tidyeval for multiple arguments taking multiple variables

``` r
# tidyeval for multiple arguments that can take multiple variables

library(dplyr)

# in the case where the verbs' arguments are ... e.g. group_by(), select(), use !!! within the function to expand the variables back out
# no enquo() or enquos() is needed because you will pass in a vars() call

group_select <- function(df, group_vars, select_vars){
  df %>%
    group_by(!!!group_vars) %>%
    select(!!!select_vars)
}

# when you call the function, pass vars() to each argument

mtcars %>%
  group_select(group_vars = vars(cyl, gear), select_vars = vars(mpg, hp))
#> Adding missing grouping variables: `cyl`, `gear`
#> # A tibble: 32 x 4
#> # Groups:   cyl, gear [8]
#>      cyl  gear   mpg    hp
#>  * <dbl> <dbl> <dbl> <dbl>
#>  1     6     4  21     110
#>  2     6     4  21     110
#>  3     4     4  22.8    93
#>  4     6     3  21.4   110
#>  5     8     3  18.7   175
#>  6     6     3  18.1   105
#>  7     8     3  14.3   245
#>  8     4     4  24.4    62
#>  9     4     4  22.8    95
#> 10     6     4  19.2   123
#> # … with 22 more rows

# (do you really need a group_select() function? probably not, this is just an example where both verbs take ... arguments)

# in the case when the verb's argument is .vars and *already expects a list of columns generated by vars()*, you don't need to use !!!

group_mean <- function(df, group_vars, mean_vars){
  df %>%
    group_by(!!!group_vars) %>%
    summarise_at(mean_vars, mean)
}

# pass the arguments in the same way, using vars()

mtcars %>%
  group_mean(group_vars = vars(cyl, gear), mean_vars = vars(mpg, hp))
#> # A tibble: 8 x 4
#> # Groups:   cyl [?]
#>     cyl  gear   mpg    hp
#>   <dbl> <dbl> <dbl> <dbl>
#> 1     4     3  21.5   97 
#> 2     4     4  26.9   76 
#> 3     4     5  28.2  102 
#> 4     6     3  19.8  108.
#> 5     6     4  19.8  116.
#> 6     6     5  19.7  175 
#> 7     8     3  15.0  194.
#> 8     8     5  15.4  300.

# i think if you are just passing ONE variable in though, you still need to use vars() for both, e.g.

mtcars %>%
  group_select(group_vars = vars(cyl), select_vars = vars(mpg))
#> Adding missing grouping variables: `cyl`
#> # A tibble: 32 x 2
#> # Groups:   cyl [3]
#>      cyl   mpg
#>  * <dbl> <dbl>
#>  1     6  21  
#>  2     6  21  
#>  3     4  22.8
#>  4     6  21.4
#>  5     8  18.7
#>  6     6  18.1
#>  7     8  14.3
#>  8     4  24.4
#>  9     4  22.8
#> 10     6  19.2
#> # … with 22 more rows

mtcars %>%
  group_mean(group_vars = vars(cyl), mean_vars = vars(mpg))
#> # A tibble: 3 x 2
#>     cyl   mpg
#>   <dbl> <dbl>
#> 1     4  26.7
#> 2     6  19.7
#> 3     8  15.1

# otherwise you would have to use the typical enquo() and !! for un-vars()'d variables, e.g. this totally fails

mtcars %>%
  group_mean(group_vars = cyl, mean_vars = mpg)
#> Error in quos(...): object 'cyl' not found
```

<sup>Created on 2019-02-09 by the [reprex package](https://reprex.tidyverse.org) (v0.2.1)</sup>