I've had a similar experience from the opposite side. I've had quite a few years of experience in Python and had to work in R for an internship during my masters.
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. Access to the underlying data was often opague and it was difficult to me at times to figure out what was happening under the hood.
Does anyone here know any research areas still using R?
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.
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
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.
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.
> 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.
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.
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.
Often reviewing is executed double blind for exactly this reason. This can be difficult in small fields where you can more-or-less guess who's working on what, but the intent is definitely there.
There is a deluge of experts in economics and other fields who clearly state this is not the right approach to bringing manufacturing to the US. Why wouldn't we trust experts in their fields?
That probably means its not a good strategy. Look at business activity, people are comparing it to early covid business conditions but its entirely self inflicted. You don't need to be an expert to see this is a failure. The assumption shouldn't be that Trump is acting in the interest of the average American or the American government.
Do they list the models of the brands they are talking about anywhere? For example, they mention Garmin but there is a huge variety of Garmin models with varying accuracy. I'm surprised I can't find it anywhere. They mention gathering data ("The majority of commercially available devices measure these basic metrics, while other metrics typically vary between different brands and models.") on different models but then group them together under a single brand? Why? Frankly, this analysis seems a bit half-assed.
