Evaluate an expression with tidyselect semantics

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.

Usage

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

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

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.
allow_predicates: If TRUE (the default), it is ok for expr to use predicates (i.e. in where()). If FALSE, will error if expr uses a predicate. Will automatically be set to FALSE if data does not support predicates (as determined by tidyselect_data_has_predicates()).
error_call: The execution environment of a currently running function, e.g. caller_env(). The function will be mentioned in error messages as the source of the error. See the call argument of abort() for more information.
include, exclude: Character vector of column names to always include or exclude from the selection.
allow_rename: If TRUE (the default), the renaming syntax c(foo = bar) is allowed. If FALSE, it causes an error. This is useful to implement purely selective behaviour.
allow_empty: If TRUE (the default), it is ok for expr to result in an empty selection. If FALSE, will error if expr yields an empty 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.

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 in eval(expr, envir, enclos) : 
#>   All renaming inputs must be named.
eval_rename(expr(c(foo = mpg)), mtcars)
#> foo 
#>   1 


# Within a function, use `enquo()` to defuse one argument:
my_function <- function(x, expr) {
  eval_select(enquo(expr), x)
}

# If your function takes dots, evaluate a defused call to `c(...)`
# with `expr(c(...))`:
my_function <- function(.x, ...) {
  eval_select(expr(c(...)), .x)
}

# If your function takes dots and a named argument, use `{{ }}`
# inside the defused expression to tunnel it inside the tidyselect DSL:
my_function <- function(.x, .expr, ...) {
  eval_select(expr(c({{ .expr }}, ...)), .x)
}

# Note that the trick above works because `expr({{ arg }})` is the
# same as `enquo(arg)`.


# 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