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An academic programming language paper about R

The R language has passed another milestone, a paper aimed at the academic programming language community (or at least one section of this community) has been written about it, Evaluating the Design of the R Language by Morandat, Hill, Osvald and Vitek. Hardly earth shattering news, but it may have some impact on how R is viewed by nonusers of the language (the many R users in finance probably don’t care that R seems to have been labeled as the language for doing statistics). The paper is well written and contains some very interesting information as well as a few mistakes, although it will probably read like gobbledygook to anybody not familiar with academic programming language research. What follows has something of the form of an R users guide to reading this paper, plus some commentary.

The paper has roughly three parts, the first gives an overview of R, the second is a formal definition of a subset and the third an initial report of an analysis of R usage. For me and I imagine you dear reader the really interesting stuff is in the third section.

When giving a language overview to people who know other computer languages it makes sense to leverage that knowledge, this is why the discussion has a world view from the perspective of languages rarely associated with R: Scheme, Haskell and CLOS. I found some of the discussion of R constructs to be much more informative and less confusing than that in nearly all R books/tutorials I have read, but then they are written from a detailed operational programming language perspective. One criticism of this overview is that it does not give any hint as to why R has such a large following (saying that users found it more useful than these languages would send the wrong kind of signal ;-).

What is a formal description of a subset of R (i.e., done purely using mathematics) doing in the second part? Well, until recently very little academic software engineering was empirically based and was populated by people I would classify as failed mathematicians without the common sense needed to be engineers. Things are starting to change but research that measures things, particularly people, is still regarded as not being respectable in some quarters. In this case the formal definition is playing the role of a virility symbol showing that the authors are obviously regular guys who happen to be indulging in a bit of empirical research.

A surprising number of papers measuring the usage of real software contain formal definitions of a subset of the language being measured. Subsets are used because handling the complete language is a big project that usually involves one or more people getting a PhD out of the work. The subset chosen have to look plausible to readers who understand the mathematics but not the programming language, broadly handle all the major constructs but not get involved with all the fiddly details that need years of work and many pages to describe.

The third part contains the real research, which is really about one implementation of R and the characteristics of R source in the CRAN and Bioconductor repositories, and contains lots of interesting information. Note: the authors are incorrect to aim nearly all of the criticisms in this subsection at R, these really apply to the current implementation of R and might not apply to a different implementation.

In a previous post I suggested some possibilities for speeding up the execution of R programs that depended on R usage characteristics. The Morandat paper goes a long way towards providing numbers for some of these usage characteristics (e.g., 37% of function parameters are assigned to and 36% of vectors contain a single value).

What do we learn from this first batch of measurements? R users rarely use many of the more complicated features (e.g., object oriented constructs {and this paper has been accepted at the European Conference on Object-Oriented Programming}), a result usually seen for other languages. I was a bit surprised that R programs were only 40% smaller than equivalent C programs. I think part of the reason is that some of the problems used for benchmarking are not the kind that would usually be solved using R and I did not see any ‘typical’ R programs being coded up in C for comparison, another possibility is that the authors were not thinking in R when writing the code.

One big measurement topic the authors missed is comparing their general findings with usage measurements of other languages. I think they will find lots of similar patterns of usage.

The complaint that R has been defined by the successive releases of its only implementation, rather than a written specification, applies to all widely used languages, at least in their early days. Back in the day a major reason for creating language standards for Pascal and then C was so that other implementations could be created; the handful of major languages whose specification was written before the first implementation (e.g., PL/1, Ada) have/are dieing out. Are multiple implementations needed in an Open Source world? The answer seems to be no for Perl and yes for PHP, Ruby etc. The effort needed to create a written specification for the R language might be better invested improving the efficiency of the current implementation so that a better alternative is not needed.

Needless to say the authors suggested committing the fatal programming language research mistake.

The authors have created an interesting set of tools for static and dynamic analysis of R and I look forward to reading more about their findings in future papers.

  1. April 27th, 2012 at 23:22 | #1

    If you think about it, having access to good numerical and statistical libraries would reduce the length of C (or another language) code dramatically. For example, Ben Klemens used the GSL and his own apophenia libraries to write Modeling with data. His code does not look dramatically longer than equivalent R code and, in many cases, is way faster.

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