Note: I believe this question is not off-topic because it meets all of the criteria for subjective questions that are allowed. I would be happy to rephrase or clarify if others disagree
I'm about to begin a two-year project which will predominantly involve longitudinal panel data. I've found numerous questions and answers (Pros and Cons of Python and R for Data Science), and blog posts (https://www.quora.com/Which-is-better-for-data-analysis-R-or-Python | http://www.kdnuggets.com/2015/05/r-vs-python-data-science.html), about the relative merits of R and Python+pandas for data science but nothing that discusses longitudinal data.
My question is therefore:
Which environment (Python+pandas or R) would you use for longitudinal data analysis, and why?
For example, I would love to see answers that:
- Tell me how you have used one or both environments to solve a particular problem with longitudinal data
- Which environment or package(s) you found easier to work with, and why
- If you used a notebook such as beaker to work with multiple environments simultaneously.
- If so, which environment did you use for which step(s) in the data analysis pipeline, and why?
- If so, did this confer advantages over just using one language: would you use such an approach again?
I am not asking which one is best (we all know that such questions are never constructive).
I am more familiar with one environment than the other, but I'm not averse to learning new skills (and both share similar syntax anyway), so I'm not going to say which one: I want answers based on your experience, not my abilities.