[baldfat]

There are so many ways to write R that can actually be very fast. R is a very different language as a whole and it has addressed a lot of problems these last 10 years. I might be biased but I really do like the functional side of R and how logical the libraries from Hadley Wickham have been designed.

Python still doesn't feel like a natural fix for data science work. I am guessing it is more bias opinion but why based on 0 for this domain????

[wiz21c]

> There are so many ways to write R that can actually be very fast.

But it means that you have to work with arrays. For me it often means breaking the flow of my (code) explanation to migrate to other data structures. Sure it is then fast, but it gets less readable and harder to update.

Now, I'm a programmer at heart, so I think in the "functional" paradigm, not the array/signal one. There's some "impedance" I guess :-)

[sidkshatriya]

I actually prefer the 0 indexing of python. Systems code (c/c++ etc.) already uses 0 based indexing. So it is nice that when you do data science the convention stays the same.

[Mikeb85]

But Fortran, R, Matlab and other tools in the domain use 1 indexing...

[runarberg]

Does it actually matter at this point? I write off by one errors all the time in JavaScript and I usually spot them immediately. I’ve written Julia and R in the past and I found it really easy to switch between contexts, and if I forget, the errors are such that it takes only a few seconds to spot and fix them.