The Best Statistical Programming Language is …Javascript?

April 27, 2012

(This article was first published on Nor Talk Too Wise » R, and kindly contributed to R-bloggers)

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 3.6.6.11
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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