Can we create a 2-dimensional array for matching combinations of two function arguments?
For example, if I write a function with 1 argument (in addition to data input argument):
choose_procedure <- function(x, what_to_do) {
switch(what_to_do,
"mean" = {...}, # mean(x) or mean(x, na.rm = TRUE) or weighted.mean(x)
"median" = {...}, # median(x)
"square" = {...}, # x * x or x ^ 2
"unique" = {...}, # unique(x)
"log" = {...} # log(x) or log10(x)
)
}
I added inline comments to imply that there could be more than one choice per what_to_do input.
When what_to_do = "mean", should it be mean(x, na.rm = TRUE) or mean(x, na.rm = FALSE)?
Likewise, when what_to_do = "log", should it be log(x) or log10(x)? Etc.
To handle this, I thought to introduce another argument to choose_procedure(), called "scenario". So if the call to choose_procedure() is:
choose_procedure(x = mtcars$mpg, what_to_do = "log", scenario = "A")
Then it would execute log(mtcars$mpg).
But if the call is
choose_procedure(x = mtcars$mpg, what_to_do = "log", scenario = "B")
then it would execute log10(mtcars$mpg).
An example with just "log" and "scenario" describes a 2x2 array:
"what_to_do" has 2 options: log() or log10()"scenario" has two options: "A" or "B"Clearly, this could be handled with 4 if-statements (one for each combination), but will become very difficult to program if we have more combinations (as in choose_procedure() example I opened with).
So I have two questions:
"what_do_to", "scenario", "sub_scenario". Etc.choose_procedure <- function(x, FUN, ...){
if(...length()) FUN(x, ...)
else FUN(x)
}
x <- c(1,3,5,NA, 10)
choose_procedure(x, mean)
[1] NA
choose_procedure(x, mean, na.rm = TRUE)
[1] 4.75
choose_procedure(x, log)
[1] 0.000000 1.098612 1.609438 NA 2.302585
choose_procedure(x, log10)
[1] 0.0000000 0.4771213 0.6989700 NA 1.0000000
This is one of those things where there are many ways to tackle the problem, and the best one is very likely to be dependant on the context you actually want to use it in. However, in the situation you outlined, here's how I would approach it:
choose_procedure <- function(x, ...) {
# Define a table of options
choices <- tibble::tribble(
~what_to_do, ~scenario, ~result,
"mean", "A", mean,
"mean", "B", ~mean(., na.rm = TRUE),
"mean", "C", weighted.mean,
"median", "A", median,
"square", "A", ~. * .,
"square", "B", ~. ^ 2,
"unique", "A", unique,
"log", "A", log,
"log", "B", log10
)
# Filter the table down to the desired option
choice <- dplyr::filter(choices, ...)
# Stop if no options available
if (nrow(choice) == 0) {
stop("No such option available")
}
# Warn if multiple options available, and use first
if (nrow(choice) > 1) {
choice <- head(choices, 1)
warning("More than one option available, using first scenario")
}
# Transform any purrr-style lambda functions to normal functions
fun <- rlang::as_function(choice$result[[1]])
# Perform the calculation
fun(x)
}
choose_procedure(x = mtcars$mpg, what_to_do == "log", scenario == "B")
#> [1] 1.322219 1.322219 1.357935 1.330414 1.271842 1.257679 1.155336 1.387390
#> [9] 1.357935 1.283301 1.250420 1.214844 1.238046 1.181844 1.017033 1.017033
#> [17] 1.167317 1.510545 1.482874 1.530200 1.332438 1.190332 1.181844 1.123852
#> [25] 1.283301 1.436163 1.414973 1.482874 1.198657 1.294466 1.176091 1.330414
Created on 2021-10-25 by the reprex package (v2.0.0)