[krastanov]

Interoperability between libraries that expect your code to be pure R / pure Python. If you use RCpp or Cython or CPython then you lose much of the magic behind the language that enables the cool (but frequently slow) features. My biggest pain point in this situation: you can not use SciPy or Pillow or Cython code with Jax/Pytorch/Tensorflow (except in very limited fashion).

Differential equation solvers that need to take a very custom function that works on fancy R/Python objects is another example of clumsiness in these drop-to-C-for-speed languages. It works and as a performance-nerd I enjoy writing such code, but it is clumsy.

That type interoperability is trivial in Julia.

[tylermw]

Once your Rcpp code is compiled, it's almost indistinguishable from base R (when you're calling it). All R functions eventually end up calling R primitives written in C, and Rcpp just simplifies the process of writing and linking C/C++ code into the R interpreter.

The only difficulty with Rcpp-based R packages is you have to ensure the target system can compile the code, which means having a suitable compiler available.

[krastanov]

I wonder how much does it differ from python's use of C or Cython (I have only superficial R skills). The prototypical example of why Python's C prevents interoperability is how the introspection needed by Jax or Tensorflow (e.g. for automatic GPU usage or automatic differentiation) fails when working on Scipy functions implemented in C.

For instance, I imagine there is an R library that makes it easy to automatically run R code on a GPU. Can that library also work with Rcpp functions?

[oxinabox]

> it's almost indistinguishable from base R (when you're calling it).

I am very surprised by this. Given how R is extremely dynamic. and has things like lazy-evaluation, that you can rewrite before it is called with substitute. Which I am sure some packages are using in scary and beautiful ways.