("Ind,near rep", "Independent", "Ind,near dem"),
#' dem = c("Not str democrat", "Strong democrat")
#' )
#' fct_count(partyid2)
fct_collapse <- function(.f, ..., other_level = NULL, group_other = "DEPRECATED") {
f <- check_factor(.f)
check_string(other_level, allow_null = TRUE, allow_na = TRUE)
if (!missing(group_other)) {
lifecycle::deprecate_warn(
when = "0.5.0",
what = "fct_collapse(group_other)",
with = "fct_collapse(other_level)",
always = TRUE
)
if (isTRUE(group_other) && is.null(other_level)) {
other_level <- "Other"
}
}
dots <- rlang::list2(...)
old <- unlist(dots, use.names = FALSE) %||% character()
new <- rep(names(dots), lengths(dots))
out <- lvls_revalue(f, lvls_rename(f, set_names(old, new)))
if (!is.null(other_level)) {
out <- lvls_other(out, levels(out) %in% new, other_level)
}
out
}
forcats/R/compat-purrr.R 0000644 0001762 0000144 00000011545 14355304071 014702 0 ustar ligges users # nocov start - compat-purrr.R
# Latest version: https://github.com/r-lib/rlang/blob/master/R/compat-purrr.R
# This file provides a minimal shim to provide a purrr-like API on top of
# base R functions. They are not drop-in replacements but allow a similar style
# of programming.
#
# Changelog:
# 2020-04-14:
# * Removed `pluck*()` functions
# * Removed `*_cpl()` functions
# * Used `as_function()` to allow use of `~`
# * Used `.` prefix for helpers
#
# 2021-05-21:
# * Fixed "object `x` not found" error in `imap()` (@mgirlich)
#
# 2021-12-15:
# * `transpose()` now supports empty lists.
map <- function(.x, .f, ...) {
.f <- as_function(.f, env = global_env())
lapply(.x, .f, ...)
}
walk <- function(.x, .f, ...) {
map(.x, .f, ...)
invisible(.x)
}
map_lgl <- function(.x, .f, ...) {
.rlang_purrr_map_mold(.x, .f, logical(1), ...)
}
map_int <- function(.x, .f, ...) {
.rlang_purrr_map_mold(.x, .f, integer(1), ...)
}
map_dbl <- function(.x, .f, ...) {
.rlang_purrr_map_mold(.x, .f, double(1), ...)
}
map_chr <- function(.x, .f, ...) {
.rlang_purrr_map_mold(.x, .f, character(1), ...)
}
.rlang_purrr_map_mold <- function(.x, .f, .mold, ...) {
.f <- as_function(.f, env = global_env())
out <- vapply(.x, .f, .mold, ..., USE.NAMES = FALSE)
names(out) <- names(.x)
out
}
map2 <- function(.x, .y, .f, ...) {
.f <- as_function(.f, env = global_env())
out <- mapply(.f, .x, .y, MoreArgs = list(...), SIMPLIFY = FALSE)
if (length(out) == length(.x)) {
set_names(out, names(.x))
} else {
set_names(out, NULL)
}
}
map2_lgl <- function(.x, .y, .f, ...) {
as.vector(map2(.x, .y, .f, ...), "logical")
}
map2_int <- function(.x, .y, .f, ...) {
as.vector(map2(.x, .y, .f, ...), "integer")
}
map2_dbl <- function(.x, .y, .f, ...) {
as.vector(map2(.x, .y, .f, ...), "double")
}
map2_chr <- function(.x, .y, .f, ...) {
as.vector(map2(.x, .y, .f, ...), "character")
}
imap <- function(.x, .f, ...) {
map2(.x, names(.x) %||% seq_along(.x), .f, ...)
}
pmap <- function(.l, .f, ...) {
.f <- as.function(.f)
args <- .rlang_purrr_args_recycle(.l)
do.call("mapply", c(
FUN = list(quote(.f)),
args, MoreArgs = quote(list(...)),
SIMPLIFY = FALSE, USE.NAMES = FALSE
))
}
.rlang_purrr_args_recycle <- function(args) {
lengths <- map_int(args, length)
n <- max(lengths)
stopifnot(all(lengths == 1L | lengths == n))
to_recycle <- lengths == 1L
args[to_recycle] <- map(args[to_recycle], function(x) rep.int(x, n))
args
}
keep <- function(.x, .f, ...) {
.x[.rlang_purrr_probe(.x, .f, ...)]
}
discard <- function(.x, .p, ...) {
sel <- .rlang_purrr_probe(.x, .p, ...)
.x[is.na(sel) | !sel]
}
map_if <- function(.x, .p, .f, ...) {
matches <- .rlang_purrr_probe(.x, .p)
.x[matches] <- map(.x[matches], .f, ...)
.x
}
.rlang_purrr_probe <- function(.x, .p, ...) {
if (is_logical(.p)) {
stopifnot(length(.p) == length(.x))
.p
} else {
.p <- as_function(.p, env = global_env())
map_lgl(.x, .p, ...)
}
}
compact <- function(.x) {
Filter(length, .x)
}
transpose <- function(.l) {
if (!length(.l)) {
return(.l)
}
inner_names <- names(.l[[1]])
if (is.null(inner_names)) {
fields <- seq_along(.l[[1]])
} else {
fields <- set_names(inner_names)
}
map(fields, function(i) {
map(.l, .subset2, i)
})
}
every <- function(.x, .p, ...) {
.p <- as_function(.p, env = global_env())
for (i in seq_along(.x)) {
if (!rlang::is_true(.p(.x[[i]], ...))) return(FALSE)
}
TRUE
}
some <- function(.x, .p, ...) {
.p <- as_function(.p, env = global_env())
for (i in seq_along(.x)) {
if (rlang::is_true(.p(.x[[i]], ...))) return(TRUE)
}
FALSE
}
negate <- function(.p) {
.p <- as_function(.p, env = global_env())
function(...) !.p(...)
