These functions were deprecated in purrr 1.0.0 because they
are slow and buggy, and we no longer think they are the right
approach to solving this problem. Please use tidyr::expand_grid()
instead.
Here is an example of equivalent usages for cross()
and
expand_grid()
:
data <- list(
id = c("John", "Jane"),
sep = c("! ", "... "),
greeting = c("Hello.", "Bonjour.")
)
# With deprecated `cross()`
data |> cross() |> map_chr(\(...) paste0(..., collapse = ""))
#> [1] "John! Hello." "Jane! Hello." "John... Hello." "Jane... Hello."
#> [5] "John! Bonjour." "Jane! Bonjour." "John... Bonjour." "Jane... Bonjour."
# With `expand_grid()`
tidyr::expand_grid(!!!data) |> pmap_chr(paste)
#> [1] "John! Hello." "John! Bonjour." "John... Hello." "John... Bonjour."
#> [5] "Jane! Hello." "Jane! Bonjour." "Jane... Hello." "Jane... Bonjour."
Usage
cross(.l, .filter = NULL)
cross2(.x, .y, .filter = NULL)
cross3(.x, .y, .z, .filter = NULL)
cross_df(.l, .filter = NULL)
Value
cross2()
, cross3()
and cross()
always return a list. cross_df()
always returns a data
frame. cross()
returns a list where each element is one
combination so that the list can be directly mapped
over. cross_df()
returns a data frame where each row is one
combination.
Details
cross2()
returns the product set of the elements of
.x
and .y
. cross3()
takes an additional
.z
argument. cross()
takes a list .l
and
returns the cartesian product of all its elements in a list, with
one combination by element. cross_df()
is like
cross()
but returns a data frame, with one combination by
row.
cross()
, cross2()
and cross3()
return the
cartesian product is returned in wide format. This makes it more
amenable to mapping operations. cross_df()
returns the output
in long format just as expand.grid()
does. This is adapted
to rowwise operations.
When the number of combinations is large and the individual
elements are heavy memory-wise, it is often useful to filter
unwanted combinations on the fly with .filter
. It must be
a predicate function that takes the same number of arguments as the
number of crossed objects (2 for cross2()
, 3 for
cross3()
, length(.l)
for cross()
) and
returns TRUE
or FALSE
. The combinations where the
predicate function returns TRUE
will be removed from the
result.
Examples
# We build all combinations of names, greetings and separators from our
# list of data and pass each one to paste()
data <- list(
id = c("John", "Jane"),
greeting = c("Hello.", "Bonjour."),
sep = c("! ", "... ")
)
data |>
cross() |>
map(lift(paste))
#> Warning: `cross()` was deprecated in purrr 1.0.0.
#> ℹ Please use `tidyr::expand_grid()` instead.
#> ℹ See <https://github.com/tidyverse/purrr/issues/768>.
#> Warning: `lift()` was deprecated in purrr 1.0.0.
#> [[1]]
#> [1] "John! Hello."
#>
#> [[2]]
#> [1] "Jane! Hello."
#>
#> [[3]]
#> [1] "John! Bonjour."
#>
#> [[4]]
#> [1] "Jane! Bonjour."
#>
#> [[5]]
#> [1] "John... Hello."
#>
#> [[6]]
#> [1] "Jane... Hello."
#>
#> [[7]]
#> [1] "John... Bonjour."
#>
#> [[8]]
#> [1] "Jane... Bonjour."
#>
# cross() returns the combinations in long format: many elements,
# each representing one combination. With cross_df() we'll get a
# data frame in long format: crossing three objects produces a data
# frame of three columns with each row being a particular
# combination. This is the same format that expand.grid() returns.
args <- data |> cross_df()
#> Warning: `cross_df()` was deprecated in purrr 1.0.0.
#> ℹ Please use `tidyr::expand_grid()` instead.
#> ℹ See <https://github.com/tidyverse/purrr/issues/768>.
# In case you need a list in long format (and not a data frame)
# just run as.list() after cross_df()
args |> as.list()
#> $id
#> [1] "John" "Jane" "John" "Jane" "John" "Jane" "John" "Jane"
#>
#> $greeting
#> [1] "Hello." "Hello." "Bonjour." "Bonjour." "Hello." "Hello."
