outer_func uses tidy evaluation for col_rfs and dflt_flag, i.e. it is possible to do col_rfs = dplyr::starts_with("col").
library(magrittr)
outer_func <- function(dat, col_rfs, dflt_flag){
col_rfs_str <- tidyselect::eval_select(rlang::enquo(col_rfs), dat) %>% names
freqs <- purrr::map(col_rfs_str, \(x) get_freq(dat, !!rlang::sym(x), {{dflt_flag}}))
return(freqs)
}
get_freq <- function(dat, rf, dflt_flag){
freq <- dat %>%
dplyr::group_by({{rf}}) %>%
dplyr::summarise(count = dplyr::n(), dr = mean({{dflt_flag}}), .groups = "drop")
return(freq)
}
dat <- tibble::tibble(dflt = sample(c(0,1), size = 1000, replace = TRUE), rf_1 = sample(c("a","b"), size = 1000, replace = TRUE), rf_2 = sample(c(1,2), size = 1000, replace = TRUE))
outer_func(dat, dplyr::starts_with("rf"), dflt)
What is a good pattern which avoids converting col_rfs to a string via tidyselect::eval_select(rlang::enquo(col_rfs), dat) %>% names and then back to an unquoted column via !!rlang::sym(x) when using purrr::map?
It is important to keep the inputs to outer_func & get_freq as unquoted strings.
You can use .data[[x]] like in the following
outer_func <- function(dat, col_rfs, dflt_flag){
dat |>
dplyr::select(all_of(col_rfs)) |>
names() |>
purrr::map(function(x) get_freq(dat, .data[[x]], {{dflt_flag}}))
}
This gives the same result as your function.
About once a week I end up referring to the Programming with dplyr article.