Check the number of arguments before a certain argument in R - r

Question

Suppose we want our function to be able to deal with two scenarios:
somefun = function(x, y, method, ...) {
res = dowith(x, y)
res
}
somefun = function(z, method, ...) {
x = z$v1
y = z$v2
res = dowith(x, y)
res
}
How can we make somefun aware of the difference between these two situations?

Answer 1

If you can guarantee that x is never going to be a list, you could just use is.list(x) to determine which version of the function is being called. Otherwise, you can use missing:
somefun<-function(x,y,method,...){
if(missing(y)){
cat("Using List version\n")
y<-x$y
x<-x$x
}
else{
cat("Using normal version\n")
}
c(x,y)
}
> somefun(list(x=1,y=2),method="a method")
Using List version
[1] 1 2
> somefun(1,2,method="a method")
Using normal version
[1] 1 2
>
However, be aware that if you do this, and you want to use the list version of the function, then method and everything after it have to be passed in by name, otherwise R is going to bind them to y:
> somefun(list(x=1,y=2),"a method")
Using normal version
$x
[1] 1
$y
[1] 2
[[3]]
[1] "a method"
> somefun(list(x=1,y=2),method="a method",5)
Using normal version
$x
[1] 1
$y
[1] 2
[[3]]
[1] 5
> somefun(list(x=1,y=2),method="a method",q=5)
Using List version
[1] 1 2

Answer 2

I don't know of an automatic way to do this, but when dealing with these types of situations, it is sometimes helpful to use switch. Here's a basic example:
somefun <- function(x, y = NULL, type = c("DF", "vecs"), method = NULL, ...) {
switch(type,
DF = sum(x[["v1"]], x[["v2"]]),
vecs = sum(x, y),
stop("'type' must be either 'DF' or 'vecs'"))
}
somefun(x = 10, y = 3, type="vecs")
# [1] 13
somefun(x = data.frame(v1 = 2, v2 = 4), type="DF")
# [1] 6
somefun(x = data.frame(v1 = 2, v2 = 4), type = "meh")
# Error in somefun(x = data.frame(v1 = 2, v2 = 4), type = "meh") :
# 'type' must be either 'DF' or 'vecs'
In the above, we're expecting that the user must enter a type argument where the acceptable values are "DF" or "vecs", and where a different set of operations has been defined for each option.
Of course, I would also script out a set of different scenarios and use some condition checking at the start of the function to make sure things will be working as expected. For instance, if you expect that most of the times, people will be inputting a data.frame, you could do something like if (is.null(y) & is.null(type)) temp <- "DF" (or insert a try type statement in there). At the end of the day, it also comes down to whether you can predict a sensible set of default values.
If your functions are complicated, you might want to separate out the steps that go into the switches into separate functions as this would probably lead to more readable (and more easily reusable) code.