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Comment by gnaddel | Hacker News Reader

I think Julias adoption is hindered by the lack of a real Julia-IDE aimed at data analysis. R has R-Studio, Python has Spyder, both of which are excellent nowadays. Julia has Juno in principle, but setup has never worked for me on multiple machines. The Julia language has a lot to offer, but there is no convenient way for people to give it a try that is comparable to what they have grown to expect from competing languages.

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There's actually Jupyter. Then there's a julia backend for ess as well.

I'm using R regularly, and I couldn't care less for R-Studio. In our stat group, only 1 statistician out of 7 is using R-studio, while all of them are using R.

The IDE has very little to do with adoption.

There can be a difference between IDE choice among professionals and the role of an IDE in introducing people to the ecosystem. On ramps don't start at the target elevation.

For what I see, the first and foremost initiating factor for a statistical package is education, and second it's available methods/packages. You have universities where you can clearly see that the predominant taught package is Stata, or R (and in the latter, the choice of UI is mostly arbitrary).

In the end though, unless you want to reimplement methods, you can count on having R packages for any method you can think of.

Few statisticians though spend the time to evaluate different IDEs than what they where taught. I've "converted" many still using Rwin.

Try Juno (a Julia IDE on top of Atom): http://junolab.org/

Recent JuliaCon talk allowing off the debugging features. https://m.youtube.com/watch?v=yDwUL3aRSRc

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