It's been a few years since I really looked at R, but I don't think the problems with R are simply that people don't learn the language. Some languages are simply not as good as others. We can all learn more about the tools we use when programming, I know that I certainly could. But this doesn't make it our fault that a language is tricky or hard to debug or hard to understand. If we worked at it, I suppose we could all write more efficient programs by using assembler, but that doesn't mean that assembler is the best possible programming language for, say, statistical programming.
Someone, Ross Ihaka, that knows a thing or two about R wrote a short post 6 years ago and said "simply start over and build something better". Take a look:
From your link, hilariously relevant to the blog post at hand:
"First, scalar computations in R are very slow. This in part because the R interpreter is very slow, but also because there are a no scalar types. By introducing scalars and using compilation it looks like its possible to get a speedup by a factor of several hundred for scalar computations. This is important because it means that many ghastly uses of array operations and the apply functions could be replaced by simple loops. The cost of these improvements is that scope declarations become mandatory and (optional) type declarations are necessary to help the compiler."
Someone, Ross Ihaka, that knows a thing or two about R wrote a short post 6 years ago and said "simply start over and build something better". Take a look:
http://www.r-bloggers.com/“simply-start-over-and-build-somet...
My hope is that Julia will eventually be adopted as a basis for a future statistical programming language.