}
reduce <- function(.x, .f, ..., .init) {
f <- function(x, y) .f(x, y, ...)
Reduce(f, .x, init = .init)
}
reduce_right <- function(.x, .f, ..., .init) {
f <- function(x, y) .f(y, x, ...)
Reduce(f, .x, init = .init, right = TRUE)
}
accumulate <- function(.x, .f, ..., .init) {
f <- function(x, y) .f(x, y, ...)
Reduce(f, .x, init = .init, accumulate = TRUE)
}
accumulate_right <- function(.x, .f, ..., .init) {
f <- function(x, y) .f(y, x, ...)
Reduce(f, .x, init = .init, right = TRUE, accumulate = TRUE)
}
detect <- function(.x, .f, ..., .right = FALSE, .p = is_true) {
.p <- as_function(.p, env = global_env())
.f <- as_function(.f, env = global_env())
for (i in .rlang_purrr_index(.x, .right)) {
if (.p(.f(.x[[i]], ...))) {
return(.x[[i]])
}
}
NULL
}
detect_index <- function(.x, .f, ..., .right = FALSE, .p = is_true) {
.p <- as_function(.p, env = global_env())
.f <- as_function(.f, env = global_env())
for (i in .rlang_purrr_index(.x, .right)) {
if (.p(.f(.x[[i]], ...))) {
return(i)
}
}
0L
}
.rlang_purrr_index <- function(x, right = FALSE) {
idx <- seq_along(x)
if (right) {
idx <- rev(idx)
}
idx
}
# nocov end
forcats/R/reorder.R 0000644 0001762 0000144 00000015113 14360012623 013677 0 ustar ligges users #' Reorder factor levels by sorting along another variable
#'
#' `fct_reorder()` is useful for 1d displays where the factor is mapped to
#' position; `fct_reorder2()` for 2d displays where the factor is mapped to
#' a non-position aesthetic. `last2()` and `first2()` are helpers for `fct_reorder2()`;
#' `last2()` finds the last value of `y` when sorted by `x`; `first2()` finds the first value.
#'
#' @param .f A factor (or character vector).
#' @param .x,.y The levels of `f` are reordered so that the values
#' of `.fun(.x)` (for `fct_reorder()`) and `fun(.x, .y)` (for `fct_reorder2()`)
#' are in ascending order.
#' @param .fun n summary function. It should take one vector for
#' `fct_reorder`, and two vectors for `fct_reorder2`, and return a single
#' value.
#' @param .na_rm Should `fct_reorder()` remove missing values?
#' If `NULL`, the default, will remove missing values with a warning.
#' Set to `FALSE` to preserve `NA`s (if you `.fun` already handles them) and
#' `TRUE` to remove silently.
#' @param .default What default value should we use for `.fun` for
#' empty levels? Use this to control where empty levels appear in the
#' output.
#' @param ... Other arguments passed on to `.fun`.
#' @param .desc Order in descending order? Note the default is different
#' between `fct_reorder` and `fct_reorder2`, in order to
#' match the default ordering of factors in the legend.
#' @export
#' @examples
#' # fct_reorder() -------------------------------------------------------------
#' # Useful when a categorical variable is mapped to position
#' boxplot(Sepal.Width ~ Species, data = iris)
#' boxplot(Sepal.Width ~ fct_reorder(Species, Sepal.Width), data = iris)
#'
#' # or with
#' library(ggplot2)
#' ggplot(iris, aes(fct_reorder(Species, Sepal.Width), Sepal.Width)) +
#' geom_boxplot()
#'
#' # fct_reorder2() -------------------------------------------------------------
#' # Useful when a categorical variable is mapped to color, size, shape etc
#'
#' chks <- subset(ChickWeight, as.integer(Chick) < 10)
#' chks <- transform(chks, Chick = fct_shuffle(Chick))
#'
#' # Without reordering it's hard to match line to legend
#' ggplot(chks, aes(Time, weight, colour = Chick)) +
#' geom_point() +
#' geom_line()
#'
#' # With reordering it's much easier
#' ggplot(chks, aes(Time, weight, colour = fct_reorder2(Chick, Time, weight))) +
#' geom_point() +
#' geom_line() +
#' labs(colour = "Chick")
fct_reorder <- function(.f,
.x,
.fun = median,
...,
.na_rm = NULL,
.default = Inf,
.desc = FALSE) {
f <- check_factor(.f)
stopifnot(length(f) == length(.x))
.fun <- as_function(.fun)
check_dots_used()
check_bool(.na_rm, allow_null = TRUE)
check_bool(.desc)
miss <- is.na(.x)
if (any(miss)) {
if (is.null(.na_rm)) {
cli::cli_warn(c(
"{.fn fct_reorder} removing {sum(miss)} missing value{?s}.",
i = "Use {.code .na_rm = TRUE} to silence this message.",
i = "Use {.code .na_rm = FALSE} to preserve NAs."
))
.na_rm <- TRUE
}
if (isTRUE(.na_rm)) {
.x <- .x[!miss]
.f <- .f[!miss]
}
}
summary <- tapply(.x, .f, function(x) .fun(x, ...), default = .default)
check_single_value_per_group(summary, ".fun")
lvls_reorder(f, order(summary, decreasing = .desc))
}
#' @export
#' @rdname fct_reorder
fct_reorder2 <- function(.f,
.x,
.y,
.fun = last2,
...,
.na_rm = NULL,
.default = -Inf,
.desc = TRUE) {
.f <- check_factor(.f)
stopifnot(length(.f) == length(.x), length(.x) == length(.y))
check_dots_used()
check_bool(.na_rm, allow_null = TRUE)
check_bool(.desc)
miss <- is.na(.x) | is.na(.y)
if (any(miss)) {
if (is.null(.na_rm)) {
cli::cli_warn(c(
"{.fn fct_reorder2} removing {sum(miss)} missing value{?s}.",
i = "Use {.code .na_rm = TRUE} to silence this message.",
i = "Use {.code .na_rm = FALSE} to preserve NAs."
