Not OP, but i think python is very far above R for engineering stuff. I built my early career on R and ran R user groups. R is great for one-off analyses, or low-volume controlled repetition like running the same report with new inputs.
For engineering stuff i want strong static analysis (type hints, pydantic, mypy), observability (logfire, structlog), and support (can i upload a package to my cloud package registry?).
For ML stuff, i want the libraries everyone else uses (pytorch, huggingface) because popularity brings a lot of development and documentation and obscure github issues the R clones lack.
Userbase matters. In R, hardly any users are doing any engineering; most R code only needs to run successfully one time. The ecosystem reflects that. The python-based ML world has the same problem, but the broader sea of python engineers helps counterbalance.
For engineering stuff i want strong static analysis (type hints, pydantic, mypy), observability (logfire, structlog), and support (can i upload a package to my cloud package registry?).
For ML stuff, i want the libraries everyone else uses (pytorch, huggingface) because popularity brings a lot of development and documentation and obscure github issues the R clones lack.
Userbase matters. In R, hardly any users are doing any engineering; most R code only needs to run successfully one time. The ecosystem reflects that. The python-based ML world has the same problem, but the broader sea of python engineers helps counterbalance.