Actually, I think your point rather showcases that Julia could be useful for the statistics community! Due to performance reasons you occasionally have to abandon R to use Numpy, but for everything else you use R. Thus, in a sense you still have the problem that Julia tries to solve: You constantly have to jump between two different languages depending on the problem size.
Also, just like you chose Numpy, someone else might choose Julia. So this turns into a Numpy versus Julia comparison. And here I feel like (and this is not really an argument, just my gut feeling) Julia might be better at attracting people with a non-CS background, who want to implement some statistical methods or analyze their biological datasets.
Yes and yes. Julia is better because, having seen the mistakes of 3 x 20 year old languages, it is creating an (incrementally) better PythonRMatlab base language (not libraries). Had Julia dropped into my lap 5-10 years ago I would have been a fool to use anything else. The fact is though, it is very late to a party where everyone has already danced the night away, and just as the music is about to change (massively parallel multi node functional).
I think (this is gut - not an authority!) that Julia is very well placed for the massively parallel multi-node functional future of which you speak. lazy.jl and the cluster implementations such as spark.jl are evolving.
Also, just like you chose Numpy, someone else might choose Julia. So this turns into a Numpy versus Julia comparison. And here I feel like (and this is not really an argument, just my gut feeling) Julia might be better at attracting people with a non-CS background, who want to implement some statistical methods or analyze their biological datasets.