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Cy('a읹w$S 7 forcats/man/ 0000755 0001762 0000144 00000000000 14004327013 012460 5 ustar ligges users forcats/man/fct_inorder.Rd 0000644 0001762 0000144 00000001701 13626262603 015260 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, 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.}
}
\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 00000002326 13626051403 015222 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{Automatically relabel factor levels, collapse 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{
Automatically relabel factor levels, collapse 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 00000001425 13764156637 014424 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}
\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 00000001166 13626252103 016114 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 NAs will be changed to.}
}
\description{
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)
f2 <- fct_explicit_na(f1)
fct_count(f2)
}
forcats/man/fct_match.Rd 0000644 0001762 0000144 00000001610 13626051403 014703 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 00000001760 14004267615 016020 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: align='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 00000001647 13626051403 014742 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{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{
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".
}
forcats/man/figures/ 0000755 0001762 0000144 00000000000 14004267500 014130 5 ustar ligges users forcats/man/figures/logo.png 0000644 0001762 0000144 00000131540 13626051403 015604 0 ustar ligges users PNG
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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 00000001372 13626051403 014560 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)
}
\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_cross.Rd 0000644 0001762 0000144 00000001571 13626252103 014746 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fct_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 00000006156 13626261320 014577 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 together factor 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, 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 \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 00000000577 13626051403 015130 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}
\usage{
fct_unique(f)
}
\arguments{
\item{f}{A factor.}
}
\description{
Unique values of a factor
}
\examples{
f <- factor(letters[rpois(100, 10)])
unique(f) # in order of appearance
fct_unique(f) # in order of 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 00000004424 13626257051 015266 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, ..., .desc = FALSE)
fct_reorder2(.f, .x, .y, .fun = last2, ..., .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}. A common argument is
\code{na.rm = TRUE}.}
\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{
df <- tibble::tribble(
~color, ~a, ~b,
"blue", 1, 2,
"green", 6, 2,
"purple", 3, 3,
"red", 2, 3,
"yellow", 5, 1
)
df$color <- factor(df$color)
fct_reorder(df$color, df$a, min)
fct_reorder2(df$color, df$a, df$b)
boxplot(Sepal.Width ~ Species, data = iris)
boxplot(Sepal.Width ~ fct_reorder(Species, Sepal.Width), data = iris)
boxplot(Sepal.Width ~ fct_reorder(Species, Sepal.Width, .desc = TRUE), data = iris)
chks <- subset(ChickWeight, as.integer(Chick) < 10)
chks <- transform(chks, Chick = fct_shuffle(Chick))
if (require("ggplot2")) {
ggplot(chks, aes(Time, weight, colour = Chick)) +
geom_point() +
geom_line()
# Note that lines match order in legend
ggplot(chks, aes(Time, weight, colour = fct_reorder2(Chick, Time, weight))) +
geom_point() +
geom_line() +
labs(colour = "Chick")
}
}
forcats/man/fct_expand.Rd 0000644 0001762 0000144 00000001143 13626051403 015067 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, ...)
}
\arguments{
\item{f}{A factor (or character vector).}
\item{...}{Additional levels to add to the factor. Levels that already
exist will be silently ignored.}
}
\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])
}
\seealso{
\code{\link[=fct_drop]{fct_drop()}} to drop unused factor levels.
}
forcats/man/fct_relevel.Rd 0000644 0001762 0000144 00000002771 13764156755 015301 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, and the return
value (which must be a character vector) will be used to relevel the
function.
Any levels not mentioned will be left in their existing order, 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 00000002300 14004341752 013414 0 ustar ligges users Package: forcats
Title: Tools for Working with Categorical Variables (Factors)
Version: 0.5.1
Authors@R:
c(person(given = "Hadley",
family = "Wickham",
