Package 'rlang'

Title: Functions for Base Types and Core R and 'Tidyverse' Features
Description: A toolbox for working with base types, core R features like the condition system, and core 'Tidyverse' features like tidy evaluation.
Authors: Lionel Henry [aut, cre], Hadley Wickham [aut], mikefc [cph] (Hash implementation based on Mike's xxhashlite), Yann Collet [cph] (Author of the embedded xxHash library), Posit, PBC [cph, fnd]
Maintainer: Lionel Henry <[email protected]>
License: MIT + file LICENSE
Version: 1.1.3.9000
Built: 2024-04-05 15:22:14 UTC
Source: https://github.com/r-lib/rlang

Help Index


Signal an error, warning, or message

Description

These functions are equivalent to base functions base::stop(), base::warning(), and base::message(). They signal a condition (an error, warning, or message respectively) and make it easy to supply condition metadata:

Certain components of condition messages are formatted with unicode symbols and terminal colours by default. These aspects can be customised, see Customising condition messages.

Usage

abort(
  message = NULL,
  class = NULL,
  ...,
  call,
  body = NULL,
  footer = NULL,
  trace = NULL,
  parent = NULL,
  use_cli_format = NULL,
  .inherit = TRUE,
  .internal = FALSE,
  .file = NULL,
  .frame = caller_env(),
  .trace_bottom = NULL,
  .subclass = deprecated()
)

warn(
  message = NULL,
  class = NULL,
  ...,
  body = NULL,
  footer = NULL,
  parent = NULL,
  use_cli_format = NULL,
  .inherit = NULL,
  .frequency = c("always", "regularly", "once"),
  .frequency_id = NULL,
  .subclass = deprecated()
)

inform(
  message = NULL,
  class = NULL,
  ...,
  body = NULL,
  footer = NULL,
  parent = NULL,
  use_cli_format = NULL,
  .inherit = NULL,
  .file = NULL,
  .frequency = c("always", "regularly", "once"),
  .frequency_id = NULL,
  .subclass = deprecated()
)

signal(message = "", class, ..., .subclass = deprecated())

reset_warning_verbosity(id)

reset_message_verbosity(id)

Arguments

message

The message to display, formatted as a bulleted list. The first element is displayed as an alert bullet prefixed with ! by default. Elements named "*", "i", "v", "x", and "!" are formatted as regular, info, success, failure, and error bullets respectively. See Formatting messages with cli for more about bulleted messaging.

If a message is not supplied, it is expected that the message is generated lazily through cnd_header() and cnd_body() methods. In that case, class must be supplied. Only inform() allows empty messages as it is occasionally useful to build user output incrementally.

If a function, it is stored in the header field of the error condition. This acts as a cnd_header() method that is invoked lazily when the error message is displayed.

class

Subclass of the condition.

...

Additional data to be stored in the condition object. If you supply condition fields, you should usually provide a class argument. You may consider prefixing condition fields with the name of your package or organisation to prevent name collisions.

call

The execution environment of a currently running function, e.g. call = caller_env(). The corresponding function call is retrieved and mentioned in error messages as the source of the error.

You only need to supply call when throwing a condition from a helper function which wouldn't be relevant to mention in the message.

Can also be NULL or a defused function call to respectively not display any call or hard-code a code to display.

For more information about error calls, see Including function calls in error messages.

body, footer

Additional bullets.

trace

A trace object created by trace_back().

parent

Supply parent when you rethrow an error from a condition handler (e.g. with try_fetch()).

  • If parent is a condition object, a chained error is created, which is useful when you want to enhance an error with more details, while still retaining the original information.

  • If parent is NA, it indicates an unchained rethrow, which is useful when you want to take ownership over an error and rethrow it with a custom message that better fits the surrounding context.

    Technically, supplying NA lets abort() know it is called from a condition handler. This helps it create simpler backtraces where the condition handling context is hidden by default.

For more information about error calls, see Including contextual information with error chains.

use_cli_format

Whether to format message lazily using cli if available. This results in prettier and more accurate formatting of messages. See local_use_cli() to set this condition field by default in your package namespace.

If set to TRUE, message should be a character vector of individual and unformatted lines. Any newline character "\\n" already present in message is reformatted by cli's paragraph formatter. See Formatting messages with cli.

.inherit

Whether the condition inherits from parent according to cnd_inherits() and try_fetch(). By default, parent conditions of higher severity are not inherited. For instance an error chained to a warning is not inherited to avoid unexpectedly catching an error downgraded to a warning.

.internal

If TRUE, a footer bullet is added to message to let the user know that the error is internal and that they should report it to the package authors. This argument is incompatible with footer.

.file

A connection or a string specifying where to print the message. The default depends on the context, see the stdout vs stderr section.

.frame

The throwing context. Used as default for .trace_bottom, and to determine the internal package to mention in internal errors when .internal is TRUE.

.trace_bottom

Used in the display of simplified backtraces as the last relevant call frame to show. This way, the irrelevant parts of backtraces corresponding to condition handling (tryCatch(), try_fetch(), abort(), etc.) are hidden by default. Defaults to call if it is an environment, or .frame otherwise. Without effect if trace is supplied.

.subclass

[Deprecated] This argument was renamed to class in rlang 0.4.2 for consistency with our conventions for class constructors documented in https://adv-r.hadley.nz/s3.html#s3-subclassing.

.frequency

How frequently should the warning or message be displayed? By default ("always") it is displayed at each time. If "regularly", it is displayed once every 8 hours. If "once", it is displayed once per session.

.frequency_id

A unique identifier for the warning or message. This is used when .frequency is supplied to recognise recurring conditions. This argument must be supplied if .frequency is not set to "always".

id

The identifying string of the condition that was supplied as .frequency_id to warn() or inform().

Details

Error prefix

As with base::stop(), errors thrown with abort() are prefixed with "Error: ". Calls and source references are included in the prefix, e.g. ⁠"Error in ⁠my_function()⁠ at myfile.R:1:2:"⁠. There are a few cosmetic differences:

If your throwing code is highly structured, you may have to explicitly inform abort() about the relevant user-facing call to include in the prefix. Internal helpers are rarely relevant to end users. See the call argument of abort().

Backtrace

abort() saves a backtrace in the trace component of the error condition. You can print a simplified backtrace of the last error by calling last_error() and a full backtrace with summary(last_error()). Learn how to control what is displayed when an error is thrown with rlang_backtrace_on_error.

Muffling and silencing conditions

Signalling a condition with inform() or warn() displays a message in the console. These messages can be muffled as usual with base::suppressMessages() or base::suppressWarnings().

inform() and warn() messages can also be silenced with the global options rlib_message_verbosity and rlib_warning_verbosity. These options take the values:

When set to quiet, the message is not displayed and the condition is not signalled.

stdout and stderr

By default, abort() and inform() print to standard output in interactive sessions. This allows rlang to be in control of the appearance of messages in IDEs like RStudio.

There are two situations where messages are streamed to stderr:

These exceptions ensure consistency of behaviour in interactive and non-interactive sessions, and when sinks are active.

See Also

Examples

# These examples are guarded to avoid throwing errors
if (FALSE) {

# Signal an error with a message just like stop():
abort("The error message.")


# Unhandled errors are saved automatically by `abort()` and can be
# retrieved with `last_error()`. The error prints with a simplified
# backtrace:
f <- function() try(g())
g <- function() evalq(h())
h <- function() abort("Tilt.")
last_error()

# Use `summary()` to print the full backtrace and the condition fields:
summary(last_error())


# Give a class to the error:
abort("The error message", "mypkg_bad_error")

# This allows callers to handle the error selectively
tryCatch(
  mypkg_function(),
  mypkg_bad_error = function(err) {
    warn(conditionMessage(err)) # Demote the error to a warning
    NA                          # Return an alternative value
  }
)

# You can also specify metadata that will be stored in the condition:
abort("The error message.", "mypkg_bad_error", data = 1:10)

# This data can then be consulted by user handlers:
tryCatch(
  mypkg_function(),
  mypkg_bad_error = function(err) {
    # Compute an alternative return value with the data:
    recover_error(err$data)
  }
)


# If you call low-level APIs it may be a good idea to create a
# chained error with the low-level error wrapped in a more
# user-friendly error. Use `try_fetch()` to fetch errors of a given
# class and rethrow them with the `parent` argument of `abort()`:
file <- "http://foo.bar/baz"
try(
  try_fetch(
    download(file),
    error = function(err) {
      msg <- sprintf("Can't download `%s`", file)
      abort(msg, parent = err)
  })
)

}

Match an argument to a character vector

Description

This is equivalent to base::match.arg() with a few differences:

arg_match() derives the possible values from the caller function.

arg_match0() is a bare-bones version if performance is at a premium. It requires a string as arg and explicit character values. For convenience, arg may also be a character vector containing every element of values, possibly permuted. In this case, the first element of arg is used.

Usage

arg_match(
  arg,
  values = NULL,
  ...,
  multiple = FALSE,
  error_arg = caller_arg(arg),
  error_call = caller_env()
)

arg_match0(arg, values, arg_nm = caller_arg(arg), error_call = caller_env())

Arguments

arg

A symbol referring to an argument accepting strings.

values

A character vector of possible values that arg can take.

...

These dots are for future extensions and must be empty.

multiple

Whether arg may contain zero or several values.

error_arg

An argument name as a string. This argument will be mentioned in error messages as the input that is at the origin of a problem.

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.

arg_nm

Same as error_arg.

Value

The string supplied to arg.

See Also

check_required()

Examples

fn <- function(x = c("foo", "bar")) arg_match(x)
fn("bar")

# Throws an informative error for mismatches:
try(fn("b"))
try(fn("baz"))

# Use the bare-bones version with explicit values for speed:
arg_match0("bar", c("foo", "bar", "baz"))

# For convenience:
fn1 <- function(x = c("bar", "baz", "foo")) fn3(x)
fn2 <- function(x = c("baz", "bar", "foo")) fn3(x)
fn3 <- function(x) arg_match0(x, c("foo", "bar", "baz"))
fn1()
fn2("bar")
try(fn3("zoo"))

Documentation anchor for error arguments

Description

Use ⁠@inheritParams rlang::args_error_context⁠ in your package to document arg and call arguments (or equivalently their prefixed versions error_arg and error_call).

Arguments

arg

An argument name as a string. This argument will be mentioned in error messages as the input that is at the origin of a problem.

error_arg

An argument name as a string. This argument will be mentioned in error messages as the input that is at the origin of a problem.

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.

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.


Convert object to a box

Description

Usage

as_box(x, class = NULL)

as_box_if(.x, .p, .class = NULL, ...)

Arguments

x, .x

An R object.

class, .class

A box class. If the input is already a box of that class, it is returned as is. If the input needs to be boxed, class is passed to new_box().

.p

A predicate function.

...

Arguments passed to .p.


Create a data mask

Description

A data mask is an environment (or possibly multiple environments forming an ancestry) containing user-supplied objects. Objects in the mask have precedence over objects in the environment (i.e. they mask those objects). Many R functions evaluate quoted expressions in a data mask so these expressions can refer to objects within the user data.

These functions let you construct a tidy eval data mask manually. They are meant for developers of tidy eval interfaces rather than for end users.

Usage

as_data_mask(data)

as_data_pronoun(data)

new_data_mask(bottom, top = bottom)

Arguments

data

A data frame or named vector of masking data.

bottom

The environment containing masking objects if the data mask is one environment deep. The bottom environment if the data mask comprises multiple environment.

If you haven't supplied top, this must be an environment that you own, i.e. that you have created yourself.

top

The last environment of the data mask. If the data mask is only one environment deep, top should be the same as bottom.

This must be an environment that you own, i.e. that you have created yourself. The parent of top will be changed by the tidy eval engine and should be considered undetermined. Never make assumption about the parent of top.

Value

A data mask that you can supply to eval_tidy().

Why build a data mask?

Most of the time you can just call eval_tidy() with a list or a data frame and the data mask will be constructed automatically. There are three main use cases for manual creation of data masks:

Building your own data mask

Unlike base::eval() which takes any kind of environments as data mask, eval_tidy() has specific requirements in order to support quosures. For this reason you can't supply bare environments.

There are two ways of constructing an rlang data mask manually:

Once you have built a data mask, simply pass it to eval_tidy() as the data argument. You can repeat this as many times as needed. Note that any objects created there (perhaps because of a call to ⁠<-⁠) will persist in subsequent evaluations.

Top and bottom of data mask

In some cases you'll need several levels in your data mask. One good reason is when you include functions in the mask. It's a good idea to keep data objects one level lower than function objects, so that the former cannot override the definitions of the latter (see examples).

In that case, set up all your environments and keep track of the bottom child and the top parent. You'll need to pass both to new_data_mask().

Note that the parent of the top environment is completely undetermined, you shouldn't expect it to remain the same at all times. This parent is replaced during evaluation by eval_tidy() to one of the following environments:

Consequently, all masking data should be contained between the bottom and top environment of the data mask.

Examples

# Evaluating in a tidy evaluation environment enables all tidy
# features:
mask <- as_data_mask(mtcars)
eval_tidy(quo(letters), mask)

# You can install new pronouns in the mask:
mask$.pronoun <- as_data_pronoun(list(foo = "bar", baz = "bam"))
eval_tidy(quo(.pronoun$foo), mask)

# In some cases the data mask can leak to the user, for example if
# a function or formula is created in the data mask environment:
cyl <- "user variable from the context"
fn <- eval_tidy(quote(function() cyl), mask)
fn()

# If new objects are created in the mask, they persist in the
# subsequent calls:
eval_tidy(quote(new <- cyl + am), mask)
eval_tidy(quote(new * 2), mask)


# In some cases your data mask is a whole chain of environments
# rather than a single environment. You'll have to use
# `new_data_mask()` and let it know about the bottom of the mask
# (the last child of the environment chain) and the topmost parent.

# A common situation where you'll want a multiple-environment mask
# is when you include functions in your mask. In that case you'll
# put functions in the top environment and data in the bottom. This
# will prevent the data from overwriting the functions.
top <- new_environment(list(`+` = base::paste, c = base::paste))

# Let's add a middle environment just for sport:
middle <- env(top)

# And finally the bottom environment containing data:
bottom <- env(middle, a = "a", b = "b", c = "c")

# We can now create a mask by supplying the top and bottom
# environments:
mask <- new_data_mask(bottom, top = top)

# This data mask can be passed to eval_tidy() instead of a list or
# data frame:
eval_tidy(quote(a + b + c), data = mask)

# Note how the function `c()` and the object `c` are looked up
# properly because of the multi-level structure:
eval_tidy(quote(c(a, b, c)), data = mask)

# new_data_mask() does not create data pronouns, but
# data pronouns can be added manually:
mask$.fns <- as_data_pronoun(top)

# The `.data` pronoun should generally be created from the
# mask. This will ensure data is looked up throughout the whole
# ancestry. Only non-function objects are looked up from this
# pronoun:
mask$.data <- as_data_pronoun(mask)
mask$.data$c

# Now we can reference values with the pronouns:
eval_tidy(quote(c(.data$a, .data$b, .data$c)), data = mask)

Coerce to an environment

Description

as_environment() coerces named vectors (including lists) to an environment. The names must be unique. If supplied an unnamed string, it returns the corresponding package environment (see pkg_env()).

Usage

as_environment(x, parent = NULL)

Arguments

x

An object to coerce.

parent

A parent environment, empty_env() by default. This argument is only used when x is data actually coerced to an environment (as opposed to data representing an environment, like NULL representing the empty environment).

Details

If x is an environment and parent is not NULL, the environment is duplicated before being set a new parent. The return value is therefore a different environment than x.

Examples

# Coerce a named vector to an environment:
env <- as_environment(mtcars)

# By default it gets the empty environment as parent:
identical(env_parent(env), empty_env())


# With strings it is a handy shortcut for pkg_env():
as_environment("base")
as_environment("rlang")

# With NULL it returns the empty environment:
as_environment(NULL)

Convert to function

Description

as_function() transforms a one-sided formula into a function. This powers the lambda syntax in packages like purrr.

Usage

as_function(
  x,
  env = global_env(),
  ...,
  arg = caller_arg(x),
  call = caller_env()
)

is_lambda(x)

Arguments

x

A function or formula.

If a function, it is used as is.

If a formula, e.g. ~ .x + 2, it is converted to a function with up to two arguments: .x (single argument) or .x and .y (two arguments). The . placeholder can be used instead of .x. This allows you to create very compact anonymous functions (lambdas) with up to two inputs. Functions created from formulas have a special class. Use is_lambda() to test for it.

If a string, the function is looked up in env. Note that this interface is strictly for user convenience because of the scoping issues involved. Package developers should avoid supplying functions by name and instead supply them by value.

env

Environment in which to fetch the function in case x is a string.

...

These dots are for future extensions and must be empty.

arg

An argument name as a string. This argument will be mentioned in error messages as the input that is at the origin of a problem.

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.

Examples

f <- as_function(~ .x + 1)
f(10)

g <- as_function(~ -1 * .)
g(4)

h <- as_function(~ .x - .y)
h(6, 3)

# Functions created from a formula have a special class:
is_lambda(f)
is_lambda(as_function(function() "foo"))

Create a default name for an R object

Description

as_label() transforms R objects into a short, human-readable description. You can use labels to:

See also as_name() for transforming symbols back to a string. Unlike as_label(), as_name() is a well defined operation that guarantees the roundtrip symbol -> string -> symbol.

In general, if you don't know for sure what kind of object you're dealing with (a call, a symbol, an unquoted constant), use as_label() and make no assumption about the resulting string. If you know you have a symbol and need the name of the object it refers to, use as_name(). For instance, use as_label() with objects captured with enquo() and as_name() with symbols captured with ensym().

Usage

as_label(x)

Arguments

x

An object.

Transformation to string

See Also

as_name() for transforming symbols back to a string deterministically.

Examples

# as_label() is useful with quoted expressions:
as_label(expr(foo(bar)))

as_label(expr(foobar))

# It works with any R object. This is also useful for quoted
# arguments because the user might unquote constant objects:
as_label(1:3)

as_label(base::list)

Extract names from symbols

Description

as_name() converts symbols to character strings. The conversion is deterministic. That is, the roundtrip symbol -> name -> symbol always gives the same result.

Usage

as_name(x)

Arguments

x

A string or symbol, possibly wrapped in a quosure. If a string, the attributes are removed, if any.

Details

rlang::as_name() is the opposite of base::as.name(). If you're writing base R code, we recommend using base::as.symbol() which is an alias of as.name() that follows a more modern terminology (R types instead of S modes).

Value

A character vector of length 1.

See Also

as_label() for converting any object to a single string suitable as a label. as_string() for a lower-level version that doesn't unwrap quosures.

Examples

# Let's create some symbols:
foo <- quote(foo)
bar <- sym("bar")

# as_name() converts symbols to strings:
foo
as_name(foo)

typeof(bar)
typeof(as_name(bar))

# as_name() unwraps quosured symbols automatically:
as_name(quo(foo))

Cast symbol to string

Description

as_string() converts symbols to character strings.

Usage

as_string(x)

Arguments

x

A string or symbol. If a string, the attributes are removed, if any.

Value

A character vector of length 1.

Unicode tags

Unlike base::as.symbol() and base::as.name(), as_string() automatically transforms unicode tags such as "<U+5E78>" to the proper UTF-8 character. This is important on Windows because:

Because it reencodes the ASCII unicode tags to their UTF-8 representation, the string -> symbol -> string roundtrip is more stable with as_string().

See Also

as_name() for a higher-level variant of as_string() that automatically unwraps quosures.

Examples

# Let's create some symbols:
foo <- quote(foo)
bar <- sym("bar")

# as_string() converts symbols to strings:
foo
as_string(foo)

typeof(bar)
typeof(as_string(bar))

Bare type predicates

Description

These predicates check for a given type but only return TRUE for bare R objects. Bare objects have no class attributes. For example, a data frame is a list, but not a bare list.

