tidyeval | R Documentation |
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.
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