role = c("aut", "cre"),
email = "hadley@rstudio.com"),
person(given = "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 (>= 3.2)
Imports: ellipsis, magrittr, rlang, tibble
Suggests: covr, dplyr, ggplot2, knitr, readr, rmarkdown, testthat
VignetteBuilder: knitr
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2021-01-27 18:16:43 UTC; hadley
Author: Hadley Wickham [aut, cre],
RStudio [cph, fnd]
Maintainer: Hadley Wickham
Repository: CRAN
Date/Publication: 2021-01-27 19:50:02 UTC
forcats/build/ 0000755 0001762 0000144 00000000000 14004327012 013003 5 ustar ligges users forcats/build/vignette.rds 0000644 0001762 0000144 00000000322 14004327012 015337 0 ustar ligges users b```b`a`f`b2 1#PHs%'妠I{委&d)+@)すQ& 7 aD0!)dKME7% 5/$~hZ8S+`zPAհe
,s\ܠL t7`~r= xA $Gs=ʕXVr7 {f forcats/tests/ 0000755 0001762 0000144 00000000000 12752645041 013063 5 ustar ligges users forcats/tests/testthat/ 0000755 0001762 0000144 00000000000 14004341752 014715 5 ustar ligges users forcats/tests/testthat/test-match.R 0000644 0001762 0000144 00000000526 13626051403 017114 0 ustar ligges users context("test-fct_match.R")
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_error(fct_match(f, "d"), "not present")
})
forcats/tests/testthat/test-relevel.R 0000644 0001762 0000144 00000002003 13626051403 017446 0 ustar ligges users context("fct_relevel")
test_that("warns about unknown levels", {
f1 <- factor(c("a", "b"))
expect_warning(f2 <- fct_relevel(f1, "d"), "Unknown levels")
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")
})
forcats/tests/testthat/test-lump.R 0000644 0001762 0000144 00000014124 13626261320 016775 0 ustar ligges users context("fct_lump")
test_that("too many arguments fails", {
f <- c("a", "b", "c")
expect_error(fct_lump(f, n = 1, count = 1))
expect_error(fct_lump(f, n = 1, prop = 0.1))
expect_error(fct_lump(f, min = 2, count = 1))
})
test_that("positive values keeps most commmon", {
f <- c("a", "a", "a", "b", "b", "c", "d", "e", "f", "g")
expect_equal(levels(fct_lump(f, n = 1)), c("a", "Other"))
expect_equal(levels(fct_lump(f, n = 2)), c("a", "b", "Other"))
expect_equal(levels(fct_lump(f, prop = 0.25)), c("a", "Other"))
expect_equal(levels(fct_lump(f, prop = 0.15)), c("a", "b", "Other"))
})
test_that("ties are respected", {
f <- c("a", "a", "a", "b", "b", "b", "c", "d")
expect_equal(levels(fct_lump(f, 1)), c("a", "b", "Other"))
})
test_that("negative values drop most common" ,{
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("return original factor when all element satisfy n / p condition", {
f <- c("a", "a", "a", "b", "b", "c", "d", "e", "f", "g")
expect_equal(levels(fct_lump(f, n = 4)), c("a", "b", "c", "d", "e", "f", "g"))
expect_equal(levels(fct_lump(f, n = 10)), c("a", "b", "c", "d", "e", "f", "g"))
expect_equal(levels(fct_lump(f, n = -10)), c("a", "b", "c", "d", "e", "f", "g"))
expect_equal(levels(fct_lump(f, prop = 0.01)), c("a", "b", "c", "d", "e", "f", "g"))
expect_equal(levels(fct_lump(f, prop = -1)), c("a", "b", "c", "d", "e", "f", "g"))
})
test_that("different behaviour when apply tie function", {
f <- c("a", "a", "a", "b", "b", "c", "d", "e", "f", "g")
expect_equal(levels(fct_lump(f, n = 4, ties.method = "min")),
c("a", "b", "c", "d", "e", "f", "g"))
expect_equal(levels(fct_lump(f, n = 4, ties.method = "max")),
c("a", "b", "Other" ))
# Rank of c, d, e, f, g is (3+4+5+6+7)/5 = 5
expect_equal(levels(fct_lump(f, n = 4, ties.method = "average")),
c("a", "b", "Other" ))
expect_equal(levels(fct_lump(f, n = 5, ties.method = "average")),
c("a", "b", "c", "d", "e", "f", "g"))
expect_equal(levels(fct_lump(f, n = 4, ties.method = "first")),
c("a", "b", "c", "d", "Other"))
if (getRversion() >= "3.3.0") {
expect_equal(levels(fct_lump(f, n = 4, ties.method = "last")),
c("a", "b", "f", "g", "Other"))
}
})
test_that("NAs included in total", {
f <- factor(c("a", "a", "b", "c", rep(NA, 7)))
o1 <- fct_lump(f, prop = 0.10)
expect_equal(levels(o1), c("a", "Other"))
o2 <- fct_lump(f, w = rep(1, 11), prop = 0.10)
expect_equal(levels(o2), c("a", "Other"))
})
test_that("bad weights generate error messages", {
expect_error(fct_lump(letters, w = letters), "must be a numeric vector")
expect_error(fct_lump(letters, w = 1:10), "must be the same length")
expect_error(fct_lump(letters, w = rep(-1, 26)), "must be non-negative")
})
test_that("values are correctly weighted", {
f <- c("a", "a", "a", "b", "b", "c", "d", "e", "f", "g")
w <- c( 0.2, 0.2, 0.6, 2, 2, 6, 4, 2, 2, 1)
f2 <- c(
"a",
rep("b", 4),
rep("c", 6),
rep("d", 4),
rep("e", 2),
rep("f", 2),
"g"
)
expect_equal(levels(fct_lump(f, w = w)), levels(fct_lump(f2)))
expect_equal(
levels(fct_lump(f, n = 1, w = w)),
levels(fct_lump(f2, n = 1))
)
expect_equal(
levels(fct_lump(f, n = -2, w = w, ties.method = "first")),
levels(fct_lump(f2, n = -2, ties.method = "first"))
)
expect_equal(
levels(fct_lump(f, n = 99, w = w)),
levels(fct_lump(f2, n = 99))
