Question on R programming with dplyr and tidy evaluation - r

Question

Folks I have a couple of questions about how tidy evaluation works with dplyr
The following code produces a tally of cars by cylinder using the mtcars dataset:
mtcars %>%
select(cyl) %>%
group_by(cyl) %>%
tally()
With output as expected:
# A tibble: 3 x 2
cyl n
* <dbl> <int>
1 4 11
2 6 7
3 8 14
If I want to pass the grouping factor as variable, then this fails:
var <- "cyl"
mtcars %>%
select(var) %>%
group_by(var) %>%
tally()
with error message:
Error: Must group by variables found in `.data`.
* Column `var` is not found.
This also fails:
var <- "cyl"
mtcars %>%
select(var) %>%
group_by({{ var}}) %>%
tally()
Producing output:
# A tibble: 1 x 2
`"cyl"` n
* <chr> <int>
1 cyl 32
This code, however, works as expected:
var <- "cyl"
mtcars %>%
select(var) %>%
group_by(.data[[ var]]) %>%
tally()
Producing the expected output:
# A tibble: 3 x 2
cyl n
* <dbl> <int>
1 4 11
2 6 7
3 8 14
I have two questions about this and wondering if someone can help!
Why does select(var) work fine without using any of the dplyr tidy evaluation extensions, such as select({{ var }}) or select(.data[[ var ]])?
What is is about group_by() that makes group_by({{ var }}) wrong but group_by(.data[[ var ]]) right?
Thanks so much!
Matt.

Answer 1

It depends on how those functions work and accept input.
If you look at the documentation at ?select the relevant part for this question is -
These helpers select variables from a character vector:
all_of(): Matches variable names in a character vector. All names must be present, otherwise an out-of-bounds error is thrown.
any_of(): Same as all_of(), except that no error is thrown for names that don't exist.
So you can use all_of and any_of in select with character vectors hence you get a warning when you run mtcars %>% select(var)
Note: Using an external vector in selections is ambiguous.
ℹ Use all_of(var) instead of var to silence this message.
and no warning with mtcars %>% select(all_of(var)).
As far as group_by is concerned there is no such specific provision and you need to use mtcars %>% group_by(.data[[var]]).