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.

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, ...)

Arguments

..f A function to lift. Default arguments for ..f. These will be evaluated only once, when the lifting factory is called. 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. A vector mold or a string describing the type of the input vectors. The latter can be any of the types returned by typeof(), or "numeric" as a shorthand for either "double" or "integer".

A function.

Details

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.

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

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

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.

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)#> [1] 51
# Or in a pipe:
mean %>% lift_dl() %>% invoke(x)#> [1] 51
# You can also use the lift() alias for this common operation:
lift(mean)(x)#> [1] 51
# Default arguments can also be specified directly in lift_dl()
list(c(1:100, NA, 1000)) %>% lift_dl(mean, na.rm = TRUE)()#> [1] 59.90099
# 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))#> [1] 7
# Now we transform it back to a variadic function
fun2 <- fun %>% lift_ld()
fun2(3, NA, 4, na.rm = TRUE)#> [1] 7
# It can sometimes be useful to make sure the lifted function's
# signature has no named parameters, as would be the case for a
# function taking only dots. The lifted function will take a list
# or vector but will not match its arguments to the names of the
# input. For instance, if you give a data frame as input to your
# lifted function, the names of the columns are probably not
# related to the function signature and should be discarded.
lifted_identical <- lift_dl(identical, .unnamed = TRUE)
mtcars[c(1, 1)] %>% lifted_identical()#> [1] TRUEmtcars[c(1, 2)] %>% lifted_identical()#> [1] FALSE#

### 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(mtcars, lift_vd(mean))#> [[1]]
#> [1] 29.90727
#>
#> [[2]]
#> [1] 29.98136
#>
#> [[3]]
#> [1] 23.59818
#>
#> [[4]]
#> [1] 38.73955
#>
#> [[5]]
#> [1] 53.66455
#>
#> [[6]]
#> [1] 35.04909
#>
#> [[7]]
#> [1] 59.72
#>
#> [[8]]
#> [1] 24.63455
#>
#> [[9]]
#> [1] 27.23364
#>
#> [[10]]
#> [1] 31.86
#>
#> [[11]]
#> [1] 31.78727
#>
#> [[12]]
#> [1] 46.43091
#>
#> [[13]]
#> [1] 46.5
#>
#> [[14]]
#> [1] 46.35
#>
#> [[15]]
#> [1] 66.23273
#>
#> [[16]]
#> [1] 66.05855
#>
#> [[17]]
#> [1] 65.97227
#>
#> [[18]]
#> [1] 19.44091
#>
#> [[19]]
#> [1] 17.74227
#>
#> [[20]]
#> [1] 18.81409
#>
#> [[21]]
#> [1] 24.88864
#>
#> [[22]]
#> [1] 47.24091
#>
#> [[23]]
#> [1] 46.00773
#>
#> [[24]]
#> [1] 58.75273
#>
#> [[25]]
#> [1] 57.37955
#>
#> [[26]]
#> [1] 18.92864
#>
#> [[27]]
#> [1] 24.77909
#>
#> [[28]]
#> [1] 24.88027
#>
#> [[29]]
#> [1] 60.97182
#>
#> [[30]]
#> [1] 34.50818
#>
#> [[31]]
#> [1] 63.15545
#>
#> [[32]]
#> [1] 26.26273
#>
# lift_vd() will collect the arguments and concatenate them to a
# vector before passing them to ..f. You can add a check to assert
# the type of vector you expect:
lift_vd(tolower, .type = character(1))("this", "is", "ok")#> [1] "this" "is"   "ok"  #

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

# cross() normally takes a list of elements and returns their
# cartesian product. By lifting it you can supply the arguments as
# if it was a function taking dots:
cross_dots <- lift_ld(cross)
out1 <- cross(list(a = 1:2, b = c("a", "b", "c")))
out2 <- cross_dots(a = 1:2, b = c("a", "b", "c"))
identical(out1, out2)#> [1] TRUE
# 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(lift_ld(some, partial(<, 200)))#> [[1]]
#> [1] FALSE
#>
#> [[2]]
#> [1] FALSE
#>
#> [[3]]
#> [1] FALSE
#>
#> [[4]]
#> [1] TRUE
#>
#> [[5]]
#> [1] TRUE
#>
#> [[6]]
#> [1] TRUE
#>
#> [[7]]
#> [1] TRUE
#>
#> [[8]]
#> [1] FALSE
#>
#> [[9]]
#> [1] FALSE
#>
#> [[10]]
#> [1] FALSE
#>
#> [[11]]
#> [1] FALSE
#>
#> [[12]]
#> [1] TRUE
#>
#> [[13]]
#> [1] TRUE
#>
#> [[14]]
#> [1] TRUE
#>
#> [[15]]
#> [1] TRUE
#>
#> [[16]]
#> [1] TRUE
#>
#> [[17]]
#> [1] TRUE
#>
#> [[18]]
#> [1] FALSE
#>
#> [[19]]
#> [1] FALSE
#>
#> [[20]]
#> [1] FALSE
#>
#> [[21]]
#> [1] FALSE
#>
#> [[22]]
#> [1] TRUE
#>
#> [[23]]
#> [1] TRUE
#>
#> [[24]]
#> [1] TRUE
#>
#> [[25]]
#> [1] TRUE
#>
#> [[26]]
#> [1] FALSE
#>
#> [[27]]
#> [1] FALSE
#>
#> [[28]]
#> [1] FALSE
#>
#> [[29]]
#> [1] TRUE
#>
#> [[30]]
#> [1] FALSE
#>
#> [[31]]
#> [1] TRUE
#>
#> [[32]]
#> [1] FALSE
#>
# Default arguments for ..f can be specified in the call to
# lift_ld()
lift_ld(cross, .filter = ==)(1:3, 1:3) %>% str()#> List of 6
#>  $:List of 2 #> ..$ : int 2
#>   ..$: int 1 #>$ :List of 2
#>   ..$: int 3 #> ..$ : int 1
#>  $:List of 2 #> ..$ : int 1
#>   ..$: int 2 #>$ :List of 2
#>   ..$: int 3 #> ..$ : int 2
#>  $:List of 2 #> ..$ : int 1
#>   ..$: int 3 #>$ :List of 2
#>   ..$: int 2 #> ..$ : int 3

# Here is another function taking a list and that we can update to
# take a vector:
glue <- function(l) {
if (!is.list(l)) stop("not a list")
l %>% invoke(paste, .)
}# NOT RUN {
letters %>% glue()           # fails because glue() expects a list
# }
letters %>% lift_lv(glue)()  # succeeds#> [1] "a b c d e f g h i j k l m n o p q r s t u v w x y z"