cross

0th

Percentile

Produce all combinations of list elements

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.

Usage
cross(.l, .filter = NULL)

cross2(.x, .y, .filter = NULL)

cross3(.x, .y, .z, .filter = NULL)

cross_df(.l, .filter = NULL)

Arguments
.l

A list of lists or atomic vectors. Alternatively, a data frame. cross_df() requires all elements to be named.

.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.

Details

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.

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.

See Also

expand.grid()

Aliases
  • cross
  • cross2
  • cross3
  • cross_df
  • cross_n
  • cross_d
Examples
# NOT RUN {
# 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 pratical for functional programming
# because applying a function to the combinations requires a loop
out <- vector("list", length = nrow(args))
for (i in seq_along(out))
  out[[i]] <- map(args, i) %>% invoke(paste, .)
out

# It's easier to transpose and then use invoke_map()
args %>% transpose() %>% map_chr(~ invoke(paste, .))

# 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:
seq_len(3) %>%
  cross2(., ., .filter = `==`) %>%
  map(setNames, c("x", "y"))

# Alternatively we can encapsulate the arguments in a named list
# before crossing to get named components:
seq_len(3) %>%
  list(x = ., y = .) %>%
  cross(.filter = `==`)
# }
Documentation reproduced from package purrr, version 0.2.5, License: GPL-3 | file LICENSE

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