RCall.jl VS cxx

I don't use it personally, but RCall.jl[1] is the main R interop package in Julia. You could call libraries that have no equivalent in Julia using that and write your own analyses in Julia instead.

[1] https://github.com/JuliaInterop/RCall.jl

You can have your cake and eat it with the likes of

* PythonCall.jl - https://github.com/cjdoris/PythonCall.jl

* NodeCall.jl - https://github.com/sunoru/NodeCall.j

* RCall.jl - https://github.com/JuliaInterop/RCall.jl

I tend to use Julia for most things and then just dip into another language’s ecosystem if I can’t find something to do the job and it’s too complex to build myself

To inter-operate Julia with the R language, the RCall package is used. Run the following commands on the Julia REPL

https://github.com/JuliaInterop/RCall.jl may help

If you need to bindings to your existing R packages then Julia is the way. Check out RCall.jl

I have no experience with R, but maybe this will be of use: https://github.com/JuliaInterop/RCall.jl

You can use RCall to use R from Julia: https://github.com/JuliaInterop/RCall.jl

I worked with R and Python during the last 3 years but learning and dabbling with Julia since 0.6. Since the availability of [PyCall.jl] and [RCall.jl], the transition to Julia can already be easier for Python/R users.

I agree that most of the time data wrangling is super confortable in R due to the syntax flexibility exploited by the big packages (tidyverse/data.table/etc). At the same time, Julia and R share a bigger heritage from Lisp influence that with Python, because R is also a Lisp-ish language (see [Advanced R, Metaprogramming]). My main grip from the R ecosystem is not that most of the perfomance sensitive packages are written in C/C++/Fortran but are written so deeply interconnect with the R environment that porting them to Julia that provide also an easy and good interface to C/C++/Fortran (and more see [Julia Interop] repo) seems impossible for some of them.

I also think that Julia reach to broader scientific programming public than R, where it overlaps with Python sometimes but provides the Matlab/Octave public with an better alternative. I don't expected to see all the habits from those communities merge into Julia ecosystem. On the other side, I think that Julia bigger reach will avoid to fall into the "base" vs "tidyverse" vs "something else in-between" that R is now.

[PyCall.jl]: https://github.com/JuliaPy/PyCall.jl

[RCall.jl]: https://github.com/JuliaInterop/RCall.jl

[Julia Interop]: https://github.com/JuliaInterop

[Advanced R, Metaprogramming] by Hadley Wickham: https://adv-r.hadley.nz/metaprogramming.html

There are cxx and autocxx, what else do you propose to do?

I would like to see a comparison of how this compares to Rust. In terms of interoperability it has Cxx (https://cxx.rs) to offer safe bindings to C++ but also has great support for Android, Linux and many other systems. You don't even need to hack together Windows bindings (as explained in the blog post) because Microsoft offers official bindings (https://crates.io/crates/windows). I'm not sure if I'd call it a superpower if any potential interoperability has to be written to be used (compared to it already being available). Or rather, in comparison to what is interoperability a Swift superpower? Certainly not C++ or C which can be used in a far wider set of targets.

We selected Qt as a cross-platform solution. The C++/Rust interface is the clunkiest and ugliest part of the application, and rather complex because some state is shared between several windows in the GUI and several threads in the backend, and any component might modify that state at any time, and updates have to be transmitted to the other components without introducing inconsistencies. Using cxx [1] helped a little, though.

The project began in 2020, and I'm not sure what I'd choose as a GUI framework today – definitely not Qt Widgets, though.

[1] https://cxx.rs/

If the build process for the C library isn't too involved I recommend using cxx bridge (https://cxx.rs/) and letting cargo handle the build and linking. cxx basically allows you to describe the bidirectional interface (although it sounds like you only need 1 direction, which is fine too) in Rust code and it provides a "good enough" API for compiling C code inside the build.rs file.

The tooling for the first kind -- calling Rust from another language -- is a bit less developed, and tends to rely on code generation that doesn't necessarily produce a natural C API. cbindgen, uniffi, cxx, and Diplomat all take this course.

I would like to utilize OMPL's functionality in Rust code, so I want to call into OMPL C++ code somehow in Rust. I've seen two (non-mutually-exclusive) options so far: - rust-cpp, which allows you to write C++ code in Rust within the cpp!() macro. - cxx, which allows you to define both sides of the FFI boundary manually (as opposed to bindgen's automatic generation).

I'm not sure how to do this in cxx; issues like https://github.com/dtolnay/cxx/issues/447 suggest that this isn't settled yet?

I use non-vendored dependencies for the Buck build in https://github.com/dtolnay/cxx.

There's also the cpp and cxx crates for doing C++/Rust interop, but they probably aren't appropriate to use in all cases. The C ABI is definitely the safest way to go unless you're really trying to marry Rust and C++ code bases, not just writing library bindings.

There's also cxx (can't vouch for it personally but it claims to make things a lot easier) https://github.com/dtolnay/cxx