Need help turning this function into a custom function

Question

I have this df with 4 columns: Name, screen_date, enroll_date, and screen2enroll_days

Name	screen_date	enroll_date	screen2enroll_days	enrollment_type
John	2020-08-20	2020-08-01	14	TypeX
Mike	2020-08-20	2020-08-01	14	TypeY
Sam	2020-10-20	2020-08-05	65	TypeY
Dan	2020-11-05	2020-08-05	90	TypeX

df <-
  data.frame(
    "Name" = c("John", "Mike", "Sam", "Dan"),
    "screen_date" = c("2020-08-01", "2020-08-20", "2020-10-20", "2020-11-05"),
    "enroll_date" = c("2020-08-01", "2020-08-01", "2020-08-05", "2020-08-05"),
    "screen2enroll_days" = c(14, 14, 65, 90),
    "enrollment_type" = c("TypeX", "TypeY", "TypeY", "TypeX")
  )

I want to create a function to read in one or more of my columns and create a new column called Action that uses the column screen2enroll_days to identify if a client needs a screening test. But ran into errors

Name	screen_date	enroll_date	screen2enroll_days	Action (new_col)
John	2020-08-14	2020-08-01	14	Up-to-date
Sam	2020-10-20	2020-08-05	65	Requires Screening
Dan	2020-11-05	2020-08-05	90	No Screening Required
Mike	2020-08-20	2020-08-01	14	No Screening Required

mutate_function <- function(df, new_col, my_col, my_col2, value1, value2, value3) {
    
    df %>% mutate(new_col = case_when(
             my_col <= 14 ~ "value1",
             my_col <= 14 & my_col2 != "TypeX" ~ "value2",
             (my_col > 14 & my_col <= 65) ~ "value3",
             TRUE ~ "value2")
    )}

mutate_function(df, Action, mycol = screen2enroll_days, my_col2 = enrollment_type, "Up-to-date", "Requires Screening", "No Screening Required")

and please share some data; use dput(yourdata) and paste the output in your edited question — Ben
– Ben, Commented Jul 20, 2021 at 18:46
@BillO'Brien: I added the info you requested. It was posted prematurely. Thanks! — Tseng
– Tseng, Commented Jul 20, 2021 at 20:33

Ben · Accepted Answer · 2021-07-21 00:21:02Z

I think there are a number of things to address to make this functional:

Inside a function you can dynamically access column names from your arguments with double curly braces ({{...}}). Alternatively, use can use the bang-bang operator with sym: !!sym(). Or, try .data[[variable]] to reference the variable from the pipe. Otherwise, it would seem you are trying to reference a column called my_col or my_col2 (for example) from df which don't exist.
If you want to set the new column values based on the value1 value2 or value3 arguments, you will want to leave off the quotes in your case_when statement
To dynamically set the new_col, use assignment (:=)
When calling the function, you may want to double check your argument names (such as mycol vs. my_col - note underscore)
Finally, you may want to double check your case_when logic. I believe the second line might never get called, as all circumstances when my_col is <= 14 will be considered as value1

library(dplyr)

mutate_function <- function(df, new_col, my_col, my_col2, value1, value2, value3) {
  df %>% mutate({{new_col}} := case_when(
    {{my_col}} <= 14 ~ value1,
    {{my_col}} <= 14 & {{my_col2}} != "TypeX" ~ value2,
    ({{my_col}} > 14 & {{my_col}} <= 65) ~ value3,
    TRUE ~ value2)
  )
}

mutate_function(df, 
                new_col = "Action", 
                my_col = "screen2enroll_days", 
                my_col2 = "enrollment_type", 
                "Up-to-date", 
                "Requires Screening", 
                "No Screening Required")

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