)
expect_equal(
levels(fct_lump(f, prop = 0.01, w = w)),
levels(fct_lump(f2, prop = 0.01))
)
expect_equal(
levels(fct_lump(f, prop = -0.25, w = w, ties.method = "max")),
levels(fct_lump(f2, prop = -0.25, ties.method = "max"))
)
})
test_that("do not change the label when no lumping occurs", {
f <- c("a", "a", "a", "a", "b", "b", "b", "c", "c", "d")
expect_equal(levels(fct_lump(f, n = 3)), c("a", "b", "c", "d"))
expect_equal(levels(fct_lump(f, prop = 0.1)), c("a", "b", "c", "d"))
})
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("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("throws error if n or prop is not numeric", {
f <- c("a", "a", "a", "a", "b", "b", "b", "c", "c", "d")
expect_error(fct_lump(f, n = "2"), "`n`")
expect_error(fct_lump(f, prop = "2"), "`prop`")
})
test_that("fct_lump_prop works when not weighted", {
f <- c("a", "a", "a", "b", "b", "c", "d", "e", "f", "g")
expect_equal(levels(fct_lump_prop(f, prop = 0.2)), c("a", "Other"))
expect_equal(levels(fct_lump_prop(f, prop = 0.1)), c("a", "b", "Other"))
})
test_that("fct_lump_prop works when weighted", {
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"))
})
# Default -----------------------------------------------------------------
test_that("lumps 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")
})
test_that("doesn't lump if none small enough", {
expect_equal(lump_test(c(2, 2, 4)), "abc")
})
test_that("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-fct_relabel.R 0000644 0001762 0000144 00000002232 13626051403 020256 0 ustar ligges users context("fct_relabel")
test_that("identity", {
f1 <- factor(c("a", "b"))
expect_identical(fct_relabel(f1, identity), f1)
})
test_that("error if not function", {
f1 <- factor("a")
expect_error(fct_relabel(f1, 1), "function")
})
test_that("error if level not character", {
f1 <- factor("a")
expect_error(fct_relabel(f1, function(x) 1), "character")
})
test_that("error if level has different length", {
f1 <- factor(letters)
expect_error(fct_relabel(f1, function(x) x[-1]), "expected 26.*got 25")
})
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], .fun=function(x) x),
factor(LETTERS[1:2])
)
}) forcats/tests/testthat/test-anon.R 0000644 0001762 0000144 00000000472 13626051403 016753 0 ustar ligges users context("test-anon.R")
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")
})
forcats/tests/testthat/test-collapse.R 0000644 0001762 0000144 00000003403 13626261320 017620 0 ustar ligges users context("test-collapse.R")
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 to Other", {
f1 <- factor(letters[1:3])
f2 <- fct_collapse(f1, xy = c("a", "b"), other_level = "Other")
expect_equal(f2, factor(c("xy", "xy", "Other"), levels = c("xy", "Other")))
})
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])
f2 <- expect_warning(
fct_collapse(f1, x1 = c("a", "d"), x2 = "c", group_other = TRUE),
"deprecated"
)
expect_equal(levels(f2), c("x1", "x2", "Other"))
})
forcats/tests/testthat/test-count.R 0000644 0001762 0000144 00000001462 13626257051 017157 0 ustar ligges users context("test-count.R")
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)$n, c(1, 0, 0))
})
test_that("counts NA even when not in levels", {
f <- factor(c("a", "a", NA))
out <- fct_count(f)
expect_equal(out$n, c(2, 1))
# and doesn't change levels
expect_equal(levels(out$f), levels(f))
})
test_that("returns marginal table", {
f <- factor(c("a", "a", "b"))
out <- fct_count(f, prop = TRUE)
expect_equal(out$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")))
})
forcats/tests/testthat/test-reorder.R 0000644 0001762 0000144 00000003367 13626257051 017477 0 ustar ligges users context("test-reorder.R")
test_that("can reorder by 2d summary", {
df <- tibble::tribble(
~g, ~x,
"a", 3,
"a", 3,
"b", 2,
"b", 2,
"b", 1
)
f1 <- fct_reorder(df$g, df$x)
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("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("complains if summary doesn't return single value", {
fun1 <- function(x, y) c(1, 2)
fun2 <- function(x, y) integer()
expect_error(fct_reorder("a", 1, fun1), "single value per group")
expect_error(fct_reorder("a", 1, fun2), "single value per group")
expect_error(fct_reorder2("a", 1, 2, fun1), "single value per group")
expect_error(fct_reorder2("a", 1, 2, fun2), "single value per group")
})
test_that("fct_infreq respects missing values", {
f <- factor(c("a", "b", "b", NA, NA, NA), exclude = FALSE)
expect_equal(levels(fct_infreq(f)), c(NA, "b", "a"))
})
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", {
f <- factor(c("c", "a", "a", "b"))
expect_error(levels(fct_inseq(f)), "level must be coercible to numeric")
})
forcats/tests/testthat/test-fct_other.R 0000644 0001762 0000144 00000001042 13626051403 017767 0 ustar ligges users context("fct_other")
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("must supply exactly one of drop and keep", {
