Keeping simple things simple

January 17, 2011

(This article was first published on Thinking inside the box , and kindly contributed to R-bloggers)

My friend Jeff deserves a sincere congratulation for finally unveiling his rebranded R
consultancy Lemnica. One notable
feature of the new website is a section called
esoteric R which
discusses less frequently-visited corners of the
R world.
It even boasts its own
CRAN package esotericR
with the example sources.
esoteric R currently
holds two articles. Jeff had sent me the one about
introducing closures
a while back, and I like it and may comment at another time. What caught me
by surprise when Lemnica finally opened
was the other article:
R calling C.

It is a fine article motivated by all the usual reasons that are e.g. mentioned in the
Google Tech Talk which
Romain and I gave last October about our work around
Rcpp. But it is
just not simple.

Allow me to explain. When Jeff showed this C language file

#include <R.h>
#include <Rinternals.h>

SEXP esoteric_rev (SEXP x) {
  SEXP res;
  int i, r, P=0;
  PROTECT(res = allocVector(REALSXP, length(x))); P++;

  for(i=length(x), r=0; i>0; i--, r++) {
     REAL(res)[r] = REAL(x)[i-1];

  copyMostAttrib(x, res);
  return res;

and then needs several paragraphs to explain what is going on, what is needed
to compile and then how to load it — I simply could not resist. Almost
immediately, I emailed back to him something as simple as this using both our
Rcpp package as well as the wonderful
inline package by Oleg
which Romain and I more or less adopted:

library(inline)  ## for cxxfunction()
src <- 'Rcpp::NumericVector x = Rcpp::NumericVector(xs);
        std::reverse(x.begin(), x.end());
fun <- cxxfunction(signature(xs="numeric"), body=src, plugin="Rcpp")
fun( seq(0, 1, 0.1) )

Here we load inline,
and then define a three-line C++ program using facilities from our
Rcpp package. All
we need to revert a vector is to first
access its R object in C++ by instantiating the R vector as a NumericVector.
These C++ classes then provide iterators which are compatible with the
Standard Template Library (STL). So we simply
call the STL function reverse pointing the beginning and end
of the vector, and are done! Rcpp then allows us the return the C++ vector
which it turns into an R vector. Efficient in-place reversal, just like Jeff
had motivated, in three lines. Best of all, we can execute this from within R itself:

R> library(inline)  ## for cxxfunction()
R> src <- 'Rcpp::NumericVector x = Rcpp::NumericVector(xs);
+         std::reverse(x.begin(), x.end());
+         return(x);'
R> fun <- cxxfunction(signature(xs="numeric"), body=src, plugin="Rcpp")
R> fun( seq(0, 1, 0.1) )
 [1] 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

Lastly, Jeff shows a more complete example wherein a new vector is created,
and any potential attributes are copied as well. Naturally, we can do that
too. First, we used clone() to make a deep copy (ie forcing
creation of a new object rather than a mere proxy) and use the same R API
function he accessed—but it our case both prefixed with ::Rf_
for R remapping (to protect clashed with other functions with identical names) and a global
namespace identifier (as it is a global C function from R).

R> library(inline)
R> src <- 'Rcpp::NumericVector x = Rcpp::clone<Rcpp::NumericVector>(xs);
+         std::reverse(x.begin(), x.end());
+         ::Rf_copyMostAttrib(xs, x);
+         return(x);'
R> fun <- cxxfunction(signature(xs="numeric"), body=src, plugin="Rcpp")
R> obj <- structure(seq(0, 1, 0.1), obligatory="hello, world!")
R> fun(obj)
 [1] 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0
[1] "hello, world!"
R> obj
 [1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
[1] "hello, world!"

Both the obj variable and the new copy contain the desired data
attribute, the new copy is reversed, the original is untouched—and all in
four lines of C++ called via one
inline call. I have
now been going on for over one hundred lines yet I never had to mention
memory management, pointers, PROTECT or other components of the
R API for C. Hopefully, this short writeup provided an idea of why
Romain and I think
Rcpp is the way to
go for creating C/C++ functions for extending and enhancing

To leave a comment for the author, please follow the link and comment on their blog: Thinking inside the box . offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.


Mango solutions

RStudio homepage

Zero Inflated Models and Generalized Linear Mixed Models with R

Quantide: statistical consulting and training


CRC R books series

Contact us if you wish to help support R-bloggers, and place your banner here.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)