understanding rlang: mutate with variable col name and variable column

Question

I'd like to define a function that takes a data.frame and a column name and returns the data.frame in with that column transformed (e.g. to lowercase). When the column name is known in advance, this is simple:

diamonds %>% mutate(cut = tolower(cut))

How do I define a function foo, such that:

col <- "cut"
foo(diamonds, col) 

does this same behavior? (Not looking for a base R or data.table answer since I want to preserve dplyr's ability to translate this into a lazily-evaluated SQL call).

If I just wanted my function to work using: foo(diamonds, cut), I just need enquo and !!

foo <- function(df, col){
    x <- enquo(col)
    mutate(df, !!x := tolower(!!x))
}

If I want to take the column name in quotes, foo(diamonds, "cut"), adding ensym is sufficient:

foo <- function(df, col){
    col <- ensym(col)
    x <- enquo(col)
    mutate(df, !!x := tolower(!!x))
}

but this fails when given a variable for an argument:

col <- "cut"
foo(diamonds, col) 

Error in ~col : object 'col' not found

What am I missing that can evaluate the variable?

Answer 1

You can also avoid tidy eval entirely here by using mutate_at().

library(tidyverse)

(x <- tibble(
  num = 1:3,
  month = month.abb[num]
))
#> # A tibble: 3 x 2
#>     num month
#>   <int> <chr>
#> 1     1 Jan  
#> 2     2 Feb  
#> 3     3 Mar

x %>%
  mutate(month = tolower(month))
#> # A tibble: 3 x 2
#>     num month
#>   <int> <chr>
#> 1     1 jan  
#> 2     2 feb  
#> 3     3 mar

foo <- function(df, col) {
  mutate_at(df, .vars = col, .funs = tolower)
}

foo(x, "month")
#> # A tibble: 3 x 2
#>     num month
#>   <int> <chr>
#> 1     1 jan  
#> 2     2 feb  
#> 3     3 mar

this <- "month"
foo(x, this)
#> # A tibble: 3 x 2
#>     num month
#>   <int> <chr>
#> 1     1 jan  
#> 2     2 feb  
#> 3     3 mar

^{Created on 2019-03-09 by the reprex package (v0.2.1.9000)}