Why is using list() critical for .dots = setNames() uses in dplyr?

I wrote an answer about why setNames() shows up sometimes in standard evaluation with dplyr.

My explanation turned into a mini-tutorial on why those standard evaluation functions have a .dots argument. The basic idea is that the usual variadic argument ... is a series of expressions that get evaluated inside of the dataframe.

library(dplyr)

# standardize and round
z_round <- . %>% scale() %>% as.numeric() %>% round(2)

# The two expressions defining zMPG, zHP are the `...`
mtcars %>% 
  mutate_(
    zMPG = ~ z_round(mpg), 
    zHP = ~ z_round(hp)
  ) %>%
  as_tibble()
#> Warning: `mutate_()` was deprecated in dplyr 0.7.0.
#> Please use `mutate()` instead.
#> See vignette('programming') for more help
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
#> # A tibble: 32 × 13
#>      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb  zMPG   zHP
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1  21       6  160    110  3.9   2.62  16.5     0     1     4     4  0.15 -0.54
#>  2  21       6  160    110  3.9   2.88  17.0     0     1     4     4  0.15 -0.54
#>  3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1  0.45 -0.78
#>  4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1  0.22 -0.54
#>  5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2 -0.23  0.41
#>  6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1 -0.33 -0.61
#>  7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4 -0.96  1.43
#>  8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2  0.72 -1.24
#>  9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2  0.45 -0.75
#> 10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4 -0.15 -0.35
#> # … with 22 more rows

If we programmatically assemble or manipulate those expressions before calling mutate_(), we can’t use that ..., because we have a list of expressions, not a series of individual expressions. We use the .dots argument instead.

exps <- list(
  zMPG = ~ z_round(mpg), 
  zHP = ~ z_round(hp)
)

mtcars %>% 
  mutate_(exps)
#> Error in `compat_lazy()`:
#> ! Can't convert a list to a quosure.

mtcars %>% 
  mutate_(.dots = exps) %>% 
  as_tibble()
#> # A tibble: 32 × 13
#>      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb  zMPG   zHP
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1  21       6  160    110  3.9   2.62  16.5     0     1     4     4  0.15 -0.54
#>  2  21       6  160    110  3.9   2.88  17.0     0     1     4     4  0.15 -0.54
#>  3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1  0.45 -0.78
#>  4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1  0.22 -0.54
#>  5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2 -0.23  0.41
#>  6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1 -0.33 -0.61
#>  7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4 -0.96  1.43
#>  8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2  0.72 -1.24
#>  9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2  0.45 -0.75
#> 10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4 -0.15 -0.35
#> # … with 22 more rows

[Jan. 4, 2021] Hello from the future

Here is how you would go about this in 2021 in the tidy evaluation framework. Use an expression-list or a quosure-list and splice them in with !!!.

exprs <- rlang::exprs(
  zMPG = z_round(mpg), 
  zHP = z_round(hp)
)

# These are just expressions
exprs
#> $zMPG
#> z_round(mpg)
#> 
#> $zHP
#> z_round(hp)

mtcars %>% 
  mutate(!!! exprs) %>% 
  as_tibble()
#> # A tibble: 32 × 13
#>      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb  zMPG   zHP
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1  21       6  160    110  3.9   2.62  16.5     0     1     4     4  0.15 -0.54
#>  2  21       6  160    110  3.9   2.88  17.0     0     1     4     4  0.15 -0.54
#>  3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1  0.45 -0.78
#>  4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1  0.22 -0.54
#>  5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2 -0.23  0.41
#>  6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1 -0.33 -0.61
#>  7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4 -0.96  1.43
#>  8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2  0.72 -1.24
#>  9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2  0.45 -0.75
#> 10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4 -0.15 -0.35
#> # … with 22 more rows

quosures <- quos(
  zMPG = z_round(mpg), 
  zHP = z_round(hp)
)

# These are expression-environment pairs
quosures
#> <list_of<quosure>>
#> 
#> $zMPG
#> <quosure>
#> expr: ^z_round(mpg)
#> env:  0x000001c4fb613818
#> 
#> $zHP
#> <quosure>
#> expr: ^z_round(hp)
#> env:  0x000001c4fb613818

mtcars %>% 
  mutate(!!! quosures) %>% 
  as_tibble()
#> # A tibble: 32 × 13
#>      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb  zMPG   zHP
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1  21       6  160    110  3.9   2.62  16.5     0     1     4     4  0.15 -0.54
#>  2  21       6  160    110  3.9   2.88  17.0     0     1     4     4  0.15 -0.54
#>  3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1  0.45 -0.78
#>  4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1  0.22 -0.54
#>  5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2 -0.23  0.41
#>  6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1 -0.33 -0.61
#>  7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4 -0.96  1.43
#>  8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2  0.72 -1.24
#>  9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2  0.45 -0.75
#> 10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4 -0.15 -0.35
#> # … with 22 more rows

---

Last knitted on 2022-05-27. Source code on GitHub.¹

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