Yes, but the discussion is about the "extremely common" case of prototyping, which definitely should not require library building, and usually should not require veering much from established libraries.
>Not sure what you were expecting
The data community can coalesce around a tool extremely quickly, in the matter of a year or two. Spark is about the same age as Julia, and has a thriving ecosystem around it. In 2004 R was a fairly esoteric analysis tool, but 2008-2010 it was the de facto data science language. Python made similar advancements in just a few years in the data science community.
Julia? I don't know a single person who uses it day-to-day, but I know a lot of people who tried very hard (myself included). The critical mass simply is not there: people aren't building packages because the users don't exist, and they don't exist because the packages don't exist. Your chart shows a linear growth in packages, which implies a constant amount of development work. This means it's not a growing language. This is what a growing language looks like: http://blog.revolutionanalytics.com/2010/01/r-package-growth...
This could be early exponential growth.
Or the constant growth may hit a tipping point as a critical mass of tools and infrastructure is developed.
Someone has to write those libraries.
> This is why Julia hasn't taken off nearly as quickly as I initially expected
Not sure what you were expecting, but consider:
- the most conservative userbase estimate I would believe is 20k users (based on mailing list subscriptions, website stats, and download numbers).
- sustained package ecosystem growth: http://pkg.julialang.org/pulse.html