Rcpp
Misc
- http://cran.r-project.org/web/packages/Rcpp/index.html
- Discussion archive
- (Video) Extending R with C++: A Brief Introduction to Rcpp
- C++14, R and Travis -- A useful hack
- For RcppEigen, it is necessary to install dependency packages.
sudo apt-get install libblas-dev liblapack-dev sudo apt-get install gfortran
Speed Comparison
- A comparison of high-performance computing techniques in R. It compares Rcpp to an R looping operator (like mapply), a parallelized version of a looping operator (like mcmapply), explicit parallelization, via the parallel package or the ParallelR suite.
- In the following example, C++ avoids the overhead of creating an intermediate object (eg vector of the same length as the original vector). The c++ uses an intermediate scalar. So C++ wins R over memory management in this case.
# http://blog.mckuhn.de/2016/03/avoiding-unnecessary-memory-allocations.html library(Rcpp) `%count<%` <- cppFunction(' size_t count_less(NumericVector x, NumericVector y) { const size_t nx = x.size(); const size_t ny = y.size(); if (nx > 1 & ny > 1) stop("Only one parameter can be a vector!"); size_t count = 0; if (nx == 1) { double c = x[0]; for (int i = 0; i < ny; i++) count += c < y[i]; } else { double c = y[0]; for (int i = 0; i < nx; i++) count += x[i] < c; } return count; } ') set.seed(42) N <- 10^7 v <- runif(N, 0, 10000) # Testing on my ODroid xu4 running ubuntu 15.10 system.time(sum(v < 5000)) # user system elapsed # 1.135 0.305 1.453 system.time(v %count<% 5000) # user system elapsed # 0.535 0.000 0.540 - Why R for data science – and not Python?
library(Rcpp) bmi_R <- function(weight, height) { weight / (height * height) } bmi_R(80, 1.85) # body mass index of person with 80 kg and 185 cm ## [1] 23.37473 cppFunction(" float bmi_cpp(float weight, float height) { return weight / (height * height); } ") bmi_cpp(80, 1.85) # same with cpp function ## [1] 23.37473 - Boost the speed of R calls from Rcpp
Use Rcpp in RStudio
RStudio makes it easy to use Rcpp package.
Open RStudio, click New File -> C++ File. It will create a C++ template on the RStudio editor
#include <Rcpp.h>
using namespace Rcpp;
// Below is a simple example of exporting a C++ function to R. You can
// source this function into an R session using the Rcpp::sourceCpp
// function (or via the Source button on the editor toolbar)
// For more on using Rcpp click the Help button on the editor toolbar
// [[Rcpp::export]]
int timesTwo(int x) {
return x * 2;
}
Now in R console, type
library(Rcpp)
sourceCpp("~/Downloads/timesTwo.cpp")
timesTwo(9)
# [1] 18
See more examples on http://adv-r.had.co.nz/Rcpp.html and Calculating a fuzzy kmeans membership matrix
If we wan to test Boost library, we can try it in RStudio. Consider the following example in stackoverflow.com.
// [[Rcpp::depends(BH)]]
#include <Rcpp.h>
#include <boost/foreach.hpp>
#include <boost/math/special_functions/gamma.hpp>
#define foreach BOOST_FOREACH
using namespace boost::math;
//[[Rcpp::export]]
Rcpp::NumericVector boost_gamma( Rcpp::NumericVector x ) {
foreach( double& elem, x ) {
elem = boost::math::tgamma(elem);
};
return x;
}
Then the R console
boost_gamma(0:10 + 1) # [1] 1 1 2 6 24 120 720 5040 40320 # [10] 362880 3628800 identical( boost_gamma(0:10 + 1), factorial(0:10) ) # [1] TRUE
Example 1. convolution example
First, Rcpp package should be installed (I am working on Linux system). Next we try one example shipped in Rcpp package.
PS. If R was not available in global environment (such as built by ourselves), we need to modify 'Makefile' file by replacing 'R' command with its complete path (4 places).
cd ~/R/x86_64-pc-linux-gnu-library/3.0/Rcpp/examples/ConvolveBenchmarks/ make R
Then type the following in an R session to see how it works. Note that we don't need to issue library(Rcpp) in R.
dyn.load("convolve3_cpp.so")
x <- .Call("convolve3cpp", 1:3, 4:6)
x # 4 13 28 27 18
If we have our own cpp file, we need to use the following way to create dynamic loaded library file. Note that the character (grave accent) ` is not (single quote)'. If you mistakenly use ', it won't work.
export PKG_CXXFLAGS=`Rscript -e "Rcpp:::CxxFlags()"` export PKG_LIBS=`Rscript -e "Rcpp:::LdFlags()"` R CMD SHLIB xxxx.cpp
Example 2. Use together with inline package
library(inline) src <-' Rcpp::NumericVector xa(a); Rcpp::NumericVector xb(b); int n_xa = xa.size(), n_xb = xb.size(); Rcpp::NumericVector xab(n_xa + n_xb - 1); for (int i = 0; i < n_xa; i++) for (int j = 0; j < n_xb; j++) xab[i + j] += xa[i] * xb[j]; return xab; ' fun <- cxxfunction(signature(a = "numeric", b = "numeric"), src, plugin = "Rcpp") fun(1:3, 1:4) # [1] 1 4 10 16 17 12