across | R Documentation |
across()
makes it easy to apply the same transformation to multiple
columns, allowing you to use select()
semantics inside in "data-masking"
functions like summarise()
and mutate()
. See vignette("colwise")
for
more details.
if_any()
and if_all()
apply the same
predicate function to a selection of columns and combine the
results into a single logical vector: if_any()
is TRUE
when
the predicate is TRUE
for any of the selected columns, if_all()
is TRUE
when the predicate is TRUE
for all selected columns.
If you just need to select columns without applying a transformation to each
of them, then you probably want to use pick()
instead.
across()
supersedes the family of "scoped variants" like
summarise_at()
, summarise_if()
, and summarise_all()
.
across(.cols, .fns, ..., .names = NULL, .unpack = FALSE)
if_any(.cols, .fns, ..., .names = NULL)
if_all(.cols, .fns, ..., .names = NULL)
.cols |
< |
.fns |
Functions to apply to each of the selected columns. Possible values are:
Within these functions you can use |
... |
Additional arguments for the function calls in |
.names |
A glue specification that describes how to name the output
columns. This can use |
.unpack |
Optionally unpack data frames returned by functions in
|
across()
typically returns a tibble with one column for each column in
.cols
and each function in .fns
. If .unpack
is used, more columns may
be returned depending on how the results of .fns
are unpacked.
if_any()
and if_all()
return a logical vector.
R code in dplyr verbs is generally evaluated once per group.
Inside across()
however, code is evaluated once for each
combination of columns and groups. If the evaluation timing is
important, for example if you're generating random variables, think
about when it should happen and place your code in consequence.
gdf <- tibble(g = c(1, 1, 2, 3), v1 = 10:13, v2 = 20:23) %>% group_by(g) set.seed(1) # Outside: 1 normal variate n <- rnorm(1) gdf %>% mutate(across(v1:v2, ~ .x + n)) #> # A tibble: 4 x 3 #> # Groups: g [3] #> g v1 v2 #> <dbl> <dbl> <dbl> #> 1 1 9.37 19.4 #> 2 1 10.4 20.4 #> 3 2 11.4 21.4 #> 4 3 12.4 22.4 # Inside a verb: 3 normal variates (ngroup) gdf %>% mutate(n = rnorm(1), across(v1:v2, ~ .x + n)) #> # A tibble: 4 x 4 #> # Groups: g [3] #> g v1 v2 n #> <dbl> <dbl> <dbl> <dbl> #> 1 1 10.2 20.2 0.184 #> 2 1 11.2 21.2 0.184 #> 3 2 11.2 21.2 -0.836 #> 4 3 14.6 24.6 1.60 # Inside `across()`: 6 normal variates (ncol * ngroup) gdf %>% mutate(across(v1:v2, ~ .x + rnorm(1))) #> # A tibble: 4 x 3 #> # Groups: g [3] #> g v1 v2 #> <dbl> <dbl> <dbl> #> 1 1 10.3 20.7 #> 2 1 11.3 21.7 #> 3 2 11.2 22.6 #> 4 3 13.5 22.7
c_across()
for a function that returns a vector
# For better printing
iris <- as_tibble(iris)
# across() -----------------------------------------------------------------
# Different ways to select the same set of columns
# See <https://tidyselect.r-lib.org/articles/syntax.html> for details
iris %>%
mutate(across(c(Sepal.Length, Sepal.Width), round))
iris %>%
mutate(across(c(1, 2), round))
iris %>%
mutate(across(1:Sepal.Width, round))
iris %>%
mutate(across(where(is.double) & !c(Petal.Length, Petal.Width), round))
# Using an external vector of names
cols <- c("Sepal.Length", "Petal.Width")
iris %>%
mutate(across(all_of(cols), round))
# If the external vector is named, the output columns will be named according
# to those names
names(cols) <- tolower(cols)
iris %>%
mutate(across(all_of(cols), round))
# A purrr-style formula
iris %>%
group_by(Species) %>%
summarise(across(starts_with("Sepal"), ~ mean(.x, na.rm = TRUE)))
# A named list of functions
iris %>%
group_by(Species) %>%
summarise(across(starts_with("Sepal"), list(mean = mean, sd = sd)))
# Use the .names argument to control the output names
iris %>%
group_by(Species) %>%
summarise(across(starts_with("Sepal"), mean, .names = "mean_{.col}"))
iris %>%
group_by(Species) %>%
summarise(across(starts_with("Sepal"), list(mean = mean, sd = sd), .names = "{.col}.{.fn}"))
# If a named external vector is used for column selection, .names will use
# those names when constructing the output names
iris %>%
group_by(Species) %>%
summarise(across(all_of(cols), mean, .names = "mean_{.col}"))
# When the list is not named, .fn is replaced by the function's position
iris %>%
group_by(Species) %>%
summarise(across(starts_with("Sepal"), list(mean, sd), .names = "{.col}.fn{.fn}"))
# When the functions in .fns return a data frame, you typically get a
# "packed" data frame back
quantile_df <- function(x, probs = c(0.25, 0.5, 0.75)) {
tibble(quantile = probs, value = quantile(x, probs))
}
iris %>%
reframe(across(starts_with("Sepal"), quantile_df))
# Use .unpack to automatically expand these packed data frames into their
# individual columns
iris %>%
reframe(across(starts_with("Sepal"), quantile_df, .unpack = TRUE))
# .unpack can utilize a glue specification if you don't like the defaults
iris %>%
reframe(across(starts_with("Sepal"), quantile_df, .unpack = "{outer}.{inner}"))
# This is also useful inside mutate(), for example, with a multi-lag helper
multilag <- function(x, lags = 1:3) {
names(lags) <- as.character(lags)
purrr::map_dfr(lags, lag, x = x)
}
iris %>%
group_by(Species) %>%
mutate(across(starts_with("Sepal"), multilag, .unpack = TRUE)) %>%
select(Species, starts_with("Sepal"))
# if_any() and if_all() ----------------------------------------------------
iris %>%
filter(if_any(ends_with("Width"), ~ . > 4))
iris %>%
filter(if_all(ends_with("Width"), ~ . > 2))
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