Usage

is_bare_list(x, n = NULL)

is_bare_atomic(x, n = NULL)

is_bare_vector(x, n = NULL)

is_bare_double(x, n = NULL)

is_bare_complex(x, n = NULL)

is_bare_integer(x, n = NULL)

is_bare_numeric(x, n = NULL)

is_bare_character(x, n = NULL)

is_bare_logical(x, n = NULL)

is_bare_raw(x, n = NULL)

is_bare_string(x, n = NULL)

is_bare_bytes(x, n = NULL)

Arguments

x

Object to be tested.

n

Expected length of a vector.

Details

See Also

type-predicates, scalar-type-predicates


Box a value

Description

new_box() is similar to base::I() but it protects a value by wrapping it in a scalar list rather than by adding an attribute. unbox() retrieves the boxed value. is_box() tests whether an object is boxed with optional class. as_box() ensures that a value is wrapped in a box. as_box_if() does the same but only if the value matches a predicate.

Usage

new_box(.x, class = NULL, ...)

is_box(x, class = NULL)

unbox(box)

Arguments

class

For new_box(), an additional class for the boxed value (in addition to rlang_box). For is_box(), a class or vector of classes passed to inherits_all().

...

Additional attributes passed to base::structure().

x, .x

An R object.

box

A boxed value to unbox.

Examples

boxed <- new_box(letters, "mybox")
is_box(boxed)
is_box(boxed, "mybox")
is_box(boxed, "otherbox")

unbox(boxed)

# as_box() avoids double-boxing:
boxed2 <- as_box(boxed, "mybox")
boxed2
unbox(boxed2)

# Compare to:
boxed_boxed <- new_box(boxed, "mybox")
boxed_boxed
unbox(unbox(boxed_boxed))

# Use `as_box_if()` with a predicate if you need to ensure a box
# only for a subset of values:
as_box_if(NULL, is_null, "null_box")
as_box_if("foo", is_null, "null_box")

Human readable memory sizes

Description

Construct, manipulate and display vectors of byte sizes. These are numeric vectors, so you can compare them numerically, but they can also be compared to human readable values such as '10MB'.

Note: A bytes() constructor will be exported soon.

Usage

as_bytes(x)

parse_bytes(x)

Arguments

x

A numeric or character vector. Character representations can use shorthand sizes (see examples).

Details

These memory sizes are always assumed to be base 1000, rather than 1024.

Examples

parse_bytes("1")
parse_bytes("1K")
parse_bytes("1Kb")
parse_bytes("1KiB")
parse_bytes("1MB")

parse_bytes("1KB") < "1MB"

sum(parse_bytes(c("1MB", "5MB", "500KB")))

Extract arguments from a call

Description

Extract arguments from a call

Usage

call_args(call)

call_args_names(call)

Arguments

call

A defused call.

Value

A named list of arguments.

See Also

fn_fmls() and fn_fmls_names()

Examples

call <- quote(f(a, b))

# Subsetting a call returns the arguments converted to a language
# object:
call[-1]

# On the other hand, call_args() returns a regular list that is
# often easier to work with:
str(call_args(call))

# When the arguments are unnamed, a vector of empty strings is
# supplied (rather than NULL):
call_args_names(call)

Inspect a call

Description

This function is a wrapper around base::match.call(). It returns its own function call.

Usage

call_inspect(...)

Arguments

...

Arguments to display in the returned call.

Examples

# When you call it directly, it simply returns what you typed
call_inspect(foo(bar), "" %>% identity())

# Pass `call_inspect` to functionals like `lapply()` or `map()` to
# inspect the calls they create around the supplied function
lapply(1:3, call_inspect)

Match supplied arguments to function definition

Description

call_match() is like match.call() with these differences:

Usage

call_match(
  call = NULL,
  fn = NULL,
  ...,
  defaults = FALSE,
  dots_env = NULL,
  dots_expand = TRUE
)

Arguments

call

A call. The arguments will be matched to fn.

fn

A function definition to match arguments to.

...

These dots must be empty.

defaults

Whether to match missing arguments to their defaults.

dots_env

An execution environment where to find dots. If supplied and dots exist in this environment, and if call includes ..., the forwarded dots are matched to numbered dots (e.g. ..1, ..2, etc). By default this is set to the empty environment which means that ... expands to nothing.

dots_expand

If FALSE, arguments passed through ... will not be spliced into call. Instead, they are gathered in a pairlist and assigned to an argument named .... Gathering dots arguments is useful if you need to separate them from the other named arguments.

Note that the resulting call is not meant to be evaluated since R does not support passing dots through a named argument, even if named "...".

Inference from the call stack

When call is not supplied, it is inferred from the call stack along with fn and dots_env.

If call is supplied, then you must supply fn as well. Also consider supplying dots_env as it is set to the empty environment when not inferred.

Examples

# `call_match()` supports matching missing arguments to their
# defaults
fn <- function(x = "default") fn
call_match(quote(fn()), fn)
call_match(quote(fn()), fn, defaults = TRUE)

Modify the arguments of a call

Description

If you are working with a user-supplied call, make sure the arguments are standardised with call_match() before modifying the call.

Usage

call_modify(
  .call,
  ...,
  .homonyms = c("keep", "first", "last", "error"),
  .standardise = NULL,
  .env = caller_env()
)

Arguments

.call

Can be a call, a formula quoting a call in the right-hand side, or a frame object from which to extract the call expression.

...

<dynamic> Named or unnamed expressions (constants, names or calls) used to modify the call. Use zap() to remove arguments. Empty arguments are preserved.

.homonyms

How to treat arguments with the same name. The default, "keep", preserves these arguments. Set .homonyms to "first" to only keep the first occurrences, to "last" to keep the last occurrences, and to "error" to raise an informative error and indicate what arguments have duplicated names.

.standardise, .env

Deprecated as of rlang 0.3.0. Please call call_match() manually.

Value

A quosure if .call is a quosure, a call otherwise.

Examples

call <- quote(mean(x, na.rm = TRUE))

# Modify an existing argument
call_modify(call, na.rm = FALSE)
call_modify(call, x = quote(y))

# Remove an argument
call_modify(call, na.rm = zap())

# Add a new argument
call_modify(call, trim = 0.1)

# Add an explicit missing argument:
call_modify(call, na.rm = )

# Supply a list of new arguments with `!!!`
newargs <- list(na.rm = NULL, trim = 0.1)
call <- call_modify(call, !!!newargs)
call

# Remove multiple arguments by splicing zaps:
newargs <- rep_named(c("na.rm", "trim"), list(zap()))
call <- call_modify(call, !!!newargs)
call


# Modify the `...` arguments as if it were a named argument:
call <- call_modify(call, ... = )
call

call <- call_modify(call, ... = zap())
call


# When you're working with a user-supplied call, standardise it
# beforehand in case it includes unmatched arguments:
user_call <- quote(matrix(x, nc = 3))
call_modify(user_call, ncol = 1)

# `call_match()` applies R's argument matching rules. Matching
# ensures you're modifying the intended argument.
user_call <- call_match(user_call, matrix)
user_call
call_modify(user_call, ncol = 1)


# By default, arguments with the same name are kept. This has
# subtle implications, for instance you can move an argument to
# last position by removing it and remapping it:
call <- quote(foo(bar = , baz))
call_modify(call, bar = NULL, bar = missing_arg())

# You can also choose to keep only the first or last homonym
# arguments:
args <-  list(bar = NULL, bar = missing_arg())
call_modify(call, !!!args, .homonyms = "first")
call_modify(call, !!!args, .homonyms = "last")

Extract function name or namespace of a call

Description

call_name() and call_ns() extract the function name or namespace of simple calls as a string. They return NULL for complex calls.

The is_call_simple() predicate helps you determine whether a call is simple. There are two invariants you can count on:

  1. If is_call_simple(x) returns TRUE, call_name(x) returns a string. Otherwise it returns NULL.

  2. If is_call_simple(x, ns = TRUE) returns TRUE, call_ns() returns a string. Otherwise it returns NULL.

Usage

call_name(call)

call_ns(call)

is_call_simple(x, ns = NULL)

Arguments

call

A defused call.

x

An object to test.

ns

Whether call is namespaced. If NULL, is_call_simple() is insensitive to namespaces. If TRUE, is_call_simple() detects namespaced calls. If FALSE, it detects unnamespaced calls.

Value

The function name or namespace as a string, or NULL if the call is not named or namespaced.

Examples

# Is the function named?
is_call_simple(quote(foo()))
is_call_simple(quote(foo[[1]]()))

# Is the function namespaced?
is_call_simple(quote(list()), ns = TRUE)
is_call_simple(quote(base::list()), ns = TRUE)

# Extract the function name from quoted calls:
call_name(quote(foo(bar)))
call_name(quo(foo(bar)))

# Namespaced calls are correctly handled:
call_name(quote(base::matrix(baz)))

# Anonymous and subsetted functions return NULL:
call_name(quote(foo$bar()))
call_name(quote(foo[[bar]]()))
call_name(quote(foo()()))

# Extract namespace of a call with call_ns():
call_ns(quote(base::bar()))

# If not namespaced, call_ns() returns NULL:
call_ns(quote(bar()))

Create a call

Description

Quoted function calls are one of the two types of symbolic objects in R. They represent the action of calling a function, possibly with arguments. There are two ways of creating a quoted call:

See section below for the difference between call2() and the base constructors.

Usage

call2(.fn, ..., .ns = NULL)

Arguments

.fn

Function to call. Must be a callable object: a string, symbol, call, or a function.

...

<dynamic> Arguments for the function call. Empty arguments are preserved.

.ns

Namespace with which to prefix .fn. Must be a string or symbol.

Difference with base constructors

call2() is more flexible than base::call():

Caveats of inlining objects in calls

call2() makes it possible to inline objects in calls, both in function and argument positions. Inlining an object or a function has the advantage that the correct object is used in all environments. If all components of the code are inlined, you can even evaluate in the empty environment.

However inlining also has drawbacks. It can cause issues with NSE functions that expect symbolic arguments. The objects may also leak in representations of the call stack, such as traceback().

See Also

call_modify

Examples

# fn can either be a string, a symbol or a call
call2("f", a = 1)
call2(quote(f), a = 1)
call2(quote(f()), a = 1)

#' Can supply arguments individually or in a list
call2(quote(f), a = 1, b = 2)
call2(quote(f), !!!list(a = 1, b = 2))

# Creating namespaced calls is easy:
call2("fun", arg = quote(baz), .ns = "mypkg")

# Empty arguments are preserved:
call2("[", quote(x), , drop = )

Find the caller argument for error messages

Description

caller_arg() is a variant of substitute() or ensym() for arguments that reference other arguments. Unlike substitute() which returns an expression, caller_arg() formats the expression as a single line string which can be included in error messages.

Arguments

arg

An argument name in the current function.

Examples

arg_checker <- function(x, arg = caller_arg(x), call = caller_env()) {
  cli::cli_abort("{.arg {arg}} must be a thingy.", arg = arg, call = call)
}

my_function <- function(my_arg) {
  arg_checker(my_arg)
}

try(my_function(NULL))

Catch a condition

Description

This is a small wrapper around tryCatch() that captures any condition signalled while evaluating its argument. It is useful for situations where you expect a specific condition to be signalled, for debugging, and for unit testing.

Usage

catch_cnd(expr, classes = "condition")

Arguments

expr

Expression to be evaluated with a catching condition handler.

classes

A character vector of condition classes to catch. By default, catches all conditions.

Value

A condition if any was signalled, NULL otherwise.

Examples

catch_cnd(10)
catch_cnd(abort("an error"))
catch_cnd(signal("my_condition", message = "a condition"))

Check that dots are empty

Description

... can be inserted in a function signature to force users to fully name the details arguments. In this case, supplying data in ... is almost always a programming error. This function checks that ... is empty and fails otherwise.

Usage

check_dots_empty(
  env = caller_env(),
  error = NULL,
  call = caller_env(),
  action = abort
)

Arguments

env

Environment in which to look for ....

error

An optional error handler passed to try_fetch(). Use this e.g. to demote an error into a warning.

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.

action

[Deprecated]

Details

In packages, document ... with this standard tag:

 @inheritParams rlang::args_dots_empty

Examples

f <- function(x, ..., foofy = 8) {
  check_dots_empty()
  x + foofy
}

# This fails because `foofy` can't be matched positionally
try(f(1, 4))

# This fails because `foofy` can't be matched partially by name
try(f(1, foof = 4))

# Thanks to `...`, it must be matched exactly
f(1, foofy = 4)

Check that all dots are unnamed

Description

In functions like paste(), named arguments in ... are often a sign of misspelled argument names. Call check_dots_unnamed() to fail with an error when named arguments are detected.

Usage

check_dots_unnamed(
  env = caller_env(),
  error = NULL,
  call = caller_env(),
  action = abort
)

Arguments

env

Environment in which to look for ....

error

An optional error handler passed to try_fetch(). Use this e.g. to demote an error into a warning.

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.

action

[Deprecated]

Examples

f <- function(..., foofy = 8) {
  check_dots_unnamed()
  c(...)
}

f(1, 2, 3, foofy = 4)

try(f(1, 2, 3, foof = 4))

Check that all dots have been used

Description

When ... arguments are passed to methods, it is assumed there method will match and use these arguments. If this isn't the case, this often indicates a programming error. Call check_dots_used() to fail with an error when unused arguments are detected.

Usage

check_dots_used(
  env = caller_env(),
  call = caller_env(),
  error = NULL,
  action = deprecated()
)

Arguments

env

Environment in which to look for ... and to set up handler.

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.

error

An optional error handler passed to try_fetch(). Use this e.g. to demote an error into a warning.

action

[Deprecated]

Details

In packages, document ... with this standard tag:

 @inheritParams rlang::args_dots_used

check_dots_used() implicitly calls on.exit() to check that all elements of ... have been used when the function exits. If you use on.exit() elsewhere in your function, make sure to use add = TRUE so that you don't override the handler set up by check_dots_used().

Examples

f <- function(...) {
  check_dots_used()
  g(...)
}

g <- function(x, y, ...) {
  x + y
}
f(x = 1, y = 2)

try(f(x = 1, y = 2, z = 3))

try(f(x = 1, y = 2, 3, 4, 5))

# Use an `error` handler to handle the error differently.
# For instance to demote the error to a warning:
fn <- function(...) {
  check_dots_empty(
    error = function(cnd) {
      warning(cnd)
    }
  )
  "out"
}
fn()

Check that arguments are mutually exclusive

Description

check_exclusive() checks that only one argument is supplied out of a set of mutually exclusive arguments. An informative error is thrown if multiple arguments are supplied.

Usage

check_exclusive(..., .require = TRUE, .frame = caller_env(), .call = .frame)

Arguments

...

Function arguments.

.require

Whether at least one argument must be supplied.

.frame

Environment where the arguments in ... are defined.

.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.

Value

The supplied argument name as a string. If .require is FALSE and no argument is supplied, the empty string "" is returned.

Examples

f <- function(x, y) {
  switch(
    check_exclusive(x, y),
    x = message("`x` was supplied."),
    y = message("`y` was supplied.")
  )
}

# Supplying zero or multiple arguments is forbidden
try(f())
try(f(NULL, NULL))

# The user must supply one of the mutually exclusive arguments
f(NULL)
f(y = NULL)


# With `.require` you can allow zero arguments
f <- function(x, y) {
  switch(
    check_exclusive(x, y, .require = FALSE),
    x = message("`x` was supplied."),
    y = message("`y` was supplied."),
    message("No arguments were supplied")
  )
}
f()

Check that argument is supplied

Description

Throws an error if x is missing.

Usage

check_required(x, arg = caller_arg(x), call = caller_env())

Arguments

x

A function argument. Must be a symbol.

arg

An argument name as a string. This argument will be mentioned in error messages as the input that is at the origin of a problem.

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.

See Also

arg_match()

Examples

f <- function(x)  {
  check_required(x)
}

# Fails because `x` is not supplied
try(f())

# Succeeds
f(NULL)

Does a condition or its ancestors inherit from a class?

Description

Like any R objects, errors captured with catchers like tryCatch() have a class() which you can test with inherits(). However, with chained errors, the class of a captured error might be different than the error that was originally signalled. Use cnd_inherits() to detect whether an error or any of its parent inherits from a class.

Whereas inherits() tells you whether an object is a particular kind of error, cnd_inherits() answers the question whether an object is a particular kind of error or has been caused by such an error.

Some chained conditions carry parents that are not inherited. See the .inherit argument of abort(), warn(), and inform().

Usage

cnd_inherits(cnd, class)

Arguments

cnd

A condition to test.

class

A class passed to inherits().

Capture an error with cnd_inherits()

Error catchers like tryCatch() and try_fetch() can only match the class of a condition, not the class of its parents. To match a class across the ancestry of an error, you'll need a bit of craftiness.

Ancestry matching can't be done with tryCatch() at all so you'll need to switch to withCallingHandlers(). Alternatively, you can use the experimental rlang function try_fetch() which is able to perform the roles of both tryCatch() and withCallingHandlers().

withCallingHandlers()

Unlike tryCatch(), withCallingHandlers() does not capture an error. If you don't explicitly jump with an error or a value throw, nothing happens.

Since we don't want to throw an error, we'll throw a value using callCC():

f <- function() {
  parent <- error_cnd("bar", message = "Bar")
  abort("Foo", parent = parent)
}

cnd <- callCC(function(throw) {
  withCallingHandlers(
    f(),
    error = function(x) if (cnd_inherits(x, "bar")) throw(x)
  )
})

class(cnd)
#> [1] "rlang_error" "error"       "condition"
class(cnd$parent)
#> [1] "bar"         "rlang_error" "error"       "condition"

try_fetch()

This pattern is easier with try_fetch(). Like withCallingHandlers(), it doesn't capture a matching error right away. Instead, it captures it only if the handler doesn't return a zap() value.

cnd <- try_fetch(
  f(),
  error = function(x) if (cnd_inherits(x, "bar")) x else zap()
)

class(cnd)
#> [1] "rlang_error" "error"       "condition"
class(cnd$parent)
#> [1] "bar"         "rlang_error" "error"       "condition"

Note that try_fetch() uses cnd_inherits() internally. This makes it very easy to match a parent condition:

cnd <- try_fetch(
  f(),
  bar = function(x) x
)

# This is the parent
class(cnd)
#> [1] "bar"         "rlang_error" "error"       "condition"

Build an error message from parts

Description

cnd_message() assembles an error message from three generics:

Methods for these generics must return a character vector. The elements are combined into a single string with a newline separator. Bullets syntax is supported, either through rlang (see format_error_bullets()), or through cli if the condition has use_cli_format set to TRUE.

The default method for the error header returns the message field of the condition object. The default methods for the body and footer return the the body and footer fields if any, or empty character vectors otherwise.

cnd_message() is automatically called by the conditionMessage() for rlang errors, warnings, and messages. Error classes created with abort() only need to implement header, body or footer methods. This provides a lot of flexibility for hierarchies of error classes, for instance you could inherit the body of an error message from a parent class while overriding the header and footer.

Usage

cnd_message(cnd, ..., inherit = TRUE, prefix = FALSE)

cnd_header(cnd, ...)

cnd_body(cnd, ...)

cnd_footer(cnd, ...)

Arguments

cnd

A condition object.

...

Arguments passed to methods.

inherit

Wether to include parent messages. Parent messages are printed with a "Caused by error:" prefix, even if prefix is FALSE.

prefix

Whether to print the full message, including the condition prefix (⁠Error:⁠, ⁠Warning:⁠, ⁠Message:⁠, or ⁠Condition:⁠). The prefix mentions the call field if present, and the srcref info if present. If cnd has a parent field (i.e. the condition is chained), the parent messages are included in the message with a ⁠Caused by⁠ prefix.

Overriding header, body, and footer methods

Sometimes the contents of an error message depends on the state of your checking routine. In that case, it can be tricky to lazily generate error messages with cnd_header(), cnd_body(), and cnd_footer(): you have the choice between overspecifying your error class hierarchies with one class per state, or replicating the type-checking control flow within the cnd_body() method. None of these options are ideal.

