Overview of selection features:
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.
Other 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.
Or from variables stored in 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 asall_of(), except that no error is thrown for names that don't exist.
Or using a predicate function:
where(): Applies a function to all variables and selects those for which the function returnsTRUE.
Simple examples
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:
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 rows
iris %>% 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 rowsSelect 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 rowsFunctions 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 rowsOperators:
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 rowsThe ! 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 rows
iris %>% 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 rowsTo 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