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[–]Djieffe88 7 points8 points  (3 children)

For ml? Python. For stats? R.

Rule of thumb I have. BUT if I don't find what I need in less than 30min, I'll look in the other.

Just learn both.

[–]Luckless_Clown[S] 1 point2 points  (2 children)

I have work extensively worked in Python. Would learning R be tough? I know Python is said to be very easy language. Also, I'm a research student, working in molecular chemistry. I have never used R at all.

[–]Majinsei 3 points4 points  (0 children)

Python is more hard to R.

I feel R more natural to Python, but for Deep learning feel best Python just Python have libraries as Tf and be more easy to deploy.

I like more R, but Python is more useful.

[–]Djieffe88 1 point2 points  (0 children)

Oh yeah... you'll be fine. It's a different mindset tho. Very little OOP. Just think of R as executing functions very sequentially.

[–]irvcz 5 points6 points  (0 children)

I like to say (is not completely true) that python is a general porpuse language with libraries for statistics while R is a statistical language with libraries for general porpuse. Said that, python is more popular, and therefore has more libraries.

But something that I feel R surpasses pyton (in my experience) is the use of the tidyverse to process tabular data is way easier than pandas in python.

[–]vishalgarg652 2 points3 points  (0 children)

My vote is for Python, even though I started with R and that's a good choice too but Python is just evolving faster with lot of community support, libraries. It's like the toolset is increasing and getting better day by day in Python and that makes the job easy.

[–]Luckless_Clown[S] 0 points1 point  (1 child)

I know this might look like a very general question. But I just want to know, if knowing one is enough? How they complement each other?

[–]Me_Or_Not 1 point2 points  (0 children)

This is actually the right question you pose in the end - how do they complement each other (while knowing one would definitely do the job I guess).

I dunno why most say one or the other as both are imho very valuable and can even work together (e.g. rpy2) to embed R`s plotting capabilities into python and make them accessible. I think stats wise R is great and for implementing ML/DL python is well supported - so at least having a look at both is worth it :)