A better option is to define header, body, or footer fields in your condition object. These can be a static string, a lambda-formula, or a function with the same signature as cnd_header(), cnd_body(), or cnd_footer(). These fields override the message generics and make it easy to generate an error message tailored to the state in which the error was constructed.


Signal a condition object

Description

cnd_signal() takes a condition as argument and emits the corresponding signal. The type of signal depends on the class of the condition:

Usage

cnd_signal(cnd, ...)

Arguments

cnd

A condition object (see cnd()). If NULL, cnd_signal() returns without signalling a condition.

...

These dots are for future extensions and must be empty.

See Also

Examples

# The type of signal depends on the class. If the condition
# inherits from "warning", a warning is issued:
cnd <- warning_cnd("my_warning_class", message = "This is a warning")
cnd_signal(cnd)

# If it inherits from "error", an error is raised:
cnd <- error_cnd("my_error_class", message = "This is an error")
try(cnd_signal(cnd))

Box a final value for early termination

Description

A value boxed with done() signals to its caller that it should stop iterating. Use it to shortcircuit a loop.

Usage

done(x)

is_done_box(x, empty = NULL)

Arguments

x

For done(), a value to box. For is_done_box(), a value to test.

empty

Whether the box is empty. If NULL, is_done_box() returns TRUE for all done boxes. If TRUE, it returns TRUE only for empty boxes. Otherwise it returns TRUE only for non-empty boxes.

Value

A boxed value.

Examples

done(3)

x <- done(3)
is_done_box(x)

.data and .env pronouns

Description

The .data and .env pronouns make it explicit where to find objects when programming with data-masked functions.

m <- 10
mtcars %>% mutate(disp = .data$disp * .env$m)

Because the lookup is explicit, there is no ambiguity between both kinds of variables. Compare:

disp <- 10
mtcars %>% mutate(disp = .data$disp * .env$disp)
mtcars %>% mutate(disp = disp * disp)

Note that .data is only a pronoun, it is not a real data frame. This means that you can't take its names or map a function over the contents of .data. Similarly, .env is not an actual R environment. For instance, it doesn't have a parent and the subsetting operators behave differently.

.data versus the magrittr pronoun .

In a magrittr pipeline, .data is not necessarily interchangeable with the magrittr pronoun .. With grouped data frames in particular, .data represents the current group slice whereas the pronoun . represents the whole data frame. Always prefer using .data in data-masked context.

Where does .data live?

The .data pronoun is automatically created for you by data-masking functions using the tidy eval framework. You don't need to import rlang::.data or use library(rlang) to work with this pronoun.

However, the .data object exported from rlang is useful to import in your package namespace to avoid a ⁠R CMD check⁠ note when referring to objects from the data mask. R does not have any way of knowing about the presence or absence of .data in a particular scope so you need to import it explicitly or equivalently declare it with utils::globalVariables(".data").

Note that rlang::.data is a "fake" pronoun. Do not refer to rlang::.data with the ⁠rlang::⁠ qualifier in data masking code. Use the unqualified .data symbol that is automatically put in scope by data-masking functions.


Dynamic dots features

Description

The base ... syntax supports:

Dynamic dots offer a few additional features, injection in particular:

  1. You can splice arguments saved in a list with the splice operator !!!.

  2. You can inject names with glue syntax on the left-hand side of ⁠:=⁠.

  3. Trailing commas are ignored, making it easier to copy and paste lines of arguments.

Add dynamic dots support in your functions

If your function takes dots, adding support for dynamic features is as easy as collecting the dots with list2() instead of list(). See also dots_list(), which offers more control over the collection.

In general, passing ... to a function that supports dynamic dots causes your function to inherit the dynamic behaviour.

In packages, document dynamic dots with this standard tag:

 @param ... <[`dynamic-dots`][rlang::dyn-dots]> What these dots do.

Examples

f <- function(...) {
  out <- list2(...)
  rev(out)
}

# Trailing commas are ignored
f(this = "that", )

# Splice lists of arguments with `!!!`
x <- list(alpha = "first", omega = "last")
f(!!!x)

# Inject a name using glue syntax
if (is_installed("glue")) {
  nm <- "key"
  f("{nm}" := "value")
  f("prefix_{nm}" := "value")
}

Embrace operator ⁠{{⁠

Description

The embrace operator ⁠{{⁠ is used to create functions that call other data-masking functions. It transports a data-masked argument (an argument that can refer to columns of a data frame) from one function to another.

my_mean <- function(data, var) {
  dplyr::summarise(data, mean = mean({{ var }}))
}

Under the hood

⁠{{⁠ combines enquo() and !! in one step. The snippet above is equivalent to:

my_mean <- function(data, var) {
  var <- enquo(var)
  dplyr::summarise(data, mean = mean(!!var))
}

See Also


Get the empty environment

Description

The empty environment is the only one that does not have a parent. It is always used as the tail of an environment chain such as the search path (see search_envs()).

Usage

empty_env()

Examples

# Create environments with nothing in scope:
child_env(empty_env())

Defuse function arguments with glue

Description

englue() creates a string with the glue operators ⁠{⁠ and ⁠{{⁠. These operators are normally used to inject names within dynamic dots. englue() makes them available anywhere within a function.

englue() must be used inside a function. englue("{{ var }}") defuses the argument var and transforms it to a string using the default name operation.

Usage

englue(x, env = caller_env(), error_call = current_env(), error_arg = "x")

Arguments

x

A string to interpolate with glue operators.

env

User environment where the interpolation data lives in case you're wrapping englue() in another function.

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.

error_arg

An argument name as a string. This argument will be mentioned in error messages as the input that is at the origin of a problem.

Details

englue("{{ var }}") is equivalent to as_label(enquo(var)). It defuses arg and transforms the expression to a string with as_label().

In dynamic dots, using only ⁠{⁠ is allowed. In englue() you must use ⁠{{⁠ at least once. Use glue::glue() for simple interpolation.

Before using englue() in a package, first ensure that glue is installed by adding it to your ⁠Imports:⁠ section.

usethis::use_package("glue", "Imports")

Wrapping englue()

You can provide englue semantics to a user provided string by supplying env. In this example we create a variant of englue() that supports a special .qux pronoun by:

my_englue <- function(text) {
  masked_env <- env(caller_env(), .qux = "QUX")

  englue(
    text,
    env = masked_env,
    error_arg = "text",
    error_call = current_env()
  )
}

# Users can then use your wrapper as they would use `englue()`:
fn <- function(x) {
  foo <- "FOO"
  my_englue("{{ x }}_{.qux}_{foo}")
}

fn(bar)
#> [1] "bar_QUX_FOO"

If you are creating a low level package on top of englue(), you should also consider exposing env, error_arg and error_call in your englue() wrapper so users can wrap your wrapper.

See Also

Examples

g <- function(var) englue("{{ var }}")
g(cyl)
g(1 + 1)
g(!!letters)

# These are equivalent to
as_label(quote(cyl))
as_label(quote(1 + 1))
as_label(letters)

Defuse function arguments

Description

enquo() and enquos() defuse function arguments. A defused expression can be examined, modified, and injected into other expressions.

Defusing function arguments is useful for:

These are advanced tools. Make sure to first learn about the embrace operator {{ in Data mask programming patterns. ⁠{{⁠ is easier to work with less theory, and it is sufficient in most applications.

Usage

enquo(arg)

enquos(
  ...,
  .named = FALSE,
  .ignore_empty = c("trailing", "none", "all"),
  .ignore_null = c("none", "all"),
  .unquote_names = TRUE,
  .homonyms = c("keep", "first", "last", "error"),
  .check_assign = FALSE
)

Arguments

arg

An unquoted argument name. The expression supplied to that argument is defused and returned.

...

Names of arguments to defuse.

.named

If TRUE, unnamed inputs are automatically named with as_label(). This is equivalent to applying exprs_auto_name() on the result. If FALSE, unnamed elements are left as is and, if fully unnamed, the list is given minimal names (a vector of ""). If NULL, fully unnamed results are left with NULL names.

.ignore_empty

Whether to ignore empty arguments. Can be one of "trailing", "none", "all". If "trailing", only the last argument is ignored if it is empty. Named arguments are not considered empty.

.ignore_null

Whether to ignore unnamed null arguments. Can be "none" or "all".

.unquote_names

Whether to treat ⁠:=⁠ as =. Unlike =, the ⁠:=⁠ syntax supports names injection.

.homonyms

How to treat arguments with the same name. The default, "keep", preserves these arguments. Set .homonyms to "first" to only keep the first occurrences, to "last" to keep the last occurrences, and to "error" to raise an informative error and indicate what arguments have duplicated names.

.check_assign

Whether to check for ⁠<-⁠ calls. When TRUE a warning recommends users to use = if they meant to match a function parameter or wrap the ⁠<-⁠ call in curly braces otherwise. This ensures assignments are explicit.

Value

enquo() returns a quosure and enquos() returns a list of quosures.

Implicit injection

Arguments defused with enquo() and enquos() automatically gain injection support.

my_mean <- function(data, var) {
  var <- enquo(var)
  dplyr::summarise(data, mean(!!var))
}

# Can now use `!!` and `{{`
my_mean(mtcars, !!sym("cyl"))

See enquo0() and enquos0() for variants that don't enable injection.

See Also

Examples

# `enquo()` defuses the expression supplied by your user
f <- function(arg) {
  enquo(arg)
}

f(1 + 1)

# `enquos()` works with arguments and dots. It returns a list of
# expressions
f <- function(...) {
  enquos(...)
}

f(1 + 1, 2 * 10)


# `enquo()` and `enquos()` enable _injection_ and _embracing_ for
# your users
g <- function(arg) {
  f({{ arg }} * 2)
}
g(100)

column <- sym("cyl")
g(!!column)

Create a new environment

Description

These functions create new environments.

Usage

env(...)

new_environment(data = list(), parent = empty_env())

Arguments

..., data

<dynamic> Named values. You can supply one unnamed to specify a custom parent, otherwise it defaults to the current environment.

parent

A parent environment.

Environments as objects

Environments are containers of uniquely named objects. Their most common use is to provide a scope for the evaluation of R expressions. Not all languages have first class environments, i.e. can manipulate scope as regular objects. Reification of scope is one of the most powerful features of R as it allows you to change what objects a function or expression sees when it is evaluated.

Environments also constitute a data structure in their own right. They are a collection of uniquely named objects, subsettable by name and modifiable by reference. This latter property (see section on reference semantics) is especially useful for creating mutable OO systems (cf the R6 package and the ggproto system for extending ggplot2).

Inheritance

All R environments (except the empty environment) are defined with a parent environment. An environment and its grandparents thus form a linear hierarchy that is the basis for lexical scoping in R. When R evaluates an expression, it looks up symbols in a given environment. If it cannot find these symbols there, it keeps looking them up in parent environments. This way, objects defined in child environments have precedence over objects defined in parent environments.

The ability of overriding specific definitions is used in the tidyeval framework to create powerful domain-specific grammars. A common use of masking is to put data frame columns in scope. See for example as_data_mask().

Reference semantics

Unlike regular objects such as vectors, environments are an uncopyable object type. This means that if you have multiple references to a given environment (by assigning the environment to another symbol with ⁠<-⁠ or passing the environment as argument to a function), modifying the bindings of one of those references changes all other references as well.

See Also

env_has(), env_bind().

Examples

# env() creates a new environment that inherits from the current
# environment by default
env <- env(a = 1, b = "foo")
env$b
identical(env_parent(env), current_env())

# Supply one unnamed argument to inherit from another environment:
env <- env(base_env(), a = 1, b = "foo")
identical(env_parent(env), base_env())


# Both env() and child_env() support tidy dots features:
objs <- list(b = "foo", c = "bar")
env <- env(a = 1, !!! objs)
env$c

# You can also unquote names with the definition operator `:=`
var <- "a"
env <- env(!!var := "A")
env$a


# Use new_environment() to create containers with the empty
# environment as parent:
env <- new_environment()
env_parent(env)

# Like other new_ constructors, it takes an object rather than dots:
new_environment(list(a = "foo", b = "bar"))

Bind symbols to objects in an environment

Description

These functions create bindings in an environment. The bindings are supplied through ... as pairs of names and values or expressions. env_bind() is equivalent to evaluating a ⁠<-⁠ expression within the given environment. This function should take care of the majority of use cases but the other variants can be useful for specific problems.

Usage

env_bind(.env, ...)

env_bind_lazy(.env, ..., .eval_env = caller_env())

env_bind_active(.env, ...)

lhs %<~% rhs

Arguments

.env

An environment.

...

<dynamic> Named objects (env_bind()), expressions env_bind_lazy(), or functions (env_bind_active()). Use zap() to remove bindings.

.eval_env

The environment where the expressions will be evaluated when the symbols are forced.

lhs

The variable name to which rhs will be lazily assigned.

rhs

An expression lazily evaluated and assigned to lhs.

Value

The input object .env, with its associated environment modified in place, invisibly.

Side effects

Since environments have reference semantics (see relevant section in env() documentation), modifying the bindings of an environment produces effects in all other references to that environment. In other words, env_bind() and its variants have side effects.

Like other side-effecty functions like par() and options(), env_bind() and variants return the old values invisibly.

See Also

env_poke() for binding a single element.

Examples

# env_bind() is a programmatic way of assigning values to symbols
# with `<-`. We can add bindings in the current environment:
env_bind(current_env(), foo = "bar")
foo

# Or modify those bindings:
bar <- "bar"
env_bind(current_env(), bar = "BAR")
bar

# You can remove bindings by supplying zap sentinels:
env_bind(current_env(), foo = zap())
try(foo)

# Unquote-splice a named list of zaps
zaps <- rep_named(c("foo", "bar"), list(zap()))
env_bind(current_env(), !!!zaps)
try(bar)

# It is most useful to change other environments:
my_env <- env()
env_bind(my_env, foo = "foo")
my_env$foo

# A useful feature is to splice lists of named values:
vals <- list(a = 10, b = 20)
env_bind(my_env, !!!vals, c = 30)
my_env$b
my_env$c

# You can also unquote a variable referring to a symbol or a string
# as binding name:
var <- "baz"
env_bind(my_env, !!var := "BAZ")
my_env$baz


# The old values of the bindings are returned invisibly:
old <- env_bind(my_env, a = 1, b = 2, baz = "baz")
old

# You can restore the original environment state by supplying the
# old values back:
env_bind(my_env, !!!old)

# env_bind_lazy() assigns expressions lazily:
env <- env()
env_bind_lazy(env, name = { cat("forced!\n"); "value" })

# Referring to the binding will cause evaluation:
env$name

# But only once, subsequent references yield the final value:
env$name

# You can unquote expressions:
expr <- quote(message("forced!"))
env_bind_lazy(env, name = !!expr)
env$name


# By default the expressions are evaluated in the current
# environment. For instance we can create a local binding and refer
# to it, even though the variable is bound in a different
# environment:
who <- "mickey"
env_bind_lazy(env, name = paste(who, "mouse"))
env$name

# You can specify another evaluation environment with `.eval_env`:
eval_env <- env(who = "minnie")
env_bind_lazy(env, name = paste(who, "mouse"), .eval_env = eval_env)
env$name

# Or by unquoting a quosure:
quo <- local({
  who <- "fievel"
  quo(paste(who, "mouse"))
})
env_bind_lazy(env, name = !!quo)
env$name

# You can create active bindings with env_bind_active(). Active
# bindings execute a function each time they are evaluated:
fn <- function() {
  cat("I have been called\n")
  rnorm(1)
}

env <- env()
env_bind_active(env, symbol = fn)

# `fn` is executed each time `symbol` is evaluated or retrieved:
env$symbol
env$symbol
eval_bare(quote(symbol), env)
eval_bare(quote(symbol), env)

# All arguments are passed to as_function() so you can use the
# formula shortcut:
env_bind_active(env, foo = ~ runif(1))
env$foo
env$foo

Browse environments

Description

Usage

env_browse(env, value = TRUE)

env_is_browsed(env)

Arguments

env

An environment.

value

Whether to browse env.

Value

env_browse() returns the previous value of env_is_browsed() (a logical), invisibly.


Cache a value in an environment

Description

env_cache() is a wrapper around env_get() and env_poke() designed to retrieve a cached value from env.

Usage

env_cache(env, nm, default)

Arguments

env

An environment.

nm

Name of binding, a string.

default

The default value to store in env if nm does not exist yet.

Value

Either the value of nm or default if it did not exist yet.

Examples

e <- env(a = "foo")

# Returns existing binding
env_cache(e, "a", "default")

# Creates a `b` binding and returns its default value
env_cache(e, "b", "default")

# Now `b` is defined
e$b

Clone or coalesce an environment

Description

Both these functions preserve active bindings and promises (the latter are only preserved on R >= 4.0.0).

Usage

env_clone(env, parent = env_parent(env))

env_coalesce(env, from)

Arguments

env

An environment.

parent

The parent of the cloned environment.

from

Environment to copy bindings from.

Examples

# A clone initially contains the same bindings as the original
# environment
env <- env(a = 1, b = 2)
clone <- env_clone(env)

env_print(clone)
env_print(env)

# But it can acquire new bindings or change existing ones without
# impacting the original environment
env_bind(clone, a = "foo", c = 3)

env_print(clone)
env_print(env)


# `env_coalesce()` copies bindings from one environment to another
lhs <- env(a = 1)
rhs <- env(a = "a", b = "b", c = "c")
env_coalesce(lhs, rhs)
env_print(lhs)

# To copy all the bindings from `rhs` into `lhs`, first delete the
# conflicting bindings from `rhs`
env_unbind(lhs, env_names(rhs))
env_coalesce(lhs, rhs)
env_print(lhs)

Depth of an environment chain

Description

This function returns the number of environments between env and the empty environment, including env. The depth of env is also the number of parents of env (since the empty environment counts as a parent).

Usage

env_depth(env)

Arguments

env

An environment.

Value

An integer.

See Also

The section on inheritance in env() documentation.

Examples

env_depth(empty_env())
env_depth(pkg_env("rlang"))

Get an object in an environment

Description

env_get() extracts an object from an enviroment env. By default, it does not look in the parent environments. env_get_list() extracts multiple objects from an environment into a named list.

Usage

env_get(env = caller_env(), nm, default, inherit = FALSE, last = empty_env())

env_get_list(
  env = caller_env(),
  nms,
  default,
  inherit = FALSE,
  last = empty_env()
)

Arguments

env

An environment.

nm

Name of binding, a string.

default

A default value in case there is no binding for nm in env.

inherit

Whether to look for bindings in the parent environments.

last

Last environment inspected when inherit is TRUE. Can be useful in conjunction with base::topenv().

nms

Names of bindings, a character vector.

Value

An object if it exists. Otherwise, throws an error.

See Also

env_cache() for a variant of env_get() designed to cache a value in an environment.

Examples

parent <- child_env(NULL, foo = "foo")
env <- child_env(parent, bar = "bar")

# This throws an error because `foo` is not directly defined in env:
# env_get(env, "foo")

# However `foo` can be fetched in the parent environment:
env_get(env, "foo", inherit = TRUE)

# You can also avoid an error by supplying a default value:
env_get(env, "foo", default = "FOO")

Does an environment have or see bindings?

Description

env_has() is a vectorised predicate that queries whether an environment owns bindings personally (with inherit set to FALSE, the default), or sees them in its own environment or in any of its parents (with inherit = TRUE).

Usage

env_has(env = caller_env(), nms, inherit = FALSE)

Arguments

env

An environment.

nms

A character vector of binding names for which to check existence.

inherit

Whether to look for bindings in the parent environments.

Value

A named logical vector as long as nms.

Examples

parent <- child_env(NULL, foo = "foo")
env <- child_env(parent, bar = "bar")

# env does not own `foo` but sees it in its parent environment:
env_has(env, "foo")
env_has(env, "foo", inherit = TRUE)

Does environment inherit from another environment?

Description

This returns TRUE if x has ancestor among its parents.

Usage

env_inherits(env, ancestor)

Arguments

env

An environment.

ancestor

Another environment from which x might inherit.


