My main beef about R (and similar tools) is that I summarize my data I get from Hadoop and find it is still too big for R to handle and I then have to summarize it some more.
[1] https://blog.javascripting.com/2014/07/28/fastest-growing-ne...
[2] http://www.pcworld.com/article/2480920/r-programming-languag...
[3] http://redmonk.com/sogrady/2015/01/14/language-rankings-1-15...
Yes I have read [R Inferno] http://www.burns-stat.com/pages/Tutor/R_inferno.pdf
By the way i should have stated that Python is a good choice for doing Data Science it is just that R works better for me.
You assessment is pretty accurate. R is almost the opposite of Python -- you HAVE to use lots of external libraries. Whereas in Python, the core is super solid, but the libraries you get are of varying quality.
R took me the longest to learn out of any language. I currently have coworkers struggling with R, and cursing it. But the learning pays off... it's pretty much the only option out there right now (and yes I use Python for almost everything, but you need R too.)
What walls and why did you switch? Did you find the shift in syntax and mindset difficult? Have you used rpy2?
There definitely seems to be more innovation in R world, but blaze, numba and bokeh have no R equal.
I work at a Microsoft Office Universe (They refuse to know of any tools besides Word and you are a tech god if you use Excel) So when I finish my reports in R I actually give them Office Documents. The presentation is actually Power Point and the documents are in Word.
I left Python and Pandas and the visualization was far behind what R had in its tool chain. I still feel that R does visualizations best.
RStudio (A true IDE) was better for my work flow than IPython/Juypter. I actually really missed IPython at first and than slowly I grew to love RStudio.
Not a wall, maybe a paper cut. A 1 index (R) over a 0 index (Python) is pretty smart for doing statistics, less errors.
I also liked the flexibility to use whatever I wanted to do my work. Was difficult at first than learned to love the fact that there were 3 good ways to do something.
Functional Programming. You can do some good functional programming with R. Functions are first class citizens.
Biggest reason for anyone to switch is Hadley Wickham and his work with RStudio. It really is amazing everything that he has done for R in the last 5 years.
Once again Python will get the job done but these are some of the reasons why I switched.
When you say "flexibility to use whatever I wanted to do my work", do you mean data munging and manipulation or statistical modeling packages?
Graphics I can use just about anything. I can use plottly, ggplot2, D3.js, PDF report, HTML, send to a blogging site, interactive graphics with ggvisand Shiney, even PowerPoint slides.
<-- Maturity -- Fundamentals well designed -->
R Python Julia
I stopped using Python for data science and use R and it really worked out great for me. R has gained a lot of momentum and I think that Pandas with Wes leaving isn't being developed as quickly. (Most of it is re-doing what R has done). I am amazed at all the FUD I was told to not use R and just use Python but after hitting so many walls I tired it and I am so glad I did. Your millage may vary.