I work with people who mostly have a background in the social sciences or humanities and who work in R pretty much every day. They dont see themselves as programmers and Python is complete gibberish for them, while R just makes sense. When i meet people from other companies in roughly the same space (i work in healthcare doing data analysis), it's mostly the same. I actually meet more people who use SAS/SPSS than Python.
For data analysis, R is in my opinion better than Python. It's when you have to integrate it in existing workflows that Python quickly becomes a better choice.
Similar story for me. I am an Engineer (the non software type). I work at an industrial plant. We use SAS pretty extensively for data analysis, time series analysis, multivariate regressions etc. As well as for BI type stuff (reports, graphing, adhoc queries).
For a while R was being pushed pretty heavily as a SAS alternative. My org paid R training courses etc. I found R and SAS pretty comparable at least the R packages we looked at (dpylr, ggplot2 etc).
I know about Python, the programming language I used PyGTK back in the day to build GUI apps. But it would not be my first thought for doing data analysis work. Does Python even offer something like R studio/ SAS Enterprise Guide and does it have a trending package?
It's not so much that Python is gibberish but that is written, as far as I know, predominantly by engineers, who aren't really experts in statistics, or experts in science for that matter. A scientist will tend to trust more code written by another scientist than code written by an engineer. At least, I would.
I find this statement interesting. Historically scientists have a reputation for writing relatively poor code. Code that runs really slowly due to things like unintended nested loops, or striding values (x,y vs y,x). And code that doesn't handle non-happy path cases very well.
Are you saying that you trust the code more because the domain knowledge make it more likely to get the right answer then? Has general knowledge increased such that scientists' code isn't as painful as it was 20 years ago?
Yeah, exactly, even though it might be terribly inefficient, I still trust scientific code more when it's written by scientists than when it's written by non-scientists, in terms of getting the right answer.
For data analysis, R is in my opinion better than Python. It's when you have to integrate it in existing workflows that Python quickly becomes a better choice.