Is frame environment user facing?

Description

Detects if env is user-facing, that is, whether it's an environment that inherits from:

If either is true, we consider env to belong to an evaluation frame that was called directly by the end user. This is by contrast to indirect calls by third party functions which are not user facing.

For instance the lifecycle package uses env_is_user_facing() to figure out whether a deprecated function was called directly or indirectly, and select an appropriate verbosity level as a function of that.

Usage

env_is_user_facing(env)

Arguments

env

An environment.

Escape hatch

You can override the return value of env_is_user_facing() by setting the global option "rlang_user_facing" to:

Examples

fn <- function() {
  env_is_user_facing(caller_env())
}

# Direct call of `fn()` from the global env
with(global_env(), fn())

# Indirect call of `fn()` from a package
with(ns_env("utils"), fn())

Label of an environment

Description

Special environments like the global environment have their own names. env_name() returns:

env_label() is exactly like env_name() but returns the memory address of anonymous environments as fallback.

Usage

env_name(env)

env_label(env)

Arguments

env

An environment.

Examples

# Some environments have specific names:
env_name(global_env())
env_name(ns_env("rlang"))

# Anonymous environments don't have names but are labelled by their
# address in memory:
env_name(env())
env_label(env())

Names and numbers of symbols bound in an environment

Description

env_names() returns object names from an enviroment env as a character vector. All names are returned, even those starting with a dot. env_length() returns the number of bindings.

Usage

env_names(env)

env_length(env)

Arguments

env

An environment.

Value

A character vector of object names.

Names of symbols and objects

Technically, objects are bound to symbols rather than strings, since the R interpreter evaluates symbols (see is_expression() for a discussion of symbolic objects versus literal objects). However it is often more convenient to work with strings. In rlang terminology, the string corresponding to a symbol is called the name of the symbol (or by extension the name of an object bound to a symbol).

Encoding

There are deep encoding issues when you convert a string to symbol and vice versa. Symbols are always in the native encoding. If that encoding (let's say latin1) cannot support some characters, these characters are serialised to ASCII. That's why you sometimes see strings looking like ⁠<U+1234>⁠, especially if you're running Windows (as R doesn't support UTF-8 as native encoding on that platform).

To alleviate some of the encoding pain, env_names() always returns a UTF-8 character vector (which is fine even on Windows) with ASCII unicode points translated back to UTF-8.

Examples

env <- env(a = 1, b = 2)
env_names(env)

Get parent environments

Description

See the section on inheritance in env()'s documentation.

Usage

env_parent(env = caller_env(), n = 1)

env_tail(env = caller_env(), last = global_env())

env_parents(env = caller_env(), last = global_env())

Arguments

env

An environment.

n

The number of generations to go up.

last

The environment at which to stop. Defaults to the global environment. The empty environment is always a stopping condition so it is safe to leave the default even when taking the tail or the parents of an environment on the search path.

env_tail() returns the environment which has last as parent and env_parents() returns the list of environments up to last.

Value

An environment for env_parent() and env_tail(), a list of environments for env_parents().

Examples

# Get the parent environment with env_parent():
env_parent(global_env())

# Or the tail environment with env_tail():
env_tail(global_env())

# By default, env_parent() returns the parent environment of the
# current evaluation frame. If called at top-level (the global
# frame), the following two expressions are equivalent:
env_parent()
env_parent(base_env())

# This default is more handy when called within a function. In this
# case, the enclosure environment of the function is returned
# (since it is the parent of the evaluation frame):
enclos_env <- env()
fn <- set_env(function() env_parent(), enclos_env)
identical(enclos_env, fn())

Poke an object in an environment

Description

env_poke() will assign or reassign a binding in env if create is TRUE. If create is FALSE and a binding does not already exists, an error is issued.

Usage

env_poke(env = caller_env(), nm, value, inherit = FALSE, create = !inherit)

Arguments

env

An environment.

nm

Name of binding, a string.

value

The value for a new binding.

inherit

Whether to look for bindings in the parent environments.

create

Whether to create a binding if it does not already exist in the environment.

Details

If inherit is TRUE, the parents environments are checked for an existing binding to reassign. If not found and create is TRUE, a new binding is created in env. The default value for create is a function of inherit: FALSE when inheriting, TRUE otherwise.

This default makes sense because the inheriting case is mostly for overriding an existing binding. If not found, something probably went wrong and it is safer to issue an error. Note that this is different to the base R operator ⁠<<-⁠ which will create a binding in the global environment instead of the current environment when no existing binding is found in the parents.

Value

The old value of nm or a zap sentinel if the binding did not exist yet.

See Also

env_bind() for binding multiple elements. env_cache() for a variant of env_poke() designed to cache values.


Pretty-print an environment

Description

This prints:

Note that printing a package namespace (see ns_env()) with env_print() will typically tag function bindings as ⁠<lazy>⁠ until they are evaluated the first time. This is because package functions are lazily-loaded from disk to improve performance when loading a package.

Usage

env_print(env = caller_env())

Arguments

env

An environment, or object that can be converted to an environment by get_env().


Remove bindings from an environment

Description

env_unbind() is the complement of env_bind(). Like env_has(), it ignores the parent environments of env by default. Set inherit to TRUE to track down bindings in parent environments.

Usage

env_unbind(env = caller_env(), nms, inherit = FALSE)

Arguments

env

An environment.

nms

A character vector of binding names to remove.

inherit

Whether to look for bindings in the parent environments.

Value

The input object env with its associated environment modified in place, invisibly.

Examples

env <- env(foo = 1, bar = 2)
env_has(env, c("foo", "bar"))

# Remove bindings with `env_unbind()`
env_unbind(env, c("foo", "bar"))
env_has(env, c("foo", "bar"))

# With inherit = TRUE, it removes bindings in parent environments
# as well:
parent <- env(empty_env(), foo = 1, bar = 2)
env <- env(parent, foo = "b")

env_unbind(env, "foo", inherit = TRUE)
env_has(env, c("foo", "bar"))
env_has(env, c("foo", "bar"), inherit = TRUE)

Evaluate an expression in an environment

Description

eval_bare() is a lower-level version of function base::eval(). Technically, it is a simple wrapper around the C function Rf_eval(). You generally don't need to use eval_bare() instead of eval(). Its main advantage is that it handles stack-sensitive calls (such as return(), on.exit() or parent.frame()) more consistently when you pass an enviroment of a frame on the call stack.

Usage

eval_bare(expr, env = parent.frame())

Arguments

expr

An expression to evaluate.

env

The environment in which to evaluate the expression.

Details

These semantics are possible because eval_bare() creates only one frame on the call stack whereas eval() creates two frames, the second of which has the user-supplied environment as frame environment. When you supply an existing frame environment to base::eval() there will be two frames on the stack with the same frame environment. Stack-sensitive functions only detect the topmost of these frames. We call these evaluation semantics "stack inconsistent".

Evaluating expressions in the actual frame environment has useful practical implications for eval_bare():

The flip side of the semantics of eval_bare() is that it can't evaluate break or next expressions even if called within a loop.

See Also

eval_tidy() for evaluation with data mask and quosure support.

Examples

# eval_bare() works just like base::eval() but you have to create
# the evaluation environment yourself:
eval_bare(quote(foo), env(foo = "bar"))

# eval() has different evaluation semantics than eval_bare(). It
# can return from the supplied environment even if its an
# environment that is not on the call stack (i.e. because you've
# created it yourself). The following would trigger an error with
# eval_bare():
ret <- quote(return("foo"))
eval(ret, env())
# eval_bare(ret, env())  # "no function to return from" error

# Another feature of eval() is that you can control surround loops:
bail <- quote(break)
while (TRUE) {
  eval(bail)
  # eval_bare(bail)  # "no loop for break/next" error
}

# To explore the consequences of stack inconsistent semantics, let's
# create a function that evaluates `parent.frame()` deep in the call
# stack, in an environment corresponding to a frame in the middle of
# the stack. For consistency with R's lazy evaluation semantics, we'd
# expect to get the caller of that frame as result:
fn <- function(eval_fn) {
  list(
    returned_env = middle(eval_fn),
    actual_env = current_env()
  )
}
middle <- function(eval_fn) {
  deep(eval_fn, current_env())
}
deep <- function(eval_fn, eval_env) {
  expr <- quote(parent.frame())
  eval_fn(expr, eval_env)
}

# With eval_bare(), we do get the expected environment:
fn(rlang::eval_bare)

# But that's not the case with base::eval():
fn(base::eval)

Evaluate an expression with quosures and pronoun support

Description

eval_tidy() is a variant of base::eval() that powers the tidy evaluation framework. Like eval() it accepts user data as argument. Whereas eval() simply transforms the data to an environment, eval_tidy() transforms it to a data mask with as_data_mask(). Evaluating in a data mask enables the following features:

Usage

eval_tidy(expr, data = NULL, env = caller_env())

Arguments

expr

An expression or quosure to evaluate.

data

A data frame, or named list or vector. Alternatively, a data mask created with as_data_mask() or new_data_mask(). Objects in data have priority over those in env. See the section about data masking.

env

The environment in which to evaluate expr. This environment is not applicable for quosures because they have their own environments.

When should eval_tidy() be used instead of eval()?

base::eval() is sufficient for simple evaluation. Use eval_tidy() when you'd like to support expressions referring to the .data pronoun, or when you need to support quosures.

If you're evaluating an expression captured with injection support, it is recommended to use eval_tidy() because users may inject quosures.

Note that unwrapping a quosure with quo_get_expr() does not guarantee that there is no quosures inside the expression. Quosures might be unquoted anywhere in the expression tree. For instance, the following does not work reliably in the presence of nested quosures:

my_quoting_fn <- function(x) {
  x <- enquo(x)
  expr <- quo_get_expr(x)
  env <- quo_get_env(x)
  eval(expr, env)
}

# Works:
my_quoting_fn(toupper(letters))

# Fails because of a nested quosure:
my_quoting_fn(toupper(!!quo(letters)))

Stack semantics of eval_tidy()

eval_tidy() always evaluates in a data mask, even when data is NULL. Because of this, it has different stack semantics than base::eval():

See also eval_bare() for more information about these differences.

See Also

Examples

# With simple defused expressions eval_tidy() works the same way as
# eval():
fruit <- "apple"
vegetable <- "potato"
expr <- quote(paste(fruit, vegetable, sep = " or "))
expr

eval(expr)
eval_tidy(expr)

# Both accept a data mask as argument:
data <- list(fruit = "banana", vegetable = "carrot")
eval(expr, data)
eval_tidy(expr, data)

# The main difference is that eval_tidy() supports quosures:
with_data <- function(data, expr) {
  quo <- enquo(expr)
  eval_tidy(quo, data)
}
with_data(NULL, fruit)
with_data(data, fruit)

# eval_tidy() installs the `.data` and `.env` pronouns to allow
# users to be explicit about variable references:
with_data(data, .data$fruit)
with_data(data, .env$fruit)

Execute a function

Description

This function constructs and evaluates a call to .fn. It has two primary uses:

Usage

exec(.fn, ..., .env = caller_env())

Arguments

.fn

A function, or function name as a string.

...

<dynamic> Arguments for .fn.

.env

Environment in which to evaluate the call. This will be most useful if .fn is a string, or the function has side-effects.

Examples

args <- list(x = c(1:10, 100, NA), na.rm = TRUE)
exec("mean", !!!args)
exec("mean", !!!args, trim = 0.2)

fs <- list(a = function() "a", b = function() "b")
lapply(fs, exec)

# Compare to do.call it will not automatically inline expressions
# into the evaluated call.
x <- 10
args <- exprs(x1 = x + 1, x2 = x * 2)
exec(list, !!!args)
do.call(list, args)

# exec() is not designed to generate pretty function calls. This is
# most easily seen if you call a function that captures the call:
f <- disp ~ cyl
exec("lm", f, data = mtcars)

# If you need finer control over the generated call, you'll need to
# construct it yourself. This may require creating a new environment
# with carefully constructed bindings
data_env <- env(data = mtcars)
eval(expr(lm(!!f, data)), data_env)

Defuse an R expression

Description

expr() defuses an R expression with injection support.

It is equivalent to base::bquote().

Arguments

expr

An expression to defuse.

See Also

Examples

# R normally returns the result of an expression
1 + 1

# `expr()` defuses the expression that you have supplied and
# returns it instead of its value
expr(1 + 1)

expr(toupper(letters))

# It supports _injection_ with `!!` and `!!!`. This is a convenient
# way of modifying part of an expression by injecting other
# objects.
var <- "cyl"
expr(with(mtcars, mean(!!sym(var))))

vars <- c("cyl", "am")
expr(with(mtcars, c(!!!syms(vars))))

# Compare to the normal way of building expressions
call("with", call("mean", sym(var)))

call("with", call2("c", !!!syms(vars)))

Print an expression

Description

expr_print(), powered by expr_deparse(), is an alternative printer for R expressions with a few improvements over the base R printer.

Usage

expr_print(x, ...)

expr_deparse(x, ..., width = peek_option("width"))

Arguments

x

An object or expression to print.

...

Arguments passed to expr_deparse().

width

The width of the deparsed or printed expression. Defaults to the global option width.

Value

expr_deparse() returns a character vector of lines. expr_print() returns its input invisibly.

Examples

# It supports any object. Non-symbolic objects are always printed
# within angular brackets:
expr_print(1:3)
expr_print(function() NULL)

# Contrast this to how the code to create these objects is printed:
expr_print(quote(1:3))
expr_print(quote(function() NULL))

# The main cause of non-symbolic objects in expressions is
# quasiquotation:
expr_print(expr(foo(!!(1:3))))


# Quosures from the global environment are printed normally:
expr_print(quo(foo))
expr_print(quo(foo(!!quo(bar))))

# Quosures from local environments are colourised according to
# their environments (if you have crayon installed):
local_quo <- local(quo(foo))
expr_print(local_quo)

wrapper_quo <- local(quo(bar(!!local_quo, baz)))
expr_print(wrapper_quo)

Ensure that all elements of a list of expressions are named

Description

This gives default names to unnamed elements of a list of expressions (or expression wrappers such as formulas or quosures), deparsed with as_label().

Usage

exprs_auto_name(
  exprs,
  ...,
  repair_auto = c("minimal", "unique"),
  repair_quiet = FALSE
)

quos_auto_name(quos)

Arguments

exprs

A list of expressions.

...

These dots are for future extensions and must be empty.

repair_auto

Whether to repair the automatic names. By default, minimal names are returned. See ?vctrs::vec_as_names for information about name repairing.

repair_quiet

Whether to inform user about repaired names.

quos

A list of quosures.


Get or set formula components

Description

f_rhs extracts the righthand side, f_lhs extracts the lefthand side, and f_env extracts the environment. All functions throw an error if f is not a formula.

Usage

f_rhs(f)

f_rhs(x) <- value

f_lhs(f)

f_lhs(x) <- value

f_env(f)

f_env(x) <- value

Arguments

f, x

A formula

value

The value to replace with.

Value

f_rhs and f_lhs return language objects (i.e. atomic vectors of length 1, a name, or a call). f_env returns an environment.

Examples

f_rhs(~ 1 + 2 + 3)
f_rhs(~ x)
f_rhs(~ "A")
f_rhs(1 ~ 2)

f_lhs(~ y)
f_lhs(x ~ y)

f_env(~ x)

Turn RHS of formula into a string or label

Description

Equivalent of expr_text() and expr_label() for formulas.

Usage

f_text(x, width = 60L, nlines = Inf)

f_name(x)

f_label(x)

Arguments

x

A formula.

width

Width of each line.

nlines

Maximum number of lines to extract.

Examples

f <- ~ a + b + bc
f_text(f)
f_label(f)

# Names a quoted with ``
f_label(~ x)
# Strings are encoded
f_label(~ "a\nb")
# Long expressions are collapsed
f_label(~ foo({
  1 + 2
  print(x)
}))

Global options for rlang

Description

rlang has several options which may be set globally to control behavior. A brief description of each is given here. If any functions are referenced, refer to their documentation for additional details.


Get or set function body

Description

fn_body() is a simple wrapper around base::body(). It always returns a ⁠\{⁠ expression and throws an error when the input is a primitive function (whereas body() returns NULL). The setter version preserves attributes, unlike ⁠body<-⁠.

Usage

fn_body(fn = caller_fn())

fn_body(fn) <- value

Arguments

fn

A function. It is looked up in the calling frame if not supplied.

value

New formals or formals names for fn.

Examples

# fn_body() is like body() but always returns a block:
fn <- function() do()
body(fn)
fn_body(fn)

# It also throws an error when used on a primitive function:
try(fn_body(base::list))

Return the closure environment of a function

Description

Closure environments define the scope of functions (see env()). When a function call is evaluated, R creates an evaluation frame that inherits from the closure environment. This makes all objects defined in the closure environment and all its parents available to code executed within the function.

Usage

fn_env(fn)

fn_env(x) <- value

Arguments

fn, x

A function.

value

A new closure environment for the function.

Details

fn_env() returns the closure environment of fn. There is also an assignment method to set a new closure environment.

Examples

env <- child_env("base")
fn <- with_env(env, function() NULL)
identical(fn_env(fn), env)

other_env <- child_env("base")
fn_env(fn) <- other_env
identical(fn_env(fn), other_env)

Extract arguments from a function

Description

fn_fmls() returns a named list of formal arguments. fn_fmls_names() returns the names of the arguments. fn_fmls_syms() returns formals as a named list of symbols. This is especially useful for forwarding arguments in constructed calls.

Usage

fn_fmls(fn = caller_fn())

fn_fmls_names(fn = caller_fn())

fn_fmls_syms(fn = caller_fn())

fn_fmls(fn) <- value

fn_fmls_names(fn) <- value

Arguments

fn

A function. It is looked up in the calling frame if not supplied.

value

New formals or formals names for fn.

Details

Unlike formals(), these helpers throw an error with primitive functions instead of returning NULL.

See Also

call_args() and call_args_names()

Examples

# Extract from current call:
fn <- function(a = 1, b = 2) fn_fmls()
fn()

# fn_fmls_syms() makes it easy to forward arguments:
call2("apply", !!! fn_fmls_syms(lapply))

# You can also change the formals:
fn_fmls(fn) <- list(A = 10, B = 20)
fn()

fn_fmls_names(fn) <- c("foo", "bar")
fn()

Format bullets for error messages

Description

format_error_bullets() takes a character vector and returns a single string (or an empty vector if the input is empty). The elements of the input vector are assembled as a list of bullets, depending on their names:

For convenience, if the vector is fully unnamed, the elements are formatted as "*" bullets.

The bullet formatting for errors follows the idea that sentences in error messages are best kept short and simple. The best way to present the information is in the cnd_body() method of an error conditon as a bullet list of simple sentences containing a single clause. The info and cross symbols of the bullets provide hints on how to interpret the bullet relative to the general error issue, which should be supplied as cnd_header().

Usage

format_error_bullets(x)

Arguments

x

A named character vector of messages. Named elements are prefixed with the corresponding bullet. Elements named with a single space " " trigger a line break from the previous bullet.

Examples

# All bullets
writeLines(format_error_bullets(c("foo", "bar")))

# This is equivalent to
writeLines(format_error_bullets(set_names(c("foo", "bar"), "*")))

# Supply named elements to format info, cross, and tick bullets
writeLines(format_error_bullets(c(i = "foo", x = "bar", v = "baz", "*" = "quux")))

# An unnamed element breaks the line
writeLines(format_error_bullets(c(i = "foo\nbar")))

# A " " element breaks the line within a bullet (with indentation)
writeLines(format_error_bullets(c(i = "foo", " " = "bar")))

Get or set the environment of an object

Description

These functions dispatch internally with methods for functions, formulas and frames. If called with a missing argument, the environment of the current evaluation frame is returned. If you call get_env() with an environment, it acts as the identity function and the environment is simply returned (this helps simplifying code when writing generic functions for environments).

