Tidyverse selections implement a dialect of R where operators make it easy to select variables:
: for selecting a range of consecutive variables.
! for taking the complement of a set of variables.
& and | for selecting the intersection or the union of two
sets of variables.
c() for combining selections.
In addition, you can use selection helpers. Some helpers select specific columns:
everything(): Matches all variables.
last_col(): Select last variable, possibly with an offset.
These helpers select variables by matching patterns in their names:
starts_with(): Starts with a prefix.
ends_with(): Ends with a suffix.
contains(): Contains a literal string.
matches(): Matches a regular expression.
num_range(): Matches a numerical range like x01, x02, x03.
These helpers select variables from a character vector:
all_of(): Matches variable names in a character vector. All
names must be present, otherwise an out-of-bounds error is
thrown.
any_of(): Same as all_of(), except that no error is thrown
for names that don't exist.
This helper selects variables with a function:
where(): Applies a function to all variables and selects those
for which the function returns TRUE.
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
#> # ... with 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
#> # ... with 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
#> # ... with 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
#> # ... with 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
#> # ... with 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
#> # ... with 83 more rows, and 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
#> # ... with 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
#> # ... with 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
#> # ... with 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
#> # ... with 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
#> # ... with 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".