A book to learn R and Python in parallel for Data Science

Post by javierluraschi » Tue, Apr 23, 2019, 1:43 PM UTC

kuzehanka wrote: post #50

Annatar wrote: post #46

Earlier quoted context omitted.

I cannot understand why I would use Python over R. R is designed from the ground up for massive amounts of data processing at speed and with ease. Even if Python continues accreting computational functionality, it will never be as fast or as efficient as R. Improving Python for something R is designed to do seems to me to be a huge waste of time: familiarity should not be the driving force behind replicating R's func…

> R is designed from the ground up for massive amounts of data processing at speed What? The R ecosystem doesn't provide meaningful out of core capabilities, nevermind the ability to handle anything approaching 'massive amounts of data'. -- Would sure love to know why an agenda-less factual comment is getting downvoted.

Post by snackematician » Tue, Apr 23, 2019, 1:55 PM UTC

kuzehanka wrote: post #22

billfruit wrote: post #13

I sometimes wonder is there any reason to learn R at all, since python eco system has absorbed most of its advanced statistical functionality, coupled with the factor that python environment is much more general, with capabilities to fetch, decode/encoded data,work with binary data databases, web frameworks for presenting etc.

I don't think there's any reason to learn R for anyone who is already proficient at programming. Despite being proficient with R, the only times I used it in the last two years were for ggplot. And even for data vis, I'm increasingly using Python and JS. There's a bunch of comments below which can be summed up with 'use R because doesn't have a direct python equivalent' but they're all missing the point that the Pyth…

Post by samt430 » Tue, Apr 23, 2019, 2:25 PM UTC

Post by spectramax » Tue, Apr 23, 2019, 2:37 PM UTC

roenxi wrote: post #49

minimaxir wrote: post #16

Earlier quoted context omitted.

I use both Python and R. tidyverse/ggplot2 alone are enough reason to use R, and are substantially faster for tasks that utilize those packages than the equivalent in Python (in my opinion). Although I haven't had as much reason to use base R. For more ML-related tasks I do go back to Python.

Here here. Tidyverse also provides a centralised 'this is how you do X' nexus really helps discover-ability. World class stuff, on tap. For example, I know the recommended pipe in R is magrittr's %>%. I have no idea what the respectable pipe library in Python is, or even if there is one. I wouldn't even know where to start finding all the tidyverse equivalents in Python. It isn't as organised and obvious as the R sta…

Post by tylermw » Tue, Apr 23, 2019, 2:47 PM UTC

Lanrei wrote: post #60

Shouldn't '<-' be used instead of '=' for variable assignments, as they aren't the same thing in R.

  divide = function(x, y) {
    return(x/y)
  }

  divide(y = 2, x = 1)
  divide(y