I know R, but don't enjoy programming in it (although I love the documentation R has for its various libraries). Luckily there's various attempts to translate all the ISLR exercises into Python on github[1][2] that I have found immensely useful in understanding implementation.
Interesting. I like Python but don't enjoy programming in it. I find programming with R much more to my liking. I do data analysis and modeling mostly.
A useful practice, in my experience, is to implement R code samples in some other language, like C/D. Implementing lower level math functions for oneself can also be fun instead of relying on a library, depending on one's ultimate goals/interests.
I think that this is a great exercise to really learn the implementation details of the methods, but the point of ISLR is more to get scientists (and the like) up and running with the usage of these methods.
I'm a data scientist without a formal background in programming. Can someone please explain why implementing math functions in C/D is different than doing it in R?
For example, I would assume that creating a mean function using numbers and operators would be language-agnostic.
It's the difference between using a calculator and knowing how to multiply two 5 digit numbers with pen and paper.
You might say, "Oh but we can do that in R". Well, if you start doing that one function at a time, very soon you find yourself with R grinding to a halt.
I think Julia would be a better choice, especially since you can directly use R & Python from it. This way you can implement the methods in a scientific language yourself and still use the original R methods.