#> [7] "Bonjour." "Bonjour."
#>
#> $sep
#> [1] "! " "! " "! " "! " "... " "... " "... " "... "
#>
# This format is often less practical for functional programming
# because applying a function to the combinations requires a loop
out <- vector("character", length = nrow(args))
for (i in seq_along(out))
out[[i]] <- invoke("paste", map(args, i))
#> Warning: `invoke()` was deprecated in purrr 1.0.0.
#> ℹ Please use `exec()` instead.
out
#> [1] "John! Hello." "Jane! Hello." "John! Bonjour."
#> [4] "Jane! Bonjour." "John... Hello." "Jane... Hello."
#> [7] "John... Bonjour." "Jane... Bonjour."
# It's easier to transpose and then use invoke_map()
args |> transpose() |> map_chr(\(x) exec(paste, !!!x))
#> [1] "John! Hello." "Jane! Hello." "John! Bonjour."
#> [4] "Jane! Bonjour." "John... Hello." "Jane... Hello."
#> [7] "John... Bonjour." "Jane... Bonjour."
# Unwanted combinations can be filtered out with a predicate function
filter <- function(x, y) x >= y
cross2(1:5, 1:5, .filter = filter) |> str()
#> Warning: `cross2()` was deprecated in purrr 1.0.0.
#> ℹ Please use `tidyr::expand_grid()` instead.
#> ℹ See <https://github.com/tidyverse/purrr/issues/768>.
#> List of 10
#> $ :List of 2
#> ..$ : int 1
#> ..$ : int 2
#> $ :List of 2
#> ..$ : int 1
#> ..$ : int 3
#> $ :List of 2
#> ..$ : int 2
#> ..$ : int 3
#> $ :List of 2
#> ..$ : int 1
#> ..$ : int 4
#> $ :List of 2
#> ..$ : int 2
#> ..$ : int 4
#> $ :List of 2
#> ..$ : int 3
#> ..$ : int 4
#> $ :List of 2
#> ..$ : int 1
#> ..$ : int 5
#> $ :List of 2
#> ..$ : int 2
#> ..$ : int 5
#> $ :List of 2
#> ..$ : int 3
#> ..$ : int 5
#> $ :List of 2
#> ..$ : int 4
#> ..$ : int 5
# To give names to the components of the combinations, we map
# setNames() on the product:
x <- seq_len(3)
cross2(x, x, .filter = `==`) |>
map(setNames, c("x", "y"))
#> [[1]]
#> [[1]]$x
#> [1] 2
#>
#> [[1]]$y
#> [1] 1
#>
#>
#> [[2]]
#> [[2]]$x
#> [1] 3
#>
#> [[2]]$y
#> [1] 1
#>
#>
#> [[3]]
#> [[3]]$x
#> [1] 1
#>
#> [[3]]$y
#> [1] 2
#>
#>
#> [[4]]
#> [[4]]$x
#> [1] 3
#>
#> [[4]]$y
#> [1] 2
#>
#>
#> [[5]]
#> [[5]]$x
#> [1] 1
#>
#> [[5]]$y
#> [1] 3
#>
#>
#> [[6]]
#> [[6]]$x
#> [1] 2
#>
#> [[6]]$y
#> [1] 3
#>
#>
# Alternatively we can encapsulate the arguments in a named list
# before crossing to get named components:
list(x = x, y = x) |>
cross(.filter = `==`)
#> [[1]]
#> [[1]]$x
#> [1] 2
#>
#> [[1]]$y
#> [1] 1
#>
#>
#> [[2]]
#> [[2]]$x
#> [1] 3
#>
#> [[2]]$y
#> [1] 1
#>
#>
#> [[3]]
#> [[3]]$x
#> [1] 1
#>
#> [[3]]$y
#> [1] 2
#>
#>
#> [[4]]
#> [[4]]$x
#> [1] 3
#>
#> [[4]]$y
#> [1] 2
#>
#>
#> [[5]]
#> [[5]]$x
#> [1] 1
#>
#> [[5]]$y
#> [1] 3
#>
#>
#> [[6]]
#> [[6]]$x
#> [1] 2
#>
#> [[6]]$y
#> [1] 3
#>
#>