tidyeval: Tidy eval helpers
In rnabioco/djvdj: A collection of single-cell V(D)J tools

tidyeval

R Documentation

Tidy eval helpers

Description

This page lists the tidy eval tools reexported in this package from rlang. To learn about using tidy eval in scripts and packages at a high level, see the dplyr programming vignette and the ggplot2 in packages vignette. The Metaprogramming section of Advanced R may also be useful for a deeper dive.

The tidy eval operators ⁠{{⁠, ⁠!!⁠, and ⁠!!!⁠ are syntactic constructs which are specially interpreted by tidy eval functions. You will mostly need ⁠{{⁠, as ⁠!!⁠ and ⁠!!!⁠ are more advanced operators which you should not have to use in simple cases.

The curly-curly operator ⁠{{⁠ allows you to tunnel data-variables passed from function arguments inside other tidy eval functions. ⁠{{⁠ is designed for individual arguments. To pass multiple arguments contained in dots, use ... in the normal way.
```
my_function <- function(data, var, ...) {
  data |>
    group_by(...) |>
    summarise(mean = mean({{ var }}))
}
```
enquo() and enquos() delay the execution of one or several function arguments. The former returns a single expression, the latter returns a list of expressions. Once defused, expressions will no longer evaluate on their own. They must be injected back into an evaluation context with ⁠!!⁠ (for a single expression) and ⁠!!!⁠ (for a list of expressions).
```
my_function <- function(data, var, ...) {
  # Defuse
  var <- enquo(var)
  dots <- enquos(...)

  # Inject
  data |>
    group_by(!!!dots) |>
    summarise(mean = mean(!!var))
}
```
In this simple case, the code is equivalent to the usage of ⁠{{⁠ and ... above. Defusing with enquo() or enquos() is only needed in more complex cases, for instance if you need to inspect or modify the expressions in some way.
The .data pronoun is an object that represents the current slice of data. If you have a variable name in a string, use the .data pronoun to subset that variable with [[.
```
my_var <- "disp"
mtcars |> summarise(mean = mean(.data[[my_var]]))
```
Another tidy eval operator is ⁠:=⁠. It makes it possible to use glue and curly-curly syntax on the LHS of =. For technical reasons, the R language doesn't support complex expressions on the left of =, so we use ⁠:=⁠ as a workaround.
```
my_function <- function(data, var, suffix = "foo") {
  # Use `{{` to tunnel function arguments and the usual glue
  # operator `{` to interpolate plain strings.
  data |>
    summarise("{{ var }}_mean_{suffix}" := mean({{ var }}))
}
```
Many tidy eval functions like dplyr::mutate() or dplyr::summarise() give an automatic name to unnamed inputs. If you need to create the same sort of automatic names by yourself, use as_label(). For instance, the glue-tunnelling syntax above can be reproduced manually with:
```
my_function <- function(data, var, suffix = "foo") {
  var <- enquo(var)
  prefix <- as_label(var)
  data |>
    summarise("{prefix}_mean_{suffix}" := mean(!!var))
}
```
Expressions defused with enquo() (or tunnelled with ⁠{{⁠) need not be simple column names, they can be arbitrarily complex. as_label() handles those cases gracefully. If your code assumes a simple column name, use as_name() instead. This is safer because it throws an error if the input is not a name as expected.