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
Here is an example of equivalent usages for
data <- list( id = c("John", "Jane"), sep = c("! ", "... "), greeting = c("Hello.", "Bonjour.") ) # With deprecated `cross()` data |> cross() |> map_chr(\(...) paste0(..., collapse = "")) #>  "John! Hello." "Jane! Hello." "John... Hello." "Jane... Hello." #>  "John! Bonjour." "Jane! Bonjour." "John... Bonjour." "Jane... Bonjour." # With `expand_grid()` tidyr::expand_grid(!!!data) |> pmap_chr(paste) #>  "John! Hello." "John! Bonjour." "John... Hello." "John... Bonjour." #>  "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)
A list of lists or atomic vectors. Alternatively, a data
A predicate function that takes the same number of arguments as the number of variables to be combined.
Lists or atomic vectors.
cross2() returns the product set of the elements of
cross3() takes an additional
cross() takes a list
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
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
FALSE. The combinations where the
predicate function returns
TRUE will be removed from the
always return a list.
cross_df() always returns a data
cross() returns a list where each element is one
combination so that the list can be directly mapped
cross_df() returns a data frame where each row is one
# 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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