))
.na_rm <- TRUE
}
if (isTRUE(.na_rm)) {
.x <- .x[!miss]
.y <- .y[!miss]
.f <- .f[!miss]
}
}
summary <- tapply(
seq_along(.x),
.f,
function(i) .fun(.x[i], .y[i], ...),
default = .default
)
check_single_value_per_group(summary, ".fun")
lvls_reorder(.f, order(summary, decreasing = .desc))
}
check_single_value_per_group <- function(x, fun_arg, call = caller_env()) {
# This is a bit of a weak test, but should detect the most common case
# where `.fun` returns multiple values.
if (is.list(x)) {
cli::cli_abort("{.arg {fun_arg}} must return a single value per group", call = call)
}
}
#' @export
#' @rdname fct_reorder
last2 <- function(.x, .y) {
terminal(.x, .y, desc = TRUE)
}
#' @export
#' @rdname fct_reorder
first2 <- function(.x, .y) {
terminal(.x, .y, desc = FALSE)
}
terminal <- function(x, y, desc) {
miss <- is.na(x) | is.na(y)
x <- x[!miss]
y <- y[!miss]
if (length(x) == 0) {
y[NA_integer_]
} else {
y[[order(x, decreasing = desc)[[1]]]]
}
}
#' Reorder factor levels by first appearance, frequency, or numeric order
#'
#' This family of functions changes only the order of the levels.
#' * `fct_inorder()`: by the order in which they first appear.
#' * `fct_infreq()`: by number of observations with each level (largest first)
#' * `fct_inseq()`: by numeric value of level.
#'
#' @inheritParams lvls_reorder
#' @param f A factor
#' @export
#' @examples
#' f <- factor(c("b", "b", "a", "c", "c", "c"))
#' f
#' fct_inorder(f)
#' fct_infreq(f)
#'
#' f <- factor(1:3, levels = c("3", "2", "1"))
#' f
#' fct_inseq(f)
fct_inorder <- function(f, ordered = NA) {
f <- check_factor(f)
check_bool(ordered, allow_na = TRUE)
idx <- as.integer(f)[!duplicated(f)]
idx <- union(idx[!is.na(idx)], lvls_seq(f))
lvls_reorder(f, idx, ordered = ordered)
}
#' @export
#' @rdname fct_inorder
#' @inheritParams fct_lump
fct_infreq <- function(f, w = NULL, ordered = NA) {
f <- check_factor(f)
w <- compute_weights(f, w)
check_bool(ordered, allow_na = TRUE)
lvls_reorder(f, order(w, decreasing = TRUE), ordered = ordered)
}
#' @export
#' @rdname fct_inorder
fct_inseq <- function(f, ordered = NA) {
f <- check_factor(f)
check_bool(ordered, allow_na = TRUE)
num_levels <- suppressWarnings(as.numeric(levels(f)))
if (all(is.na(num_levels))) {
cli::cli_abort("At least one existing level must be coercible to numeric.")
}
lvls_reorder(f, order(num_levels), ordered = ordered)
}
forcats/R/expand.R 0000644 0001762 0000144 00000001371 14360012612 013513 0 ustar ligges users #' Add additional levels to a factor
#'
#' @param f A factor (or character vector).
#' @param ... Additional levels to add to the factor. Levels that already
#' exist will be silently ignored.
#' @inheritParams fct_relevel
#' @export
#' @seealso [fct_drop()] to drop unused factor levels.
#' @examples
#' f <- factor(sample(letters[1:3], 20, replace = TRUE))
#' f
#' fct_expand(f, "d", "e", "f")
#' fct_expand(f, letters[1:6])
#' fct_expand(f, "Z", after = 0)
fct_expand <- function(f, ..., after = Inf) {
f <- check_factor(f)
check_dots_unnamed()
check_number_decimal(after, min = 0)
old_levels <- levels(f)
new_levels <- chr(...)
new_levels <- append(old_levels, setdiff(new_levels, old_levels), after = after)
lvls_expand(f, new_levels)
}
forcats/R/other.R 0000644 0001762 0000144 00000002404 14360012612 013353 0 ustar ligges users #' Manually replace levels with "other"
#'
#' @inheritParams fct_lump
#' @param keep,drop Pick one of `keep` and `drop`:
#' * `keep` will preserve listed levels, replacing all others with
#' `other_level`.
#' * `drop` will replace listed levels with `other_level`, keeping all
#' as is.
#' @seealso [fct_lump()] to automatically convert the rarest (or most
#' common) levels to "other".
#' @export
#' @examples
#' x <- factor(rep(LETTERS[1:9], times = c(40, 10, 5, 27, 1, 1, 1, 1, 1)))
#'
#' fct_other(x, keep = c("A", "B"))
#' fct_other(x, drop = c("A", "B"))
fct_other <- function(f, keep, drop, other_level = "Other") {
f <- check_factor(f)
check_exclusive(keep, drop)
check_string(other_level, allow_na = TRUE)
if (!missing(keep)) {
check_character(keep)
lvls_other(f, levels(f) %in% keep, other_level)
} else {
check_character(drop)
lvls_other(f, !levels(f) %in% drop, other_level)
}
}
# Replace specified levels (if any), with other.
# @param keep A logical vector the same length as `levels(f)`
lvls_other <- function(f, keep, other_level = "Other") {
if (all(keep)) {
f
} else {
new_levels <- ifelse(keep, levels(f), other_level)
f <- lvls_revalue(f, new_levels)
fct_relevel(f, other_level, after = Inf)
}
}
forcats/R/c.R 0000644 0001762 0000144 00000002356 14360012612 012462 0 ustar ligges users #' Concatenate factors, combining levels
#'
#' This is a useful way of patching together factors from multiple sources
#' that really should have the same levels but don't.
