Is there any project for making base R faster

I just encountered this link.!topic/julia-users/Uu_UcYp49Qo

It talks about how julia is faster than r and python. And the person poses a question if R can be fast enough by using the same techniques. Its a must read link. And the answer that he gets is there are projects for making it faster.

I googled it and found projects like RHO. But they are not being worked upon.

So my question is this what and how many (if there are any) projects are trying to make R more faster. And can we implement an LLVM COMPILER in R as well.

There’s a fork of R that tries to speed up R and sometimes code from the author is pulled into the main R codebase: I’ve never used it so I can’t vouch for it.

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First, yes there faster versions of base like depending on what you are doing.

But I would like to respond with a question in return- Because using R is never going to be the fastest way of doing a task (for instance to writing something in pure C/C++ to do something), the question becomes "What tasks are you trying to do that the speed of R is a problem?"

Because depending on the task, it may be something R will never be the fastest at (and so would be better to use other tools) or it may be something where the manner in which you approach the problem is not an R way of doing so in the first place, so the approach is creating slowdowns.

Certainly, when I was at the stage of using datasets large enough to notice, that was when I found that the naïve code I was writing could be sped up by hundreds of times. Not be underlying computer changes, but by thinking about what I needed to do with the data in the course of the analysis and how I was managing that.


@thoughtfulnz I understand your point. But My question is entirely different I want to know if there are any projects running to make the Base R faster. That's it.

I have started learning R 2 years ago and Now I see myself writing more code and working with more databases than I ever thought I would. My job forced me to learn JSON, CSS, HTML and many more thing. I think I am more involved in the programming aspect of the language than the statistical one.:sunglasses:

I want to know can I utilize my knowledge of R in building something concrete out of it. In statistics speed doesn't matter I agree but In programming it does. R is both a programming language and a statistical language. Because a few of my collegues have started taking classes on JULIA I wanted to know about the future of language which I totally love and care about.

I am just being curious nothing else.:grin:

They are still trying to make stable version of 2.5 when we are at 3.4. But thanks for introducing me to such an endeaver. I will totally keep an eye on the projects. thanks a lot

Tibco has released it's own version of R called TERR, which you can try for free:

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ALTREP is faster in some cases.

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cf. Microsoft R Open being faster - it is possible to build R from source and include the BLAS libraries to get basically the same thing

In fact, this is recommended on Linux-based operating systems, because it allows you to have multiple concurrent installed versions of R.

If you are concerned about speed and speaking about databases, I definitely recommend checking out and These don't really get at making R faster, but translating R code (specifically dplyr code) into other languages via dbplyr or sparklyr is very core to the R language. The first was Fortran, C/C++ are increasingly common, and the pattern continues to be elaborated upon (i.e. you can use profvis to find performance hits and translate those functions into C/C++). Plus, with these approaches, you save a ton of time on I/O.


Thanks a lot @cole. I knew about those things. My question was different though. It was just about knowing running projects in R community. But thanx a lot for taking your time to reply me. Really appreciate it

Sure thing. Always good to share in case others who find your thread are unaware :slight_smile:

You may have seen this page as well, but this is probably a good page to follow / contribute to if you have interest in contributing to R's speed / performance! I generally lean towards contributing to and optimizing the language itself rather than the many forks that are out there.

CRAN Task View - High Performance Computing: