[bdod6]

Can someone explain how this is more powerful than someone use an Python/R based workflow? E.g., I currently use a combination .ipynb, python scripts, and RStudio and this feels like it covers everything I need for any data science project.

[jointpdf]

I think Julia has a cleaner focus on scientific and mathematical computing than either R or Python (both for performance and understanding). i.e. the language is designed in such a way that corresponds more directly to mathematical notation and ways of thinking. If you’ve been in a graduate program that’s heavily mathematical, where you spend equal time doing pen and paper proofs and hacking together simulations and such (and frantically trying to learn a language like R/MATLAB/Python while staying afloat in your courses), you’ll appreciate the advantage of this. To my eyes, Python is too verbose and “computer science-y” and R is too quirky to fulfill this niche (I say this as someone that bleeds RStudio blue, and enjoys using Python+SciPy). I don’t think Julia is aimed at garden-variety / enterprise data science workflows. Caveat—I’m not a Julia user currently, so this is sort of a hot take.

The “Ju” in Jupyter is for Julia, so it’s designed to be used as an interactive notebook language also. The Juno IDE is modeled after RStudio.

[anthony_doan]

> R is too quirky to fulfill this niche

I'd like to offer a counter point or add on to this.

It's quirky enough to have many packages backed by some expert statistician.

I hope Julia get to be successful in this regard too.

[jointpdf]

The way I wrote that comes off as more dismissive than I intended. I think it’s quirky in the sense that there is a wide variance in styles of accomplishing things in (base) R, so something that appears perfectly natural to me can look foreign to someone else. I think this is partly the user base and partly the language itself, and of course the two are interdependent. To me, it’s a joy to write R code because of it’s flexibility and power, but I often have dreaded sharing it with others (especially as a beginner). It’s easy to look at someone else’s R scripts and think “this is horrifying”. By the way, this is referring more to scientific/statistical workflows—for more general purpose data science in R, the Tidyverse (or even just the pipe operator %>% around which the Tidyverse is built) goes a long, long ways towards helping people write expressive but readable code.

By contrast, Python feels a bit too rigid/standardized. Everyone’s code looks like it was copy+pasted from a book of truth somewhere. This is good for sharing and engineering, not as good for expressing mathematical ideas.

So whereas R has evolved organically over decades and Python is for everyone (and alternatives like MATLAB or SAS are first and foremost software for industry rather than languages), Julia seems to be thoughtfully purpose-built to be a modern language for numerical/scientific computing. It polishes off the rough edges and blends some of the best features of each language. Again, this is just an impression from someone who already thinks in R but is learning both Python/Julia.

More to your point, maybe Julia is at a stage of development where it’s good for both students (for developing computational and mathematical thinking) and experts (for slinging concise but performant code), but not yet the rank-and-file users looking to just get things done.

[snicker7]

Fast for-loop, the ability to microoptimize numerical code (skip bounds checking in array access, SIMD optimations), GPU vector computing can use exact same code as CPU due to Julia functions being highly polymorphic. Your research code is your production code.

Also the macro system allows one to define powerful DSLs (see Gen.jl for AI).