Every time I try Julia on my computer, there is so much lag between input and output, I just go back to R.

Startup time is not the only issue with Julia. Its error messages are long-winded and cryptic, and its manpages can be quite sketchy.

Even so, Julia is a good tool for intensive computation, and I think it will continue to replace Fortran for some such work.

Balance is the key. I use R all day long for data analysis, and don't really see Julia as competitive for such work. However, when it comes to numerical modelling, Julia competes well with Fortran and C/C++.

I think Julia should be thought of as a Fortran replacement for overnight/overweek jobs, not an R replacement for interactive work.

I'm kind of on the fence with Julia, never used it in anger. But to me it seems much more of a Matlab or Python replacement than a Fortran replacement? Meaning that (in my perhaps limited view) it's main usefulness is in making algorithm development much faster and smoother.

However, once a new very fast algorithm has been discovered and tested and everyone agrees it is good, someone will probably implement it in Fortran or C or even assembly and optimize the shit out of it.

One thing that supports this view is that there are several Julia packages that are wrappers around existing C/Fortran/C++ libraries, and basically no examples (that I know) of people porting existing libraries to Julia.