The Best Statistical Programming Language is …Javascript?

[This article was first published on Nor Talk Too Wise » R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

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:

JuliaPythonMatlabOctaveRJavaScript
3f670da02.7.1R2011a3.42.14.2V8 3.6.6.11
fib1.9731.471336.372383.80225.231.55
parse_int1.4416.50815.196454.50337.522.17
quicksort1.4955.84132.713127.50713.774.11
mandel5.5531.1565.44824.68156.685.67
pi_sum0.7418.031.08328.33164.690.75
rand_
mat_stat
3.3739.3411.6454.5422.078.12
rand_
mat_mul
1.001.180.701.658.6441.79

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.

To leave a comment for the author, please follow the link and comment on their blog: Nor Talk Too Wise » R.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)