The Best Statistical Programming Language is …Javascript?

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R-Bloggers has recently been buzzing about Julia, the new kid on the statistical programming block.  Julia, however, is hardly the sole contender for the market of R defectors, with Clojure-fork Incanter generating buzz as well.  Even with these two making noise, I think there’s a huge point that everyone is missing, and it’s front-and-center on the Julia homepage:

Julia Python Matlab Octave R JavaScript
3f670da0 2.7.1 R2011a 3.4 2.14.2 V8
fib 1.97 31.47 1336.37 2383.80 225.23 1.55
parse_int 1.44 16.50 815.19 6454.50 337.52 2.17
quicksort 1.49 55.84 132.71 3127.50 713.77 4.11
mandel 5.55 31.15 65.44 824.68 156.68 5.67
pi_sum 0.74 18.03 1.08 328.33 164.69 0.75
rand_ mat_stat 3.37 39.34 11.64 54.54 22.07 8.12
rand_ mat_mul 1.00 1.18 0.70 1.65 8.64 41.79

Julia kicks ass on the benchmarks, but it also has a severe uphill battle.  It’s new, it’s Linux, it’s command-line-only, and it doesn’t have support for the wide array of Statistical functionality available in R.  But besides the obvious my-language-can-beat-up-your-language comparisons, notice anything interesting?  Think orders of magnitude?

Javascript performs nearly as well as Julia down the board.  This nearly floored me.

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