gistfile1.txt

get_counts <- function(dataset, var){

    dataset %>%
      group_by(am) %>%
      mutate("change_{{var}}" := n_distinct({{var}})) %>%
      mutate(total = across(starts_with("change"), identity)) %>%  
      mutate(total = unlist(total)) %>%  
      filter(total == 11) %>%  
      select(-total)

  }

get_counts(mtcars, hp)

@b-rodrigues this works, but I'm not entirely sure it's the most efficient way:

get_counts <- function(dataset, var) {
  var <- rlang::enexpr(var)
  new_var <- glue::glue("change_{eval(quote(var))}")
  dataset %>%
    dplyr::group_by(am) %>%
    dplyr::mutate({{new_var}} := dplyr::n_distinct({{var}})) %>% 
    dplyr::filter(.data[[new_var]] == 11)
}

@b-rodrigues this works, but I'm not entirely sure it's the most efficient way:

get_counts <- function(dataset, var) {
  var <- rlang::enexpr(var)
  new_var <- glue::glue("change_{eval(quote(var))}")
  dataset %>%
    dplyr::group_by(am) %>%
    dplyr::mutate({{new_var}} := dplyr::n_distinct({{var}})) %>% 
    dplyr::filter(.data[[new_var]] == 11)
}

This is quite nice, and more efficient than my "solution"! Thanks