Dear R community,
I do know, that an R function is constructing a copy of any object passed as
argument into a function. I program on a larger S4 project for a package, and I
arrived at a point where I have to think a little harder on implementation
style (especially to spare users complex object handling).
I have a function foo(), taking as input arguments two S4 objects of different
class type
foo <- function(o1, o2) {
o1@att1 <- producesomething()
o2@att2 <- producesomethingelse()
}
Of course, this functions does not change the objects in the global
environment. Now I have two choices
1. Change the objects and return a list with both objects:
foo <- function(o1, o2) {
o1@att1 <- producesomething()
o2@att2 <- producesomethingelse()
l <- list(O1 = o1, O2 = o2)
return(l)
}
This is cumbersome for users, as they have then to assign the objects inside
the returned list to the symbols used in the global environment. But it is the
way intended by R.
2. Change the objects of the global environment inside the function:
foo <- function(o1, o2) {
o1@att1 <- producesomething()
o2@att2 <- producesomethingelse()
assign("o1", o1, .GlobalEnv)
assign("o2", o2, .GlobalEnv)
}
This is for users very practical, as the symbols used before refer now to
objects with changed attributes and can be used immediately. BUT: It is not
intended to be done like this.
I spared any solution using one function for every single object type, as this
becomes even more cumbersome than choice 1 above, in my opinion.
What is your opinion on the trade-off between user-friendly handling of objects
and R-intended programming style? Do I miss another choice, combining both?
Best
Simon
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