Pass multiple column names in function to dplyr::distinct() with Spark - r

Question

I want to specify an unknown number of column names in a function that will use dplyr::distinct(). My current attempt is:
myFunction <- function(table, id) {
table %>%
dplyr::distinct(.data[[id]])
}
I'm trying the above [.data[[id]]] because the data-masking section of this dplyr blog states:
When you have an env-variable that is a character vector, you need to index into the .data pronoun with [[, like summarise(df, mean = mean(.data[[var]])).
and the documentation for dplyr::distinct() says about its second argument:
<data-masking> Optional variables to use when determining uniqueness. If there are multiple rows for a given combination of inputs, only the first row will be preserved. If omitted, will use all variables.
Spark
More specifically, I'm trying to use this function with Spark.
sc <- sparklyr::spark_connect(local = "master")
mtcars_tbl <- sparklyr::copy_to(sc, mtcars, "mtcars_spark")
##### desired return
mtcars_tbl %>% dplyr::distinct(cyl, gear)
# Source: spark<?> [?? x 2]
cyl gear
<dbl> <dbl>
1 6 4
2 4 4
3 6 3
4 8 3
5 4 3
6 4 5
7 8 5
8 6 5
##### myFunction fails
id = c("cyl", "gear")
myFunction(mtcars_tbl, id)
Error: Can't convert a call to a string
Run `rlang::last_error()` to see where the error occurred.
Following this comment, I have other failed attempts:
myFunction <- function(table, id) {
table %>%
dplyr::distinct(.dots = id)
}
myFunction(mtcars_tbl, id)
# Source: spark<?> [?? x 1]
.dots
<list>
1 <named list [2]>
#####
myFunction <- function(table, id) {
table %>%
dplyr::distinct_(id)
}
myFunction(mtcars_tbl, id)
Error in UseMethod("distinct_") :
no applicable method for 'distinct_' applied to an object of class "c('tbl_spark', 'tbl_sql', 'tbl_lazy', 'tbl')"

Answer 1

Distinct applies to all columns of a table at once. Consider an example table:
A B
1 4
1 4
2 3
2 3
3 3
3 5
It is not clear what applying distinct to only column A, but not column B should return. The following example is clearly not a good choice because it breaks the relationship between columns A and B. For example, there is no (A = 2, B = 4) row in the original dataset.
A B
1 4
2 4
3 3
3
3
5
Hence the best approach is to select only those columns you want first, and then take the distinct. Something more like:
myFunction <- function(table, id) {
table %>%
dplyr::select(dplyr::all_of(id)) %>%
dplyr::distinct()
}