tidyselect implements a DSL for selecting variables. It provides helpers for selecting variables:
var1:var10: variables lying between var1 on the left and var10 on the right.
starts_with("a"): names that start with "a".
ends_with("z"): names that end with "z".
contains("b"): names that contain "b".
matches("x.y"): names that match regular expression x.y.
num_range(x, 1:4): names following the pattern, x1, x2, ..., x4.
all_of(vars)/any_of(vars):
matches names stored in the character vector vars. all_of(vars) will
error if the variables aren't present; any_of(var) will match just the
variables that exist.
everything(): all variables.
last_col(): furthest column on the right.
where(is.numeric): all variables where
is.numeric() returns TRUE.
As well as operators for combining those selections:
!selection: only variables that don't match selection.
selection1 & selection2: only variables included in both selection1 and selection2.
selection1 | selection2: all variables that match either selection1 or selection2.
When writing code inside packages you can substitute "var" for var to avoid R CMD check notes.
Here we show the usage for the basic selection operators. See the
specific help pages to learn about helpers like starts_with().
The selection language can be used in functions like
dplyr::select() or tidyr::pivot_longer(). Let's first attach
the tidyverse:
library(tidyverse)# For better printing
iris <- as_tibble(iris)
Select variables by name:
starwars %>% select(height)
#> # A tibble: 87 x 1
#> height
#> <int>
#> 1 172
#> 2 167
#> 3 96
#> 4 202
#> # i 83 more rowsiris %>% pivot_longer(Sepal.Length)
#> # A tibble: 150 x 6
#> Sepal.Width Petal.Length Petal.Width Species name value
#> <dbl> <dbl> <dbl> <fct> <chr> <dbl>
#> 1 3.5 1.4 0.2 setosa Sepal.Length 5.1
#> 2 3 1.4 0.2 setosa Sepal.Length 4.9
#> 3 3.2 1.3 0.2 setosa Sepal.Length 4.7
#> 4 3.1 1.5 0.2 setosa Sepal.Length 4.6
#> # i 146 more rows
Select multiple variables by separating them with commas. Note how the order of columns is determined by the order of inputs:
starwars %>% select(homeworld, height, mass)
#> # A tibble: 87 x 3
#> homeworld height mass
#> <chr> <int> <dbl>
#> 1 Tatooine 172 77
#> 2 Tatooine 167 75
#> 3 Naboo 96 32
#> 4 Tatooine 202 136
#> # i 83 more rows
Functions like tidyr::pivot_longer() don't take variables with
dots. In this case use c() to select multiple variables:
iris %>% pivot_longer(c(Sepal.Length, Petal.Length))
#> # A tibble: 300 x 5
#> Sepal.Width Petal.Width Species name value
#> <dbl> <dbl> <fct> <chr> <dbl>
#> 1 3.5 0.2 setosa Sepal.Length 5.1
#> 2 3.5 0.2 setosa Petal.Length 1.4
#> 3 3 0.2 setosa Sepal.Length 4.9
#> 4 3 0.2 setosa Petal.Length 1.4
#> # i 296 more rows
The : operator selects a range of consecutive variables:
starwars %>% select(name:mass)
#> # A tibble: 87 x 3
#> name height mass
#> <chr> <int> <dbl>
#> 1 Luke Skywalker 172 77
#> 2 C-3PO 167 75
#> 3 R2-D2 96 32
#> 4 Darth Vader 202 136
#> # i 83 more rows
The ! operator negates a selection:
starwars %>% select(!(name:mass))
#> # A tibble: 87 x 11
#> hair_color skin_color eye_color birth_year sex gender homeworld species
#> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr>
#> 1 blond fair blue 19 male masculine Tatooine Human
#> 2 <NA> gold yellow 112 none masculine Tatooine Droid
#> 3 <NA> white, blue red 33 none masculine Naboo Droid
#> 4 none white yellow 41.9 male masculine Tatooine Human
#> # i 83 more rows
#> # i 3 more variables: films <list>, vehicles <list>, starships <list>iris %>% select(!c(Sepal.Length, Petal.Length))
#> # A tibble: 150 x 3
#> Sepal.Width Petal.Width Species
#> <dbl> <dbl> <fct>
#> 1 3.5 0.2 setosa
#> 2 3 0.2 setosa
#> 3 3.2 0.2 setosa
#> 4 3.1 0.2 setosa
#> # i 146 more rows
iris %>% select(!ends_with("Width"))
#> # A tibble: 150 x 3
#> Sepal.Length Petal.Length Species
#> <dbl> <dbl> <fct>
#> 1 5.1 1.4 setosa
#> 2 4.9 1.4 setosa
#> 3 4.7 1.3 setosa
#> 4 4.6 1.5 setosa
#> # i 146 more rows
& and | take the intersection or the union of two selections:
iris %>% select(starts_with("Petal") & ends_with("Width"))
#> # A tibble: 150 x 1
#> Petal.Width
#> <dbl>
#> 1 0.2
#> 2 0.2
#> 3 0.2
#> 4 0.2
#> # i 146 more rowsiris %>% select(starts_with("Petal") | ends_with("Width"))
#> # A tibble: 150 x 3
#> Petal.Length Petal.Width Sepal.Width
#> <dbl> <dbl> <dbl>
#> 1 1.4 0.2 3.5
#> 2 1.4 0.2 3
#> 3 1.3 0.2 3.2
#> 4 1.5 0.2 3.1
#> # i 146 more rows
To take the difference between two selections, combine the & and
! operators:
iris %>% select(starts_with("Petal") & !ends_with("Width"))
#> # A tibble: 150 x 1
#> Petal.Length
#> <dbl>
#> 1 1.4
#> 2 1.4
#> 3 1.3
#> 4 1.5
#> # i 146 more rows
The order of selected columns is determined by the inputs.
all_of(c("foo", "bar")) selects "foo" first.
c(starts_with("c"), starts_with("d")) selects all columns
starting with "c" first, then all columns starting with "d".