f <- factor(c("a", "b"))
expect_error(fct_other(f), "exactly one")
expect_error(fct_other(f, keep = "a", drop = "a"), "exactly one")
})
forcats/tests/testthat/test-fct_recode.R 0000644 0001762 0000144 00000002135 13626051403 020113 0 ustar ligges users context("fct_recode")
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", {
expect_error(check_recode_levels(a = 1), "Problems at positions: 1")
})
test_that("new levels must be length 1", {
expect_error(check_recode_levels(a = c("a", "b")), "Problems at positions: 1")
})
forcats/tests/testthat/test-shift.R 0000644 0001762 0000144 00000000603 13626051403 017131 0 ustar ligges users context("test-shift.R")
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)
})
forcats/tests/testthat/test-fct_c.R 0000644 0001762 0000144 00000000570 13626051403 017075 0 ustar ligges users context("fct_c")
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())
})
forcats/tests/testthat/test-fct_cross.R 0000644 0001762 0000144 00000004127 13626252103 020006 0 ustar ligges users context("fct_cross")
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"))
})
forcats/tests/testthat/test-explicit_na.R 0000644 0001762 0000144 00000000746 13626051403 020323 0 ustar ligges users context("test-explicit_na.R")
test_that("factor unchanged if no missing levels", {
f1 <- factor(letters[1:3])
f2 <- fct_explicit_na(f1)
expect_identical(f1, f2)
})
test_that("converts implicit NA", {
f1 <- factor(c("a", NA))
f2 <- fct_explicit_na(f1)
expect_equal(f2, fct_inorder(c("a", "(Missing)")))
})
test_that("converts explicit NA", {
f1 <- factor(c("a", NA), exclude = NULL)
f2 <- fct_explicit_na(f1)
expect_equal(f2, fct_inorder(c("a", "(Missing)")))
})
forcats/tests/testthat/test-lvls_reorder.R 0000644 0001762 0000144 00000002146 13626051403 020522 0 ustar ligges users context("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/test-fct_drop.R 0000644 0001762 0000144 00000001162 13626051403 017615 0 ustar ligges users context("fct_drop")
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"))
})
forcats/tests/testthat/test-utils.R 0000644 0001762 0000144 00000000422 13626051403 017153 0 ustar ligges users context("test-utils.R")
test_that("check_factor() fails when needed", {
expect_error(check_factor(NA), "factor")
})
test_that("check_factor_list() fails when needed", {
expect_error(check_factor_list(1), "list")
expect_error(check_factor_list(list(1)), "factor")
})
forcats/tests/testthat/test-shuffle.R 0000644 0001762 0000144 00000000265 13626051403 017454 0 ustar ligges users context("test-shuffle.R")
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 00000000227 13626051403 016612 0 ustar ligges users context("test-rev.R")
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 00000000731 13626461724 017772 0 ustar ligges users context("as_factor")
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/helper-lump.R 0000644 0001762 0000144 00000000150 12754332005 017267 0 ustar ligges users lump_test <- function(x) {
paste(ifelse(in_smallest(x), "X", letters[seq_along(x)]), collapse = "")
}
forcats/tests/testthat/test-lvls.R 0000644 0001762 0000144 00000002466 13626051403 017005 0 ustar ligges users context("lvls")
# 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_error(lvls_expand(f1, c("a", "c")), "include all existing levels")
})
# 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 be a character", {
f1 <- factor(c("a", "b"))
expect_error(lvls_revalue(f1, 1:5), "character vector")
})
test_that("`new_levels` must be same length as existing levels", {
f1 <- factor(c("a", "b"))
expect_error(lvls_revalue(f1, c("a")), "same length")
expect_error(lvls_revalue(f1, c("a", "b", "c")), "same length")
})
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 14004327013 013715 5 ustar ligges users forcats/vignettes/forcats.Rmd 0000644 0001762 0000144 00000012145 14004267612 016035 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}
ggplot(starwars, aes(x = hair_color)) +
geom_bar() +
coord_flip()
```
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}
ggplot(starwars, aes(x = fct_infreq(hair_color))) +
geom_bar() +
coord_flip()
```
Note that `fct_infreq()` it automatically puts NA at the top, even though that doesn't have the smallest number of entries.
## 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}
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", "$1000 to 2999"), after = 1) %>%
levels()
```
forcats/R/ 0000755 0001762 0000144 00000000000 13764156755 012137 5 ustar ligges users forcats/R/count.R 0000644 0001762 0000144 00000001514 13626257051 013377 0 ustar ligges users #' Count entries in a factor
#'
#' @param f A factor (or character vector).
#' @param sort If `TRUE`, sort the result so that the most common values
#' float to the top.
#' @param prop If `TRUE`, compute the fraction of marginal table.
