[R-pkg-devel] Question on best approach to develop R package that wraps old complex Python2 software

I would suggest the reticulate library in R. The few most important for
your case are reticulate::use_python_version and reticulate::import.
For example, in your R package, you should start with:


# change this to the name of the module you need
numpy <- NULL


.onLoad <- function (libname, pkgname)
{
    reticulate::use_python_version("2.7")  # change this as you need to


    # .onLoad happens before the namespace is locked, so this is legitimate
    numpy <<- reticulate::import("numpy", delay_load = list(
        on_error = function(c) stop(
            "unable to import 'numpy', try ",
            sQuote("reticulate::py_install(\"numpy\")"),
            " if it is not installed:\n  ",
            conditionMessage(c)
        )
    ))
}


when your package's namespace is loaded, this will load the version of
python you need to use, and will lazy-import the module you need for your
python session.

On Tue, Jan 25, 2022 at 8:52 AM Alexandru Voda <alexandru.voda at seh.ox.ac.uk>
wrote:

Hi!

How would one best write an R wrapper package over a complex Python2
software (such as https://github.com/bulik/ldsc), that is still very
widely used in statistical genetics?

I'm writting an R package (that currently passes all --as-cran checks) for
multiple other C++ softwares on the same topic as the one above, but this
Python2 one I've difficulties with - it just looks like a bunch of hackish
system() calls... And while it works on Linux and Mac, I've no idea whether
it'd work on Windows.

While it may seem easy to dismiss, actually LDSC is widely used in the
statistical genetics field, and lots of people find it difficult to work
with because of all the dependency files and weirdly documented commands,
and because... well... Python2...

Any tips? Or do you know anyone that I should contact/ask?

Best wishes,
Alexandru

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