[levocardia]

>I agree that RStudio isn't too awful, but the packaging management and reproducibility situation in R is dire, even compared to Python.

I've had exactly the opposite experience. For R, I download R and install it, and download Rstudio and install it. Then when I need a new package I just install.packages("coolnewpackage") and it just works (TM). Occasionally I get info messages about packages being built in newer versions of R, and once a year or so I eventually get around to looking up how to use the updateR() function, but in five years of doing biostats in R I can't remember a single time I had a dependency issue.

Python, on the other hand, is a nightmare. Conda makes life a lot easier, but it is not easy to learn if you are not a software engineer (remember, R was made not just by statisticians, but for them as well). For many projects, my Python flow was something like...

Try creating a new conda env with the packages I think I need. Try starting the project, oops I don't have spyder-kernels installed. Oh, and my environment isn't compatible with it. How about just running it in VScode? Well now I don't have my variable explorer. How about Jupyter? How do I get Jupyter to find my conda env again? Oh wait I need this other library it's only on conda-forge, and then the conda environment solver fails. I guess I'll start from scratch with a new conda env, and maybe after several trial-and-error sessions of carefully composing the correct "conda create -n ..." incantation in a text editor before copy-pasting them to the command line, I might get the environment I need up and running, after conda finishes its 10-minute compatibility search and downloads 80 GB of python libraries.

And using conda is the easy way of doing it! Don't even get me started on pip and venv...

[th0ma5]

With R on Windows, you get some binary dependencies, but on Linux you need the system libraries for any package that uses an external library. R uses the HTTP headers to determine which binary package to send you and no roll-your-own package system for virus scanning and the like supports either the Conda contrib patterns nor the R HTTP code binary scheme. I think Conda used to be kind of cool, but I have the same problems, and its position was always to make a ton of assumptions about what you want to do. R is like that... Sensible and automatic defaults that you can't find or aren't told about.

[0thgen]

I have never needed anything more than pip in 8 years of development, and have always run into issues with r packages (every new version of r seems to break 30% of existing tidyverse packages)

[disgruntledphd2]

Do you do much DS/ML in Python? I definitely agree that pip is totally fine otherwise.

At work, I've been giving out about pip to one of our DEs for a while, and when he needed to upgrade a bunch of DS packages he finally started coming around to my opinion.

[extr]

Great summary of the situation. If you've ever been in the position of trying to explain to a bunch of R users why Python packaging is so much harder to deal with, you know the struggle. R/RStudio really makes it incredibly easy to get up and going for non-developers in a way that's probably hard to appreciate for many people on HN who are SWEs by trade.

[listenallyall]

Your own experience seems to disprove the claim that conda makes running analytical/numerical code easier in Python. Simple venv and pip really is the simpler choice.