#' @return A tibble with columns `f`, `n` and `p`, if prop is `TRUE`.
#' @export
#' @examples
#' f <- factor(sample(letters)[rpois(1000, 10)])
#' table(f)
#' fct_count(f)
#' fct_count(f, sort = TRUE)
#' fct_count(f, sort = TRUE, prop = TRUE)
fct_count <- function(f, sort = FALSE, prop = FALSE) {
f2 <- check_factor(f)
n_na <- sum(is.na(f))
df <- tibble::tibble(
f = fct_inorder(c(levels(f2), if (n_na > 0) NA)),
n = c(tabulate(f2, nlevels(f)), if (n_na > 0) n_na)
)
if (sort) {
df <- df[order(df$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 00000001251 13626051403 013376 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(f) {
if (is.character(f)) {
factor(f)
} else if (is.factor(f)) {
f
} else {
stop("`f` must be a factor (or character vector).", call. = FALSE)
}
}
check_factor_list <- function(fs, arg_name = "fs") {
if (!is.list(fs)) {
stop("`fs` must be a list", call. = FALSE)
}
is_factor <- vapply(fs, is.factor, logical(1))
if (any(!is_factor)) {
stop("All elements of `", arg_name, "` must be factors", call. = FALSE)
}
fs
}
forcats/R/anon.R 0000644 0001762 0000144 00000001321 13626257051 013176 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 = "") {
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/fct_cross.R 0000644 0001762 0000144 00000002323 13626252103 014224 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) {
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/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 00000015270 13764156637 013243 0 ustar ligges users #' Lump together factor 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 `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")) {
ties.method <- match.arg(ties.method)
check_calc_levels(f, w)
if (missing(n) && missing(prop)) {
fct_lump_lowfreq(f, other_level = other_level)
} else if (missing(prop)) {
fct_lump_n(f, n, w, other_level, ties.method)
} else if (missing(n)) {
fct_lump_prop(f, prop, w, other_level)
} else {
abort("Must supply only one of `n` and `prop`")
}
}
#' @export
#' @rdname fct_lump
fct_lump_min <- function(f, min, w = NULL, other_level = "Other") {
calcs <- check_calc_levels(f, w)
f <- calcs$f
if (!is.numeric(min) || length(min) != 1 || min < 0) {
rlang::abort("`min` must be a positive number")
}
new_levels <- ifelse(calcs$count >= min, levels(f), other_level)
if (other_level %in% new_levels) {
f <- lvls_revalue(f, new_levels)
fct_relevel(f, other_level, after = Inf)
} else {
f
}
}
#' @export
#' @rdname fct_lump
fct_lump_prop <- function(f, prop, w = NULL, other_level = "Other") {
calcs <- check_calc_levels(f, w)
f <- calcs$f
if (!is.numeric(prop) || length(prop) != 1) {
rlang::abort("`prop` must be a number")
}
prop_n <- calcs$count / calcs$total
if (prop < 0) {
new_levels <- ifelse(prop_n <= -prop, levels(f), other_level)
} else {
new_levels <- ifelse(prop_n > prop, levels(f), other_level)
}
if (prop > 0 && sum(prop_n <= prop) <= 1) {
# No lumping needed
return(f)
}
if (other_level %in% new_levels) {
f <- lvls_revalue(f, new_levels)
fct_relevel(f, other_level, after = Inf)
} else {
f
}
}
#' @export
#' @rdname fct_lump
fct_lump_n <- function(f, n, w = NULL, other_level = "Other",
ties.method = c("min", "average", "first", "last", "random", "max")) {
ties.method <- match.arg(ties.method)
calcs <- check_calc_levels(f, w)
f <- calcs$f
if (!is.numeric(n) || length(n) != 1) {
rlang::abort("`n` must be a number")
}
if (n < 0) {
rank <- rank(calcs$count, ties.method = ties.method)
n <- -n
} else {
rank <- rank(-calcs$count, ties.method = ties.method)
}
new_levels <- ifelse(rank <= n, levels(f), other_level)
if (sum(rank > n) <= 1) {
# No lumping needed
return(f)
}
if (other_level %in% new_levels) {
f <- lvls_revalue(f, new_levels)
fct_relevel(f, other_level, after = Inf)
} else {
f
}
}
#' @export
#' @rdname fct_lump
fct_lump_lowfreq <- function(f, other_level = "Other") {
calcs <- check_calc_levels(f, NULL)
f <- calcs$f
new_levels <- ifelse(!in_smallest(calcs$count), levels(f), other_level)
if (other_level %in% new_levels) {
f <- lvls_revalue(f, new_levels)
fct_relevel(f, other_level, after = Inf)
} else {
f
}
}
check_calc_levels <- function(f, w = NULL) {
f <- check_factor(f)
w <- check_weights(w, length(f))
if (is.null(w)) {
count <- as.vector(table(f))
total <- length(f)
} else {
count <- as.vector(tapply(w, f, FUN = sum))
total <- sum(w)
}
list(f = f, count = count, total = total)
}
# 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)) {
if (is.null(w)) {
return(w)
}
if (!is.numeric(w)) {
stop("`w` must be a numeric vector", call. = FALSE)
}
if (length(w) != n) {
stop(
"`w` must be the same length as `f` (", n, "), not length ", length(w),
call. = FALSE
)
}
bad <- w < 0 | is.na(w)
if (any(bad)) {
stop(
"All `w` must be non-negative and non-missing. Problems at positions: ",
paste0(which(bad), collapse = ", "),
call. = FALSE
)
}
w
}