#'
#' @param ... <[`dynamic-dots`][rlang::dyn-dots]> Individual
#' factors. Uses tidy dots, so you can splice in a list of factors
#' with `!!!`.
#' @export
#' @examples
#' fa <- factor("a")
#' fb <- factor("b")
#' fab <- factor(c("a", "b"))
#'
#' c(fa, fb, fab)
#' fct_c(fa, fb, fab)
#'
#' # You can also pass a list of factors with !!!
#' fs <- list(fa, fb, fab)
#' fct_c(!!!fs)
fct_c <- function(...) {
fs <- list2(...)
fs <- check_factor_list(fs, "...")
if (length(fs) == 0) {
return(factor())
}
levels <- lvls_union(fs)
all <- unlist(fct_unify(fs, levels), use.names = FALSE)
factor(all, levels = levels, exclude = NULL)
}
#' Unify the levels in a list of factors
#'
#' @param fs A list of factors
#' @param levels Set of levels to apply to every factor. Default to
#' union of all factor levels
#' @export
#' @examples
#' fs <- list(factor("a"), factor("b"), factor(c("a", "b")))
#' fct_unify(fs)
fct_unify <- function(fs, levels = lvls_union(fs)) {
fs <- check_factor_list(fs)
check_character(levels)
lapply(fs, lvls_expand, new_levels = levels)
}
forcats/R/cross.R 0000644 0001762 0000144 00000002453 14360012612 013367 0 ustar ligges users #' Combine levels from two or more factors to create a new factor
#'
#' Computes a factor whose levels are all the combinations of the levels of the input factors.
#'
#' @param ... <[`dynamic-dots`][rlang::dyn-dots]> Additional factors
#' or character vectors.
#' @param sep A character string to separate the levels
#' @param keep_empty If TRUE, keep combinations with no observations as levels
#' @return The new factor
#'
#' @export
#' @examples
#' fruit <- factor(c("apple", "kiwi", "apple", "apple"))
#' colour <- factor(c("green", "green", "red", "green"))
#' eaten <- c("yes", "no", "yes", "no")
#' fct_cross(fruit, colour)
#' fct_cross(fruit, colour, eaten)
#' fct_cross(fruit, colour, keep_empty = TRUE)
fct_cross <- function(..., sep = ":", keep_empty = FALSE) {
check_dots_unnamed()
check_string(sep, allow_empty = TRUE)
check_bool(keep_empty)
flist <- list2(...)
if (length(flist) == 0) {
return(factor())
}
.data <- tibble::as_tibble(flist, .name_repair = "minimal")
.data <- lapply(.data, check_factor)
newf <- exec(paste, !!!.data, sep = sep)
old_levels <- lapply(.data, levels)
grid <- exec(expand.grid, old_levels)
new_levels <- exec(paste, !!!grid, sep = sep)
if (!keep_empty) {
new_levels <- intersect(new_levels, newf)
}
factor(newf, levels = new_levels)
}
forcats/NEWS.md 0000644 0001762 0000144 00000016236 14364755560 013040 0 ustar ligges users # forcats 1.0.0
## New features
* New `fct_na_value_to_level()` and `fct_na_level_to_value()` to convert
NA values to NA levels and vice versa (#337).
## Minor improvement and bug fixes
* All functions now validate their inputs, giving more useful errors if you
accidentally misspecify an input.
* `fct_collapse()` can now use `other_level = NA` (#291).
* `fct_count()` works with factors that contain `NA`s in levels.
* `fct_explicit_na()` is deprecated in favour of `fct_na_value_to_level()`.
* `fct_expand()` gains an `after` argument so that you can choose where
the new levels are placed (#138).
* `fct_infreq()` gains the ability to weight by another variable using the `w`
argument (#261).
* `fct_inorder()` now works when not all levels appear in the data (#262).
* `fct_lump_prop()` and friends now work correctly if you supply weights
and have empty levels (#292).
* `fct_lump_n()` and `fct_lump_prop()` will now create an "Other" level even
if it only consists of a single level. This makes them consistent with the
other `fct_lump_*` functions (#274).
* `fct_other()` no longer generates a warning if no levels are replaced with
other (#265).
* `fct_relevel()`, `fct_cross()`, and `fct_expand()` now error if you name the
arguments in `...` since those names are ignored and your code probably
doesn't do what you think it does (#319).
* `fct_reorder()` and `fct_reorder2()` now remove `NA` values in `.x` with a
warning (like `ggplot2::geom_point()` and friends). You can suppress the
warning by setting `.na_rm = TRUE` (#315).
* `fct_reorder()` and `fct_reorder2()` gain a new `.default` argument that
controls the placement of empty levels (including levels that might become
empty after removing missing values in `.x`) (#266).
* `fct_unique()` now captures implicit missing values if present (#293).
# forcats 0.5.2
* New `fct()` which works like `factor()` but errors if values of `x`
are not included in the levels specification (#299)
* `first2()` and `last2()` now ignore missing values in both `x` and `y` (#303).
* Error messages are more informative.
# forcats 0.5.1
* Re-license as MIT (#277).
* `fct_lump_n()` no longer uses a partial argument name (@malcolmbarrett, #276).
# forcats 0.5.0
* `as_factor()` gains a logical method that always returns a factor with
levels "FALSE" and "TRUE" (#185).
* `fct_c()`, `fct_collapse()` and `fct_recode()` are now explicitly
documented as using [dynamic dots](https://rlang.r-lib.org/reference/dyn-dots.html)
(@labouz, #234).
* `fct_collapse()` now accepts a `other_level` argument, to allow a
user-specified `Other` level (@gtm19, #194). It now correctly collapses
factors when `other_level` is not `NULL` (#172), and makes `"Other"` the
last level (#202) (@gtm19, #172 & #202)
* `fct_count()` no longer converts implicit NAs into explicit NAs (#151).
