Rasmus Bååth writes about Bayesian statistics, R, and data analysis, and has built small tools such as a browser MCMC library.
Commenters recommend his article on why pandas feels clunky coming from R, and his online Bayesian alternative to the t-test.
> We found R has a really hard time integrating into data pipelines and was best used as a standalone tool by individuals
In my org we have several 100% R teams (including mine) that have been developing and maintaining business-critical, data-intensive applications for a decade now. We don't find R difficult to integrate into data pipelines. We write our data pipelines in R, and we find it very efficient to do so. They talk to databases, APIs, command line tools, etc without issue.
Doing what we do in Python is unimaginable, especially if pandas is the tabular lingua franca in the team. I vehemently agree with this article on the clunkiness of pandas from a sister comment: https://www.sumsar.net/blog/pandas-feels-clunky-when-coming-from-r/. Compared to dplyr and the tidyverse, pandas very noticeably gets in your way rather than being a tool of thought. (For what it's worth, there are other teams in my org that use Python for entirely justified reasons, and they use polars these days, not pandas.)
If I had to complain about anything in R these days, it would be the increasing complexity and illegibility of error messages. Tidyverse tracebacks are often dozens or hundreds of lines. This is made much worse if you have a web app in the Shiny framework, as Shiny seems to mangle and garble what little useful information you can get (my kingdom for an error with a file name and line number). Even outside of advanced packages like Shiny, the reporting of error messages suffers…
- Marginal Revolution (Tyler Cowen & Alex Tabarrok): economics https://marginalrevolution.com
- John Cochrane: economics https://johnhcochrane.blogspot.com
- Interfluidity (Steve Randy Waldman): all over the place, but mostly economics-focused https://www.interfluidity.com
- FT Alphaville: finance and economics, free signup required https://ftalphaville.ft.com
- Money Stuff (Matt Levine): finance and economics, very entertaining and accessible https://www.bloomberg.com/view/contributors/ARbTQlRLRjE/matthew-s-levine
- Andrew Gelman: statistics with an emphasis on applications in the social sciences http://andrewgelman.com
- Flowing Data (Nathan Yau): data visualization and statistics https://flowingdata.com
- Shravan Vasishth: statistics and statistical computing, often very technical https://vasishth-statistics.blogspot.com
- Rasmus Bååth: statistics and statistical computing (inactive but worth mentioning) http://www.sumsar.net/blog/archives
Pure python scripts, maybe using the #%%-convention (https://code.visualstudio.com/docs/python/jupyter-support-py#_jupyter-code-cells) so you get the best of both notebooks and scripts, in a right-sized instance/container/machine. And if you need to run jobs in parallel, then orchestrate using make, like so: https://www.sumsar.net/blog/makefile-recipe-python-data-pipelines/
https://brohrer.github.io/how_bayesian_inference_works.html
https://oscarbonilla.com/2009/05/visualizing-bayes-theorem/
https://plus.maths.org/content/maths-minute-bayes-theorem
http://www.sumsar.net/blog/2017/02/introduction-to-bayesian-data-analysis-part-one/
http://www.yudkowsky.net/rational/bayes
Gateway drug: Everyone's favorite old-school t-test, brought into the modern world: https://www.sumsar.net/best_online/
Here's the story of the tool: https://jkkweb.sitehost.iu.edu/articles/Kruschke2013JEPG.pdf
(Kruschke's books and papers are a terrific resource for learning real-world applied stats)
Basically with tidyverse, R can let you write less code and keep it readable: https://www.sumsar.net/blog/pandas-feels-clunky-when-coming-from-r/
Can't speak to abandonment, but it seems a lot of recent devel is occurring inside the the tidyverse, which is deprecating a whole bunch of other stuff.