Usage

get_env(env, default = NULL)

set_env(env, new_env = caller_env())

env_poke_parent(env, new_env)

Arguments

env

An environment.

default

The default environment in case env does not wrap an environment. If NULL and no environment could be extracted, an error is issued.

new_env

An environment to replace env with.

Details

While set_env() returns a modified copy and does not have side effects, env_poke_parent() operates changes the environment by side effect. This is because environments are uncopyable. Be careful not to change environments that you don't own, e.g. a parent environment of a function from a package.

See Also

quo_get_env() and quo_set_env() for versions of get_env() and set_env() that only work on quosures.

Examples

# Environment of closure functions:
fn <- function() "foo"
get_env(fn)

# Or of quosures or formulas:
get_env(~foo)
get_env(quo(foo))


# Provide a default in case the object doesn't bundle an environment.
# Let's create an unevaluated formula:
f <- quote(~foo)

# The following line would fail if run because unevaluated formulas
# don't bundle an environment (they didn't have the chance to
# record one yet):
# get_env(f)

# It is often useful to provide a default when you're writing
# functions accepting formulas as input:
default <- env()
identical(get_env(f, default), default)

# set_env() can be used to set the enclosure of functions and
# formulas. Let's create a function with a particular environment:
env <- child_env("base")
fn <- set_env(function() NULL, env)

# That function now has `env` as enclosure:
identical(get_env(fn), env)
identical(get_env(fn), current_env())

# set_env() does not work by side effect. Setting a new environment
# for fn has no effect on the original function:
other_env <- child_env(NULL)
set_env(fn, other_env)
identical(get_env(fn), other_env)

# Since set_env() returns a new function with a different
# environment, you'll need to reassign the result:
fn <- set_env(fn, other_env)
identical(get_env(fn), other_env)

Entrace unexpected errors

Description

global_entrace() enriches base errors, warnings, and messages with rlang features.

Set global entracing in your RProfile with:

rlang::global_entrace()

Usage

global_entrace(enable = TRUE, class = c("error", "warning", "message"))

Arguments

enable

Whether to enable or disable global handling.

class

A character vector of one or several classes of conditions to be entraced.

Inside RMarkdown documents

Call global_entrace() inside an RMarkdown document to cause errors and warnings to be promoted to rlang conditions that include a backtrace. This needs to be done in a separate setup chunk before the first error or warning.

This is useful in conjunction with rlang_backtrace_on_error_report and rlang_backtrace_on_warning_report. To get full entracing in an Rmd document, include this in a setup chunk before the first error or warning is signalled.

```{r setup}
rlang::global_entrace()
options(rlang_backtrace_on_warning_report = "full")
options(rlang_backtrace_on_error_report = "full")
```

Under the hood

On R 4.0 and newer, global_entrace() installs a global handler with globalCallingHandlers(). On older R versions, entrace() is set as an option(error = ) handler. The latter method has the disadvantage that only one handler can be set at a time. This means that you need to manually switch between entrace() and other handlers like recover(). Also this causes a conflict with IDE handlers (e.g. in RStudio).


Register default global handlers

Description

global_handle() sets up a default configuration for error, warning, and message handling. It calls:

Usage

global_handle(entrace = TRUE, prompt_install = TRUE)

Arguments

entrace

Passed as enable argument to global_entrace().

prompt_install

Passed as enable argument to global_prompt_install().


Prompt user to install missing packages

Description

When enabled, packageNotFoundError thrown by loadNamespace() cause a user prompt to install the missing package and continue without interrupting the current program.

This is similar to how check_installed() prompts users to install required packages. It uses the same install strategy, using pak if available and install.packages() otherwise.

Usage

global_prompt_install(enable = TRUE)

Arguments

enable

Whether to enable or disable global handling.


Name injection with "{" and "{{"

Description

Dynamic dots (and data-masked dots which are dynamic by default) have built-in support for names interpolation with the glue package.

tibble::tibble(foo = 1)
#> # A tibble: 1 x 1
#>     foo
#>   <dbl>
#> 1     1

foo <- "name"
tibble::tibble("{foo}" := 1)
#> # A tibble: 1 x 1
#>    name
#>   <dbl>
#> 1     1

Inside functions, embracing an argument with {{ inserts the expression supplied as argument in the string. This gives an indication on the variable or computation supplied as argument:

tib <- function(x) {
  tibble::tibble("var: {{ x }}" := x)
}

tib(1 + 1)
#> # A tibble: 1 x 1
#>   `var: 1 + 1`
#>          <dbl>
#> 1            2

See also englue() to string-embrace outside of dynamic dots.

g <- function(x) {
  englue("var: {{ x }}")
}

g(1 + 1)
#> [1] "var: 1 + 1"

Technically, ⁠"{{"⁠ defuses a function argument, calls as_label() on the expression supplied as argument, and inserts the result in the string.

⁠"{"⁠ and ⁠"{{"⁠

While glue::glue() only supports ⁠"{"⁠, dynamic dots support both ⁠"{"⁠ and ⁠"{{"⁠. The double brace variant is similar to the embrace operator {{ available in data-masked arguments.

In the following example, the embrace operator is used in a glue string to name the result with a default name that represents the expression supplied as argument:

my_mean <- function(data, var) {
  data %>% dplyr::summarise("{{ var }}" := mean({{ var }}))
}

mtcars %>% my_mean(cyl)
#> # A tibble: 1 x 1
#>     cyl
#>   <dbl>
#> 1  6.19

mtcars %>% my_mean(cyl * am)
#> # A tibble: 1 x 1
#>   `cyl * am`
#>        <dbl>
#> 1       2.06

⁠"{{"⁠ is only meant for inserting an expression supplied as argument to a function. The result of the expression is not inspected or used. To interpolate a string stored in a variable, use the regular glue operator ⁠"{"⁠ instead:

my_mean <- function(data, var, name = "mean") {
  data %>% dplyr::summarise("{name}" := mean({{ var }}))
}

mtcars %>% my_mean(cyl)
#> # A tibble: 1 x 1
#>    mean
#>   <dbl>
#> 1  6.19

mtcars %>% my_mean(cyl, name = "cyl")
#> # A tibble: 1 x 1
#>     cyl
#>   <dbl>
#> 1  6.19

Using the wrong operator causes unexpected results:

x <- "name"

list2("{{ x }}" := 1)
#> $`"name"`
#> [1] 1

list2("{x}" := 1)
#> $name
#> [1] 1

Ideally, using ⁠{{⁠ on regular objects would be an error. However for technical reasons it is not possible to make a distinction between function arguments and ordinary variables. See Does {{ work on regular objects? for more information about this limitation.

Allow overriding default names

The implementation of my_mean() in the previous section forces a default name onto the result. But what if the caller wants to give it a different name? In functions that take dots, it is possible to just supply a named expression to override the default. In a function like my_mean() that takes a named argument we need a different approach.

This is where englue() becomes useful. We can pull out the default name creation in another user-facing argument like this:

my_mean <- function(data, var, name = englue("{{ var }}")) {
  data %>% dplyr::summarise("{name}" := mean({{ var }}))
}

Now the user may supply their own name if needed:

mtcars %>% my_mean(cyl * am)
#> # A tibble: 1 x 1
#>   `cyl * am`
#>        <dbl>
#> 1       2.06

mtcars %>% my_mean(cyl * am, name = "mean_cyl_am")
#> # A tibble: 1 x 1
#>   mean_cyl_am
#>         <dbl>
#> 1        2.06

What's the deal with ⁠:=⁠?

Name injection in dynamic dots was originally implemented with ⁠:=⁠ instead of = to allow complex expressions on the LHS:

x <- "name"
list2(!!x := 1)
#> $name
#> [1] 1

Name-injection with glue operations was an extension of this existing feature and so inherited the same interface. However, there is no technical barrier to using glue strings on the LHS of =.

As we are now moving away from !! for common tasks, we are considering enabling glue strings with = and superseding ⁠:=⁠ usage. Track the progress of this change in issue 1296.

Using glue syntax in packages

Since rlang does not depend directly on glue, you will have to ensure that glue is installed by adding it to your ⁠Imports:⁠ section.

usethis::use_package("glue", "Imports")

Does an object have an element with this name?

Description

This function returns a logical value that indicates if a data frame or another named object contains an element with a specific name. Note that has_name() only works with vectors. For instance, environments need the specialised function env_has().

Usage

has_name(x, name)

Arguments

x

A data frame or another named object

name

Element name(s) to check

Details

Unnamed objects are treated as if all names are empty strings. NA input gives FALSE as output.

Value

A logical vector of the same length as name

Examples

has_name(iris, "Species")
has_name(mtcars, "gears")

Hashing

Description

The generated hash is guaranteed to be reproducible across platforms that have the same endianness and are using the same R version.

Usage

hash(x)

hash_file(path)

Arguments

x

An object.

path

A character vector of paths to the files to be hashed.

Details

These hashers use the XXH128 hash algorithm of the xxHash library, which generates a 128-bit hash. Both are implemented as streaming hashes, which generate the hash with minimal extra memory usage.

For hash(), objects are converted to binary using R's native serialization tools. On R >= 3.5.0, serialization version 3 is used, otherwise version 2 is used. See serialize() for more information about the serialization version.

Value

Examples

hash(c(1, 2, 3))
hash(mtcars)

authors <- file.path(R.home("doc"), "AUTHORS")
copying <- file.path(R.home("doc"), "COPYING")
hashes <- hash_file(c(authors, copying))
hashes

# If you need a single hash for multiple files,
# hash the result of `hash_file()`
hash(hashes)

Does an object inherit from a set of classes?

Description

Usage

inherits_any(x, class)

inherits_all(x, class)

inherits_only(x, class)

Arguments

x

An object to test for inheritance.

class

A character vector of classes.

Examples

obj <- structure(list(), class = c("foo", "bar", "baz"))

# With the _any variant only one class must match:
inherits_any(obj, c("foobar", "bazbaz"))
inherits_any(obj, c("foo", "bazbaz"))

# With the _all variant all classes must match:
inherits_all(obj, c("foo", "bazbaz"))
inherits_all(obj, c("foo", "baz"))

# The order of classes must match as well:
inherits_all(obj, c("baz", "foo"))

# inherits_only() checks that the class vectors are identical:
inherits_only(obj, c("foo", "baz"))
inherits_only(obj, c("foo", "bar", "baz"))

Inject objects in an R expression

Description

inject() evaluates an expression with injection support. There are three main usages:

Usage

inject(expr, env = caller_env())

Arguments

expr

An argument to evaluate. This argument is immediately evaluated in env (the current environment by default) with injected objects and expressions.

env

The environment in which to evaluate expr. Defaults to the current environment. For expert use only.

Examples

# inject() simply evaluates its argument with injection
# support. These expressions are equivalent:
2 * 3
inject(2 * 3)
inject(!!2 * !!3)

# Injection with `!!` can be useful to insert objects or
# expressions within other expressions, like formulas:
lhs <- sym("foo")
rhs <- sym("bar")
inject(!!lhs ~ !!rhs + 10)

# Injection with `!!!` splices lists of arguments in function
# calls:
args <- list(na.rm = TRUE, finite = 0.2)
inject(mean(1:10, !!!args))

Injection operator ⁠!!⁠

Description

The injection operator ⁠!!⁠ injects a value or expression inside another expression. In other words, it modifies a piece of code before R evaluates it.

There are two main cases for injection. You can inject constant values to work around issues of scoping ambiguity, and you can inject defused expressions like symbolised column names.

Where does ⁠!!⁠ work?

⁠!!⁠ does not work everywhere, you can only use it within certain special functions:

All data-masking verbs in the tidyverse support injection operators out of the box. With base functions, you need to use inject() to enable ⁠!!⁠. Using ⁠!!⁠ out of context may lead to incorrect results, see What happens if I use injection operators out of context?.

The examples below are built around the base function with(). Since it's not a tidyverse function we will use inject() to enable ⁠!!⁠ usage.

Injecting values

Data-masking functions like with() are handy because you can refer to column names in your computations. This comes at the price of data mask ambiguity: if you have defined an env-variable of the same name as a data-variable, you get a name collisions. This collision is always resolved by giving precedence to the data-variable (it masks the env-variable):

cyl <- c(100, 110)
with(mtcars, mean(cyl))
#> [1] 6.1875

The injection operator offers one way of solving this. Use it to inject the env-variable inside the data-masked expression:

inject(
  with(mtcars, mean(!!cyl))
)
#> [1] 105

Note that the .env pronoun is a simpler way of solving the ambiguity. See The data mask ambiguity for more about this.

Injecting expressions

Injection is also useful for modifying parts of a defused expression. In the following example we use the symbolise-and-inject pattern to inject a column name inside a data-masked expression.

var <- sym("cyl")
inject(
  with(mtcars, mean(!!var))
)
#> [1] 6.1875

Since with() is a base function, you can't inject quosures, only naked symbols and calls. This isn't a problem here because we're injecting the name of a data frame column. If the environment is important, try injecting a pre-computed value instead.

When do I need ⁠!!⁠?

With tidyverse APIs, injecting expressions with ⁠!!⁠ is no longer a common pattern. First, the .env pronoun solves the ambiguity problem in a more intuitive way:

cyl <- 100
mtcars %>% dplyr::mutate(cyl = cyl * .env$cyl)

Second, the embrace operator {{ makes the defuse-and-inject pattern easier to learn and use.

my_mean <- function(data, var) {
  data %>% dplyr::summarise(mean({{ var }}))
}

# Equivalent to
my_mean <- function(data, var) {
  data %>% dplyr::summarise(mean(!!enquo(var)))
}

⁠!!⁠ is a good tool to learn for advanced applications but our hope is that it isn't needed for common data analysis cases.

See Also


Is object a call?

Description

This function tests if x is a call. This is a pattern-matching predicate that returns FALSE if name and n are supplied and the call does not match these properties.

Usage

is_call(x, name = NULL, n = NULL, ns = NULL)

Arguments

x

An object to test. Formulas and quosures are treated literally.

name

An optional name that the call should match. It is passed to sym() before matching. This argument is vectorised and you can supply a vector of names to match. In this case, is_call() returns TRUE if at least one name matches.

n

An optional number of arguments that the call should match.

ns

The namespace of the call. If NULL, the namespace doesn't participate in the pattern-matching. If an empty string "" and x is a namespaced call, is_call() returns FALSE. If any other string, is_call() checks that x is namespaced within ns.

Can be a character vector of namespaces, in which case the call has to match at least one of them, otherwise is_call() returns FALSE.

See Also

is_expression()

Examples

is_call(quote(foo(bar)))

# You can pattern-match the call with additional arguments:
is_call(quote(foo(bar)), "foo")
is_call(quote(foo(bar)), "bar")
is_call(quote(foo(bar)), quote(foo))

# Match the number of arguments with is_call():
is_call(quote(foo(bar)), "foo", 1)
is_call(quote(foo(bar)), "foo", 2)


# By default, namespaced calls are tested unqualified:
ns_expr <- quote(base::list())
is_call(ns_expr, "list")

# You can also specify whether the call shouldn't be namespaced by
# supplying an empty string:
is_call(ns_expr, "list", ns = "")

# Or if it should have a namespace:
is_call(ns_expr, "list", ns = "utils")
is_call(ns_expr, "list", ns = "base")

# You can supply multiple namespaces:
is_call(ns_expr, "list", ns = c("utils", "base"))
is_call(ns_expr, "list", ns = c("utils", "stats"))

# If one of them is "", unnamespaced calls will match as well:
is_call(quote(list()), "list", ns = "base")
is_call(quote(list()), "list", ns = c("base", ""))
is_call(quote(base::list()), "list", ns = c("base", ""))


# The name argument is vectorised so you can supply a list of names
# to match with:
is_call(quote(foo(bar)), c("bar", "baz"))
is_call(quote(foo(bar)), c("bar", "foo"))
is_call(quote(base::list), c("::", ":::", "$", "@"))

Is object an empty vector or NULL?

Description

Is object an empty vector or NULL?

Usage

is_empty(x)

Arguments

x

object to test

Examples

is_empty(NULL)
is_empty(list())
is_empty(list(NULL))

Is object an environment?

Description

is_bare_environment() tests whether x is an environment without a s3 or s4 class.

Usage

is_environment(x)

is_bare_environment(x)

Arguments

x

object to test


Is an object an expression?

Description

In rlang, an expression is the return type of parse_expr(), the set of objects that can be obtained from parsing R code. Under this definition expressions include numbers, strings, NULL, symbols, and function calls. These objects can be classified as:

is_expression() returns TRUE if the input is either a symbolic object or a syntactic literal. If a call, the elements of the call must all be expressions as well. Unparsable calls are not considered expressions in this narrow definition.

Note that in base R, there exists expression() vectors, a data type similar to a list that supports special attributes created by the parser called source references. This data type is not supported in rlang.

Usage

is_expression(x)

is_syntactic_literal(x)

is_symbolic(x)

Arguments

x

An object to test.

Details

is_symbolic() returns TRUE for symbols and calls (objects with type language). Symbolic objects are replaced by their value during evaluation. Literals are the complement of symbolic objects. They are their own value and return themselves during evaluation.

is_syntactic_literal() is a predicate that returns TRUE for the subset of literals that are created by R when parsing text (see parse_expr()): numbers, strings and NULL. Along with symbols, these literals are the terminating nodes in an AST.

Note that in the most general sense, a literal is any R object that evaluates to itself and that can be evaluated in the empty environment. For instance, quote(c(1, 2)) is not a literal, it is a call. However, the result of evaluating it in base_env() is a literal(in this case an atomic vector).

As the data structure for function arguments, pairlists are also a kind of language objects. However, since they are mostly an internal data structure and can't be returned as is by the parser, is_expression() returns FALSE for pairlists.

See Also

is_call() for a call predicate.

Examples

q1 <- quote(1)
is_expression(q1)
is_syntactic_literal(q1)

q2 <- quote(x)
is_expression(q2)
is_symbol(q2)

q3 <- quote(x + 1)
is_expression(q3)
is_call(q3)


# Atomic expressions are the terminating nodes of a call tree:
# NULL or a scalar atomic vector:
is_syntactic_literal("string")
is_syntactic_literal(NULL)

is_syntactic_literal(letters)
is_syntactic_literal(quote(call()))

# Parsable literals have the property of being self-quoting:
identical("foo", quote("foo"))
identical(1L, quote(1L))
identical(NULL, quote(NULL))

# Like any literals, they can be evaluated within the empty
# environment:
eval_bare(quote(1L), empty_env())

# Whereas it would fail for symbolic expressions:
# eval_bare(quote(c(1L, 2L)), empty_env())


# Pairlists are also language objects representing argument lists.
# You will usually encounter them with extracted formals:
fmls <- formals(is_expression)
typeof(fmls)

# Since they are mostly an internal data structure, is_expression()
# returns FALSE for pairlists, so you will have to check explicitly
# for them:
is_expression(fmls)
is_pairlist(fmls)

Is object a formula?

Description

is_formula() tests whether x is a call to ~. is_bare_formula() tests in addition that x does not inherit from anything else than "formula".

Note: When we first implemented is_formula(), we thought it best to treat unevaluated formulas as formulas by default (see section below). Now we think this default introduces too many edge cases in normal code. We recommend always supplying scoped = TRUE. Unevaluated formulas can be handled via a is_call(x, "~") branch.

Usage

is_formula(x, scoped = NULL, lhs = NULL)

is_bare_formula(x, scoped = TRUE, lhs = NULL)

Arguments

x

An object to test.

scoped

A boolean indicating whether the quosure is scoped, that is, has a valid environment attribute and inherits from "formula". If NULL, the scope is not inspected.

lhs

A boolean indicating whether the formula has a left-hand side. If NULL, the LHS is not inspected and is_formula() returns TRUE for both one- and two-sided formulas.

Dealing with unevaluated formulas

At parse time, a formula is a simple call to ~ and it does not have a class or an environment. Once evaluated, the ~ call becomes a properly structured formula. Unevaluated formulas arise by quotation, e.g. ~~foo, quote(~foo), or substitute(arg) with arg being supplied a formula. Use the scoped argument to check whether the formula carries an environment.