* `fct_inseq()` behaves more robustly when factor levels aren't all numbers
(#221).
* `fct_lump()` has been split up into three new functions: `fct_lump_prop()`,
`fct_lump_n()`, and `fct_lump_lowfreq()`. (@jonocarroll, #167, #142).
All `fct_lump_()` functions check their inputs more carefully
(@robinson_es, #169)
* `fct_reorder2()` gains a helper function `first2()`, that sorts `.y` by the
first value of `.x` (@jtr13).
# forcats 0.4.0
## New features
* `fct_collapse()` gains a `group_other` argument to allow you to group all
un-named levels into `"Other"`. (#100, @AmeliaMN)
* `fct_cross()` creates a new factor containing the combined levels from two
or more input factors, similar to `base::interaction` (@tslumley, #136)
* `fct_inseq()` reorders labels in numeric order, if possible (#145, @kbodwin).
* `fct_lump_min()` preserves levels that appear at least `min` times (can also
be used with the `w` weighted argument) (@robinsones, #142).
* `fct_match()` performs validated matching, providing a safer alternative to
`f %in% c("x", "y")` which silently returns `FALSE` if `"x"` or `"y"`
are not levels of `f` (e.g. because of a typo) (#126, @jonocarroll).
* `fct_relevel()` can now level factors using a function that is passed the
current levels (#117).
* `as_factor()` now has a numeric method. By default, orders factors in numeric
order, unlike the other methods which default to order of appearance.
(#145, @kbodwin)
## Minor bug fixes and improvements
* `fct_count()` gains a parameter to also compute the proportion
(@zhiiiyang, #146).
* `fct_lump()` now does not change the label if no lumping occurs
(@zhiiiyang, #130).
* `fct_relabel()` now accepts character input.
* `fct_reorder()` and `fct_reorder2()` no longer require that the summary
function return a numeric vector of length 1; instead it can return any
orderable vector of length 1 (#147).
* `fct_reorder()`, `fct_reorder2()` and `as_factor()` now use the ellipsis
package to warn if you pass in named components to `...` (#174).
# forcats 0.3.0
## API changes
* `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_reorder()` and `fct_reorder2()` have renamed `fun` to `.fun` to
avoid spurious matching of named arguments.
## New features
* 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 `.` prefix
in order to avoid unhelpful matching of named arguments (#110).
* `fct_lump()` gains `w` argument (#70, @wilkox) to weight value
frequencies before lumping them together (#68).
## Improvements to NA handling
* `as_factor()` and `fct_inorder()` accept NA levels (#98).
* `fct_explicit_na()` also replaces NAs encoded in levels.
* `fct_lump()` correctly accounts for `NA` values in input (#41)
* `lvls_revalue()` preserves NA levels.
## Minor improvements and bug fixes
* Test coverage increased from 80% to 99%.
* `fct_drop()` now preserves attributes (#83).
* `fct_expand()` and `lvls_expand()` now also take character vectors (#99).
* `fct_relabel()` now accepts objects coercible to functions
by `rlang::as_function` (#91, @alistaire47)
# forcats 0.2.0
## New functions
* `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).
## Minor improvements and bug fixes
* `fct_c()` can take either a list of factors or individual factors (#42).
* `fct_drop()` gains `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).
* `fct_relevel()` gains `after` argument so you can also move levels
to the end (or any other position you like) (#29).
* `lvls_reorder()`, `fct_inorder()`, and `fct_infreq()` gain an `ordered`
argument, allowing you to override the existing "ordered" status (#54).
# forcats 0.1.1
* Minor fixes for R CMD check
* Add package docs
forcats/MD5 0000644 0001762 0000144 00000015474 14365570422 012246 0 ustar ligges users a4f2bfc739dd8db1f4ca5c7fda94fb5b *DESCRIPTION
500bcbd26c47066e09614bf631a00918 *LICENSE
d8643b38be14ae3050e4c946bcf48fd0 *NAMESPACE
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forcats/inst/ 0000755 0001762 0000144 00000000000 14364755577 012717 5 ustar ligges users forcats/inst/doc/ 0000755 0001762 0000144 00000000000 14364755577 013464 5 ustar ligges users forcats/inst/doc/forcats.R 0000644 0001762 0000144 00000005602 14364755577 015253 0 ustar ligges users ## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----message = FALSE----------------------------------------------------------
library(dplyr)
library(ggplot2)
library(forcats)
## ----initial-plot-------------------------------------------------------------
ggplot(starwars, aes(y = hair_color)) +
geom_bar()
## ----fct-infreq-hair----------------------------------------------------------
ggplot(starwars, aes(y = fct_infreq(hair_color))) +
geom_bar()
## -----------------------------------------------------------------------------
f <- factor(c("x", "y", NA))
levels(f)
is.na(f)
## -----------------------------------------------------------------------------
f <- factor(c("x", "y", NA), exclude = NULL)
levels(f)
is.na(f)
## -----------------------------------------------------------------------------
ggplot(starwars, aes(y = fct_infreq(fct_na_value_to_level(hair_color)))) +
geom_bar() +
labs(y = "Hair color")
## -----------------------------------------------------------------------------
starwars %>%
count(skin_color, sort = TRUE)
## -----------------------------------------------------------------------------
starwars %>%
mutate(skin_color = fct_lump(skin_color, n = 5)) %>%
count(skin_color, sort = TRUE)
## -----------------------------------------------------------------------------
starwars %>%
mutate(skin_color = fct_lump(skin_color, prop = .1)) %>%
count(skin_color, sort = TRUE)
## -----------------------------------------------------------------------------
starwars %>%
mutate(skin_color = fct_lump(skin_color, prop = .1, other_level = "extra")) %>%
count(skin_color, sort = TRUE)
## ----fct-lump-mean------------------------------------------------------------
avg_mass_eye_color <- starwars %>%
mutate(eye_color = fct_lump(eye_color, n = 6)) %>%
group_by(eye_color) %>%
summarise(mean_mass = mean(mass, na.rm = TRUE))
avg_mass_eye_color
## ----fct-reorder--------------------------------------------------------------
avg_mass_eye_color %>%
mutate(eye_color = fct_reorder(eye_color, mean_mass)) %>%
ggplot(aes(x = eye_color, y = mean_mass)) +
geom_col()
## -----------------------------------------------------------------------------
gss_cat %>%
count(rincome)
## -----------------------------------------------------------------------------
levels(gss_cat$rincome)
## -----------------------------------------------------------------------------
reshuffled_income <- gss_cat$rincome %>%
fct_shuffle()
levels(reshuffled_income)
## -----------------------------------------------------------------------------
fct_relevel(reshuffled_income, c("Lt $1000", "$1000 to 2999")) %>%
levels()
## -----------------------------------------------------------------------------
fct_relevel(reshuffled_income, c("Lt $1000", "$1000 to 2999"), after = 1) %>%
levels()
forcats/inst/doc/forcats.Rmd 0000644 0001762 0000144 00000016710 14360103324 015543 0 ustar ligges users ---
title: "Introduction to forcats"
author: "Emily Robinson"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Introduction to forcats}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
The goal of the **forcats** package is to provide a suite of useful tools that solve common problems with factors.
