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

[Andrew Simmons]

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 using 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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