eval_select() and eval_rename() evaluate defused R code (i.e. quoted expressions) according to the special rules of the tidyselect syntax. They power functions like dplyr::select(), dplyr::rename(), or tidyr::pivot_longer().

See the Get started vignette to learn how to use eval_select() and eval_rename() in your packages.

eval_rename(
  expr,
  data,
  env = caller_env(),
  ...,
  strict = TRUE,
  name_spec = NULL
)

eval_select(
  expr,
  data,
  env = caller_env(),
  ...,
  include = NULL,
  exclude = NULL,
  strict = TRUE,
  name_spec = NULL
)

Arguments

expr

Defused R code describing a selection according to the tidyselect syntax.

data

A named list, data frame, or atomic vector. Technically, data can be any vector with names() and "[[" implementations.

env

The environment in which to evaluate expr. Discarded if expr is a quosure.

...

These dots are for future extensions and must be empty.

strict

If TRUE, out-of-bounds errors are thrown if expr attempts to select or rename a variable that doesn't exist. If FALSE, failed selections or renamings are ignored.

name_spec

A name specification describing how to combine or propagate names. This is used only in case nested c() expressions like c(foo = c(bar = starts_with("foo"))). See the name_spec argument of vctrs::vec_c() for a description of valid name specs.

include, exclude

Character vector of column names to always include or exclude from the selection.

Value

A named vector of numeric locations, one for each of the selected elements.

The names are normally the same as in the input data, except when the user supplied named selections with c(). In the latter case, the names reflect the new names chosen by the user.

A given element may be selected multiple times under different names, in which case the vector might contain duplicate locations.

Details

The select and rename variants take the same types of inputs and have the same type of return value. However eval_rename() has a few extra constraints. It requires named inputs, and will fail if a data frame column is renamed to another existing column name. See the selecting versus renaming section in the syntax vignette for a description of the differences.

See also

Examples

library(rlang) # Interpret defused code as selection: x <- expr(mpg:cyl) eval_select(x, mtcars)
#> mpg cyl #> 1 2
# Interpret defused code as a renaming selection. All inputs must # be named within `c()`: try(eval_rename(expr(mpg), mtcars))
#> Error : All renaming inputs must be named.
eval_rename(expr(c(foo = mpg)), mtcars)
#> foo #> 1
# The evaluators return a named vector of locations. Here are # examples of using these location vectors to implement `select()` # and `rename()`: select <- function(.x, ...) { pos <- eval_select(expr(c(...)), .x) set_names(.x[pos], names(pos)) } rename <- function(.x, ...) { pos <- eval_rename(expr(c(...)), .x) names(.x)[pos] <- names(pos) .x } select(mtcars, mpg:cyl)
#> mpg cyl #> Mazda RX4 21.0 6 #> Mazda RX4 Wag 21.0 6 #> Datsun 710 22.8 4 #> Hornet 4 Drive 21.4 6 #> Hornet Sportabout 18.7 8 #> Valiant 18.1 6 #> Duster 360 14.3 8 #> Merc 240D 24.4 4 #> Merc 230 22.8 4 #> Merc 280 19.2 6 #> Merc 280C 17.8 6 #> Merc 450SE 16.4 8 #> Merc 450SL 17.3 8 #> Merc 450SLC 15.2 8 #> Cadillac Fleetwood 10.4 8 #> Lincoln Continental 10.4 8 #> Chrysler Imperial 14.7 8 #> Fiat 128 32.4 4 #> Honda Civic 30.4 4 #> Toyota Corolla 33.9 4 #> Toyota Corona 21.5 4 #> Dodge Challenger 15.5 8 #> AMC Javelin 15.2 8 #> Camaro Z28 13.3 8 #> Pontiac Firebird 19.2 8 #> Fiat X1-9 27.3 4 #> Porsche 914-2 26.0 4 #> Lotus Europa 30.4 4 #> Ford Pantera L 15.8 8 #> Ferrari Dino 19.7 6 #> Maserati Bora 15.0 8 #> Volvo 142E 21.4 4
rename(mtcars, foo = mpg)
#> foo cyl disp hp drat wt qsec vs am gear carb #> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2