Factors are useful when you have categorical data, variables that have a fixed and known set of values, and when you want to display character vectors in non-alphabetical order.
If you want to learn more, the best place to start is the [chapter on factors](https://r4ds.had.co.nz/factors.html) in R for Data Science.
## Ordering by frequency
```{r message = FALSE}
library(dplyr)
library(ggplot2)
library(forcats)
```
Let's try answering the question, "what are the most common hair colors of star wars characters?" Let's start off by making a bar plot:
```{r initial-plot}
#| fig.alt: >
#| A bar chart of hair color of starwars characters. The bars are
#| alphabetically ordered, making it hard to see general patterns.
ggplot(starwars, aes(y = hair_color)) +
geom_bar()
```
That's okay, but it would be more helpful the graph was ordered by count.
This is a case of an **unordered** categorical variable where we want it ordered by its frequency.
To do so, we can use the function `fct_infreq()`:
```{r fct-infreq-hair}
#| fig.alt: >
#| The bar chart of hair color, now ordered so that the least
#| frequent colours come first and the most frequent colors come last.
#| This makes it easy to see that the most common hair color is none
#| (~35), followed by brown (~18), then black (~12). Surprisingly,
#| NAs are at the top of the graph, even though there are ~5 NAs and
#| other colors have smaller values.
ggplot(starwars, aes(y = fct_infreq(hair_color))) +
geom_bar()
```
Note that `fct_infreq()` it automatically puts NA at the top, even though that doesn't have the smallest number of entries.
It's a little surprising that the `NA` bar isn't ordered by frequency.
To understand why we need to make a brief digression to discuss `NA`s in values vs. `NA`s in levels
## `NAs`s in levels and values
There are two ways to represent a missing value in a factor:
- You can include it in the values of the factor; it does not appear in the levels and `is.na()` reports it as missing.
This is how missing values are encoded in a factor by default:
```{r}
f <- factor(c("x", "y", NA))
levels(f)
is.na(f)
```
- You can include it in the levels of the factor and `is.na()` does not report it as missing.
This requires a little more work to create, because, by default, `factor()` uses `exclude = NA`, meaning that missing values are not included in the levels.
You can force `NA` to be included by setting `exclude = NULL`:
```{r}
f <- factor(c("x", "y", NA), exclude = NULL)
levels(f)
is.na(f)
```
`NA`s in the values tend to be best for data analysis, since `is.na()` works as you'd expect.
`NA`s in the levels can be useful for display if you need to control where they appear in a table or a plot.
To fix the issue above, we can use `fct_na_value_to_level()` to convert the `NA` in the value to an NA in the levels.
Then they appear where you'd expect:
```{r}
#| fig.alt: >
#| The bar chart of hair color, now ordered so that NAs are
#| ordered where you'd expect: in between white (4) and black (12).
ggplot(starwars, aes(y = fct_infreq(fct_na_value_to_level(hair_color)))) +
geom_bar() +
labs(y = "Hair color")
```
(If you need the opposite operation, you can use `fct_na_level_to_value()`.)
## Combining levels
Let's take a look at skin color now:
```{r}
starwars %>%
count(skin_color, sort = TRUE)
```
We see that there's 31 different skin colors - if we want to make a plot this would be way too many to display!
Let's reduce it to only be the top 5.
We can use `fct_lump()` to "lump" all the infrequent colors into one factor, "other." The argument `n` is the number of levels we want to keep.
```{r}
starwars %>%
mutate(skin_color = fct_lump(skin_color, n = 5)) %>%
count(skin_color, sort = TRUE)
```
We could also have used `prop` instead, which keeps all the levels that appear at least `prop` of the time.
For example, let's keep skin colors that at least 10% of the characters have:
```{r}
starwars %>%
mutate(skin_color = fct_lump(skin_color, prop = .1)) %>%
count(skin_color, sort = TRUE)
```
Only light and fair remain; everything else is other.
If you wanted to call it something than "other", you can change it with the argument `other_level`:
```{r}
starwars %>%
mutate(skin_color = fct_lump(skin_color, prop = .1, other_level = "extra")) %>%
count(skin_color, sort = TRUE)
```
What if we wanted to see if the average mass differed by eye color?
We'll only look at the 6 most popular eye colors and remove `NA`s.