Examples

is_formula(~10)
is_formula(10)

# If you don't supply `lhs`, both one-sided and two-sided formulas
# will return `TRUE`
is_formula(disp ~ am)
is_formula(~am)

# You can also specify whether you expect a LHS:
is_formula(disp ~ am, lhs = TRUE)
is_formula(disp ~ am, lhs = FALSE)
is_formula(~am, lhs = TRUE)
is_formula(~am, lhs = FALSE)

# Handling of unevaluated formulas is a bit tricky. These formulas
# are special because they don't inherit from `"formula"` and they
# don't carry an environment (they are not scoped):
f <- quote(~foo)
f_env(f)

# By default unevaluated formulas are treated as formulas
is_formula(f)

# Supply `scoped = TRUE` to ensure you have an evaluated formula
is_formula(f, scoped = TRUE)

# By default unevaluated formulas not treated as bare formulas
is_bare_formula(f)

# If you supply `scoped = TRUE`, they will be considered bare
# formulas even though they don't inherit from `"formula"`
is_bare_formula(f, scoped = TRUE)

Is object a function?

Description

The R language defines two different types of functions: primitive functions, which are low-level, and closures, which are the regular kind of functions.

Usage

is_function(x)

is_closure(x)

is_primitive(x)

is_primitive_eager(x)

is_primitive_lazy(x)

Arguments

x

Object to be tested.

Details

Closures are functions written in R, named after the way their arguments are scoped within nested environments (see https://en.wikipedia.org/wiki/Closure_(computer_programming)). The root environment of the closure is called the closure environment. When closures are evaluated, a new environment called the evaluation frame is created with the closure environment as parent. This is where the body of the closure is evaluated. These closure frames appear on the evaluation stack, as opposed to primitive functions which do not necessarily have their own evaluation frame and never appear on the stack.

Primitive functions are more efficient than closures for two reasons. First, they are written entirely in fast low-level code. Second, the mechanism by which they are passed arguments is more efficient because they often do not need the full procedure of argument matching (dealing with positional versus named arguments, partial matching, etc). One practical consequence of the special way in which primitives are passed arguments is that they technically do not have formal arguments, and formals() will return NULL if called on a primitive function. Finally, primitive functions can either take arguments lazily, like R closures do, or evaluate them eagerly before being passed on to the C code. The former kind of primitives are called "special" in R terminology, while the latter is referred to as "builtin". is_primitive_eager() and is_primitive_lazy() allow you to check whether a primitive function evaluates arguments eagerly or lazily.

You will also encounter the distinction between primitive and internal functions in technical documentation. Like primitive functions, internal functions are defined at a low level and written in C. However, internal functions have no representation in the R language. Instead, they are called via a call to base::.Internal() within a regular closure. This ensures that they appear as normal R function objects: they obey all the usual rules of argument passing, and they appear on the evaluation stack as any other closures. As a result, fn_fmls() does not need to look in the .ArgsEnv environment to obtain a representation of their arguments, and there is no way of querying from R whether they are lazy ('special' in R terminology) or eager ('builtin').

You can call primitive functions with .Primitive() and internal functions with .Internal(). However, calling internal functions in a package is forbidden by CRAN's policy because they are considered part of the private API. They often assume that they have been called with correctly formed arguments, and may cause R to crash if you call them with unexpected objects.

Examples

# Primitive functions are not closures:
is_closure(base::c)
is_primitive(base::c)

# On the other hand, internal functions are wrapped in a closure
# and appear as such from the R side:
is_closure(base::eval)

# Both closures and primitives are functions:
is_function(base::c)
is_function(base::eval)

# Many primitive functions evaluate arguments eagerly:
is_primitive_eager(base::c)
is_primitive_eager(base::list)
is_primitive_eager(base::`+`)

# However, primitives that operate on expressions, like quote() or
# substitute(), are lazy:
is_primitive_lazy(base::quote)
is_primitive_lazy(base::substitute)

Are packages installed in any of the libraries?

Description

These functions check that packages are installed with minimal side effects. If installed, the packages will be loaded but not attached.

You can disable the prompt by setting the rlib_restart_package_not_found global option to FALSE. In that case, missing packages always cause an error.

Usage

is_installed(pkg, ..., version = NULL, compare = NULL)

check_installed(
  pkg,
  reason = NULL,
  ...,
  version = NULL,
  compare = NULL,
  action = NULL,
  call = caller_env()
)

Arguments

pkg

The package names. Can include version requirements, e.g. "pkg (>= 1.0.0)".

...

These dots must be empty.

version

Minimum versions for pkg. If supplied, must be the same length as pkg. NA elements stand for any versions.

compare

A character vector of comparison operators to use for version. If supplied, must be the same length as version. If NULL, >= is used as default for all elements. NA elements in compare are also set to >= by default.

reason

Optional string indicating why is pkg needed. Appears in error messages (if non-interactive) and user prompts (if interactive).

action

An optional function taking pkg and ... arguments. It is called by check_installed() when the user chooses to update outdated packages. The function is passed the missing and outdated packages as a character vector of names.

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.

Value

is_installed() returns TRUE if all package names provided in pkg are installed, FALSE otherwise. check_installed() either doesn't return or returns NULL.

Handling package not found errors

check_installed() signals error conditions of class rlib_error_package_not_found. The error includes pkg and version fields. They are vectorised and may include several packages.

The error is signalled with a rlib_restart_package_not_found restart on the stack to allow handlers to install the required packages. To do so, add a calling handler for rlib_error_package_not_found, install the required packages, and invoke the restart without arguments. This restarts the check from scratch.

The condition is not signalled in non-interactive sessions, in the restarting case, or if the rlib_restart_package_not_found user option is set to FALSE.

Examples

is_installed("utils")
is_installed(c("base", "ggplot5"))
is_installed(c("base", "ggplot5"), version = c(NA, "5.1.0"))

Is a vector integer-like?

Description

These predicates check whether R considers a number vector to be integer-like, according to its own tolerance check (which is in fact delegated to the C library). This function is not adapted to data analysis, see the help for base::is.integer() for examples of how to check for whole numbers.

Things to consider when checking for integer-like doubles:

Usage

is_integerish(x, n = NULL, finite = NULL)

is_bare_integerish(x, n = NULL, finite = NULL)

is_scalar_integerish(x, finite = NULL)

Arguments

x

Object to be tested.

n

Expected length of a vector.

finite

Whether all values of the vector are finite. The non-finite values are NA, Inf, -Inf and NaN. Setting this to something other than NULL can be expensive because the whole vector needs to be traversed and checked.

See Also

is_bare_numeric() for testing whether an object is a base numeric type (a bare double or integer vector).

Examples

is_integerish(10L)
is_integerish(10.0)
is_integerish(10.0, n = 2)
is_integerish(10.000001)
is_integerish(TRUE)

Is R running interactively?

Description

Like base::interactive(), is_interactive() returns TRUE when the function runs interactively and FALSE when it runs in batch mode. It also checks, in this order:

with_interactive() and local_interactive() set the global option conveniently.

Usage

is_interactive()

local_interactive(value = TRUE, frame = caller_env())

with_interactive(expr, value = TRUE)

Arguments

value

A single TRUE or FALSE. This overrides the return value of is_interactive().

frame

The environment of a running function which defines the scope of the temporary options. When the function returns, the options are reset to their original values.

expr

An expression to evaluate with interactivity set to value.


Is object named?

Description

Usage

is_named(x)

is_named2(x)

have_name(x)

Arguments

x

A vector to test.

Details

is_named() always returns TRUE for empty vectors because

Value

is_named() and is_named2() are scalar predicates that return TRUE or FALSE. have_name() is vectorised and returns a logical vector as long as the input.

Examples

# is_named() is a scalar predicate about the whole vector of names:
is_named(c(a = 1, b = 2))
is_named(c(a = 1, 2))

# Unlike is_named2(), is_named() returns `FALSE` for empty vectors
# that don't have a `names` attribute.
is_named(list())
is_named2(list())

# have_name() is a vectorised predicate
have_name(c(a = 1, b = 2))
have_name(c(a = 1, 2))

# Empty and missing names are treated as invalid:
invalid <- set_names(letters[1:5])
names(invalid)[1] <- ""
names(invalid)[3] <- NA

is_named(invalid)
have_name(invalid)

# A data frame normally has valid, unique names
is_named(mtcars)
have_name(mtcars)

# A matrix usually doesn't because the names are stored in a
# different attribute
mat <- matrix(1:4, 2)
colnames(mat) <- c("a", "b")
is_named(mat)
names(mat)

Is an object a namespace environment?

Description

Is an object a namespace environment?

Usage

is_namespace(x)

Arguments

x

An object to test.


Is object a symbol?

Description

Is object a symbol?

Usage

is_symbol(x, name = NULL)

Arguments

x

An object to test.

name

An optional name or vector of names that the symbol should match.


Is object identical to TRUE or FALSE?

Description

These functions bypass R's automatic conversion rules and check that x is literally TRUE or FALSE.

Usage

is_true(x)

is_false(x)

Arguments

x

object to test

Examples

is_true(TRUE)
is_true(1)

is_false(FALSE)
is_false(0)

Is object a weak reference?

Description

Is object a weak reference?

Usage

is_weakref(x)

Arguments

x

An object to test.


Last abort() error

Description

Usage

last_error()

last_trace(drop = NULL)

Arguments

drop

Whether to drop technical calls. These are hidden from users by default, set drop to FALSE to see the full backtrace.

See Also


Display last messages and warnings

Description

last_warnings() and last_messages() return a list of all warnings and messages that occurred during the last R command.

global_entrace() must be active in order to log the messages and warnings.

By default the warnings and messages are printed with a simplified backtrace, like last_error(). Use summary() to print the conditions with a full backtrace.

Usage

last_warnings(n = NULL)

last_messages(n = NULL)

Arguments

n

How many warnings or messages to display. Defaults to all.

Examples

Enable backtrace capture with global_entrace():

global_entrace()

Signal some warnings in nested functions. The warnings inform about which function emitted a warning but they don't provide information about the call stack:

f <- function() { warning("foo"); g() }
g <- function() { warning("bar", immediate. = TRUE); h() }
h <- function() warning("baz")

f()
#> Warning in g() : bar
#> Warning messages:
#> 1: In f() : foo
#> 2: In h() : baz

Call last_warnings() to see backtraces for each of these warnings:

last_warnings()
#> [[1]]
#> <warning/rlang_warning>
#> Warning in `f()`:
#> foo
#> Backtrace:
#>     x
#>  1. \-global f()
#>
#> [[2]]
#> <warning/rlang_warning>
#> Warning in `g()`:
#> bar
#> Backtrace:
#>     x
#>  1. \-global f()
#>  2.   \-global g()
#>
#> [[3]]
#> <warning/rlang_warning>
#> Warning in `h()`:
#> baz
#> Backtrace:
#>     x
#>  1. \-global f()
#>  2.   \-global g()
#>  3.     \-global h()

This works similarly with messages:

f <- function() { inform("Hey!"); g() }
g <- function() { inform("Hi!"); h() }
h <- function() inform("Hello!")

f()
#> Hey!
#> Hi!
#> Hello!

rlang::last_messages()
#> [[1]]
#> <message/rlang_message>
#> Message:
#> Hey!
#> ---
#> Backtrace:
#>     x
#>  1. \-global f()
#>
#> [[2]]
#> <message/rlang_message>
#> Message:
#> Hi!
#> ---
#> Backtrace:
#>     x
#>  1. \-global f()
#>  2.   \-global g()
#>
#> [[3]]
#> <message/rlang_message>
#> Message:
#> Hello!
#> ---
#> Backtrace:
#>     x
#>  1. \-global f()
#>  2.   \-global g()
#>  3.     \-global h()

See Also

last_error()


Collect dynamic dots in a list

Description

list2(...) is equivalent to list(...) with a few additional features, collectively called dynamic dots. While list2() hard-code these features, dots_list() is a lower-level version that offers more control.

Usage

list2(...)

dots_list(
  ...,
  .named = FALSE,
  .ignore_empty = c("trailing", "none", "all"),
  .preserve_empty = FALSE,
  .homonyms = c("keep", "first", "last", "error"),
  .check_assign = FALSE
)

Arguments

...

Arguments to collect in a list. These dots are dynamic.

.named

If TRUE, unnamed inputs are automatically named with as_label(). This is equivalent to applying exprs_auto_name() on the result. If FALSE, unnamed elements are left as is and, if fully unnamed, the list is given minimal names (a vector of ""). If NULL, fully unnamed results are left with NULL names.

.ignore_empty

Whether to ignore empty arguments. Can be one of "trailing", "none", "all". If "trailing", only the last argument is ignored if it is empty.

.preserve_empty

Whether to preserve the empty arguments that were not ignored. If TRUE, empty arguments are stored with missing_arg() values. If FALSE (the default) an error is thrown when an empty argument is detected.

.homonyms

How to treat arguments with the same name. The default, "keep", preserves these arguments. Set .homonyms to "first" to only keep the first occurrences, to "last" to keep the last occurrences, and to "error" to raise an informative error and indicate what arguments have duplicated names.

.check_assign

Whether to check for ⁠<-⁠ calls. When TRUE a warning recommends users to use = if they meant to match a function parameter or wrap the ⁠<-⁠ call in curly braces otherwise. This ensures assignments are explicit.

Details

For historical reasons, dots_list() creates a named list by default. By comparison list2() implements the preferred behaviour of only creating a names vector when a name is supplied.

Value

A list containing the ... inputs.

Examples

# Let's create a function that takes a variable number of arguments:
numeric <- function(...) {
  dots <- list2(...)
  num <- as.numeric(dots)
  set_names(num, names(dots))
}
numeric(1, 2, 3)

# The main difference with list(...) is that list2(...) enables
# the `!!!` syntax to splice lists:
x <- list(2, 3)
numeric(1, !!! x, 4)

# As well as unquoting of names:
nm <- "yup!"
numeric(!!nm := 1)


# One useful application of splicing is to work around exact and
# partial matching of arguments. Let's create a function taking
# named arguments and dots:
fn <- function(data, ...) {
  list2(...)
}

# You normally cannot pass an argument named `data` through the dots
# as it will match `fn`'s `data` argument. The splicing syntax
# provides a workaround:
fn("wrong!", data = letters)  # exact matching of `data`
fn("wrong!", dat = letters)   # partial matching of `data`
fn(some_data, !!!list(data = letters))  # no matching

# Empty trailing arguments are allowed:
list2(1, )

# But non-trailing empty arguments cause an error:
try(list2(1, , ))

# Use the more configurable `dots_list()` function to preserve all
# empty arguments:
list3 <- function(...) dots_list(..., .preserve_empty = TRUE)

# Note how the last empty argument is still ignored because
# `.ignore_empty` defaults to "trailing":
list3(1, , )

# The list with preserved empty arguments is equivalent to:
list(1, missing_arg())


# Arguments with duplicated names are kept by default:
list2(a = 1, a = 2, b = 3, b = 4, 5, 6)

# Use the `.homonyms` argument to keep only the first of these:
dots_list(a = 1, a = 2, b = 3, b = 4, 5, 6, .homonyms = "first")

# Or the last:
dots_list(a = 1, a = 2, b = 3, b = 4, 5, 6, .homonyms = "last")

# Or raise an informative error:
try(dots_list(a = 1, a = 2, b = 3, b = 4, 5, 6, .homonyms = "error"))


# dots_list() can be configured to warn when a `<-` call is
# detected:
my_list <- function(...) dots_list(..., .check_assign = TRUE)
my_list(a <- 1)

# There is no warning if the assignment is wrapped in braces.
# This requires users to be explicit about their intent:
my_list({ a <- 1 })

Temporarily change bindings of an environment

Description

Usage

local_bindings(..., .env = .frame, .frame = caller_env())

with_bindings(.expr, ..., .env = caller_env())

Arguments

...

Pairs of names and values. These dots support splicing (with value semantics) and name unquoting.

.env

An environment.

.frame

The frame environment that determines the scope of the temporary bindings. When that frame is popped from the call stack, bindings are switched back to their original values.

.expr

An expression to evaluate with temporary bindings.

Value

local_bindings() returns the values of old bindings invisibly; with_bindings() returns the value of expr.

Examples

foo <- "foo"
bar <- "bar"

# `foo` will be temporarily rebinded while executing `expr`
with_bindings(paste(foo, bar), foo = "rebinded")
paste(foo, bar)

Set local error call in an execution environment

Description

local_error_call() is an alternative to explicitly passing a call argument to abort(). It sets the call (or a value that indicates where to find the call, see below) in a local binding that is automatically picked up by abort().

Usage

local_error_call(call, frame = caller_env())

Arguments

call

This can be:

  • A call to be used as context for an error thrown in that execution environment.

  • The NULL value to show no context.

  • An execution environment, e.g. as returned by caller_env(). The sys.call() for that environment is taken as context.

frame

The execution environment in which to set the local error call.

Motivation for setting local error calls

By default abort() uses the function call of its caller as context in error messages:

foo <- function() abort("Uh oh.")
foo()
#> Error in `foo()`: Uh oh.

This is not always appropriate. For example a function that checks an input on the behalf of another function should reference the latter, not the former:

arg_check <- function(arg,
                      error_arg = as_string(substitute(arg))) {
  abort(cli::format_error("{.arg {error_arg}} is failing."))
}

foo <- function(x) arg_check(x)
foo()
#> Error in `arg_check()`: `x` is failing.

The mismatch is clear in the example above. arg_check() does not have any x argument and so it is confusing to present arg_check() as being the relevant context for the failure of the x argument.

One way around this is to take a call or error_call argument and pass it to abort(). Here we name this argument error_call for consistency with error_arg which is prefixed because there is an existing arg argument. In other situations, taking arg and call arguments might be appropriate.

arg_check <- function(arg,
                      error_arg = as_string(substitute(arg)),
                      error_call = caller_env()) {
  abort(
    cli::format_error("{.arg {error_arg}} is failing."),
    call = error_call
  )
}

foo <- function(x) arg_check(x)
foo()
#> Error in `foo()`: `x` is failing.

This is the generally recommended pattern for argument checking functions. If you mention an argument in an error message, provide your callers a way to supply a different argument name and a different error call. abort() stores the error call in the call condition field which is then used to generate the "in" part of error messages.

In more complex cases it's often burdensome to pass the relevant call around, for instance if your checking and throwing code is structured into many different functions. In this case, use local_error_call() to set the call locally or instruct abort() to climb the call stack one level to find the relevant call. In the following example, the complexity is not so important that sparing the argument passing makes a big difference. However this illustrates the pattern:

arg_check <- function(arg,
                      error_arg = caller_arg(arg),
                      error_call = caller_env()) {
  # Set the local error call
  local_error_call(error_call)

  my_classed_stop(
    cli::format_error("{.arg {error_arg}} is failing.")
  )
}

my_classed_stop <- function(message) {
  # Forward the local error call to the caller's
  local_error_call(caller_env())

  abort(message, class = "my_class")
}

foo <- function(x) arg_check(x)
foo()
#> Error in `foo()`: `x` is failing.

Error call flags in performance-critical functions

The call argument can also be the string "caller". This is equivalent to caller_env() or parent.frame() but has a lower overhead because call stack introspection is only performed when an error is triggered. Note that eagerly calling caller_env() is fast enough in almost all cases.

If your function needs to be really fast, assign the error call flag directly instead of calling local_error_call():

.__error_call__. <- "caller"

Examples

# Set a context for error messages
function() {
  local_error_call(quote(foo()))
  local_error_call(sys.call())
}

# Disable the context
function() {
  local_error_call(NULL)
}

# Use the caller's context
function() {
  local_error_call(caller_env())
}

Change global options

Description

Usage

local_options(..., .frame = caller_env())

with_options(.expr, ...)

push_options(...)

peek_options(...)

peek_option(name)

Arguments

...

For local_options() and push_options(), named values defining new option values. For peek_options(), strings or character vectors of option names.

.frame

The environment of a stack frame which defines the scope of the temporary options. When the frame returns, the options are set back to their original values.

