select: Subset columns using their names and types

Description

Select (and optionally rename) variables in a data frame, using a concise mini-language that makes it easy to refer to variables based on their name (e.g. a:f selects all columns from a on the left to f on the right). You can also use predicate functions like is.numeric to select variables based on their properties.

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.

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.

Usage

select(.data, ...)

Value

An object of the same type as .data. The output has the following properties:

Rows are not affected.
Output columns are a subset of input columns, potentially with a different order. Columns will be renamed if new_name = old_name form is used.
Data frame attributes are preserved.
Groups are maintained; you can't select off grouping variables.

Arguments

.data: A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
...: <tidy-select> One or more unquoted expressions separated by commas. Variable names can be used as if they were positions in the data frame, so expressions like x:y can be used to select a range of variables.

Methods

This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages: dplyr:::methods_rd("select").

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:

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 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
#> # ... 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

Operators:

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_c~1 eye_c~2 birth~3 sex   gender homew~4 species films vehic~5
#>   <chr>      <chr>    <chr>     <dbl> <chr> <chr>  <chr>   <chr>   <lis> <list> 
#> 1 blond      fair     blue       19   male  mascu~ Tatooi~ Human   <chr> <chr>  
#> 2 <NA>       gold     yellow    112   none  mascu~ Tatooi~ Droid   <chr> <chr>  
#> 3 <NA>       white, ~ red        33   none  mascu~ Naboo   Droid   <chr> <chr>  
#> 4 none       white    yellow     41.9 male  mascu~ Tatooi~ Human   <chr> <chr>  
#> # ... with 83 more rows, 1 more variable: starships <list>, and abbreviated
#> #   variable names 1: skin_color, 2: eye_color, 3: birth_year, 4: homeworld,
#> #   5: vehicles
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 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
#> # ... 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