```{r fct-lump-mean}
avg_mass_eye_color <- starwars %>%
mutate(eye_color = fct_lump(eye_color, n = 6)) %>%
group_by(eye_color) %>%
summarise(mean_mass = mean(mass, na.rm = TRUE))
avg_mass_eye_color
```
## Ordering by another variable
It looks like people (or at least one person) with orange eyes are definitely heavier!
If we wanted to make a graph, it would be nice if it was ordered by `mean_mass`.
We can do this with `fct_reorder()`, which reorders one variable by another.
```{r fct-reorder}
#| fig-alt: >
#| A column chart with eye color on the x-axis and mean mass on the
#| y-axis. The bars are ordered by mean_mass, so that the tallest bar
#| (orange eye color with mean mass of ~275) is at the far right.
avg_mass_eye_color %>%
mutate(eye_color = fct_reorder(eye_color, mean_mass)) %>%
ggplot(aes(x = eye_color, y = mean_mass)) +
geom_col()
```
## Manually reordering
Let's switch to using another dataset, `gss_cat`, the general social survey.
What is the income distribution among the respondents?
```{r}
gss_cat %>%
count(rincome)
```
Notice that the income levels are in the correct order - they start with the non-answers and then go from highest to lowest.
This is the same order you'd see if you plotted it as a bar chart.
This is not a coincidence.
When you're working with ordinal data, where there is an order, you can have an ordered factor.
You can examine them with the base function `levels()`, which prints them in order:
```{r}
levels(gss_cat$rincome)
```
But what if your factor came in the wrong order?
Let's simulate that by reordering the levels of `rincome` randomly with `fct_shuffle()`:
```{r}
reshuffled_income <- gss_cat$rincome %>%
fct_shuffle()
levels(reshuffled_income)
```
Now if we plotted it, it would show in this order, which is all over the place!
How can we fix this and put it in the right order?
We can use the function `fct_relevel()` when we need to manually reorder our factor levels.
In addition to the factor, you give it a character vector of level names, and specify where you want to move them.
It defaults to moving them to the front, but you can move them after another level with the argument `after`.
If you want to move it to the end, you set `after` equal to `Inf`.
For example, let's say we wanted to move `Lt $1000` and `$1000 to 2999` to the front.
We would write:
```{r}
fct_relevel(reshuffled_income, c("Lt $1000", "$1000 to 2999")) %>%
levels()
```
What if we want to move them to the second and third place?
```{r}
fct_relevel(reshuffled_income, c("Lt $1000", "$1000 to 2999"), after = 1) %>%
levels()
```
forcats/inst/doc/forcats.html 0000644 0001762 0000144 00000354660 14364755577 016031 0 ustar ligges users
Introduction to forcats
Introduction to forcats
Emily Robinson
The goal of the forcats package is to provide a
suite of useful tools that solve common problems with factors. Factors
are useful when you have categorical data, variables that have a fixed
and known set of values, and when you want to display character vectors
in non-alphabetical order. If you want to learn more, the best place to
start is the chapter on
factors in R for Data Science.
Ordering by frequency
library (dplyr)library (ggplot2)library (forcats)Let’s try answering the question, “what are the most common hair
colors of star wars characters?” Let’s start off by making a bar
plot:
ggplot (starwars, aes (y = hair_color)) + geom_bar ()
That’s okay, but it would be more helpful the graph was ordered by
count. This is a case of an unordered categorical
variable where we want it ordered by its frequency. To do so, we can use
the function fct_infreq():
ggplot (starwars, aes (y = fct_infreq (hair_color))) + geom_bar ()
Note that fct_infreq() it automatically puts NA at the
top, even though that doesn’t have the smallest number of entries.
It’s a little surprising that the NA bar isn’t ordered
by frequency. To understand why we need to make a brief digression to
discuss NAs in values vs. NAs in levels
NAss in levels and valuesThere are two ways to represent a missing value in a factor:
You can include it in the values of the factor; it does not
appear in the levels and is.na() reports it as missing.
This is how missing values are encoded in a factor by default:
<- factor (c ("x" , "y" , NA ))levels (f)#> [1] "x" "y" is.na (f)#> [1] FALSE FALSE TRUE You can include it in the levels of the factor and
is.na() does not report it as missing. This requires a
little more work to create, because, by default, factor()
uses exclude = NA, meaning that missing values are not
included in the levels. You can force NA to be included by
setting exclude = NULL:
<- factor (c ("x" , "y" , NA ), exclude = NULL )levels (f)#> [1] "x" "y" NA is.na (f)#> [1] FALSE FALSE FALSE
NAs in the values tend to be best for data analysis,
since is.na() works as you’d expect. NAs in
the levels can be useful for display if you need to control where they
appear in a table or a plot.
To fix the issue above, we can use
fct_na_value_to_level() to convert the NA in
the value to an NA in the levels. Then they appear where you’d
expect:
ggplot (starwars, aes (y = fct_infreq (fct_na_value_to_level (hair_color)))) + geom_bar () + labs (y = "Hair color" )
(If you need the opposite operation, you can use
fct_na_level_to_value().)
Combining levels
Let’s take a look at skin color now:
%>% count (skin_color, sort = TRUE )#> # A tibble: 31 × 2 #> skin_color n #> <chr> <int> #> 1 fair 17 #> 2 light 11 #> 3 dark 6 #> 4 green 6 #> 5 grey 6 #> 6 pale 5 #> 7 brown 4 #> 8 blue 2 #> 9 blue, grey 2 #> 10 orange 2 #> # … with 21 more rows We see that there’s 31 different skin colors - if we want to make a
plot this would be way too many to display! Let’s reduce it to only be
the top 5. We can use fct_lump() to “lump” all the
infrequent colors into one factor, “other.” The argument n
is the number of levels we want to keep.
