Evolving notes, images and sounds by Luis Apiolaza

Category: julia

Teaching code, production code, benchmarks and new languages

I’m a bit obsessive with words. May be I should have used learning in the title, rather than teaching code. Or perhaps remembering code. You know? Code where one actually has very clear idea of what is going on; for example, let’s say that we are calculating the average of a bunch of n numbers, we can have a loop that will add up each of them and then divide the total by n. Of course we wouldn’t do that in R, but use a simple function: mean(x).

In a previous post I compared R and Julia code and one of the commenters (Andrés) rightly pointed out that the code was inefficient. It was possible to speed up the calculation many times (and he sent me the code to back it up), because we could reuse intermediate results, generate batches of random numbers, etc. However, if you have studied the genomic selection problem, the implementations in my post are a lot closer to the algorithm. It is easier to follow and to compare, but not too flash in the speed department; for the latter we’d move to production code, highly optimized but not very similar to the original explanation.

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Late-April flotsam

It has been month and a half since I compiled a list of statistical/programming internet flotsam and jetsam.

  • Via Lambda The Ultimate: Evaluating the Design of the R Language: Objects and Functions For Data Analysis (PDF). A very detailed evaluation of the design and performance of R. HT: Christophe Lalanne. If you are in statistical genetics and Twitter Christophe is the man to follow.
  • Attributed to John Tukey, “without assumptions there can be no conclusions” is an extremely important point, which comes to mind when listening to the fascinating interview to Richard Burkhauser on the changes of income for the middle class in USA. Changes to the definition of the unit of analysis may give a completely different result. By the way, does someone have a first-hand reference to Tukey’s quote?
  • Nature news publishes RNA studies under fire: High-profile results challenged over statistical analysis of sequence data. I expect to see happening more often once researchers get used to upload the data and code for their papers.
  • Bob O’Hara writes on Why simple models are better, which is not positive towards the machine learning crowd.
  • A Matlab Programmer’s Take On Julia, and a Python developer interacts with Julia developers. Not everything is smooth. HT: Mike Croucher. ?
  • Dear NASA: No More Rainbow Color Scales, Please. HT: Mike Dickinson. Important: this applies to R graphs too.
  • Rafael Maia asks “are programmers trying on purpose to come up with names for their languages that make it hard to google for info?” These are the suggestions if one searches Google for Julia:
Unhelpful search suggestions.

That’s all folks.

R, Julia and genome wide selection

— “You are a pussy” emailed my friend.
— “Sensu cat?” I replied.
— “No. Sensu chicken” blurbed my now ex-friend.

What was this about? He read my post on R, Julia and the shiny new thing, which prompted him to assume that I was the proverbial old dog unwilling (or was it unable?) to learn new tricks. (Incidentally, with friends like this who needs enemies? Hi, Gus.)
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R, Julia and the shiny new thing

My head exploded a while ago. Perhaps not my head but my brain was all mushy after working every day of March and first week of April; an explanation—as good as any—for the post hiatus. Back to the post title.

It is not a mystery that for a while there have been some underlying unhappiness in the R world. Ross Ihaka and Duncan Temple Long have mused on starting over (PDF, 2008). Similar comments were voiced by Ihaka in Christian Robert’s blog (2010) and were probably at the root of the development of Incanter (based on Clojure). Vince Buffalo pointed out the growing pains of R but it was a realist on his post about Julia: one thing is having a cool base language but a different one is recreating R’s package ecosystem.

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