Assigning Rcpp::XPtr on the R side

I'm learning about external pointers, XPtr, in Rcpp. I made the following test functions:

// [[Rcpp::export]]
Rcpp::XPtr<arma::Mat<int>> create_xptr(int i, int j) {
  arma::Mat<int>* ptr(new arma::Mat<int>(i, j));
  Rcpp::XPtr<arma::Mat<int>> p(ptr, true);
  return p;
}

// [[Rcpp::export]]
void fill_xptr(Rcpp::XPtr<arma::Mat<int>>& xptr, int k) {
  (*xptr).fill(k);
}

// [[Rcpp::export]]
arma::Mat<int> return_val(Rcpp::XPtr<arma::Mat<int>>& xptr) {
  return *xptr;
}

Now on the R side I can of-course create an instance and work with it:

x <- create_xptr(1000, 1000) # (1)

Say, for some reason I accidentally called create_xptr again and assigned the result to x, i.e

x <- create_xptr(1000, 1000) # (2)

Then, I have no longer access to the pointer i created in (1) which makes sense and hence, I cannot free the memory. What I would like is, that the second time (2) it just overwrite the first one (1). And secondly, if I create an external pointer in some local scope (say a simple for loop), should the memory used then be freed automatically when it goes out of scope? I've tried the following:

for (i in 1:3) {
  a <- create_xptr(1000, 100000)
  fill_xptr(a, 1)
}

But it just adds to the memory usage for each i.

I have tried reading some code on different git-repos, old posts here on stack read a little about finalizers and garbage collection in R. I can't seem to put together the puzzle.

Solution

We use external pointers for things that do not have already existing interfaces such as database connections or objects from other new libraries. You may be better off looking at some existing uses of XPtr (or the other external pointer variants in some other packages, there are two small ones on CRAN).

And I don't think I can think of an example directly referencing this in R. It is mostly for "wrapping" external objects to hold on to them and to pass them around for further use elsewhere. And you are correct that you need to read up a little on finalizers. I find reading Writing R Extensions, as dense as it is, to be the best source because you need to get the initial "basics" in C right first.