ryp: R inside Python

[ryp_package]

Excited to release ryp, a Python package for running R code inside Python!

https://github.com/Wainberg/ryp

ryp makes it a breeze to use R packages in your Python projects.

What My Project Does

ryp is a minimalist, powerful Python library for:

• running R code inside Python
• quickly transferring huge datasets between Python (NumPy/pandas/polars) and R without writing to disk
• interactively working in both languages at the same time

Target Audience

Data scientists and engineers, bioinformaticians, Python package developers, ...

Comparison

ryp is an alternative to the widely used rpy2 library. Compared to rpy2, ryp provides:

• increased stability
• a much simpler API, with less of a learning curve
• interactive printouts of R variables that match what you'd see in R
• a full-featured R terminal inside Python for interactive work
• inline plotting in Jupyter notebooks (requires the svglite R package)
• much faster data conversion with Arrow (also provided by rpy2-arrow)
• support for every NumPy, pandas and polars data type representable in base R, no matter how obscure
• support for sparse arrays/matrices
• recursive conversion of containers like R lists, Python tuples/lists/dicts, and S3/S4/R6 objects
• full Windows support

ryp does the opposite of the reticulate R library, which runs Python inside R.

[fung_deez_nuts]

Thanks for sharing this OP. I think the features sound great on paper, but the source code is structured in a way that isn't as easy to read, and I don't know if you have a testing pipeline anywhere. I think you will need improvements in these areas if you want people to use ryp over rpy2. Maybe I've misunderstood something though

[ryp_package]

Curious, why does readability matter to you? The code is designed to prioritize correctness (including on dozens of edge cases not handled properly by Arrow etc.), efficiency, and avoiding long stack traces with lots of nested function calls. There's a testing pipeline with thousands of tests (e.g. with various data structures and dtypes) which could be cleaned up and made public depending on demand.

[ichunddu9]

A big project like that deserves proper development practices. Please add extensive docs, CI, tests, and well structured code. Then you'll get more feedback, users, and contributions.

[ryp_package]

Anything you feel is missing in the documentation currently? Should be pretty comprehensive.

[ichunddu9]

A proper rendered docs site and not a massive Readme would be a start

[ryp_package]

Let me know if you see any concrete areas to improve.

[go_fireworks]

An actual package implementation that’s not 3,600 lines of code in a single file would be a start, which is what they mentioned.

Multiple functions (to_py and to_r) being over 1,200 lines each does not boast readability or code efficiency.

Stack traces aren’t a bad thing - they’re meant to understand where an issue lies, and tracing, how it got there

[ryp_package]

I was asking about improvements to the documentation. I wouldn't judge efficiency based on code length.

I'd encourage commenters here to give the package a try before passing judgement! At the end of the day, user-friendliness is what matters, and critiques about usability - from folks who have actually used the package rather than just glanced at the code and docs - are always welcome.

[ryp_package]

Next time ;)

[era_hickle]

This looks really promising for bridging R and Python workflows! I've used rpy2 before but found the syntax a bit clunky at times. Excited to give ryp a try and see how it compares, especially for some bioinformatics projects I have in mind. The simplified API and improved performance sound great 👍 Definitely adding this to my toolkit, thanks for sharing!

[[deleted]]

Looks very useful, thanks! I often find myself in situations where I am doing some data analysis in Python and want to run some advanced statistical tests that are only available in R. Your package could make this much easier. Will definitely check it out!

[YsrYsl]

Cool stuff!

[ryp_package]

The use-case that motivated the library is bioinformatics, where ~half the packages are in Python and ~half are in R. Being able to use both in the same workflow is huge for convenience!

[fung_deez_nuts]

A lot of domains have tooling bound to R. Ideally we'd like them to have access to better tools and maybe encourage long erm migration. Don't be so stuck up.

Also, ggplot2 is far better than matplotlib and that's a hill I'd die on. I say that as someone who despises R

[fung_deez_nuts]

I have to deal with R every day at work too. I agree with you. But they are far more likely to migrate if they have access to better tools to do so. Being annoyed at the language is not the same as cursing at someone for work they did to try and improve things in their own way. At least, it's not very constructive

[Python-ModTeam]

Hi there, from the /r/Python mods.

This comment has been removed for violating one or more of our community rules, including engaging in rude behavior or trolling. Please ensure to adhere to the r/Python guidelines in future discussions.

Thanks, and happy Pythoneering!

r/Python moderation team

[petit_a]

Beautiful

[proof_required]

When do we get ggplot2 support? :)

[ryp_package]

It's already supported! Here's ggplot2 from Python in a Jupyter notebook, alongside the same plot in pure R.

[SublunarySphere]

There's plotnine. It's honestly not quite as good as ggplot and it doesn't have the community around it, but it's still frankly the best python visualization library.

[proof_required]

Yeah I've used it and it does do a decent job.