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My Journey from R to Julia

drtomasaragon.github.io

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Re: My Journey from R to Julia

#21

Post by rlh2 »

karencarits wrote: post #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/

The obsession with cpu speed almost always confuses me in these topics. Time it takes to program is way more important, and that’s where a terse language like R shines. The base/most common functions are almost always executing C anyway. It’s kind of like lisp in that it’s easy to write slow code, but who cares if it’s “fast enough”? Also, it’s almost always easy to speed up if necessary at the R level and R’s C API is also easy to use for for numeric computing/optimization which is exposed at the C level if you want to use it.

Re: My Journey from R to Julia

#22

Post by borodi »

sfpotter wrote: post #11
hnarayanan wrote: post #7

I get confused by this every time this comes up. Is multiple dispatch the same as function-overloading (e.g. in C++)?

They're different. IIRC, multiple dispatch is dynamic (i.e., happens at runtime) while C++'s function overloading is static (happens at compile time).

One interesting thing is that if julia can prove what types a function will be called with at compile time, it doesn't have to do dynamic dispatch, so it has no overhead. It's what the julia folks call type-stable code

Re: My Journey from R to Julia

#23

Post by thejosh »

BrandonS113 wrote: post #16
Lyngbakr wrote: post #13

Earlier quoted context omitted.

I only used Julia for a short time, but I didn't see the blazing fast speeds I was promised. I've seen the benchmarks, of course, on which the claims are founded, but the C-like speeds weren't obvious to me in everyday data science workflows. In the end, there wasn't sufficient motivation for me to switch to Julia as my weapon of choice. I do like Pluto[0], though... [0] https://plutojl.org/

If doing data science, I find Julia's tools to be inferior to Python and R. But in my work, when it comes to long computations, not only does Julia usually vastly outperform both, we write Julia code faster with fewer errors.

What kind of questions are you trying to answer?

What's the area for the long computations you're doing?

Just curious :)

Re: My Journey from R to Julia

#24

Post by bluedino »

I work with PhD chemists at a F500 company, most everyone uses Python, we have a pocket of users that are on the R train. Mostly Rstudio mixed with Python.

Someone just asked to install Julia on the compute cluster just last week so we'll see how many others start using it.

Re: My Journey from R to Julia

#25

Post by rightbyte »

Lyngbakr wrote: post #13
BrandonS113 wrote: post #8

Just 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.

I only used Julia for a short time, but I didn't see the blazing fast speeds I was promised. I've seen the benchmarks, of course, on which the claims are founded, but the C-like speeds weren't obvious to me in everyday data science workflows. In the end, there wasn't sufficient motivation for me to switch to Julia as my weapon of choice. I do like Pluto[0], though... [0] https://plutojl.org/

You need to know how the compiler propagates types in detail to write performant code. It is quite hard.

Re: My Journey from R to Julia

#26

Post by mxkopy »

adenozine wrote: post #6

I’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…

> no traits, no interfaces, and no classes. So, the amount of organization in his code is effectively zero.

This is an utterly deranged take. Do you mean to say that adherence to OOP and code organization are the same thing ?

Re: My Journey from R to Julia

#27

Post by kkoncevicius »

rlh2 wrote: post #21
karencarits wrote: post #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/

The obsession with cpu speed almost always confuses me in these topics. Time it takes to program is way more important, and that’s where a terse language like R shines. The base/most common functions are almost always executing C anyway. It’s kind of like lisp in that it’s easy to write slow code, but who cares if it’s “fast enough”? Also, it’s almost always easy to speed up if necessary at the R level and R’s C API…

It depends. Take for example any omic dataset where you might need to run a GLM model on ~500,000 rows. Codes I've seen for this operation can range in time from taking 30 minutes to 2 days.

My take away here is that, sure, for one operation the speed is not that critical, but there is always the case where that one operation will be used close to a million times in one analysis and then it all adds up. On top of that if it's implemented in C then the invocation from R to C and back will be happening that many times which adds to the slowness.

Re: My Journey from R to Julia

#28

Post by Annatar »

bluenose69 wrote: post #17

The key comment is that it's hard to know more than 1.5 languages. I think everyone has their own number for that. My number is higher than the author's. I use R for most work, but a lot of my computations involved large binary datasets that are best read with C/C++, so I use C/C++ and R in tandem for my data-analysis work. Separate from that, I use python when I'm writing (undemanding) system-level work. I see it as…

[dead]

Re: My Journey from R to Julia

#29

Post by BrandonS113 »

bluenose69 wrote: post #17

The key comment is that it's hard to know more than 1.5 languages. I think everyone has their own number for that. My number is higher than the author's. I use R for most work, but a lot of my computations involved large binary datasets that are best read with C/C++, so I use C/C++ and R in tandem for my data-analysis work. Separate from that, I use python when I'm writing (undemanding) system-level work. I see it as…

1.5 is on the low side. I use Python, R, Julia, and Latex professionally. Python for op system/internet/data, R for stats and Julia for numerical calculations (some very large scale). So know the useful parts of all 4.

Re: My Journey from R to Julia

#30

Post by ChrisRackauckas »

BrandonS113 wrote: post #8

Just 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.

Did you use JuMP? It would be interesting to see the JuMP code.
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