I don't know if you've heard about it, because it is a relatively recent development, but the tidymodels ecosystem of packages (https://www.tidymodels.org) is also breaching the gap from data exploration/visualization to advanced modeling and machine learning in a way that feels really natural if you're used to the tidyverse way of doing things. It's developed by RStudio as the improved version of caret. I've been using it for differential gene expression analysis and it's a game changer in how much time it saves me.

What about python and its countless packages? (Honest question, I 'grew up' using python in an academic setting, but haven't caught up with the latest developments)

I use python for more data engineering, large scale processing, and PyTorch.

In my experience, the specific things R does well, python does it in a clunkier way.

Statistical software written by statisticians in academia, bioconductor, and quick prototyping is still much faster in R than in python.

My use case is to prototype in R, then move to python if things become more production rather than exploratory.

Python can of course do the same, its just so much clunkier to do it.

Python isn't really an advancement. But it's a more obvious choice for people with a background in software engineering. I have some hopes for Julia though.

As someone who used ruby (yes real ruby, not rails) before python or R, I definately think R is better for data science and ruby better for everything else. Sadly, I predict a future where python rules over everything.

I've been using https://exploratory.io/ a lot, which is r in a really nice wrapper where you can do everything point and click, by writing code by hand or a mix.

Tidyverse!