forcats/R/drop.R 0000644 0001762 0000144 00000001526 13626051403 013207 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) {
f <- check_factor(f)
levels <- levels(f)
count <- table(f)
to_drop <- levels[count == 0]
if (!missing(only)) {
to_drop <- intersect(to_drop, only)
}
refactor(f, new_levels = setdiff(levels, to_drop))
}
forcats/R/shift.R 0000644 0001762 0000144 00000001370 13626051403 013355 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) {
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 00000001131 12754704614 013156 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}
#' \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/lvls.R 0000644 0001762 0000144 00000006071 13626051403 013223 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)) {
stop("`idx` must be numeric", call. = FALSE)
}
if (!setequal(idx, lvls_seq(f)) || length(idx) != nlevels(f)) {
stop("`idx` must contain one integer for each level of `f`", call. = FALSE)
}
refactor(f, levels(f)[idx], ordered = ordered)
}
#' @export
#' @rdname lvls
lvls_revalue <- function(f, new_levels) {
f <- check_factor(f)
if (!is.character(new_levels)) {
stop("`new_levels` must be a character vector", call. = FALSE)
}
if (length(new_levels) != nlevels(f)) {
stop(
"`new_levels` must be the same length as `levels(f)`: expected ",
nlevels(f), " new levels, got ", length(new_levels), ".",
call. = FALSE
)
}
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) {
stop(
"Must include all existing levels. Missing: ", paste0(missing, collapse = ", "),
call. = FALSE)
}
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 00000000461 13626252033 013550 0 ustar ligges users #' Unique values of a factor
#'
#' @param f A factor.
#' @export
#' @examples
#' f <- factor(letters[rpois(100, 10)])
#'
#' unique(f) # in order of appearance
#' fct_unique(f) # in order of levels
fct_unique <- function(f) {
factor(levels(f), levels(f), exclude = NULL, ordered = is.ordered(f))
}
forcats/R/relevel.R 0000644 0001762 0000144 00000004216 13764156755 013723 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, and the return
#' value (which must be a character vector) will be used to relevel the
#' function.
#'
#' Any levels not mentioned will be left in their existing order, 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)
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)) {
stop("Re-leveling function must return character vector", call. = FALSE)
}
} else {
first_levels <- chr(...)
}
unknown <- setdiff(first_levels, old_levels)
if (length(unknown) > 0) {
warning("Unknown levels in `f`: ", paste(unknown, collapse = ", "), call. = FALSE)
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 00000002313 13626252103 013644 0 ustar ligges users #' Automatically relabel factor levels, collapse 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/explicit_na.R 0000644 0001762 0000144 00000001474 13626252103 014544 0 ustar ligges users #' Make missing values explicit
#'
#' 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 NAs will be changed to.
#' @export
#' @examples
#' f1 <- factor(c("a", "a", NA, NA, "a", "b", NA, "c", "a", "c", "b"))
#' fct_count(f1)
#'
#' f2 <- fct_explicit_na(f1)
#' fct_count(f2)
fct_explicit_na <- function(f, na_level = "(Missing)") {
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 00000002067 13626051403 013340 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)
bad_lvls <- setdiff(lvls, levels(f))
bad_lvls <- bad_lvls[!is.na(bad_lvls)]
if (length(bad_lvls) > 0) {
stop(
"Levels not present in factor: ",
paste0(encodeString(bad_lvls, quote = '"'), collapse = ", "),
call. = FALSE
)
}
f %in% lvls
}
forcats/R/forcats-package.R 0000644 0001762 0000144 00000000062 13626252103 015267 0 ustar ligges users #' @keywords internal
#' @import rlang
"_PACKAGE"
forcats/R/as_factor.R 0000644 0001762 0000144 00000002524 13626461710 014211 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, ...) {
ellipsis::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 00000004154 13764157242 013517 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]
}
# 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)
lvls_revalue(f, old_levels)
}
check_recode_levels <- function(...) {
levels <- rlang::list2(...)
is_ok <- function(x) is.character(x) && length(x) == 1
ok <- vapply(levels, is_ok, logical(1))
if (!all(ok)) {
stop(
"Each input to fct_recode must be a single named string. ",
"Problems at positions: ", paste0(which(!ok), collapse = ", "),
call. = FALSE
)
}
unlist(levels)
}
forcats/R/collapse.R 0000644 0001762 0000144 00000002746 13626261320 014053 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") {
if (!missing(group_other)) {
warning("`group_other` is deprecated. Please use `other_level` instead")
if (isTRUE(group_other) && is.null(other_level)) {
other_level <- "Other"
}
}
new <- rlang::list2(...)
