The document discusses various ways to speed up R code by integrating it with C/C++ code. It covers using .C to call C functions from R, .Call which allows more direct access to R data structures, and Rcpp which allows using C++ objects. The goal is to reduce data copying, access more of R's data and functionality, and optimize performance-critical parts of code. Examples are provided for each method.

1. Turbocharge your R
Rob Zinkov
July 12th, 2011
Rob Zinkov () Turbocharge your R July 12th, 2011 1 / 23

2. Outline
1 Introduction
2 .C
3 .Call
4 Rcpp
Rob Zinkov () Turbocharge your R July 12th, 2011 2 / 23

3. Introduction
What is the point of this talk?
Show you how to speed up your R code
Rob Zinkov () Turbocharge your R July 12th, 2011 3 / 23

4. Introduction
Caveats
• Please try to optimize your R code first
• Some of these mechanisms will make coding harder
Rob Zinkov () Turbocharge your R July 12th, 2011 4 / 23

5. .C
• This is the basic mechanism
• Explicitly copies the data into C
• Only accepts integer vectors
Rob Zinkov () Turbocharge your R July 12th, 2011 5 / 23

6. .C
Step 1. Put function in file (foo.c)
void foo(int *nin, double *x)
{
int n = nin[0];
int i;
for (i=0; i<n; i++)
x[i] = x[i] * x[i];
}
Rob Zinkov () Turbocharge your R July 12th, 2011 6 / 23

7. .C
• Note this is a void function
• Note arguments are passed in as pointers
• Try to limit one function per file
Rob Zinkov () Turbocharge your R July 12th, 2011 7 / 23

8. .C
Step 2. Compile file with R
$ R CMD SHLIB foo.c
Rob Zinkov () Turbocharge your R July 12th, 2011 8 / 23

9. .C
Step 3. Load into R
> dyn.load("foo.so")
Rob Zinkov () Turbocharge your R July 12th, 2011 9 / 23

10. .C
Step 4. Call your code
.C("foo", n=as.integer(5), x=as.double(rnorm(5)))
Rob Zinkov () Turbocharge your R July 12th, 2011 10 / 23

11. .C
• Arguments to .C are name of function followed by arguments
• Arguments must be the right type
• Touching C code runs risks of segfaults
Rob Zinkov () Turbocharge your R July 12th, 2011 11 / 23

12. .Call
Why?
• Less copying of data structures (lower memory)
• Access more of R data structures
• Access more kinds of R data
• Do more in C
Rob Zinkov () Turbocharge your R July 12th, 2011 12 / 23

13. .Call
.Call code
#include <R.h>
#include <Rinternals.h>
#include <Rmath.h>
SEXP vecSum(SEXP Rvec){
int i, n;
double *vec, value = 0;
vec = REAL(Rvec);
n = length(Rvec);
for (i = 0; i < n; i++) value += vec[i];
printf("The value is: %4.6f n", value);
return R_NilValue;
}
Rob Zinkov () Turbocharge your R July 12th, 2011 13 / 23

14. .Call
R CMD SHLIB vecSum.c
dyn.load("vecSum.so")
.Call("vecSum", rnorm(10))
Rob Zinkov () Turbocharge your R July 12th, 2011 14 / 23

15. .Call
SEXP ab(SEXP Ra, SEXP Rb){
int i, a, b;
SEXP Rval;
Ra = coerceVector(Ra, INTSXP);
Rb = coerceVector(Rb, INTSXP);
a = INTEGER(Ra)[0];
b = INTEGER(Rb)[0];
PROTECT(Rval = allocVector(INTSXP, b - a + 1));
for (i = a; i <= b; i++)
INTEGER(Rval)[i - a] = i;
UNPROTECT(1);
return Rval;
}
Rob Zinkov () Turbocharge your R July 12th, 2011 15 / 23

16. .Call
Since memory is shared explicit care must be taken not to collide with R
Rob Zinkov () Turbocharge your R July 12th, 2011 16 / 23

17. Rcpp
Why?
• Use C++ instead of C
• Ability to use objects to represent R more naturally
• Easier to load code
Rob Zinkov () Turbocharge your R July 12th, 2011 17 / 23

18. Rcpp
src <- ’
IntegerVector tmp(clone(x));
double rate = as< double >(y);
int tmpsize = tmp.size();
RNGScope scope;
for (int ii =0; ii < tmpsize; ii++) {
tmp(ii) = Rf_rbinom(tmp(ii), rate);
};
return tmp;
’
require(inline)
## compile the function, inspect the process with verbose=T
testfun2 = cxxfunction(signature(x=’integer’, y=’numeric’),
src, plugin=’Rcpp’, verbose=T)
Rob Zinkov () Turbocharge your R July 12th, 2011 18 / 23

19. Rcpp
require(inline)
testfun = cxxfunction(
signature(x="numeric",
i="integer"),
body = ’
NumericVector xx(x);
int ii = as<int>(i);
xx = xx * ii;
return( xx );
’, plugin="Rcpp")
testfun(1:5, 3)
Rob Zinkov () Turbocharge your R July 12th, 2011 19 / 23

20. Rcpp
Conclusions
It is fairly easy to make R faster
Rob Zinkov () Turbocharge your R July 12th, 2011 20 / 23

21. Rcpp
Conclusions
Now go make your R code faster
Rob Zinkov () Turbocharge your R July 12th, 2011 21 / 23

22. Rcpp
References
• http://www.stat.umn.edu/ charlie/rc/
• http://helmingstay.blogspot.com/2011/06/efficient-loops-in-r-
complexity-versus.html
• http://www.sfu.ca/ sblay/R-C-interface.ppt
• http://www.biostat.jhsph.edu/ bcaffo/statcomp/files/dotCall.pdf
• http://cran.r-project.org/web/packages/Rcpp/vignettes/Rcpp-
quickref.pdf
• http://www.jstatsoft.org/v40/i08/paper
Rob Zinkov () Turbocharge your R July 12th, 2011 22 / 23

23. Rcpp
Questions?
Rob Zinkov () Turbocharge your R July 12th, 2011 23 / 23