[gnaddel]

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.

[usernam]

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.

[cheriot]

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.

[usernam]

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.

[ssivark]

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