levs <- as.list(unlist(new, use.names = FALSE))
if (!is.null(other_level)){
f <- check_factor(.f)
levels <- levels(f)
new[[other_level]] <- levels[!levels %in% levs]
levs <- c(levs, new[[other_level]])
}
names(levs) <- names(new)[rep(seq_along(new), vapply(new, length, integer(1)))]
out <- fct_recode(.f, !!!levs)
if (any(levels(out) == other_level)){
fct_relevel(out, other_level, after = Inf)
} else return(out)
}
forcats/R/reorder.R 0000644 0001762 0000144 00000010517 13626262434 013715 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 ... Other arguments passed on to `.fun`. A common argument is
#' `na.rm = TRUE`.
#' @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.
#' @importFrom stats median
#' @export
#' @examples
#' df <- tibble::tribble(
#' ~color, ~a, ~b,
#' "blue", 1, 2,
#' "green", 6, 2,
#' "purple", 3, 3,
#' "red", 2, 3,
#' "yellow", 5, 1
#' )
#' df$color <- factor(df$color)
#' fct_reorder(df$color, df$a, min)
#' fct_reorder2(df$color, df$a, df$b)
#'
#' boxplot(Sepal.Width ~ Species, data = iris)
#' boxplot(Sepal.Width ~ fct_reorder(Species, Sepal.Width), data = iris)
#' boxplot(Sepal.Width ~ fct_reorder(Species, Sepal.Width, .desc = TRUE), data = iris)
#'
#' chks <- subset(ChickWeight, as.integer(Chick) < 10)
#' chks <- transform(chks, Chick = fct_shuffle(Chick))
#'
#' if (require("ggplot2")) {
#' ggplot(chks, aes(Time, weight, colour = Chick)) +
#' geom_point() +
#' geom_line()
#'
#' # Note that lines match order in legend
#' 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, ..., .desc = FALSE) {
f <- check_factor(.f)
stopifnot(length(f) == length(.x))
ellipsis::check_dots_used()
summary <- tapply(.x, .f, .fun, ...)
# This is a bit of a weak test, but should detect the most common case
# where `.fun` returns multiple values.
if (is.list(summary)) {
stop("`fun` must return a single value per group", call. = FALSE)
}
lvls_reorder(f, order(summary, decreasing = .desc))
}
#' @export
#' @rdname fct_reorder
fct_reorder2 <- function(.f, .x, .y, .fun = last2, ..., .desc = TRUE) {
f <- check_factor(.f)
stopifnot(length(f) == length(.x), length(.x) == length(.y))
ellipsis::check_dots_used()
summary <- tapply(seq_along(.x), f, function(i) .fun(.x[i], .y[i], ...))
if (is.list(summary)) {
stop("`fun` must return a single value per group", call. = FALSE)
}
lvls_reorder(.f, order(summary, decreasing = .desc))
}
#' @export
#' @rdname fct_reorder
last2 <- function(.x, .y) {
.y[order(.x, na.last = FALSE)][length(.y)]
}
#' @export
#' @rdname fct_reorder
first2 <- function(.x, .y) {
.y[order(.x)][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)
idx <- as.integer(f)[!duplicated(f)]
idx <- idx[!is.na(idx)]
lvls_reorder(f, idx, ordered = ordered)
}
#' @export
#' @rdname fct_inorder
fct_infreq <- function(f, ordered = NA) {
f <- check_factor(f)
lvls_reorder(f, order(table(f), decreasing = TRUE), ordered = ordered)
}
#' @export
#' @rdname fct_inorder
fct_inseq <- function(f, ordered = NA) {
f <- check_factor(f)
num_levels <- suppressWarnings(as.numeric(levels(f)))
if (all(is.na(num_levels))) {
stop("At least one existing level must be coercible to numeric.", call. = FALSE)
}
lvls_reorder(f, order(num_levels), ordered = ordered)
}
forcats/R/expand.R 0000644 0001762 0000144 00000001024 13626252103 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.
#' @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 <- function(f, ...) {
f <- check_factor(f)
new_levels <- chr(...)
