Add multiple output variables using purrr and a predefined function - r

Question

Take this simple dataset and function (representative of more complex problems):
x <- data.frame(a = 1:3, b = 2:4)
mult <- function(a,b,n) (a + b) * n
Using base R's Map I could do this to add 2 new columns in a vectorised fashion:
ns <- 1:2
x[paste0("new",seq_along(ns))] <- Map(mult, x["a"], x["b"], n=ns)
x
# a b new1 new2
#1 1 2 3 6
#2 2 3 5 10
#3 3 4 7 14
purrr attempt via pmap gets close with a list output:
library(purrr)
library(dplyr)
x %>% select(a,b) %>% pmap(mult, n=1:2)
#[[1]]
#[1] 3 6
#
#[[2]]
#[1] 5 10
#
#[[3]]
#[1] 7 14
My attempts from here with pmap_dfr etc all seem to error out in trying to map this back to new columns.
How do I end up making 2 further variables which match my current "new1"/"new2"? I'm sure there is a simple incantation, but I'm clearly overlooking it or using the wrong *map* function.
There is some useful discussion here - How to use map from purrr with dplyr::mutate to create multiple new columns based on column pairs - but it seems overly hacky and inflexible for what I imagined was a simple problem.

Answer 1

The best approach I've found (which is still not terribly elegant) is to pipe into bind_cols. To get pmap_dfr to work correctly, the function should return a named list (which may or may not be a data frame):
library(tidyverse)
x <- data.frame(a = 1:3, b = 2:4)
mult <- function(a,b,n) as.list(set_names((a + b) * n, paste0('new', n)))
x %>% bind_cols(pmap_dfr(., mult, n = 1:2))
#> a b new1 new2
#> 1 1 2 3 6
#> 2 2 3 5 10
#> 3 3 4 7 14
To avoid changing the definition of mult, you can wrap it in an anonymous function:
mult <- function(a,b,n) (a + b) * n
x %>% bind_cols(pmap_dfr(
.,
~as.list(set_names(
mult(...),
paste0('new', 1:2)
)),
n = 1:2
))
#> a b new1 new2
#> 1 1 2 3 6
#> 2 2 3 5 10
#> 3 3 4 7 14
In this particular case, it's not actually necessary to iterate over rows, though, because you can vectorize the inputs from x and instead iterate over n. The advantage is that usually n > p, so the number of iterations will be [potentially much] lower. To be clear, whether such an approach is possible depends on for which parameters the function can accept vector arguments.
mult still needs to be called on the variables of x. The simplest way to do this is to pass them explicitly:
x %>% bind_cols(map_dfc(1:2, ~mult(x$a, x$b, .x)))
#> a b V1 V2
#> 1 1 2 3 6
#> 2 2 3 5 10
#> 3 3 4 7 14
...but this loses the benefit of pmap that named variables will automatically get passed to the correct parameter. You can get that back by using purrr::lift, which is an adverb that changes the domain of a function so it accepts a list by wrapping it in do.call. The returned function can be called on x and the value of n for that iteration:
x %>% bind_cols(map_dfc(1:2, ~lift(mult)(x, n = .x)))
This is equivalent to
x %>% bind_cols(map_dfc(1:2, ~invoke(mult, x, n = .x)))
but the advantage of the former is that it returns a function which can be partially applied on x so it only has an n parameter left, and thus requires no explicit references to x and so pipes better:
x %>% bind_cols(map_dfc(1:2, partial(lift(mult), .)))
All return the same thing. Names can be fixed after the fact with %>% set_names(~sub('^V(\\d+)$', 'new\\1', .x)), if you like.

Answer 2

Here is one possibility.
library(purrr)
library(dplyr)
n <- 1:2
x %>%
mutate(val = pmap(., mult, n = n)) %>%
unnest() %>%
mutate(var = rep(paste0("new", n), nrow(.) / length(n))) %>%
spread(var, val)
# a b new1 new2
#1 1 2 3 6
#2 2 3 5 10
#3 3 4 7 14
Not pretty, so I'm also curious to see alternatives. A lot of excess comes about from unnesting the list column and spreading into new columns.
Here is another possibility using pmap_dfc plus an ugly as.data.frame(t(...)) call
bind_cols(x, as.data.frame(t(pmap_dfc(x, mult, n = n))))
# a b V1 V2
#1 1 2 3 6
#2 2 3 5 10
#3 3 4 7 14
Sample data
x <- data.frame(a = 1:3, b = 2:4)
mult <- function(a,b,n) (a + b) * n

Answer 3

To mimic the input format for Map, we could call pmap from purrr in this way:
x[paste0("new",seq_along(ns))] <- pmap(list(x['a'], x['b'], ns), mult)
To fit this in a pipe:
x %>%
{list(.['a'], .['b'], ns)} %>%
pmap(mult) %>%
setNames(paste0('new', seq_along(ns))) %>%
cbind(x)
# new1 new2 a b
# 1 3 6 1 2
# 2 5 10 2 3
# 3 7 14 3 4
Apparently, this looks ugly compared to the concise base R code. But I could not think of a better way.