My Journey from R to Julia
drtomasaragon.github.io
My Journey from R to Julia
1–10 of 120 posts
Re: My Journey from R to Julia
#2> For example, in R, we try to avoid loops because they are very inefficient
This was true before, but the performance of for loops has been improved a lot later years, and while vectorization is still faster, for loops are no longer a no-no
See https://www.r-bloggers.com/2022/02/avoid-loops-in-r-really/
Re: My Journey from R to Julia
#3> For example, in R, we try to avoid loops because they are very inefficient This was true before, but the performance of for loops has been improved a lot later years, and while vectorization is still faster, for loops are no longer a no-no See https://www.r-bloggers.com/2022/02/avoid-loops-in-r-really/
It's a really sticky misconception. I've seen many beginners telling others to "never ever use loops in R", and so you end up with nested sapply()s or whatever soon-to-be-deprecated tidyverse functions are in vogue that nobody can reason about.
Re: My Journey from R to Julia
#4It’s important to note that R’s S4 Object System now supports multiple dispatch & I have enjoyed using it. I would agree that it’s not quite as elegant as Julia’s.
See https://www.mpjon.es/2021/05/31/r-julia-multiple-dispatch/
Re: My Journey from R to Julia
#5It’s important to note that R’s S4 Object System now supports multiple dispatch & I have enjoyed using it. I would agree that it’s not quite as elegant as Julia’s. See https://www.mpjon.es/2021/05/31/r-julia-multiple-dispatch/
> now
It's 25 years old!
Re: My Journey from R to Julia
#6I’ve made most of my career turning scientific and mathematical code into maintainable and aesthetic code, and the red flag for me in this article is that he evidently couldn’t keep up with the Python learning curve and chose instead a language with no traits, no interfaces, and no classes. So, the amount of organization in his code is effectively zero.
I understand that Julia 2.0 is slated to have some sort of concrete interface mechanism, so that’s good. Thus far, I’ve seen some pretty low quality results. There’s just no way to have intuition about what method is going to be called in Julia. In python, I know it’s either going to be somewhere in dir(some-obj) or it’s gonna be some funky meta class stuff. Either way, pycharm can literally just hyperlink me.
Until Julia has the same capability, it just won’t be suitable for general purpose code. I know there will be some Julia fan in the replies about how I can approximate the behavior, and how Julia is the future and blah blah blah.
Just fix interfaces. It’s not that hard. They’ve got MIT grads for crying out loud!
I’m a little appalled there’s PhDs doing computer science work with public money that can’t wrap their head around python. That’s a failed curriculum imo.
Re: My Journey from R to Julia
#7I get confused by this every time this comes up. Is multiple dispatch the same as function-overloading (e.g. in C++)?
Re: My Journey from R to Julia
#8Just now I was thinking of moving a long calculation from R to Julia (non-linear optimisation of a simple function with multiple local minima, for a lot of different datasets). No loops. Embarrassingly parallel. And to my great surprise, R and Julia took the same time.
Re: My Journey from R to Julia
#9> For example, in R, we try to avoid loops because they are very inefficient This was true before, but the performance of for loops has been improved a lot later years, and while vectorization is still faster, for loops are no longer a no-no See https://www.r-bloggers.com/2022/02/avoid-loops-in-r-really/
It's a really sticky misconception. I've seen many beginners telling others to "never ever use loops in R", and so you end up with nested sapply()s or whatever soon-to-be-deprecated tidyverse functions are in vogue that nobody can reason about.
So Rob Pike’s rule 1 and 2 again:
Rule 1. You can't tell where a program is going to spend its time. Bottlenecks occur in surprising places, so don't try to second guess and put in a speed hack until you've proven that's where the bottleneck is.
Rule 2. Measure. Don't tune for speed until you've measured, and even then don't unless one part of the code overwhelms the rest.
Re: My Journey from R to Julia
#10I’ve made most of my career turning scientific and mathematical code into maintainable and aesthetic code, and the red flag for me in this article is that he evidently couldn’t keep up with the Python learning curve and chose instead a language with no traits, no interfaces, and no classes. So, the amount of organization in his code is effectively zero. I understand that Julia 2.0 is slated to have some sort of concr…
That is harsh. I know PhDs doing comp science work, hey with PhDs in comp science, coming to same conclusion. Python is an excellent language. But coding in numpy is not its strength.