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Factors with forcats : : CHEAT SHEET
Change the value of levels
The forcats package provides tools for working with factors, which are R's data structure for categorical data.
R represents categorical
data with factors. A factor
is an integer vector with a
levels attribute that stores
a set of mappings between
integers and categorical values. When you view a factor, R
displays not the integers, but the values associated with them.
fct_c(…) Combine factors
with different levels.
f1 <- factor(c("a", "c"))
f2 <- factor(c("b", "a"))
fct_c(f1, f2)
fct_unify(fs, levels =
lvls_union(fs)) Standardize
levels across a list of factors.
fct_unify(list(f2, f1))
Inspect Factors
RStudio® is a trademark of RStudio, Inc. • CC BY SA RStudio • info@rstudio.com • 844-448-1212 • rstudio.com • Learn more at forcats.tidyverse.org • Diagrams inspired by @LVaudor ! • forcats 0.3.0 • Updated: 2019-02
Factors
Create a factor with factor()
factor(x = character(), levels,
labels = levels, exclude = NA, ordered
= is.ordered(x), nmax = NA) Convert
a vector to a factor. Also as_factor.
f <- factor(c("a", "c", "b", "a"),
levels = c("a", "b", "c"))
Return its levels with levels()
levels(x) Return/set the levels of a
factor. levels(f); levels(f) <- c("x","y","z")
Use unclass() to see its structure
Add or drop levelsCombine Factors
fct_drop(f, only) Drop unused levels.
f5 <- factor(c("a","b"),c("a","b","x"))
f6 <- fct_drop(f5)
fct_expand(f, …) Add levels to
a factor. fct_expand(f6, "x")
fct_explicit_na(f, na_level="(Missing)")
Assigns a level to NAs to ensure they
appear in plots, etc.
fct_explicit_na(factor(c("a", "b", NA)))
fct_count(f, sort = FALSE)
Count the number of values
with each level. fct_count(f)
fct_unique(f) Return the
unique values, removing
duplicates. fct_unique(f)
fct_recode(.f, ...) Manually change
levels. Also fct_relabel which obeys
purrr::map syntax to apply a function
or expression to each level.
fct_recode(f, v = "a", x = "b", z = "c")
fct_relabel(f, ~ paste0("x", .x))
fct_anon(f, prefix = ""))
Anonymize levels with random
integers. fct_anon(f)
fct_collapse(.f, ...) Collapse levels
into manually defined groups.
fct_collapse(f, x = c("a", "b"))
fct_lump(f, n, prop, w = NULL,
other_level = "Other", ties.method =
c("min", "average", "first", "last",
"random", "max")) Lump together
least/most common levels into a
single level. Also fct_lump_min.
fct_lump(f, n = 1)
fct_other(f, keep, drop, other_level =
"Other") Replace levels with "other."
fct_other(f, keep = c("a", "b"))
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Change the order of levels
fct_relevel(.f, ..., after = 0L)
Manually reorder factor levels.
fct_relevel(f, c("b", "c", "a"))
fct_infreq(f, ordered = NA)
Reorder levels by the frequency
in which they appear in the
data (highest frequency first).
f3 <- factor(c("c", "c", "a"))
fct_infreq(f3)
fct_inorder(f, ordered = NA)
Reorder levels by order in
which they appear in the data.
fct_inorder(f2)
fct_rev(f) Reverse level order.
f4 <- factor(c("a","b","c"))
fct_rev(f4)
fct_shift(f) Shift levels to left
or right, wrapping around end.
fct_shift(f4)
fct_shuffle(f, n = 1L) Randomly
permute order of factor levels.
fct_shuffle(f4)
fct_reorder(.f, .x, .fun=median, ...,
.desc = FALSE) Reorder levels by
their relationship with another
variable.
boxplot(data = iris, Sepal.Width ~
fct_reorder(Species, Sepal.Width))
fct_reorder2(.f, .x, .y, .fun =
last2, ..., .desc = TRUE) Reorder
levels by their final values when
plotted with two other variables.
ggplot(data = iris,
aes(Sepal.Width, Sepal.Length,
color = fct_reorder2(Species,
Sepal.Width, Sepal.Length))) +
geom_smooth()
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