.expr

An expression to evaluate with temporary options.

name

An option name as string.

Value

For local_options() and push_options(), the old option values. peek_option() returns the current value of an option while the plural peek_options() returns a list of current option values.

Life cycle

These functions are experimental.

Examples

# Store and retrieve a global option:
push_options(my_option = 10)
peek_option("my_option")

# Change the option temporarily:
with_options(my_option = 100, peek_option("my_option"))
peek_option("my_option")

# The scoped variant is useful within functions:
fn <- function() {
  local_options(my_option = 100)
  peek_option("my_option")
}
fn()
peek_option("my_option")

# The plural peek returns a named list:
peek_options("my_option")
peek_options("my_option", "digits")

Generate or handle a missing argument

Description

These functions help using the missing argument as a regular R object.

Usage

missing_arg()

is_missing(x)

maybe_missing(x, default = missing_arg())

Arguments

x

An object that might be the missing argument.

default

The object to return if the input is missing, defaults to missing_arg().

Other ways to reify the missing argument

is_missing() and default arguments

The base function missing() makes a distinction between default values supplied explicitly and default values generated through a missing argument:

fn <- function(x = 1) base::missing(x)

fn()
#> [1] TRUE
fn(1)
#> [1] FALSE

This only happens within a function. If the default value has been generated in a calling function, it is never treated as missing:

caller <- function(x = 1) fn(x)
caller()
#> [1] FALSE

rlang::is_missing() simplifies these rules by never treating default arguments as missing, even in internal contexts:

fn <- function(x = 1) rlang::is_missing(x)

fn()
#> [1] FALSE
fn(1)
#> [1] FALSE

This is a little less flexible because you can't specialise behaviour based on implicitly supplied default values. However, this makes the behaviour of is_missing() and functions using it simpler to understand.

Fragility of the missing argument object

The missing argument is an object that triggers an error if and only if it is the result of evaluating a symbol. No error is produced when a function call evaluates to the missing argument object. For instance, it is possible to bind the missing argument to a variable with an expression like x[[1]] <- missing_arg(). Likewise, x[[1]] is safe to use as argument, e.g. list(x[[1]]) even when the result is the missing object.

However, as soon as the missing argument is passed down between functions through a bare variable, it is likely to cause a missing argument error:

x <- missing_arg()
list(x)
#> Error:
#> ! argument "x" is missing, with no default

To work around this, is_missing() and maybe_missing(x) use a bit of magic to determine if the input is the missing argument without triggering a missing error.

x <- missing_arg()
list(maybe_missing(x))
#> [[1]]
#>

maybe_missing() is particularly useful for prototyping meta-programming algorithms in R. The missing argument is a likely input when computing on the language because it is a standard object in formals lists. While C functions are always allowed to return the missing argument and pass it to other C functions, this is not the case on the R side. If you're implementing your meta-programming algorithm in R, use maybe_missing() when an input might be the missing argument object.

Examples

# The missing argument usually arises inside a function when the
# user omits an argument that does not have a default:
fn <- function(x) is_missing(x)
fn()

# Creating a missing argument can also be useful to generate calls
args <- list(1, missing_arg(), 3, missing_arg())
quo(fn(!!! args))

# Other ways to create that object include:
quote(expr = )
expr()

# It is perfectly valid to generate and assign the missing
# argument in a list.
x <- missing_arg()
l <- list(missing_arg())

# Just don't evaluate a symbol that contains the empty argument.
# Evaluating the object `x` that we created above would trigger an
# error.
# x  # Not run

# On the other hand accessing a missing argument contained in a
# list does not trigger an error because subsetting is a function
# call:
l[[1]]
is.null(l[[1]])

# In case you really need to access a symbol that might contain the
# empty argument object, use maybe_missing():
maybe_missing(x)
is.null(maybe_missing(x))
is_missing(maybe_missing(x))


# Note that base::missing() only works on symbols and does not
# support complex expressions. For this reason the following lines
# would throw an error:

#> missing(missing_arg())
#> missing(l[[1]])

# while is_missing() will work as expected:
is_missing(missing_arg())
is_missing(l[[1]])

Get names of a vector

Description

names2() always returns a character vector, even when an object does not have a names attribute. In this case, it returns a vector of empty names "". It also standardises missing names to "".

The replacement variant ⁠names2<-⁠ never adds NA names and instead fills unnamed vectors with "".

Usage

names2(x)

names2(x) <- value

Arguments

x

A vector.

value

New names.

Examples

names2(letters)

# It also takes care of standardising missing names:
x <- set_names(1:3, c("a", NA, "b"))
names2(x)

# Replacing names with the base `names<-` function may introduce
# `NA` values when the vector is unnamed:
x <- 1:3
names(x)[1:2] <- "foo"
names(x)

# Use the `names2<-` variant to avoid this
x <- 1:3
names2(x)[1:2] <- "foo"
names(x)

Create a formula

Description

Create a formula

Usage

new_formula(lhs, rhs, env = caller_env())

Arguments

lhs, rhs

A call, name, or atomic vector.

env

An environment.

Value

A formula object.

See Also

new_quosure()

Examples

new_formula(quote(a), quote(b))
new_formula(NULL, quote(b))

Create a function

Description

This constructs a new function given its three components: list of arguments, body code and parent environment.

Usage

new_function(args, body, env = caller_env())

Arguments

args

A named list or pairlist of default arguments. Note that if you want arguments that don't have defaults, you'll need to use the special function pairlist2(). If you need quoted defaults, use exprs().

body

A language object representing the code inside the function. Usually this will be most easily generated with base::quote()

env

The parent environment of the function, defaults to the calling environment of new_function()

Examples

f <- function() letters
g <- new_function(NULL, quote(letters))
identical(f, g)

# Pass a list or pairlist of named arguments to create a function
# with parameters. The name becomes the parameter name and the
# argument the default value for this parameter:
new_function(list(x = 10), quote(x))
new_function(pairlist2(x = 10), quote(x))

# Use `exprs()` to create quoted defaults. Compare:
new_function(pairlist2(x = 5 + 5), quote(x))
new_function(exprs(x = 5 + 5), quote(x))

# Pass empty arguments to omit defaults. `list()` doesn't allow
# empty arguments but `pairlist2()` does:
new_function(pairlist2(x = , y = 5 + 5), quote(x + y))
new_function(exprs(x = , y = 5 + 5), quote(x + y))

Create a quosure from components

Description

Usage

new_quosure(expr, env = caller_env())

as_quosure(x, env = NULL)

is_quosure(x)

Arguments

expr

An expression to wrap in a quosure.

env

The environment in which the expression should be evaluated. Only used for symbols and calls. This should normally be the environment in which the expression was created.

x

An object to test.

See Also

Examples

# `new_quosure()` creates a quosure from its components. These are
# equivalent:
new_quosure(quote(foo), current_env())

quo(foo)

# `new_quosure()` always rewraps its input into a new quosure, even
# if the input is itself a quosure:
new_quosure(quo(foo))

# This is unlike `as_quosure()` which preserves its input if it's
# already a quosure:
as_quosure(quo(foo))


# `as_quosure()` uses the supplied environment with naked expressions:
env <- env(var = "thing")
as_quosure(quote(var), env)

# If the expression already carries an environment, this
# environment is preserved. This is the case for formulas and
# quosures:
as_quosure(~foo, env)

as_quosure(~foo)

# An environment must be supplied when the input is a naked
# expression:
try(
  as_quosure(quote(var))
)

Create a list of quosures

Description

This small S3 class provides methods for [ and c() and ensures the following invariants:

new_quosures() takes a list of quosures and adds the quosures class and a vector of empty names if needed. as_quosures() calls as_quosure() on all elements before creating the quosures object.

Usage

new_quosures(x)

as_quosures(x, env, named = FALSE)

is_quosures(x)

Arguments

x

A list of quosures or objects to coerce to quosures.

env

The default environment for the new quosures.

named

Whether to name the list with quos_auto_name().


Create a weak reference

Description

A weak reference is a special R object which makes it possible to keep a reference to an object without preventing garbage collection of that object. It can also be used to keep data about an object without preventing GC of the object, similar to WeakMaps in JavaScript.

Objects in R are considered reachable if they can be accessed by following a chain of references, starting from a root node; root nodes are specially-designated R objects, and include the global environment and base environment. As long as the key is reachable, the value will not be garbage collected. This is true even if the weak reference object becomes unreachable. The key effectively prevents the weak reference and its value from being collected, according to the following chain of ownership: weakref <- key -> value.

When the key becomes unreachable, the key and value in the weak reference object are replaced by NULL, and the finalizer is scheduled to execute.

Usage

new_weakref(key, value = NULL, finalizer = NULL, on_quit = FALSE)

Arguments

key

The key for the weak reference. Must be a reference object – that is, an environment or external pointer.

value

The value for the weak reference. This can be NULL, if you want to use the weak reference like a weak pointer.

finalizer

A function that is run after the key becomes unreachable.

on_quit

Should the finalizer be run when R exits?

See Also

is_weakref(), wref_key() and wref_value().

Examples

e <- env()

# Create a weak reference to e
w <- new_weakref(e, finalizer = function(e) message("finalized"))

# Get the key object from the weak reference
identical(wref_key(w), e)

# When the regular reference (the `e` binding) is removed and a GC occurs,
# the weak reference will not keep the object alive.
rm(e)
gc()
identical(wref_key(w), NULL)


# A weak reference with a key and value. The value contains data about the
# key.
k <- env()
v <- list(1, 2, 3)
w <- new_weakref(k, v)

identical(wref_key(w), k)
identical(wref_value(w), v)

# When v is removed, the weak ref keeps it alive because k is still reachable.
rm(v)
gc()
identical(wref_value(w), list(1, 2, 3))

# When k is removed, the weak ref does not keep k or v alive.
rm(k)
gc()
identical(wref_key(w), NULL)
identical(wref_value(w), NULL)

Run expressions on load

Description

on_load() is for your own package and runs expressions when the namespace is not sealed yet. This means you can modify existing binding or create new ones. This is not the case with on_package_load() which runs expressions after a foreign package has finished loading, at which point its namespace is sealed.

Usage

on_load(expr, env = parent.frame(), ns = topenv(env))

run_on_load(ns = topenv(parent.frame()))

on_package_load(pkg, expr, env = parent.frame())

Arguments

expr

An expression to run on load.

env

The environment in which to evaluate expr. Defaults to the current environment, which is your package namespace if you run on_load() at top level.

ns

The namespace in which to hook expr.

pkg

Package to hook expression into.

When should I run expressions on load?

There are two main use cases for running expressions on load:

  1. When a side effect, such as registering a method with s3_register(), must occur in the user session rather than the package builder session.

  2. To avoid hard-coding objects from other packages in your namespace. If you assign foo::bar or the result of foo::baz() in your package, they become constants. Any upstream changes in the foo package will not be reflected in the objects you've assigned in your namespace. This often breaks assumptions made by the authors of foo and causes all sorts of issues.

    Recreating the foreign objects each time your package is loaded makes sure that any such changes will be taken into account. In technical terms, running an expression on load introduces indirection.

Comparison with .onLoad()

on_load() has the advantage that hooked expressions can appear in any file, in context. This is unlike .onLoad() which gathers disparate expressions in a single block.

on_load() is implemented via .onLoad() and requires run_on_load() to be called from that hook.

The expressions inside on_load() do not undergo static analysis by ⁠R CMD check⁠. Therefore, it is advisable to only use simple function calls inside on_load().

Examples

quote({  # Not run

# First add `run_on_load()` to your `.onLoad()` hook,
# then use `on_load()` anywhere in your package
.onLoad <- function(lib, pkg) {
  run_on_load()
}

# Register a method on load
on_load({
  s3_register("foo::bar", "my_class")
})

# Assign an object on load
var <- NULL
on_load({
  var <- foo()
})

# To use `on_package_load()` at top level, wrap it in `on_load()`
on_load({
  on_package_load("foo", message("foo is loaded"))
})

# In functions it can be called directly
f <- function() on_package_load("foo", message("foo is loaded"))

})

Infix attribute accessor and setter

Description

This operator extracts or sets attributes for regular objects and S4 fields for S4 objects.

Usage

x %@% name

x %@% name <- value

Arguments

x

Object

name

Attribute name

value

New value for attribute name.

Examples

# Unlike `@`, this operator extracts attributes for any kind of
# objects:
factor(1:3) %@% "levels"
mtcars %@% class

mtcars %@% class <- NULL
mtcars

# It also works on S4 objects:
.Person <- setClass("Person", slots = c(name = "character", species = "character"))
fievel <- .Person(name = "Fievel", species = "mouse")
fievel %@% name

Default value for NULL

Description

This infix function makes it easy to replace NULLs with a default value. It's inspired by the way that Ruby's or operation (||) works.

Usage

x %||% y

Arguments

x, y

If x is NULL, will return y; otherwise returns x.

Examples

1 %||% 2
NULL %||% 2

Collect dynamic dots in a pairlist

Description

This pairlist constructor uses dynamic dots. Use it to manually create argument lists for calls or parameter lists for functions.

Usage

pairlist2(...)

Arguments

...

<dynamic> Arguments stored in the pairlist. Empty arguments are preserved.

Examples

# Unlike `exprs()`, `pairlist2()` evaluates its arguments.
new_function(pairlist2(x = 1, y = 3 * 6), quote(x * y))
new_function(exprs(x = 1, y = 3 * 6), quote(x * y))

# It preserves missing arguments, which is useful for creating
# parameters without defaults:
new_function(pairlist2(x = , y = 3 * 6), quote(x * y))

Parse R code

Description

These functions parse and transform text into R expressions. This is the first step to interpret or evaluate a piece of R code written by a programmer.

Usage

parse_expr(x)

parse_exprs(x)

parse_quo(x, env)

parse_quos(x, env)

Arguments

x

Text containing expressions to parse_expr for parse_expr() and parse_exprs(). Can also be an R connection, for instance to a file. If the supplied connection is not open, it will be automatically closed and destroyed.

env

The environment for the quosures. The global environment (the default) may be the right choice when you are parsing external user inputs. You might also want to evaluate the R code in an isolated context (perhaps a child of the global environment or of the base environment).

Details

Unlike base::parse(), these functions never retain source reference information, as doing so is slow and rarely necessary.

Value

parse_expr() returns an expression, parse_exprs() returns a list of expressions. Note that for the plural variants the length of the output may be greater than the length of the input. This would happen is one of the strings contain several expressions (such as "foo; bar"). The names of x are preserved (and recycled in case of multiple expressions). The ⁠_quo⁠ suffixed variants return quosures.

See Also

base::parse()

Examples

# parse_expr() can parse any R expression:
parse_expr("mtcars %>% dplyr::mutate(cyl_prime = cyl / sd(cyl))")

# A string can contain several expressions separated by ; or \n
parse_exprs("NULL; list()\n foo(bar)")

# Use names to figure out which input produced an expression:
parse_exprs(c(foo = "1; 2", bar = "3"))

# You can also parse source files by passing a R connection. Let's
# create a file containing R code:
path <- tempfile("my-file.R")
cat("1; 2; mtcars", file = path)

# We can now parse it by supplying a connection:
parse_exprs(file(path))

Show injected expression

Description

qq_show() helps examining injected expressions inside a function. This is useful for learning about injection and for debugging injection code.

Arguments

expr

An expression involving injection operators.

Examples

qq_show() shows the intermediary expression before it is evaluated by R:

list2(!!!1:3)
#> [[1]]
#> [1] 1
#> 
#> [[2]]
#> [1] 2
#> 
#> [[3]]
#> [1] 3

qq_show(list2(!!!1:3))
#> list2(1L, 2L, 3L)

It is especially useful inside functions to reveal what an injected expression looks like:

my_mean <- function(data, var) {
  qq_show(data %>% dplyr::summarise(mean({{ var }})))
}

mtcars %>% my_mean(cyl)
#> data %>% dplyr::summarise(mean(^cyl))

See Also


Squash a quosure

Description

quo_squash() flattens all nested quosures within an expression. For example it transforms ⁠^foo(^bar(), ^baz)⁠ to the bare expression foo(bar(), baz).

This operation is safe if the squashed quosure is used for labelling or printing (see as_label(), but note that as_label() squashes quosures automatically). However if the squashed quosure is evaluated, all expressions of the flattened quosures are resolved in a single environment. This is a source of bugs so it is good practice to set warn to TRUE to let the user know about the lossy squashing.

Usage

quo_squash(quo, warn = FALSE)

Arguments

quo

A quosure or expression.

warn

Whether to warn if the quosure contains other quosures (those will be collapsed). This is useful when you use quo_squash() in order to make a non-tidyeval API compatible with quosures. In that case, getting rid of the nested quosures is likely to cause subtle bugs and it is good practice to warn the user about it.

Examples

# Quosures can contain nested quosures:
quo <- quo(wrapper(!!quo(wrappee)))
quo

# quo_squash() flattens all the quosures and returns a simple expression:
quo_squash(quo)

Quosure getters, setters and predicates

Description

These tools inspect and modify quosures, a type of defused expression that includes a reference to the context where it was created. A quosure is guaranteed to evaluate in its original environment and can refer to local objects safely.

All quo_ prefixed functions expect a quosure and will fail if supplied another type of object. Make sure the input is a quosure with is_quosure().

Usage

quo_is_missing(quo)

quo_is_symbol(quo, name = NULL)

quo_is_call(quo, name = NULL, n = NULL, ns = NULL)

quo_is_symbolic(quo)

quo_is_null(quo)

quo_get_expr(quo)

quo_get_env(quo)

quo_set_expr(quo, expr)

quo_set_env(quo, env)

Arguments

quo

A quosure to test.

name

The name of the symbol or function call. If NULL the name is not tested.

n

An optional number of arguments that the call should match.

ns

The namespace of the call. If NULL, the namespace doesn't participate in the pattern-matching. If an empty string "" and x is a namespaced call, is_call() returns FALSE. If any other string, is_call() checks that x is namespaced within ns.

Can be a character vector of namespaces, in which case the call has to match at least one of them, otherwise is_call() returns FALSE.

expr

A new expression for the quosure.

env

A new environment for the quosure.

Empty quosures and missing arguments

When missing arguments are captured as quosures, either through enquo() or quos(), they are returned as an empty quosure. These quosures contain the missing argument and typically have the empty environment as enclosure.

Use quo_is_missing() to test for a missing argument defused with enquo().

See Also

Examples

quo <- quo(my_quosure)
quo


# Access and set the components of a quosure:
quo_get_expr(quo)
quo_get_env(quo)

quo <- quo_set_expr(quo, quote(baz))
quo <- quo_set_env(quo, empty_env())
quo

# Test wether an object is a quosure:
is_quosure(quo)

# If it is a quosure, you can use the specialised type predicates
# to check what is inside it:
quo_is_symbol(quo)
quo_is_call(quo)
quo_is_null(quo)

# quo_is_missing() checks for a special kind of quosure, the one
# that contains the missing argument:
quo()
quo_is_missing(quo())

fn <- function(arg) enquo(arg)
fn()
quo_is_missing(fn())

Create vectors matching the length of a given vector

Description

These functions take the idea of seq_along() and apply it to repeating values.

Usage

rep_along(along, x)

rep_named(names, x)

Arguments

along

Vector whose length determine how many times x is repeated.

x

Values to repeat.

names

Names for the new vector. The length of names determines how many times x is repeated.

See Also

new-vector

Examples

x <- 0:5
rep_along(x, 1:2)
rep_along(x, 1)

# Create fresh vectors by repeating missing values:
rep_along(x, na_int)
rep_along(x, na_chr)

# rep_named() repeats a value along a names vectors
rep_named(c("foo", "bar"), list(letters))

Display backtrace on error

Description

rlang errors carry a backtrace that can be inspected by calling last_error(). You can also control the default display of the backtrace by setting the option rlang_backtrace_on_error to one of the following values:

rlang errors are normally thrown with abort(). If you promote base errors to rlang errors with global_entrace(), rlang_backtrace_on_error applies to all errors.

