Category Archives: sas

Teaching code, production code, benchmarks and new languages

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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.

This reminded me of the controversial Julia benchmarks, which implemented a series of 7 toy problems in a number of languages, following the ‘teaching code’ approach. Forcing ‘teaching code’ makes the comparison simple, as different language implementations look similar to each other. However, one is also throwing away the very thing that makes languages different: they have been designed and optimized using different typical uses in mind. For example, execution time for the pisum benchmark can be reduced sixty times just by replacing the internal loop with sum(1/c(1:10000)^2). Making comparisons easily understandable (so the code looks very similar) is orthogonal to making them realistic.

Gratuitous picture: looking for the right bicycle in Uppsala (Photo: Luis).

Tony Boyles asked, tongue in cheek, ‘The Best Statistical Programming Language is …Javascript?’ Well, if you define best as fastest language running seven benchmarks that may bear some resemblance to what you’d like to do (despite not having any statistical libraries) maybe the answer is yes.

I have to admit that I’m a sucker for languages; I like to understand new ways of expressing problems and, some times, one is lucky and finds languages that even allow tackling new problems. At the same time, most of my tests never progress beyond the ‘ah, cool, but it isn’t really that interesting’. So, if you are going to design a new language for statistical analyses you may want to:

  • Piggyback on another language. Case in point: R, which is far from an immaculate conception but an implementation of S, which gave it a start with both users and code. It may be that you extend a more general language (e.g. Incanter) rather than a domain specific one.
  • However, one needs a clean syntax from the beginning (ah, Python); my theory is that this partly explains why Incanter got little traction. (((too-many-parentheses-are-annoying)))
  • Related to the previous point, make extensibility to other languages very simple. R’s is a pain, while Julia’s seems to be much straightforward (judging by Douglas Bates’s examples).
  • Indices start at 1, not 0. Come on! This is stats and maths, not computer science. It is easier for us, your audience, to count from 1.
  • Programming structures are first class citizens, not half-assed constructs that complicate life. A clear counterexample are SAS’s macros and PROC IML, which are not conducive to people writing their own libraries/modules and sharing them with the community: they are poorly integrated with the rest of the SAS system.
  • Rely since day one on creating a community; as I pointed out in a previous post one thing is having a cool base language but a different one is recreating R’s package ecosystem. Good luck recreating R’s ecosystem working on your own.
  • However, you have to create a base language with sane basics included: access to both plain text and databases, easy summary functions (Xapply doesn’t cut it), glms and cool graphics (ggplot like) or people may not get hooked with the language, so they start contributing.

Two interesting resources discussing the adoption of R are this paper (PDF) by John Fox and this presentation by John Cook. Incidentally, John Fox is the author of my favorite book on regression and generalized linear models, no R code at all, just explanations and the math behind it all. John Cook writes The Endeavour, a very interesting blog with mathematical/programming bent.

On R versus SAS

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A short while ago there was a discussion on linkedin about the use of SAS versus R for the enterprise. I have thought a bit about the issue but, as I do not use Linkedin, I did not make any comments there.

Disclaimer: I did use SAS a lot between 1992 and 1997, mostly for genetic evaluation, heavily relying on BASE, STAT, IML and GRAPH. From that point on, I was a light SAS user (mostly STAT and IML) until 2009. The main reason I left SAS was that I started using ASReml in 1997 and, around two years ago asreml-R, the R package version of ASReml. Through my job I can access any statistical software; if the university does not have a license, I can buy an academic one without any issues.

I think it is important to make a distinction between enterprise use and huge datasets. Some companies have large datasets, but there probably are many companies that need to analyze large numbers of small to medium size datasets. If we accept this premise, there is room to use a diversity of statistical packages, including both SAS and R.

Another topic that often appears in the R vs. SAS discussion is cost. SAS licenses are not cheap, but for many large companies the cost of having expensive researchers with lower productivity while they learn another “free” system can be really high. Same issue applies if there are legacy programs: converting software to a new system can be expensive and time consuming. Of course this situation is changing: new graduates are being exposed much more to R than to SAS in many departments. We now use R in many courses and students may end up working in a small company that will be happy not to spend any money to pay for a SAS license.

The problem of openness versus closed source is, in my opinion, a bit of a red herring. Most users of statistical software will never have a look at statistical code (think of people driving software with menus). Most users will not be tempted about reading the source code. Most users will not need to recompile the software to make it work in some strange supercomputer. Besides the merits of “feeling good about oneself” for using open software, most users will not worry about losing access to SAS software, as the company has been on business for several decades (typical scenario put forward by open source advocates). After making clear the previous few points I should highlight why I choose R over SAS for both academic and commercial use:

  • There is good integration between the programming language and the statistical functions. Both SAS macros and IML are poorly integrated with the data step and procs.
  • R is highly conducive to exploratory data analysis; visualization functions (either the lattice or the ggplot 2 packages) produce high quality plots that really help developing ideas to build models.
  • Statistics is not defined by the software. If someone develops a new methodology or algorithm chances are that there will be an R implementation almost immediately. If I want to test a new idea I can scramble to write some code that connects packages developed by other researchers.
  • It is relatively easy to integrate R with other languages, for example Python, to glue a variety of systems.
  • asreml-r!
  • I can exchange ideas with a huge number of people, because slowly R is becoming the de facto standard for many disciplines that make use of statistics.

Of course R has many drawbacks when compared to SAS; for example:

  • The default editor in the Windows version is pathetic, while the one in OS X is pasable (code folding and proper refreshing would be great additions).
  • R syntax can be horribly inconsistent across packages, making the learning process more difficult.
  • There are many, too many, ways of doing the same thing, which can be confusing, particularly for newbies. For example, summarizing data by combinations of factors could be done using aggregate, summarize (from Hmisc), functions of the apply family, doBy, etc. Compare this situation to proc means.

No, I did not mention technical support (which I find a non-issue), access to large data sets (it is possible to integrate R with databases and ongoing work to process data that can’t fit in memory) or documentation. Concerning the latter, it would be helpful to have better R documentation, but SAS would also benefit from better manuals. There has been a huge number of books using R published recently and the documentation gap is closing. R would benefit of having good canonical documentation, something that all users could see first as the default documentation. The documentation included with the system is, how to call it, Spartan, and sometimes plain useless and confusing. A gigantic link to a searchable version of the R users email list from the main R project page would be great.