Pass a vector of arguments to map function - r

Question

I'm trying to create a function that will map across a nested tibble. This function needs to take a vector of parameters that will vary for each row.
When I call purrr:map2() on the nested data, purrr tries to loop over all values of the parameter vector and all rows in the dataset. What can I do to pass the entire vector as a single argument?
library(tidyverse)
myf <- function(x, params) {
print(params)
x %>%
mutate(new_mpg = mpg + rnorm(n(), params[1], params[2])) %>%
summarise(old = mean(mpg), new = mean(new_mpg)) %>%
as.list()
}
# Calling function with params defined is great!
myf(mtcars, params = c(5, 10))
#> [1] 5 10
#> $old
#> [1] 20.09062
#>
#> $new
#> [1] 25.62049
# Cannot work in purr as vector, tries to loop over param
mtcars %>%
group_by(cyl) %>% # from base R
nest() %>%
mutate(
newold = map2(data, c(5, 10), myf),
)
#> [1] 5
#> Warning in rnorm(n(), params[1], params[2]): NAs produced
#> [1] 10
#> Warning in rnorm(n(), params[1], params[2]): NAs produced
#> Error: Problem with `mutate()` column `newold`.
#> ℹ `newold = map2(data, c(5, 10), myf)`.
#> ℹ `newold` must be size 1, not 2.
#> ℹ The error occurred in group 1: cyl = 4.
# New function wrapper with hard-coded params
myf2 <- function(x){
myf(x, c(5, 10))
}
# works great! but not what I need
mtcars %>%
group_by(cyl) %>% # from base R
nest() %>%
mutate(
mean = 5,
sd = 10,
newold = map(data, myf2),
)
#> [1] 5 10
#> [1] 5 10
#> [1] 5 10
#> # A tibble: 3 × 5
#> # Groups: cyl [3]
#> cyl data mean sd newold
#> <dbl> <list> <dbl> <dbl> <list>
#> 1 6 <tibble [7 × 10]> 5 10 <named list [2]>
#> 2 4 <tibble [11 × 10]> 5 10 <named list [2]>
#> 3 8 <tibble [14 × 10]> 5 10 <named list [2]>
Created on 2021-11-29 by the reprex package (v2.0.0)

Answer 1

Skip the group_by() step and just use nest() - otherwise your data will remain grouped after nesting and need to be ungrouped. To get your function to work, just pass the parameters as a list.
library(tidyverse)
mtcars %>%
nest(data = -cyl) %>%
mutate(
newold = map2_df(data, list(c(5, 10)), myf)
) %>%
unpack(newold)
# A tibble: 3 x 4
cyl data old new
<dbl> <list> <dbl> <dbl>
1 6 <tibble [7 x 10]> 19.7 30.7
2 4 <tibble [11 x 10]> 26.7 31.1
3 8 <tibble [14 x 10]> 15.1 17.0

Answer 2

You don't need map2. I think what you need is map.
mtcars %>%
group_by(cyl) %>% # from base R
nest() %>%
mutate(
newold = map(data, myf, params = c(5, 10)),
)
# [1] 5 10
# [1] 5 10
# [1] 5 10
# # A tibble: 3 x 3
# # Groups: cyl [3]
# cyl data newold
# <dbl> <list> <list>
# 1 6 <tibble [7 x 10]> <named list [2]>
# 2 4 <tibble [11 x 10]> <named list [2]>
# 3 8 <tibble [14 x 10]> <named list [2]>
If you have multiple sets of params. You can ungroup your data frame, add a list column with your params, and use map2.
mtcars %>%
group_by(cyl) %>%
nest() %>%
ungroup() %>%
# Add different sets of params
mutate(Params = list(a = c(5, 10), b = c(6, 11), c = c(7, 12))) %>%
mutate(
newold = map2(data, Params, myf)
)
# [1] 5 10
# [1] 6 11
# [1] 7 12
# # A tibble: 3 x 4
# cyl data Params newold
# <dbl> <list> <named list> <list>
# 1 6 <tibble [7 x 10]> <dbl [2]> <named list [2]>
# 2 4 <tibble [11 x 10]> <dbl [2]> <named list [2]>
# 3 8 <tibble [14 x 10]> <dbl [2]> <named list [2]>