cross | R Documentation |
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."
cross(.l, .filter = NULL) cross2(.x, .y, .filter = NULL) cross3(.x, .y, .z, .filter = NULL) cross_df(.l, .filter = NULL)
.l |
A list of lists or atomic vectors. Alternatively, a data
frame. |
.filter |
A predicate function that takes the same number of arguments as the number of variables to be combined. |
.x, .y, .z |
Lists or atomic vectors. |
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.
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.
expand.grid()
# 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)) # 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() # In case you need a list in long format (and not a data frame) # just run as.list() after cross_df() args |> as.list() # 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)) out # It's easier to transpose and then use invoke_map() args |> transpose() |> map_chr(\(x) exec(paste, !!!x)) # Unwanted combinations can be filtered out with a predicate function filter <- function(x, y) x >= y cross2(1:5, 1:5, .filter = filter) |> str() # 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")) # Alternatively we can encapsulate the arguments in a named list # before crossing to get named components: list(x = x, y = x) |> cross(.filter = `==`)
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