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.
across()
supersedes the family of "scoped variants" like
summarise_at()
, summarise_if()
, and summarise_all()
.
Usage
across(.cols = everything(), .fns = NULL, ..., .names = NULL)
if_any(.cols = everything(), .fns = NULL, ..., .names = NULL)
if_all(.cols = everything(), .fns = NULL, ..., .names = NULL)
Arguments
- .cols, cols
<
tidy-select
> Columns to transform. Becauseacross()
is used within functions likesummarise()
andmutate()
, you can't select or compute upon grouping variables.- .fns
Functions to apply to each of the selected columns. Possible values are:
A function, e.g.
mean
.A purrr-style lambda, e.g.
~ mean(.x, na.rm = TRUE)
A list of functions/lambdas, e.g.
list(mean = mean, n_miss = ~ sum(is.na(.x))
NULL
: the default value, returns the selected columns in a data frame without applying a transformation. This is useful for when you want to use a function that takes a data frame.
Within these functions you can use
cur_column()
andcur_group()
to access the current column and grouping keys respectively.- ...
Additional arguments for the function calls in
.fns
. Using these...
is strongly discouraged because of issues of timing of evaluation.- .names
A glue specification that describes how to name the output columns. This can use
{.col}
to stand for the selected column name, and{.fn}
to stand for the name of the function being applied. The default (NULL
) is equivalent to"{.col}"
for the single function case and"{.col}_{.fn}"
for the case where a list is used for.fns
.
Value
across()
returns a tibble with one column for each column in .cols
and each function in .fns
.
if_any()
and if_all()
return a logical vector.
Timing of evaluation
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 × 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
## # A tibble: 4 × 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 × 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
See also
c_across()
for a function that returns a vector
Examples
# across() -----------------------------------------------------------------
# Different ways to select the same set of columns
# See <https://tidyselect.r-lib.org/articles/syntax.html> for details
iris %>%
as_tibble() %>%
mutate(across(c(Sepal.Length, Sepal.Width), round))
#> # A tibble: 150 × 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 5 4 1.4 0.2 setosa
#> 2 5 3 1.4 0.2 setosa
#> 3 5 3 1.3 0.2 setosa
#> 4 5 3 1.5 0.2 setosa
#> 5 5 4 1.4 0.2 setosa
#> 6 5 4 1.7 0.4 setosa
#> 7 5 3 1.4 0.3 setosa
#> 8 5 3 1.5 0.2 setosa
#> 9 4 3 1.4 0.2 setosa
#> 10 5 3 1.5 0.1 setosa
#> # … with 140 more rows
iris %>%
as_tibble() %>%
mutate(across(c(1, 2), round))
#> # A tibble: 150 × 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 5 4 1.4 0.2 setosa
#> 2 5 3 1.4 0.2 setosa
#> 3 5 3 1.3 0.2 setosa
#> 4 5 3 1.5 0.2 setosa
#> 5 5 4 1.4 0.2 setosa
#> 6 5 4 1.7 0.4 setosa
#> 7 5 3 1.4 0.3 setosa
#> 8 5 3 1.5 0.2 setosa
#> 9 4 3 1.4 0.2 setosa
#> 10 5 3 1.5 0.1 setosa
#> # … with 140 more rows
iris %>%
as_tibble() %>%
mutate(across(1:Sepal.Width, round))
#> # A tibble: 150 × 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 5 4 1.4 0.2 setosa
#> 2 5 3 1.4 0.2 setosa
#> 3 5 3 1.3 0.2 setosa
#> 4 5 3 1.5 0.2 setosa
#> 5 5 4 1.4 0.2 setosa
#> 6 5 4 1.7 0.4 setosa
#> 7 5 3 1.4 0.3 setosa
#> 8 5 3 1.5 0.2 setosa
#> 9 4 3 1.4 0.2 setosa
#> 10 5 3 1.5 0.1 setosa
#> # … with 140 more rows
iris %>%
as_tibble() %>%
