A better data masking indirection example in the `programming.Rmd` vignette: `filter(df, {{ var }})`

[wbzyl]

This code does not work:

f <- function(df, var) filter(df, {{ var }})
f(mtcars, am)
# Error in `filter()`:
# ℹ In argument: `vs`.
# Caused by error:
# ! `..1` must be a logical vector, not a double vector.

This code works:

f <- function(df, var) filter(df, {{ var }} == 0)
f(mtcars, vs)

[hrryt]

The above code is working as expected, as filter() expects logical vectors.
See the argument documentation for ... (?dplyr::filter):

     ...: <‘data-masking’> Expressions that return a logical value, and
          are defined in terms of the variables in ‘.data’. If multiple
          expressions are included, they are combined with the ‘&’
          operator. Only rows for which all conditions evaluate to
          ‘TRUE’ are kept.

filter(df, {{ var }}) therefore works well as an example of indirection, as long as var evaluates to a logical vector:

library(dplyr)

(df <- tibble(a = 1:3, b = a != 2))
#>  # A tibble: 3 × 2
#>       a b    
#>   <int> <lgl>
#> 1     1 TRUE 
#> 2     2 FALSE
#> 3     3 TRUE 

f <- function(df, var) filter(df, {{ var }})

f(df, b)
#>  # A tibble: 2 × 2
#>       a b    
#>   <int> <lgl>
#> 1     1 TRUE 
#> 2     3 TRUE

f(df, a == 1)
#>  # A tibble: 1 × 2
#>       a b    
#>   <int> <lgl>
#> 1     1 TRUE