Promote base errors to rlang errors

You can use options(error = rlang::entrace) to promote base errors to rlang errors. This does two things:

Warnings and errors in RMarkdown

The display of errors depends on whether they're expected (i.e. chunk option error = TRUE) or unexpected:

When knitr is running (as determined by the knitr.in.progress global option), the default top environment for backtraces is set to the chunk environment knitr::knit_global(). This ensures that the part of the call stack belonging to knitr does not end up in backtraces. If needed, you can override this by setting the rlang_trace_top_env global option.

Similarly to rlang_backtrace_on_error_report, you can set rlang_backtrace_on_warning_report inside RMarkdown documents to tweak the display of warnings. This is useful in conjunction with global_entrace(). Because of technical limitations, there is currently no corresponding rlang_backtrace_on_warning option for normal R sessions.

To get full entracing in an Rmd document, include this in a setup chunk before the first error or warning is signalled.

```{r setup}
rlang::global_entrace()
options(rlang_backtrace_on_warning_report = "full")
options(rlang_backtrace_on_error_report = "full")
```

See Also

rlang_backtrace_on_warning

Examples

# Display a simplified backtrace on error for both base and rlang
# errors:

# options(
#   rlang_backtrace_on_error = "branch",
#   error = rlang::entrace
# )
# stop("foo")

Errors of class rlang_error

Description

abort() and error_cnd() create errors of class "rlang_error". The differences with base errors are:


Scalar type predicates

Description

These predicates check for a given type and whether the vector is "scalar", that is, of length 1.

In addition to the length check, is_string() and is_bool() return FALSE if their input is missing. This is useful for type-checking arguments, when your function expects a single string or a single TRUE or FALSE.

Usage

is_scalar_list(x)

is_scalar_atomic(x)

is_scalar_vector(x)

is_scalar_integer(x)

is_scalar_double(x)

is_scalar_complex(x)

is_scalar_character(x)

is_scalar_logical(x)

is_scalar_raw(x)

is_string(x, string = NULL)

is_scalar_bytes(x)

is_bool(x)

Arguments

x

object to be tested.

string

A string to compare to x. If a character vector, returns TRUE if at least one element is equal to x.

See Also

type-predicates, bare-type-predicates


Increasing sequence of integers in an interval

Description

These helpers take two endpoints and return the sequence of all integers within that interval. For seq2_along(), the upper endpoint is taken from the length of a vector. Unlike base::seq(), they return an empty vector if the starting point is a larger integer than the end point.

Usage

seq2(from, to)

seq2_along(from, x)

Arguments

from

The starting point of the sequence.

to

The end point.

x

A vector whose length is the end point.

Value

An integer vector containing a strictly increasing sequence.

Examples

seq2(2, 10)
seq2(10, 2)
seq(10, 2)

seq2_along(10, letters)

Set names of a vector

Description

This is equivalent to stats::setNames(), with more features and stricter argument checking.

Usage

set_names(x, nm = x, ...)

Arguments

x

Vector to name.

nm, ...

Vector of names, the same length as x. If length 1, nm is recycled to the length of x following the recycling rules of the tidyverse..

You can specify names in the following ways:

  • If not supplied, x will be named to as.character(x).

  • If x already has names, you can provide a function or formula to transform the existing names. In that case, ... is passed to the function.

  • Otherwise if ... is supplied, x is named to c(nm, ...).

  • If nm is NULL, the names are removed (if present).

Life cycle

set_names() is stable and exported in purrr.

Examples

set_names(1:4, c("a", "b", "c", "d"))
set_names(1:4, letters[1:4])
set_names(1:4, "a", "b", "c", "d")

# If the second argument is ommitted a vector is named with itself
set_names(letters[1:5])

# Alternatively you can supply a function
set_names(1:10, ~ letters[seq_along(.)])
set_names(head(mtcars), toupper)

# If the input vector is unnamed, it is first named after itself
# before the function is applied:
set_names(letters, toupper)

# `...` is passed to the function:
set_names(head(mtcars), paste0, "_foo")

# If length 1, the second argument is recycled to the length of the first:
set_names(1:3, "foo")
set_names(list(), "")

Splice values at dots collection time

Description

The splicing operator ⁠!!!⁠ operates both in values contexts like list2() and dots_list(), and in metaprogramming contexts like expr(), enquos(), or inject(). While the end result looks the same, the implementation is different and much more efficient in the value cases. This difference in implementation may cause performance issues for instance when going from:

xs <- list(2, 3)
list2(1, !!!xs, 4)

to:

inject(list2(1, !!!xs, 4))

In the former case, the performant value-splicing is used. In the latter case, the slow metaprogramming splicing is used.

A common practical case where this may occur is when code is wrapped inside a tidyeval context like dplyr::mutate(). In this case, the metaprogramming operator ⁠!!!⁠ will take over the value-splicing operator, causing an unexpected slowdown.

To avoid this in performance-critical code, use splice() instead of ⁠!!!⁠:

# These both use the fast splicing:
list2(1, splice(xs), 4)
inject(list2(1, splice(xs), 4))

Usage

splice(x)

is_spliced(x)

is_spliced_bare(x)

Arguments

x

A list or vector to splice non-eagerly.


Splice operator ⁠!!!⁠

Description

The splice operator ⁠!!!⁠ implemented in dynamic dots injects a list of arguments into a function call. It belongs to the family of injection operators and provides the same functionality as do.call().

The two main cases for splice injection are:

Where does ⁠!!!⁠ work?

⁠!!!⁠ does not work everywhere, you can only use it within certain special functions:

Most tidyverse functions support ⁠!!!⁠ out of the box. With base functions you need to use inject() to enable ⁠!!!⁠.

Using the operator out of context may lead to incorrect results, see What happens if I use injection operators out of context?.

Splicing a list of arguments

Take a function like base::rbind() that takes data in .... This sort of functions takes a variable number of arguments.

df1 <- data.frame(x = 1)
df2 <- data.frame(x = 2)

rbind(df1, df2)
#>   x
#> 1 1
#> 2 2

Passing individual arguments is only possible for a fixed amount of arguments. When the arguments are in a list whose length is variable (and potentially very large), we need a programmatic approach like the splicing syntax ⁠!!!⁠:

dfs <- list(df1, df2)

inject(rbind(!!!dfs))
#>   x
#> 1 1
#> 2 2

Because rbind() is a base function we used inject() to explicitly enable ⁠!!!⁠. However, many functions implement dynamic dots with ⁠!!!⁠ implicitly enabled out of the box.

tidyr::expand_grid(x = 1:2, y = c("a", "b"))
#> # A tibble: 4 x 2
#>       x y    
#>   <int> <chr>
#> 1     1 a    
#> 2     1 b    
#> 3     2 a    
#> 4     2 b

xs <- list(x = 1:2, y = c("a", "b"))
tidyr::expand_grid(!!!xs)
#> # A tibble: 4 x 2
#>       x y    
#>   <int> <chr>
#> 1     1 a    
#> 2     1 b    
#> 3     2 a    
#> 4     2 b

Note how the expanded grid has the right column names. That's because we spliced a named list. Splicing causes each name of the list to become an argument name.

tidyr::expand_grid(!!!set_names(xs, toupper))
#> # A tibble: 4 x 2
#>       X Y    
#>   <int> <chr>
#> 1     1 a    
#> 2     1 b    
#> 3     2 a    
#> 4     2 b

Splicing a list of expressions

Another usage for ⁠!!!⁠ is to inject defused expressions into data-masked dots. However this usage is no longer a common pattern for programming with tidyverse functions and we recommend using other patterns if possible.

First, instead of using the defuse-and-inject pattern with ..., you can simply pass them on as you normally would. These two expressions are completely equivalent:

my_group_by <- function(.data, ...) {
  .data %>% dplyr::group_by(!!!enquos(...))
}

# This equivalent syntax is preferred
my_group_by <- function(.data, ...) {
  .data %>% dplyr::group_by(...)
}

Second, more complex applications such as transformation patterns can be solved with the across() operation introduced in dplyr 1.0. Say you want to take the mean() of all expressions in .... Before across(), you had to defuse the ... expressions, wrap them in a call to mean(), and inject them in summarise().

my_mean <- function(.data, ...) {
  # Defuse dots and auto-name them
  exprs <- enquos(..., .named = TRUE)

  # Wrap the expressions in a call to `mean()`
  exprs <- purrr::map(exprs, ~ call("mean", .x, na.rm = TRUE))

  # Inject them
  .data %>% dplyr::summarise(!!!exprs)
}

It is much easier to use across() instead:

my_mean <- function(.data, ...) {
  .data %>% dplyr::summarise(across(c(...), ~ mean(.x, na.rm = TRUE)))
}

Performance of injected dots and dynamic dots

Take this dynamic dots function:

n_args <- function(...) {
  length(list2(...))
}

Because it takes dynamic dots you can splice with ⁠!!!⁠ out of the box.

n_args(1, 2)
#> [1] 2

n_args(!!!mtcars)
#> [1] 11

Equivalently you could enable ⁠!!!⁠ explicitly with inject().

inject(n_args(!!!mtcars))
#> [1] 11

While the result is the same, what is going on under the hood is completely different. list2() is a dots collector that special-cases ⁠!!!⁠ arguments. On the other hand, inject() operates on the language and creates a function call containing as many arguments as there are elements in the spliced list. If you supply a list of size 1e6, inject() is creating one million arguments before evaluation. This can be much slower.

xs <- rep(list(1), 1e6)

system.time(
  n_args(!!!xs)
)
#>    user  system elapsed
#>   0.009   0.000   0.009

system.time(
  inject(n_args(!!!xs))
)
#>    user  system elapsed
#>   0.445   0.012   0.457

The same issue occurs when functions taking dynamic dots are called inside a data-masking function like dplyr::mutate(). The mechanism that enables ⁠!!!⁠ injection in these arguments is the same as in inject().

See Also


Get properties of the current or caller frame

Description

These accessors retrieve properties of frames on the call stack. The prefix indicates for which frame a property should be accessed:

The suffix indicates which property to retrieve:

Usage

current_call()

current_fn()

current_env()

caller_call(n = 1)

caller_fn(n = 1)

caller_env(n = 1)

frame_call(frame = caller_env())

frame_fn(frame = caller_env())

Arguments

n

The number of callers to go back.

frame

A frame environment of a currently running function, as returned by caller_env(). NULL is returned if the environment does not exist on the stack.

See Also

caller_env() and current_env()


Create a symbol or list of symbols

Description

Symbols are a kind of defused expression that represent objects in environments.

Only tidy eval APIs support the .data pronoun. With base R functions, use simple symbols created with sym() or syms().

Usage

sym(x)

syms(x)

data_sym(x)

data_syms(x)

Arguments

x

For sym() and data_sym(), a string. For syms() and data_syms(), a list of strings.

Value

For sym() and syms(), a symbol or list of symbols. For data_sym() and data_syms(), calls of the form .data$foo.

See Also

Examples

# Create a symbol
sym("cyl")

# Create a list of symbols
syms(c("cyl", "am"))

# Symbolised names refer to variables
eval(sym("cyl"), mtcars)

# Beware of scoping issues
Cyl <- "wrong"
eval(sym("Cyl"), mtcars)

# Data symbols are explicitly scoped in the data mask
try(eval_tidy(data_sym("Cyl"), mtcars))

# These can only be used with tidy eval functions
try(eval(data_sym("Cyl"), mtcars))

# The empty string returns the missing argument:
sym("")

# This way sym() and as_string() are inverse of each other:
as_string(missing_arg())
sym(as_string(missing_arg()))

Capture a backtrace

Description

A backtrace captures the sequence of calls that lead to the current function (sometimes called the call stack). Because of lazy evaluation, the call stack in R is actually a tree, which the print() method for this object will reveal.

Users rarely need to call trace_back() manually. Instead, signalling an error with abort() or setting up global_entrace() is the most common way to create backtraces when an error is thrown. Inspect the backtrace created for the most recent error with last_error().

trace_length() returns the number of frames in a backtrace.

Usage

trace_back(top = NULL, bottom = NULL)

trace_length(trace)

Arguments

top

The first frame environment to be included in the backtrace. This becomes the top of the backtrace tree and represents the oldest call in the backtrace.

This is needed in particular when you call trace_back() indirectly or from a larger context, for example in tests or inside an RMarkdown document where you don't want all of the knitr evaluation mechanisms to appear in the backtrace.

If not supplied, the rlang_trace_top_env global option is consulted. This makes it possible to trim the embedding context for all backtraces created while the option is set. If knitr is in progress, the default value for this option is knitr::knit_global() so that the knitr context is trimmed out of backtraces.

bottom

The last frame environment to be included in the backtrace. This becomes the rightmost leaf of the backtrace tree and represents the youngest call in the backtrace.

Set this when you would like to capture a backtrace without the capture context.

Can also be an integer that will be passed to caller_env().

trace

A backtrace created by trace_back().

Examples

# Trim backtraces automatically (this improves the generated
# documentation for the rlang website and the same trick can be
# useful within knitr documents):
options(rlang_trace_top_env = current_env())

f <- function() g()
g <- function() h()
h <- function() trace_back()

# When no lazy evaluation is involved the backtrace is linear
# (i.e. every call has only one child)
f()

# Lazy evaluation introduces a tree like structure
identity(identity(f()))
identity(try(f()))
try(identity(f()))

# When printing, you can request to simplify this tree to only show
# the direct sequence of calls that lead to `trace_back()`
x <- try(identity(f()))
x
print(x, simplify = "branch")

# With a little cunning you can also use it to capture the
# tree from within a base NSE function
x <- NULL
with(mtcars, {x <<- f(); 10})
x


# Restore default top env for next example
options(rlang_trace_top_env = NULL)

# When code is executed indirectly, i.e. via source or within an
# RMarkdown document, you'll tend to get a lot of guff at the beginning
# related to the execution environment:
conn <- textConnection("summary(f())")
source(conn, echo = TRUE, local = TRUE)
close(conn)

# To automatically strip this off, specify which frame should be
# the top of the backtrace. This will automatically trim off calls
# prior to that frame:
top <- current_env()
h <- function() trace_back(top)

conn <- textConnection("summary(f())")
source(conn, echo = TRUE, local = TRUE)
close(conn)

Try an expression with condition handlers

Description

[Experimental]

try_fetch() establishes handlers for conditions of a given class ("error", "warning", "message", ...). Handlers are functions that take a condition object as argument and are called when the corresponding condition class has been signalled.

A condition handler can:

Whereas tryCatch() catches conditions (discarding any running code along the way) and then calls the handler, try_fetch() first calls the handler with the condition on top of the currently running code (fetches it where it stands) and then catches the return value. This is a subtle difference that has implications for the debuggability of your functions. See the comparison with tryCatch() section below.

Another difference between try_fetch() and the base equivalent is that errors are matched across chains, see the parent argument of abort(). This is a useful property that makes try_fetch() insensitive to changes of implementation or context of evaluation that cause a classed error to suddenly get chained to a contextual error. Note that some chained conditions are not inherited, see the .inherit argument of abort() or warn(). In particular, downgraded conditions (e.g. from error to warning or from warning to message) are not matched across parents.

Usage

try_fetch(expr, ...)

Arguments

expr

An R expression.

...

<dynamic-dots> Named condition handlers. The names specify the condition class for which a handler will be called.

Stack overflows

A stack overflow occurs when a program keeps adding to itself until the stack memory (whose size is very limited unlike heap memory) is exhausted.

# A function that calls itself indefinitely causes stack overflows
f <- function() f()
f()
#> Error: C stack usage  9525680 is too close to the limit

Because memory is very limited when these errors happen, it is not possible to call the handlers on the existing program stack. Instead, error conditions are first caught by try_fetch() and only then error handlers are called. Catching the error interrupts the program up to the try_fetch() context, which allows R to reclaim stack memory.

The practical implication is that error handlers should never assume that the whole call stack is preserved. For instance a trace_back() capture might miss frames.

Note that error handlers are only run for stack overflows on R >= 4.2. On older versions of R the handlers are simply not run. This is because these errors do not inherit from the class stackOverflowError before R 4.2. Consider using tryCatch() instead with critical error handlers that need to capture all errors on old versions of R.

Comparison with tryCatch()

try_fetch() generalises tryCatch() and withCallingHandlers() in a single function. It reproduces the behaviour of both calling and exiting handlers depending the on the return value of the handler. If the handler returns the zap() sentinel, it is taken as a calling handler that declines to recover from a condition. Otherwise, it is taken as an exiting handler which returns a value from the catching site.

The important difference between tryCatch() and try_fetch() is that the program in expr is still fully running when an error handler is called. Because the call stack is preserved, this makes it possible to capture a full backtrace from within the handler, e.g. when rethrowing the error with abort(parent = cnd). Technically, try_fetch() is more similar to (and implemented on top of) base::withCallingHandlers() than ⁠tryCatch().⁠


Type predicates

Description

These type predicates aim to make type testing in R more consistent. They are wrappers around base::typeof(), so operate at a level beneath S3/S4 etc.

Usage

is_list(x, n = NULL)

is_atomic(x, n = NULL)

is_vector(x, n = NULL)

is_integer(x, n = NULL)

is_double(x, n = NULL, finite = NULL)

is_complex(x, n = NULL, finite = NULL)

is_character(x, n = NULL)

is_logical(x, n = NULL)

is_raw(x, n = NULL)

is_bytes(x, n = NULL)

is_null(x)

Arguments

x

Object to be tested.

n

Expected length of a vector.

finite

Whether all values of the vector are finite. The non-finite values are NA, Inf, -Inf and NaN. Setting this to something other than NULL can be expensive because the whole vector needs to be traversed and checked.

Details

Compared to base R functions:

See Also

bare-type-predicates scalar-type-predicates


Create vectors

Description

[Questioning]

The atomic vector constructors are equivalent to c() but:

Usage

lgl(...)

int(...)

dbl(...)

cpl(...)

chr(...)

bytes(...)

Arguments

...

Components of the new vector. Bare lists and explicitly spliced lists are spliced.

Life cycle

Examples

# These constructors are like a typed version of c():
c(TRUE, FALSE)
lgl(TRUE, FALSE)

# They follow a restricted set of coercion rules:
int(TRUE, FALSE, 20)

# Lists can be spliced:
dbl(10, !!! list(1, 2L), TRUE)


# They splice names a bit differently than c(). The latter
# automatically composes inner and outer names:
c(a = c(A = 10), b = c(B = 20, C = 30))

# On the other hand, rlang's constructors use the inner names and issue a
# warning to inform the user that the outer names are ignored:
dbl(a = c(A = 10), b = c(B = 20, C = 30))
dbl(a = c(1, 2))

# As an exception, it is allowed to provide an outer name when the
# inner vector is an unnamed scalar atomic:
dbl(a = 1)

# Spliced lists behave the same way:
dbl(!!! list(a = 1))
dbl(!!! list(a = c(A = 1)))

# bytes() accepts integerish inputs
bytes(1:10)
bytes(0x01, 0xff, c(0x03, 0x05), list(10, 20, 30L))

Get key/value from a weak reference object

Description

Get key/value from a weak reference object

Usage

wref_key(x)

wref_value(x)

Arguments

x

A weak reference object.

See Also

is_weakref() and new_weakref().


Create zap objects

Description

zap() creates a sentinel object that indicates that an object should be removed. For instance, named zaps instruct env_bind() and call_modify() to remove those objects from the environment or the call.

The advantage of zap objects is that they unambiguously signal the intent of removing an object. Sentinels like NULL or missing_arg() are ambiguous because they represent valid R objects.

Usage

zap()

is_zap(x)

Arguments

x

An object to test.

Examples

# Create one zap object:
zap()

# Create a list of zaps:
rep(list(zap()), 3)
rep_named(c("foo", "bar"), list(zap()))

Zap source references

Description

There are a number of situations where R creates source references:

These source references take up space and might cause a number of issues. zap_srcref() recursively walks through expressions and functions to remove all source references.

Usage

zap_srcref(x)

Arguments

x

An R object. Functions and calls are walked recursively.