missing_arg: Generate or handle a missing argument

In tidyverse/rlang: Functions for Base Types and Core R and 'Tidyverse' Features

Generate or handle a missing argument

Description

These functions help using the missing argument as a regular R object.

• missing_arg() generates a missing argument.

• is_missing() is like base::missing() but also supports testing for missing arguments contained in other objects like lists. It is also more consistent with default arguments which are never treated as missing (see section below).

• maybe_missing() is useful to pass down an input that might be missing to another function, potentially substituting by a default value. It avoids triggering an "argument is missing" error.

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

• base::quote(expr = ) is the canonical way to create a missing argument object.

• expr() called without argument creates a missing argument.

• quo() called without argument creates an empty quosure, i.e. a quosure containing the missing argument object.

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]])

tidyverse/rlang documentation built on Oct. 31, 2024, 5:35 p.m.