lvls_expand(f, union(levels(f), new_levels))
}
forcats/R/other.R 0000644 0001762 0000144 00000001751 13626051403 013364 0 ustar ligges users #' 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)
if (!xor(missing(keep), missing(drop))) {
stop("Must supply exactly one of `keep` and `drop`", call. = FALSE)
}
levels <- levels(f)
if (!missing(keep)) {
levels[!levels %in% keep] <- other_level
} else {
levels[levels %in% drop] <- other_level
}
f <- lvls_revalue(f, levels)
fct_relevel(f, other_level, after = Inf)
}
forcats/R/c.R 0000644 0001762 0000144 00000002333 13626252103 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 <- rlang::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)
lapply(fs, lvls_expand, new_levels = levels)
}
forcats/NEWS.md 0000644 0001762 0000144 00000012103 14004275144 013007 0 ustar ligges users # 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 00000011076 14004341752 012230 0 ustar ligges users 983fd92fcc3d571a2d5630dde33ccedf *DESCRIPTION
500bcbd26c47066e09614bf631a00918 *LICENSE
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forcats/inst/ 0000755 0001762 0000144 00000000000 14004327012 012661 5 ustar ligges users forcats/inst/doc/ 0000755 0001762 0000144 00000000000 14004327012 013426 5 ustar ligges users forcats/inst/doc/forcats.R 0000644 0001762 0000144 00000004713 14004327012 015217 0 ustar ligges users ## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----message = FALSE----------------------------------------------------------
library(dplyr)
library(ggplot2)
library(forcats)
## ----initial-plot-------------------------------------------------------------
ggplot(starwars, aes(x = hair_color)) +
geom_bar() +
coord_flip()
## ----fct-infreq-hair----------------------------------------------------------
ggplot(starwars, aes(x = fct_infreq(hair_color))) +
geom_bar() +
coord_flip()
## -----------------------------------------------------------------------------
starwars %>%
count(skin_color, sort = TRUE)
## -----------------------------------------------------------------------------
starwars %>%
mutate(skin_color = fct_lump(skin_color, n = 5)) %>%
count(skin_color, sort = TRUE)
## -----------------------------------------------------------------------------
starwars %>%
mutate(skin_color = fct_lump(skin_color, prop = .1)) %>%
count(skin_color, sort = TRUE)
## -----------------------------------------------------------------------------
starwars %>%
mutate(skin_color = fct_lump(skin_color, prop = .1, other_level = "extra")) %>%
count(skin_color, sort = TRUE)
## ----fct-lump-mean------------------------------------------------------------
avg_mass_eye_color <- starwars %>%
mutate(eye_color = fct_lump(eye_color, n = 6)) %>%
group_by(eye_color) %>%
summarise(mean_mass = mean(mass, na.rm = TRUE))
avg_mass_eye_color
## ----fct-reorder--------------------------------------------------------------
avg_mass_eye_color %>%
mutate(eye_color = fct_reorder(eye_color, mean_mass)) %>%
ggplot(aes(x = eye_color, y = mean_mass)) +
geom_col()
## -----------------------------------------------------------------------------
gss_cat %>%
count(rincome)
## -----------------------------------------------------------------------------
levels(gss_cat$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", "$1000 to 2999"), after = 1) %>%
levels()
forcats/inst/doc/forcats.Rmd 0000644 0001762 0000144 00000012145 14004267612 015547 0 ustar ligges users ---
title: "Introduction to forcats"
author: "Emily Robinson"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Introduction to forcats}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::opts_chunk$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}
ggplot(starwars, aes(x = hair_color)) +
geom_bar() +
coord_flip()
```
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}
ggplot(starwars, aes(x = fct_infreq(hair_color))) +
geom_bar() +
coord_flip()
```
Note that `fct_infreq()` it automatically puts NA at the top, even though that doesn't have the smallest number of entries.
## 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}
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 00000276162 14004327012 015773 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 (x = hair_color)) + geom_bar () + coord_flip ()
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 (x = fct_infreq (hair_color))) + geom_bar () + coord_flip ()
Note that fct_infreq() it automatically puts NA at the top, even though that doesn’t have the smallest number of entries.
Combining levels
Let’s take a look at skin color now:
%>% count (skin_color, sort = TRUE )#> # A tibble: 31 x 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 x 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 x 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 x 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 x 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 x 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] "$8000 to 9999" "Refused" "$4000 to 4999" "$6000 to 6999" #> [5] "$5000 to 5999" "$10000 - 14999" "$1000 to 2999" "Lt $1000" #> [9] "Don't know" "No answer" "$7000 to 7999" "Not applicable" #> [13] "$3000 to 3999" "$25000 or more" "$20000 - 24999" "$15000 - 19999" 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" "$8000 to 9999" "Refused" #> [5] "$4000 to 4999" "$6000 to 6999" "$5000 to 5999" "$10000 - 14999" #> [9] "Don't know" "No answer" "$7000 to 7999" "Not applicable" #> [13] "$3000 to 3999" "$25000 or more" "$20000 - 24999" "$15000 - 19999" 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] "$8000 to 9999" "Lt $1000" "$1000 to 2999" "Refused" #> [5] "$4000 to 4999" "$6000 to 6999" "$5000 to 5999" "$10000 - 14999" #> [9] "Don't know" "No answer" "$7000 to 7999" "Not applicable" #> [13] "$3000 to 3999" "$25000 or more" "$20000 - 24999" "$15000 - 19999"
[](https://cran.r-project.org/package=forcats)
[](https://github.com/tidyverse/forcats/actions)
[](https://codecov.io/gh/tidyverse/forcats?branch=master)
## 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.had.co.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
