It is generally a good idea to use TeX Live via a dev container because this enforces the same setup for all coauthors. The container is compatible with but not part of the SoDa Replicator itself because it is not technically required and because it demands prior knowledge of WSL, Docker, etc. 2/2

Hence, the package itself is very quick and simple to install from source. It is easy to use and comes with a number of examples.

Yes, I agree, Python's package management is a terrible user experience. CRAN feels ok to users, but is a bad package developer experience 😅.

That depends on the field. Many people in my environment (economists) have migrated to Python.
Modern R and Python packages are just C and C++ wrappers anyway. Native R and Python are not performant enough for modern tasks.

Statistical programs in Python tend to build on numpy and pandas. Given how mature and stable these packages are, I would not consider these dependencies a major drawback of Python nowadays.
Base R also only has dense matrices. More elaborate matrix types require a package.

Fair point 😄.

Unfortunately, Julia never gained enough traction and never developed a reasonably sophisticated package ecosystem. So, users will stick to the mediocre choices of #rstats and Python. 6/n

According to my own observation, there is a considerable shift from #rstats to Python among empirical researchers. A key driver of this is the Python-first nature of machine learning APIs. I think that #rstats will lose market share to Python, but it will not die soon. 5/n

For now, the simplicity of #rstats makes it a better choice than Python for many users who do not need the versatility of a general purpose programming language. It keeps attracting users from outdated commercial software like Stata or SPSS. 4/n

The poor management of CRAN does not help either. Both R Core and CRAN need to fundamentally change for #rstats remain popular. 3/n

Compare the release notes of base R and #Python over the course of the past years. Python is leaping forward while base R is devoid of innovation. #rstats is kept alive and thriving by its packages. Unfortunately, the severe limitations of base R also limit the scope of package development. 2/n

😅 Considering R Core's absence of innovation in base R development, a reimplementation in Rust would certainly be a surprise.

We already got complaints from a data editor when code using pre-processed data took a few hours to run on his laptop. Code on raw data does not run at all or takes weeks on end user devices.