Spencer Would it not be easier to include this kind of test in a small file in the tests/ directory?
Paul -----Original Message----- From: r-devel-boun...@r-project.org [mailto:r-devel-boun...@r-project.org] On Behalf Of Spencer Graves Sent: January 17, 2011 3:58 PM To: Dominick Samperi Cc: Patrick Leyshock; r-devel@r-project.org; Dirk Eddelbuettel Subject: Re: [Rd] R vs. C For me, a major strength of R is the package development process. I've found this so valuable that I created a Wikipedia entry by that name and made additions to a Wikipedia entry on "software repository", noting that this process encourages good software development practices that I have not seen standardized for other languages. I encourage people to review this material and make additions or corrections as they like (or sent me suggestions for me to make appropriate changes). While R has other capabilities for unit and regression testing, I often include unit tests in the "examples" section of documentation files. To keep from cluttering the examples with unnecessary material, I often include something like the following: A1 <- myfunc() # to test myfunc A0 <- ("manual generation of the correct answer for A1") \dontshow{stopifnot(} # so the user doesn't see "stopifnot(" all.equal(A1, A0) # compare myfunc output with the correct answer \dontshow{)} # close paren on "stopifnot(". This may not be as good in some ways as a full suite of unit tests, which could be provided separately. However, this has the distinct advantage of including unit tests with the documentation in a way that should help users understand "myfunc". (Unit tests too detailed to show users could be completely enclosed in "\dontshow". Spencer On 1/17/2011 11:38 AM, Dominick Samperi wrote: > On Mon, Jan 17, 2011 at 2:08 PM, Spencer Graves< > spencer.gra...@structuremonitoring.com> wrote: > >> Another point I have not yet seen mentioned: If your code is >> painfully slow, that can often be fixed without leaving R by experimenting >> with different ways of doing the same thing -- often after using profiling >> your code to find the slowest part as described in chapter 3 of "Writing R >> Extensions". >> >> >> If I'm given code already written in C (or some other language), >> unless it's really simple, I may link to it rather than recode it in R. >> However, the problems with portability, maintainability, transparency to >> others who may not be very facile with C, etc., all suggest that it's well >> worth some effort experimenting with alternate ways of doing the same thing >> in R before jumping to C or something else. >> >> Hope this helps. >> Spencer >> >> >> >> On 1/17/2011 10:57 AM, David Henderson wrote: >> >>> I think we're also forgetting something, namely testing. If you write >>> your >>> routine in C, you have placed additional burden upon yourself to test your >>> C >>> code through unit tests, etc. If you write your code in R, you still need >>> the >>> unit tests, but you can rely on the well tested nature of R to allow you >>> to >>> reduce the number of tests of your algorithm. I routinely tell people at >>> Sage >>> Bionetworks where I am working now that your new C code needs to >>> experience at >>> least one order of magnitude increase in performance to warrant the effort >>> of >>> moving from R to C. >>> >>> But, then again, I am working with scientists who are not primarily, or >>> even >>> secondarily, coders... >>> >>> Dave H >>> >>> > This makes sense, but I have seem some very transparent algorithms turned > into vectorized R code > that is difficult to read (and thus to maintain or to change). These chunks > of optimized R code are like > embedded assembly, in the sense that nobody is likely to want to mess with > it. This could be addressed > by including pseudo code for the original (more transparent) algorithm as a > comment, but I have never > seen this done in practice (perhaps it could be enforced by R CMD check?!). > > On the other hand, in principle a well-documented piece of C/C++ code could > be much easier to understand, > without paying a performance penalty...but "coders" are not likely to place > this high on their > list of priorities. > > The bottom like is that R is an adaptor ("glue") language like Lisp that > makes it easy to mix and > match functions (using classes and generic functions), many of which are > written in C (or C++ > or Fortran) for performance reasons. Like any object-based system there can > be a lot of > object copying, and like any functional programming system, there can be a > lot of function > calls, resulting in poor performance for some applications. > > If you can vectorize your R code then you have effectively found a way to > benefit from > somebody else's C code, thus saving yourself some time. For operations other > than pure > vector calculations you will have to do the C/C++ programming yourself (or > call a library > that somebody else has written). > > Dominick > > > >>> ----- Original Message ---- >>> From: Dirk Eddelbuettel<e...@debian.org> >>> To: Patrick Leyshock<ngkbr...@gmail.com> >>> Cc: r-devel@r-project.org >>> Sent: Mon, January 17, 2011 10:13:36 AM >>> Subject: Re: [Rd] R vs. C >>> >>> >>> On 17 January 2011 at 09:13, Patrick Leyshock wrote: >>> | A question, please about development of R packages: >>> | >>> | Are there any guidelines or best practices for deciding when and why to >>> | implement an operation in R, vs. implementing it in C? The "Writing R >>> | Extensions" recommends "working in interpreted R code . . . this is >>> normally >>> | the best option." But we do write C-functions and access them in R - >>> the >>> | question is, when/why is this justified, and when/why is it NOT >>> justified? >>> | >>> | While I have identified helpful documents on R coding standards, I have >>> not >>> | seen notes/discussions on when/why to implement in R, vs. when to >>> implement >>> | in C. >>> >>> The (still fairly recent) book 'Software for Data Analysis: Programming >>> with >>> R' by John Chambers (Springer, 2008) has a lot to say about this. John >>> also >>> gave a talk in November which stressed 'multilanguage' approaches; see >>> e.g. >>> >>> http://blog.revolutionanalytics.com/2010/11/john-chambers-on-r-and-multilingualism.html >>> >>> >>> In short, it all depends, and it is unlikely that you will get a coherent >>> answer that is valid for all circumstances. We all love R for how >>> expressive >>> and powerful it is, yet there are times when something else is called for. >>> Exactly when that time is depends on a great many things and you have not >>> mentioned a single metric in your question. So I'd start with John's >>> book. >>> >>> Hope this helps, Dirk >>> >> ______________________________________________ >> R-devel@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-devel ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel ==================================================================================== La version française suit le texte anglais. ------------------------------------------------------------------------------------ This email may contain privileged and/or confidential information, and the Bank of Canada does not waive any related rights. 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