111–120 of 246 posts
Julia seems like such a superior language compared to R. What would be required for it to supplant R for statistical work (or some subset of it)?
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I am using Julia extensively since 2013, and I can say that it's awesome! But don't try to use it if you're looking for a general-purpose scripting language: Python is far better suited for this. Similarly, if you want to produce standalone executables, C++, Rust, Go or Nim are better. However, Julia is perfect if you write mathematical/physical/engineering simulations and data analysis codes, which is my typical use…
>Python is far better suited for this. Similarly, if you want to produce standalone executables, C++, Rust, Go or Nim are better. That's the case now, because Julia made a design decision to focus on extreme composability, dynamism, generic codegen etc which involved compiler tradeoffs...but it's not inherent to the langauge. For scripting, interpreted Julia is coming. For executables, small binary compilation is as…
Is the Julia business model similar to Redhat or Canonical where they sell consulting services?
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“Only” to write a very high amount of high-quality statistical and plot packages…
Right. R's killer feature is its ecosystem. I'm wondering if most statisticians or researchers deal with data big enough that massively better performance would be enough motivation to switch.
I'm confused as to why Julia, a programming language is worth so much money. If the makers of Julia have already given away their source code here, https://github.com/JuliaLang/julia what are they selling that's worth a 24 million series A round? Is the Julia business model similar to Redhat or Canonical where they sell consulting services?
This is addressed in several places in the comment thread by Keno and Chris.
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What's SDR in this context? Not software-defined radio, right? (Though I suppose Julia is a good fit for signal processing!)
Yes, software-defined radio, we have a very broad set of interests, and that happens to be one of the open jobs :).
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"Massively better performance" is a bit misleading: Julia is only massively better at certain workflows. The fastest data.frame library in ALL interpreted languages is consistently data.table, which is R. For in-memory data analysis, Julia will have to offer more than performance to win over statisticians/researchers. Benchmarks: https://www.ritchievink.com/blog/2021/02/28/i-wrote-one-of-t...
In addition to the comment about df.jl catching up, they aren't comparable at all. Julia's DF library is generic and allows user defined ops and types. You can put in GPU vectors, distributed vectors, custom number types etc. Julia optimizes all this stuff. data.frame is just a giant chunk of c (c++) code that one must interact with in very specific ways
These features aren't of interest to practicing statisticians, which the parent comment was talking about.
> data.frame is just a giant chunk of c (c++) code that one must interact with in very specific ways
I don't understand this criticism: yes, data.table has an API.
I'm confused as to why Julia, a programming language is worth so much money. If the makers of Julia have already given away their source code here, https://github.com/JuliaLang/julia what are they selling that's worth a 24 million series A round? Is the Julia business model similar to Redhat or Canonical where they sell consulting services?
Hi. Good question. This is addressed in several places in the comment thread by Keno and Chris.
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thanks for the answer Keno. i guess an example Stream 1 product is Pumas. i didn't realize it's a separate product from Julia. my background is in finance and i am curious if you have any plans to break into that domain (examples on your website include julia language use)
Finance was a focus area early on and we have a fair number of consulting clients there and JuliaHub is available of course, but we were never able to figure out a dedicated domain-specific, non-niche product to sell into the space. Maybe in the future.
YMMV, but I find it is fantastic in this use case. And I don't have to worry about semantic space.
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Wanted to ask if JuliaDB is something that might get more development attention? Or will that remain a community project? (I see it’s been in need of a release for awhile.)
In general, the community has discussed reviving the project (or at least the ideas and some of its codebase). Julia computing will also be contributing as part of that revival.