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Comment by vitorsr | Hacker News Reader

This has been said before multiple times over but with these languages it is rarely about the languages themselves but their ecosystems:

https://cran.r-project.org/web/packages/available_packages_b...

To go from R to Julia, as an example, one would have to give up on a hundred or so high-quality packages potentially related to their activities.

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Having looked at a large number of R packages source code, I do hesitate to freely label R packages as generally high-quality. I’ve been operating on the “trust, but verify” principle

You don't have to. Just use Rcall.

Of course R has been here longer. Eleven years after its creation, R had fewer than 500 packages. Julia was released in 2012 and today has over 7,000 packages.

    R
    Cited in: 8,589 Publications
    7,353 [Citing Publications in] Statistics (62-XX)
    https://zbmath.org/software/771

    Julia
    Cited in: 442 Publications
    64 [Citing Publications in] Statistics (62-XX)
    https://zbmath.org/software/13986

That is exactly the issue. No language comes close to the richness of the R statistical package ecosystem.

It's not an issue at all with RCall and PyCall.

Of course it is, sometimes one needs to pass data represented as R objects (like zoo) to R functions, and receiving something that is an R object and to be worked on by R calls before being passed on. That is very clumsy to manage with RCall.

A perfect example of this is image processing.

Is is delightful in working with spatial raster data. It's an 'it just works' space, especially with raster and stars. There have been several attempts to disrupt the raster package but none have really stuck.

Granted, it's not fast. However trying to do the same kinds of things in python with rasterio is just cludgy. And half the time you end up making system calls to gdal anyways. Guess what? I can make the system calls from R too.

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