purrr (version 1.0.2)

lift: Lift the domain of a function

Description

lift_xy() is a composition helper. It helps you compose functions by lifting their domain from a kind of input to another kind. The domain can be changed from and to a list (l), a vector (v) and dots (d). For example, lift_ld(fun) transforms a function taking a list to a function taking dots.

The most important of those helpers is probably lift_dl() because it allows you to transform a regular function to one that takes a list. This is often essential for composition with purrr functional tools. Since this is such a common function, lift() is provided as an alias for that operation.

These functions were superseded in purrr 1.0.0 because we no longer believe "lifting" to be a mainstream operation, and we are striving to reduce purrr to its most useful core. Superseded functions will not go away, but will only receive critical bug fixes.

Usage

lift(..f, ..., .unnamed = FALSE)
lift_dl(..f, ..., .unnamed = FALSE)
lift_dv(..f, ..., .unnamed = FALSE)
lift_vl(..f, ..., .type)
lift_vd(..f, ..., .type)
lift_ld(..f, ...)
lift_lv(..f, ...)

Value

A function.

Arguments

..f

A function to lift.

...

Default arguments for ..f. These will be evaluated only once, when the lifting factory is called.

.unnamed

If TRUE, ld or lv will not name the parameters in the lifted function signature. This prevents matching of arguments by name and match by position instead.

.type

Can be a vector mold specifying both the type and the length of the vectors to be concatenated, such as numeric(1) or integer(4). Alternatively, it can be a string describing the type, one of: "logical", "integer", "double", "complex", "character" or "raw".

from ... to <code>list(...)</code> or <code>c(...)</code>

Here dots should be taken here in a figurative way. The lifted functions does not need to take dots per se. The function is simply wrapped a function in do.call(), so instead of taking multiple arguments, it takes a single named list or vector which will be interpreted as its arguments. This is particularly useful when you want to pass a row of a data frame or a list to a function and don't want to manually pull it apart in your function.

from <code>c(...)</code> to <code>list(...)</code> or <code>...</code>

These factories allow a function taking a vector to take a list or dots instead. The lifted function internally transforms its inputs back to an atomic vector. purrr does not obey the usual R casting rules (e.g., c(1, "2") produces a character vector) and will produce an error if the types are not compatible. Additionally, you can enforce a particular vector type by supplying .type.

from list(...) to c(...) or ...

lift_ld() turns a function that takes a list into a function that takes dots. lift_vd() does the same with a function that takes an atomic vector. These factory functions are the inverse operations of lift_dl() and lift_dv().

lift_vd() internally coerces the inputs of ..f to an atomic vector. The details of this coercion can be controlled with .type.

See Also

invoke()

Examples

### Lifting from ... to list(...) or c(...)

x <- list(x = c(1:100, NA, 1000), na.rm = TRUE, trim = 0.9)
lift_dl(mean)(x)
# You can also use the lift() alias for this common operation:
lift(mean)(x)
# now:
exec(mean, !!!x)

# Default arguments can also be specified directly in lift_dl()
list(c(1:100, NA, 1000)) |> lift_dl(mean, na.rm = TRUE)()
# now:
mean(c(1:100, NA, 1000), na.rm = TRUE)

# lift_dl() and lift_ld() are inverse of each other.
# Here we transform sum() so that it takes a list
fun <- sum |> lift_dl()
fun(list(3, NA, 4, na.rm = TRUE))
# now:
fun <- function(x) exec("sum", !!!x)
exec(sum, 3, NA, 4, na.rm = TRUE)
### Lifting from c(...) to list(...) or ...

# In other situations we need the vector-valued function to take a
# variable number of arguments as with pmap(). This is a job for
# lift_vd():
pmap_dbl(mtcars, lift_vd(mean))
# now
pmap_dbl(mtcars, \(...) mean(c(...)))
### Lifting from list(...) to c(...) or ...

# This kind of lifting is sometimes needed for function
# composition. An example would be to use pmap() with a function
# that takes a list. In the following, we use some() on each row of
# a data frame to check they each contain at least one element
# satisfying a condition:
mtcars |> pmap_lgl(lift_ld(some, partial(`<`, 200)))
# now
mtcars |> pmap_lgl(\(...) any(c(...) > 200))