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
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:#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); UNPROTECT(P); return res; }
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 alibrary(inline) ## for cxxfunction() src <- 'Rcpp::NumericVector x = Rcpp::NumericVector(xs); std::reverse(x.begin(), x.end()); return(x);' fun <- cxxfunction(signature(xs="numeric"), body=src, plugin="Rcpp") fun( seq(0, 1, 0.1) )
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 R>
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).
Both theR> 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 attr(,"obligatory") [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 attr(,"obligatory") [1] "hello, world!" R>
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
R.