Data masking inside pmap() when value for data mask is provided by a column

[florisvdh]

I'd like to implement pmap() on a nested tibble and do data masking within the .f argument, taking the data masking value from a column. Unfortunately the data masking seems to work only when the data masking value is given by a scalar object from the environment. Is this a bug?

How can this be solved, both for the mutate() example 1, and the group_by() example 2?

suppressPackageStartupMessages(library(dplyr))
library(tidyr)
library(purrr)

# EXAMPLE 1 (mutate). Does not work, and didn't find a solution here:
iris %>% 
    nest(measured = -Species) %>% 
    mutate(to_duplicate = ifelse(Species == "setosa",
                                 "Sepal.Length",
                                 "Petal.Width"),
           measured = 
               map2(measured, to_duplicate, function(m, d) {
                   m %>% 
                       mutate(newcol = .data[[d]])
           }))
#> Error in splice(dot_call(capture_dots, frame_env = frame_env, named = named, : object 'd' not found

# EXAMPLE 2 (group_by). Not working:
iris %>% 
    mutate(subspecies = rep(LETTERS[1:15], each = 10),
           groupvariable = "subspecies") %>% 
    nest(measured = -c(Species, groupvariable)) %>% 
    mutate(measured = 
               map2(measured, groupvariable, function(m, g) {
                   m %>% 
                       group_by(.data[[g]]) %>% 
                       summarise(newcol = mean(Petal.Width))
               }))
#> Error in splice(dot_call(capture_dots, frame_env = frame_env, named = named, : object 'g' not found

# Workaround for example 2.
# group_by_() still works to do that, however it is deprecated:
iris %>% 
    mutate(subspecies = rep(LETTERS[1:15], each = 10),
           groupvariable = "subspecies") %>% 
    nest(measured = -c(Species, groupvariable)) %>% 
    mutate(measured = 
               map2(measured, groupvariable, function(m, g) {
                   m %>% 
                       group_by_(g) %>% 
                       summarise(newcol = mean(Petal.Width))
               }))
#> Warning: `group_by_()` was deprecated in dplyr 0.7.0.
#> Please use `group_by()` instead.
#> See vignette('programming') for more help
#> # A tibble: 3 x 3
#>   Species    groupvariable measured            
#>   <fct>      <chr>         <list>              
#> 1 setosa     subspecies    <tibble[,2] [5 × 2]>
#> 2 versicolor subspecies    <tibble[,2] [5 × 2]>
#> 3 virginica  subspecies    <tibble[,2] [5 × 2]>

# In the above example, g had a constant value throughout iris, so assigning as a global variable works
# However I'd like it to work as well when its value varies in iris, so this is not a solution
g <- "subspecies"
iris %>% 
    mutate(subspecies = rep(LETTERS[1:15], each = 10)) %>% 
    nest(measured = -Species) %>% 
    mutate(measured = 
               map(measured, function(m) {
                   m %>% 
                       group_by(.data[[g]]) %>% 
                       summarise(newcol = mean(Petal.Width))
               }))
#> # A tibble: 3 x 2
#>   Species    measured            
#>   <fct>      <list>              
#> 1 setosa     <tibble[,2] [5 × 2]>
#> 2 versicolor <tibble[,2] [5 × 2]>
#> 3 virginica  <tibble[,2] [5 × 2]>

^{Created on 2021-04-28 by the reprex package (v2.0.0)}

Session info

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