[Rd] R vs. C

Patrick Burns pburns at pburns.seanet.com
Tue Jan 18 10:53:50 CET 2011

I'm not at all a fan of thinking
of the examples as being tests.

Examples should clarify the thinking
of potential users.  Tests should
clarify the space in which the code
is correct.  These two goals are
generally at odds.

On 17/01/2011 22:15, Spencer Graves wrote:
> Hi, Paul:
> The "Writing R Extensions" manual says that *.R code in a "tests"
> directory is run during "R CMD check". I suspect that many R programmers
> do this routinely. I probably should do that also. However, for me, it's
> simpler to have everything in the "examples" section of *.Rd files. I
> think the examples with independently developed answers provides useful
> documentation.
> Spencer
> On 1/17/2011 1:52 PM, Paul Gilbert wrote:
>> 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-bounces at r-project.org
>> [mailto:r-devel-bounces at r-project.org] On Behalf Of Spencer Graves
>> Sent: January 17, 2011 3:58 PM
>> To: Dominick Samperi
>> Cc: Patrick Leyshock; r-devel at 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.graves at 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<edd at debian.org>
>>>>> To: Patrick Leyshock<ngkbr8es at gmail.com>
>>>>> Cc: r-devel at 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
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Patrick Burns
pburns at pburns.seanet.com
twitter: @portfolioprobe
(home of 'Some hints for the R beginner'
and 'The R Inferno')

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