%>% mutate (skin_color = fct_lump (skin_color, n = 5 )) %>% count (skin_color, sort = TRUE )#> # A tibble: 6 × 2 #> skin_color n #> <fct> <int> #> 1 Other 41 #> 2 fair 17 #> 3 light 11 #> 4 dark 6 #> 5 green 6 #> 6 grey 6 We could also have used prop instead, which keeps all
the levels that appear at least prop of the time. For
example, let’s keep skin colors that at least 10% of the characters
have:
%>% mutate (skin_color = fct_lump (skin_color, prop = .1 )) %>% count (skin_color, sort = TRUE )#> # A tibble: 3 × 2 #> skin_color n #> <fct> <int> #> 1 Other 59 #> 2 fair 17 #> 3 light 11 Only light and fair remain; everything else is other.
If you wanted to call it something than “other”, you can change it
with the argument other_level:
%>% mutate (skin_color = fct_lump (skin_color, prop = .1 , other_level = "extra" )) %>% count (skin_color, sort = TRUE )#> # A tibble: 3 × 2 #> skin_color n #> <fct> <int> #> 1 extra 59 #> 2 fair 17 #> 3 light 11 What if we wanted to see if the average mass differed by eye color?
We’ll only look at the 6 most popular eye colors and remove
NAs.
<- starwars %>% mutate (eye_color = fct_lump (eye_color, n = 6 )) %>% group_by (eye_color) %>% summarise (mean_mass = mean (mass, na.rm = TRUE ))#> # A tibble: 7 × 2 #> eye_color mean_mass #> <fct> <dbl> #> 1 black 76.3 #> 2 blue 86.5 #> 3 brown 66.1 #> 4 orange 282. #> 5 red 81.4 #> 6 yellow 81.1 #> 7 Other 68.4
Ordering by another variable
It looks like people (or at least one person) with orange eyes are
definitely heavier! If we wanted to make a graph, it would be nice if it
was ordered by mean_mass. We can do this with
fct_reorder(), which reorders one variable by another.
%>% mutate (eye_color = fct_reorder (eye_color, mean_mass)) %>% ggplot (aes (x = eye_color, y = mean_mass)) + geom_col ()
Manually reordering
Let’s switch to using another dataset, gss_cat, the
general social survey. What is the income distribution among the
respondents?
%>% count (rincome)#> # A tibble: 16 × 2 #> rincome n #> <fct> <int> #> 1 No answer 183 #> 2 Don't know 267 #> 3 Refused 975 #> 4 $25000 or more 7363 #> 5 $20000 - 24999 1283 #> 6 $15000 - 19999 1048 #> 7 $10000 - 14999 1168 #> 8 $8000 to 9999 340 #> 9 $7000 to 7999 188 #> 10 $6000 to 6999 215 #> 11 $5000 to 5999 227 #> 12 $4000 to 4999 226 #> 13 $3000 to 3999 276 #> 14 $1000 to 2999 395 #> 15 Lt $1000 286 #> 16 Not applicable 7043 Notice that the income levels are in the correct order - they start
with the non-answers and then go from highest to lowest. This is the
same order you’d see if you plotted it as a bar chart. This is not a
coincidence. When you’re working with ordinal data, where there is an
order, you can have an ordered factor. You can examine them with the
base function levels(), which prints them in order:
levels (gss_cat$ rincome)#> [1] "No answer" "Don't know" "Refused" "$25000 or more" #> [5] "$20000 - 24999" "$15000 - 19999" "$10000 - 14999" "$8000 to 9999" #> [9] "$7000 to 7999" "$6000 to 6999" "$5000 to 5999" "$4000 to 4999" #> [13] "$3000 to 3999" "$1000 to 2999" "Lt $1000" "Not applicable" But what if your factor came in the wrong order? Let’s simulate that
by reordering the levels of rincome randomly with
fct_shuffle():
<- gss_cat$ rincome %>% fct_shuffle ()levels (reshuffled_income)#> [1] "Refused" "$1000 to 2999" "$7000 to 7999" "$10000 - 14999" #> [5] "$25000 or more" "$15000 - 19999" "Lt $1000" "$3000 to 3999" #> [9] "$6000 to 6999" "$5000 to 5999" "$8000 to 9999" "No answer" #> [13] "Not applicable" "$20000 - 24999" "Don't know" "$4000 to 4999" Now if we plotted it, it would show in this order, which is all over
the place! How can we fix this and put it in the right order?
We can use the function fct_relevel() when we need to
manually reorder our factor levels. In addition to the factor, you give
it a character vector of level names, and specify where you want to move
them. It defaults to moving them to the front, but you can move them
after another level with the argument after. If you want to
move it to the end, you set after equal to
Inf.
For example, let’s say we wanted to move Lt $1000 and
$1000 to 2999 to the front. We would write:
fct_relevel (reshuffled_income, c ("Lt $1000" , "$1000 to 2999" )) %>% levels ()#> [1] "Lt $1000" "$1000 to 2999" "Refused" "$7000 to 7999" #> [5] "$10000 - 14999" "$25000 or more" "$15000 - 19999" "$3000 to 3999" #> [9] "$6000 to 6999" "$5000 to 5999" "$8000 to 9999" "No answer" #> [13] "Not applicable" "$20000 - 24999" "Don't know" "$4000 to 4999" What if we want to move them to the second and third place?
fct_relevel (reshuffled_income, c ("Lt $1000" , "$1000 to 2999" ), after = 1 ) %>% levels ()#> [1] "Refused" "Lt $1000" "$1000 to 2999" "$7000 to 7999" #> [5] "$10000 - 14999" "$25000 or more" "$15000 - 19999" "$3000 to 3999" #> [9] "$6000 to 6999" "$5000 to 5999" "$8000 to 9999" "No answer" #> [13] "Not applicable" "$20000 - 24999" "Don't know" "$4000 to 4999"
[](https://cran.r-project.org/package=forcats)
[](https://github.com/tidyverse/forcats/actions/workflows/R-CMD-check.yaml)
[](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("devtools")
devtools::install_github("tidyverse/forcats")
## Cheatsheet
