crossmap provides an extension to purrr’s family of mapping functions. xmap() works like purrr::pmap(), but applies a function to every combination of elements in a list of inputs.
crossmap also includes a few other general purpose and specialized functions for working with combinations of list elements.
You can install the released version of crossmap from CRAN with:
install.packages("crossmap")
or the development version from GitHub with:
# install.packages("pak")
pak::pkg_install("rossellhayes/crossmap")
While purrr::pmap() applies a function to list elements pairwise, xmap() applies a function to all combinations of elements.
pmap_chr(list(1:3, 1:3), ~ paste(.x, "*", .y, "=", .x * .y)) #> [1] "1 * 1 = 1" "2 * 2 = 4" "3 * 3 = 9" xmap_chr(list(1:3, 1:3), ~ paste(.x, "*", .y, "=", .x * .y)) #> [1] "1 * 1 = 1" "2 * 1 = 2" "3 * 1 = 3" "1 * 2 = 2" "2 * 2 = 4" "3 * 2 = 6" #> [7] "1 * 3 = 3" "2 * 3 = 6" "3 * 3 = 9"
xmap_mat() formats xmap() results into a matrix.
xmap_mat(list(1:3, 1:6), prod) #> 1 2 3 4 5 6 #> 1 1 2 3 4 5 6 #> 2 2 4 6 8 10 12 #> 3 3 6 9 12 15 18
crossmap also integrates with furrr to offer parallelized versions of the xmap() functions.
future::plan("multisession")
future_xmap_chr(list(1:3, 1:3), ~ paste(.x, "*", .y, "=", .x * .y))
#> [1] "1 * 1 = 1" "2 * 1 = 2" "3 * 1 = 3" "1 * 2 = 2" "2 * 2 = 4" "3 * 2 = 6"
#> [7] "1 * 3 = 3" "2 * 3 = 6" "3 * 3 = 9"
cross_fit() is an easy wrapper for an important use of crossmap, crossing model specifications with different formulas, subsets, and weights.
cross_fit( mtcars, formulas = list(hp = mpg ~ hp, drat = mpg ~ drat), cols = c(cyl, vs), weights = c(wt, NA) ) #> # A tibble: 40 × 21 #> model weights cyl vs term estimate std.error statistic p.value #> <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 hp NA 4 0 (Interce… 26 NaN NaN NaN #> 2 hp NA 4 0 hp NA NA NA NA #> 3 hp NA 4 1 (Interce… 36.0 5.52e+ 0 6.52e+ 0 1.85e- 4 #> 4 hp NA 4 1 hp -0.113 6.55e- 2 -1.73e+ 0 1.21e- 1 #> 5 hp NA 6 0 (Interce… 23.2 1.98e-14 1.17e+15 5.43e-16 #> 6 hp NA 6 0 hp -0.0200 1.46e-16 -1.37e+14 4.66e-15 #> 7 hp NA 6 1 (Interce… 24.2 1.41e+ 1 1.72e+ 0 2.28e- 1 #> 8 hp NA 6 1 hp -0.0440 1.22e- 1 -3.61e- 1 7.52e- 1 #> 9 hp NA 8 0 (Interce… 18.1 2.99e+ 0 6.05e+ 0 5.74e- 5 #> 10 hp NA 8 0 hp -0.0142 1.39e- 2 -1.02e+ 0 3.26e- 1 #> # ℹ 30 more rows #> # ℹ 12 more variables: r.squared <dbl>, adj.r.squared <dbl>, sigma <dbl>, #> # model.statistic <dbl>, model.p.value <dbl>, df <dbl>, logLik <dbl>, #> # AIC <dbl>, BIC <dbl>, deviance <dbl>, df.residual <int>, nobs <int>
cross_list() finds all combinations of elements from a set of lists.
cross_list(number = 1:3, letter = letters[1:3]) #> $number #> [1] 1 2 3 1 2 3 1 2 3 #> #> $letter #> [1] "a" "a" "a" "b" "b" "b" "c" "c" "c" cross_tbl(number = 1:3, letter = letters[1:3]) #> # A tibble: 9 × 2 #> number letter #> <int> <chr> #> 1 1 a #> 2 2 a #> 3 3 a #> 4 1 b #> 5 2 b #> 6 3 b #> 7 1 c #> 8 2 c #> 9 3 c
And cross_join() finds all combinations of the rows of data frames.
cross_join(
tibble(
color = c("red", "yellow", "orange"),
fruit = c("apple", "banana", "cantaloupe")
),
tibble(dessert = c("cupcake", "muffin", "streudel"), makes = c(8, 6, 1))
)
#> # A tibble: 9 × 4
#> color fruit dessert makes
#> <chr> <chr> <chr> <dbl>
#> 1 red apple cupcake 8
#> 2 red apple muffin 6
#> 3 red apple streudel 1
#> 4 yellow banana cupcake 8
#> 5 yellow banana muffin 6
#> 6 yellow banana streudel 1
#> 7 orange cantaloupe cupcake 8
#> 8 orange cantaloupe muffin 6
#> 9 orange cantaloupe streudel 1
map_vec() and variants automatically determine output types. This means you don’t have to worry about adding _int(), _dbl() or _chr().
map_vec(sample(5), ~ . ^ 2)
#> [1] 25 9 4 1 16
map_vec(c("apple", "banana", "cantaloupe"), paste0, "s")
#> [1] "apples" "bananas" "cantaloupes"
Hex sticker font is Source Sans by Adobe.
Please note that crossmap is released with a Contributor Code of Conduct.
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