lmap(), lmap_at() and lmap_if() are similar to map(), map_at() and map_if(), with the difference that they operate exclusively on functions that take and return a list (or data frame). Thus, instead of mapping the elements of a list (as in .x[[i]]), they apply a function .f to each subset of size 1 of that list (as in .x[i]). We call those elements list-elements).

lmap(.x, .f, ...)

lmap_if(.x, .p, .f, ..., .else = NULL)

lmap_at(.x, .at, .f, ...)

Arguments

.x

A list or data frame.

.f

A function that takes and returns a list or data frame.

...

Additional arguments passed on to the mapped function.

.p

A single predicate function, a formula describing such a predicate function, or a logical vector of the same length as .x. Alternatively, if the elements of .x are themselves lists of objects, a string indicating the name of a logical element in the inner lists. Only those elements where .p evaluates to TRUE will be modified.

.else

A function applied to elements of .x for which .p returns FALSE.

.at

A character vector of names, positive numeric vector of positions to include, or a negative numeric vector of positions to exlude. Only those elements corresponding to .at will be modified. If the tidyselect package is installed, you can use vars() and the tidyselect helpers to select elements.

Value

If .x is a list, a list. If .x is a data frame, a data frame.

Details

Mapping the list-elements .x[i] has several advantages. It makes it possible to work with functions that exclusively take a list or data frame. It enables .f to access the attributes of the encapsulating list, like the name of the components it receives. It also enables .f to return a larger list than the list-element of size 1 it got as input. Conversely, .f can also return empty lists. In these cases, the output list is reshaped with a different size than the input list .x.

See also

Other map variants: imap, invoke, map2, map, modify

Examples

# Let's write a function that returns a larger list or an empty list # depending on some condition. This function also uses the names # metadata available in the attributes of the list-element maybe_rep <- function(x) { n <- rpois(1, 2) out <- rep_len(x, n) if (length(out) > 0) { names(out) <- paste0(names(x), seq_len(n)) } out } # The output size varies each time we map f() x <- list(a = 1:4, b = letters[5:7], c = 8:9, d = letters[10]) x %>% lmap(maybe_rep)
#> $a1 #> [1] 1 2 3 4 #> #> $b1 #> [1] "e" "f" "g" #> #> $b2 #> [1] "e" "f" "g" #> #> $c1 #> [1] 8 9 #> #> $c2 #> [1] 8 9 #> #> $c3 #> [1] 8 9 #> #> $d1 #> [1] "j" #> #> $d2 #> [1] "j" #> #> $d3 #> [1] "j" #> #> $d4 #> [1] "j" #>
# We can apply f() on a selected subset of x x %>% lmap_at(c("a", "d"), maybe_rep)
#> $b #> [1] "e" "f" "g" #> #> $c #> [1] 8 9 #> #> $d1 #> [1] "j" #> #> $d2 #> [1] "j" #>
# Or only where a condition is satisfied x %>% lmap_if(is.character, maybe_rep)
#> $a #> [1] 1 2 3 4 #> #> $b1 #> [1] "e" "f" "g" #> #> $b2 #> [1] "e" "f" "g" #> #> $b3 #> [1] "e" "f" "g" #> #> $b4 #> [1] "e" "f" "g" #> #> $b5 #> [1] "e" "f" "g" #> #> $b6 #> [1] "e" "f" "g" #> #> $c #> [1] 8 9 #> #> $d1 #> [1] "j" #>
# A more realistic example would be a function that takes discrete # variables in a dataset and turns them into disjunctive tables, a # form that is amenable to fitting some types of models. # A disjunctive table contains only 0 and 1 but has as many columns # as unique values in the original variable. Ideally, we want to # combine the names of each level with the name of the discrete # variable in order to identify them. Given these requirements, it # makes sense to have a function that takes a data frame of size 1 # and returns a data frame of variable size. disjoin <- function(x, sep = "_") { name <- names(x) x <- as.factor(x[[1]]) out <- lapply(levels(x), function(level) { as.numeric(x == level) }) names(out) <- paste(name, levels(x), sep = sep) out } # Now, we are ready to map disjoin() on each categorical variable of a # data frame: iris %>% lmap_if(is.factor, disjoin)
#> # A tibble: 150 x 7 #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species_setosa #> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 5.1 3.5 1.4 0.2 1 #> 2 4.9 3 1.4 0.2 1 #> 3 4.7 3.2 1.3 0.2 1 #> 4 4.6 3.1 1.5 0.2 1 #> 5 5 3.6 1.4 0.2 1 #> 6 5.4 3.9 1.7 0.4 1 #> 7 4.6 3.4 1.4 0.3 1 #> 8 5 3.4 1.5 0.2 1 #> 9 4.4 2.9 1.4 0.2 1 #> 10 4.9 3.1 1.5 0.1 1 #> # … with 140 more rows, and 2 more variables: Species_versicolor <dbl>, #> # Species_virginica <dbl>
mtcars %>% lmap_at(c("cyl", "vs", "am"), disjoin)
#> # A tibble: 32 x 15 #> mpg cyl_4 cyl_6 cyl_8 disp hp drat wt qsec vs_0 vs_1 am_0 am_1 #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 21 0 1 0 160 110 3.9 2.62 16.5 1 0 0 1 #> 2 21 0 1 0 160 110 3.9 2.88 17.0 1 0 0 1 #> 3 22.8 1 0 0 108 93 3.85 2.32 18.6 0 1 0 1 #> 4 21.4 0 1 0 258 110 3.08 3.22 19.4 0 1 1 0 #> 5 18.7 0 0 1 360 175 3.15 3.44 17.0 1 0 1 0 #> 6 18.1 0 1 0 225 105 2.76 3.46 20.2 0 1 1 0 #> 7 14.3 0 0 1 360 245 3.21 3.57 15.8 1 0 1 0 #> 8 24.4 1 0 0 147. 62 3.69 3.19 20 0 1 1 0 #> 9 22.8 1 0 0 141. 95 3.92 3.15 22.9 0 1 1 0 #> 10 19.2 0 1 0 168. 123 3.92 3.44 18.3 0 1 1 0 #> # … with 22 more rows, and 2 more variables: gear <dbl>, carb <dbl>