As an R user, I get what you mean. If you need to do things that don't fit well in the "tidyverse" model, you have three options:

1. Wrap the complicated bits in functions, then force it into the tidyverse model by abusing summarize and mutate.

2. Use data.table. It's very adaptable and handles arbitrary multiline expressions (returning a data.table if the last expression returns a list, otherwise returning the object as-is).

3. Use base R. It's not as bad as people make it out to be. You'll need to learn it to anyway, if you want to do anything beyond the basics.

Everyone in statistics, and lots of people applying statistics in other disciplines (anthropology etc.).

In addition to stats, R is widely used in computational biology and bioinformatics domains. It’s also widely used in the biopharma industry for a variety of other purposes.

IME (bioinformatics PhD in the netherlands a number of years ago) it's mostly still preferred in a (pre-)clinical context, not so much in academia itself

R is used extensively in quant finance. The quant traders, portfolio managers, and risk managers with whom I work all use R.

> My impression was that it's pretty easy to do straightforward things like the examples described in the article. But when you have to do complicated or unusual things with your data I found it very frustrating to work with.

That's where I realised that the "modern" approach was taken in the article - which obviously I had not looked at.

Not really research pr se, but it's used extensively in banking here in Norway for anything from statistical model development to basic analysis and reporting.

Totally agree, I think this whole conversation is a first language thing. I learned pandas first, and found the whole R ecosystem to be a complete mess.

So many different types of object, so many different syntaxes. The tidyverse makes sense, and sure, is elegant, but if your colleagues are using base R. Don't even get me started on docs and Stackoverflow for R. I much, and always will prefer Python.

The one area I still go back to R is on proper survey work. I've looked for years and haven't found anything equivalent to the survey package for Python. I do like that R tends to start from the assumption that data is weighted.

Fortunately I don't do surveys much anymore.

Survey people use r already, or maybe stata, so there isn’t any need for a python package. It’s sort of trivial to implement a jackknife loop in python, if you really had to. A python survey package would not be pythonic.

Yeah, I've cludged together all sorts of stuff, just to avoid working in R. Terrible use of my time.

The big issue isn't necessarily around jackknife etc. (as you say, pretty trivial and I think perhaps in statsmodels), but around regression weighting and ensuring compatibility with colleague's work.

The R survey package, Stata and SPSS all support things like survey design in their regressions, python does not out of the box. Even simple things like weighted frequencies end up with some pretty awkward code in Python.

I can imagine a pythonic survey package that extends pandas and statsmodels, but as you say, survey people use R and there's just not a scene for it.

Past life for me anyway, surveys are not my bag.

> The tidyverse makes sense, and sure, is elegant, but if your colleagues are using base R

Then you probably need to get new colleagues.

Lovely people, terrible stack. What I found more than anything else was I needed different problems to work on!

R is very heavily used in Statistics. It's also common in other sciences. I've worked a fair bit with biologists and that's what they're using too for data analysis and visualization.