mutate(across(where(is.double) & !c(Petal.Length, Petal.Width), round))
#> # A tibble: 150 × 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 5 4 1.4 0.2 setosa
#> 2 5 3 1.4 0.2 setosa
#> 3 5 3 1.3 0.2 setosa
#> 4 5 3 1.5 0.2 setosa
#> 5 5 4 1.4 0.2 setosa
#> 6 5 4 1.7 0.4 setosa
#> 7 5 3 1.4 0.3 setosa
#> 8 5 3 1.5 0.2 setosa
#> 9 4 3 1.4 0.2 setosa
#> 10 5 3 1.5 0.1 setosa
#> # … with 140 more rows
# A purrr-style formula
iris %>%
group_by(Species) %>%
summarise(across(starts_with("Sepal"), ~ mean(.x, na.rm = TRUE)))
#> # A tibble: 3 × 3
#> Species Sepal.Length Sepal.Width
#> <fct> <dbl> <dbl>
#> 1 setosa 5.01 3.43
#> 2 versicolor 5.94 2.77
#> 3 virginica 6.59 2.97
# A named list of functions
iris %>%
group_by(Species) %>%
summarise(across(starts_with("Sepal"), list(mean = mean, sd = sd)))
#> # A tibble: 3 × 5
#> Species Sepal.Length_me… Sepal.Length_sd Sepal.Width_mean Sepal.Width_sd
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 setosa 5.01 0.352 3.43 0.379
#> 2 versic… 5.94 0.516 2.77 0.314
#> 3 virgin… 6.59 0.636 2.97 0.322
# Use the .names argument to control the output names
iris %>%
group_by(Species) %>%
summarise(across(starts_with("Sepal"), mean, .names = "mean_{.col}"))
#> # A tibble: 3 × 3
#> Species mean_Sepal.Length mean_Sepal.Width
#> <fct> <dbl> <dbl>
#> 1 setosa 5.01 3.43
#> 2 versicolor 5.94 2.77
#> 3 virginica 6.59 2.97
iris %>%
group_by(Species) %>%
summarise(across(starts_with("Sepal"), list(mean = mean, sd = sd), .names = "{.col}.{.fn}"))
#> # A tibble: 3 × 5
#> Species Sepal.Length.me… Sepal.Length.sd Sepal.Width.mean Sepal.Width.sd
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 setosa 5.01 0.352 3.43 0.379
#> 2 versic… 5.94 0.516 2.77 0.314
#> 3 virgin… 6.59 0.636 2.97 0.322
# 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}"))
#> # A tibble: 3 × 5
#> Species Sepal.Length.fn1 Sepal.Length.fn2 Sepal.Width.fn1
#> <fct> <dbl> <dbl> <dbl>
#> 1 setosa 5.01 0.352 3.43
#> 2 versicolor 5.94 0.516 2.77
#> 3 virginica 6.59 0.636 2.97
#> # … with 1 more variable: Sepal.Width.fn2 <dbl>
# across() returns a data frame, which can be used as input of another function
df <- data.frame(
x1 = c(1, 2, NA),
x2 = c(4, NA, 6),
y = c("a", "b", "c")
)
df %>%
mutate(x_complete = complete.cases(across(starts_with("x"))))
#> x1 x2 y x_complete
#> 1 1 4 a TRUE
#> 2 2 NA b FALSE
#> 3 NA 6 c FALSE
df %>%
filter(complete.cases(across(starts_with("x"))))
#> x1 x2 y
#> 1 1 4 a
# if_any() and if_all() ----------------------------------------------------
iris %>%
filter(if_any(ends_with("Width"), ~ . > 4))
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 5.7 4.4 1.5 0.4 setosa
#> 2 5.2 4.1 1.5 0.1 setosa
#> 3 5.5 4.2 1.4 0.2 setosa
iris %>%
filter(if_all(ends_with("Width"), ~ . > 2))
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 6.3 3.3 6.0 2.5 virginica
#> 2 7.1 3.0 5.9 2.1 virginica
#> 3 6.5 3.0 5.8 2.2 virginica
#> 4 7.6 3.0 6.6 2.1 virginica
#> 5 7.2 3.6 6.1 2.5 virginica
#> 6 6.8 3.0 5.5 2.1 virginica
#> 7 5.8 2.8 5.1 2.4 virginica
#> 8 6.4 3.2 5.3 2.3 virginica
#> 9 7.7 3.8 6.7 2.2 virginica
#> 10 7.7 2.6 6.9 2.3 virginica
#> 11 6.9 3.2 5.7 2.3 virginica
#> 12 6.7 3.3 5.7 2.1 virginica
#> 13 6.4 2.8 5.6 2.1 virginica
#> 14 6.4 2.8 5.6 2.2 virginica
#> 15 7.7 3.0 6.1 2.3 virginica
#> 16 6.3 3.4 5.6 2.4 virginica
#> 17 6.9 3.1 5.4 2.1 virginica
#> 18 6.7 3.1 5.6 2.4 virginica
#> 19 6.9 3.1 5.1 2.3 virginica
#> 20 6.8 3.2 5.9 2.3 virginica
#> 21 6.7 3.3 5.7 2.5 virginica
#> 22 6.7 3.0 5.2 2.3 virginica
#> 23 6.2 3.4 5.4 2.3 virginica