Last updated: 2020-02-25
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#include <RcppArmadillo.h>
// Note #include <Rcpp.h> implied by RcppArmadillo.
using namespace Rcpp;Let’s begin by examining to very simple examples of calling C++ functions within R :
// [[Rcpp::export]]
int square(int x) {
return x*x;
}
// [[Rcpp::export]]
int add(int x, int y, int z) {
int sum = x + y + z;
return sum;
}Note the // [[Rcpp::export]] line allows the function to be exported into R . Within R , I can now call:
square(7L)[1] 49add(1, 2, 3)[1] 6It is very straightforward to call the C++ implemented function within R. The motivation behind Rcpp comes when you run into a computation in R that create a performance bottleneck in your code. By instead writing your function in C++, you can achieve a massive speed boost. Rcpp seeks to make the interaction between R and C++ as seamless and convenient as possible.
It can be even easier to parse C++ code in R
Rcpp::cppFunction('double accu(NumericVector x) { return(
std::accumulate(x.begin(), x.end(), 0.0)
);
}')
res <- accu(seq(1, 10, by=0.5))
res[1] 104.5cppFunction parses C++ code and compiles it for use within R. If you have an external .cpp file you want to call within R, can use sourceCpp. You can also evaluate a single C++ statement with evalCpp()
# Showing maximum value of double.
Rcpp::evalCpp('std::numeric_limits<double>::max()')[1] 1.797693e+308Here’s a 2-minute C++ introduction:
// This is a comment
/* Also this */
// [[Rcpp::export]]
double sumC(NumericVector x) {
int n = x.size();
double total = 0;
for(int i = 0; i < n; ++i) {
total += x[i]; if(total > 100) break;
}
return total;
}sumC(seq(1:10))[1] 55Notice the following things about C++:
A quick introduction to Rcpp objects and operations:
Example:
// Rcpp implementation
// [[Rcpp::export]]
NumericVector pdistCpp(double x, NumericVector ys) {
return pow((x-ys), 2);
}# R implementation
pdistR <- function(x, ys) {
(x - ys)^2
}
pdistR(5.0, c(4.1, -9.3, 0, 13.7))[1] 0.81 204.49 25.00 75.69pdistCpp(5.0, c(4.1, -9.3, 0, 13.7))[1] 0.81 204.49 25.00 75.69Note the similarity of the Rcpp and R implementations. Rcpp performs R styled vectorizations.
// two integer vectors of the same size
NumericVector x;
NumericVector y;
// expressions involving two vectors
LogicalVector res = x < y;
LogicalVector res = x != y;
// one vector, one single value
LogicalVector res = x < 2;
// two expressions
LogicalVector res = (x + y) == (x*x);
// functions producing single boolean result
all(x*x < 3);
any(x*x < 3);There are many functions similar to what exists inside R:
is_na(x);
seq_along(x);
sapply( seq_len(10), square<int>() );
ifelse( x < y, x, (x+y)*y );
pmin( x, x*x);
diff( xx );
intersect( xx, yy); //returns interserct of two vectors
unique( xx ); // subset of unique values in input vecto
// math functions
abs(x); exp(x); log(x); ceil(x);
sqrt(x); sin(x); gamma(x);
range(x);
mean(x); sd(x); var(x);
which_min(x); which_max(x);
// A bunch moreRcpp has access to the same density, distribution, and RNG functions used by R itself. The seed you set in R will even be passed into Rcpp. For example, you can draw from a gamma distribution with scale and shape parameters equal to 1 with:
RNGScope scope;
// [[Rcpp::export]]
NumericVector getRGamma() {
NumericVector x = rgamma( 10, 1, 1 );
return x;
}set.seed(1234)
getRGamma() [1] 0.01072755 0.74699906 0.78632796 0.11687523 0.92185869 0.17617898
[7] 1.43714223 0.15671970 0.21950202 3.52757261set.seed(1234)
rgamma(10, 1, 1) [1] 0.01072755 0.74699906 0.78632796 0.11687523 0.92185869 0.17617898
[7] 1.43714223 0.15671970 0.21950202 3.52757261Don’t use stdout and stderr as you would in C++. Instead, use Rcpp::Rcout or Rcpp::print.
// [[Rcpp::export]]
void useOperatorOnVector(NumericVector x) {
Rcpp::Rcout << "Rcpp vector is " << std::endl << x << std::endl;
}
// [[Rcpp::export]]
void callPrint(RObject x) {
Rcpp::print(x); // will work on any SEXP object
}v <- seq(0.0, 10.0, by=2.5)
useOperatorOnVector(v)Rcpp vector is
0 2.5 5 7.5 10callPrint(v)[1] 0.0 2.5 5.0 7.5 10.0// [[Rcpp::export]]
NumericVector logRcpp1(NumericVector invec) {
NumericVector outvec(invec);
for(int i=0; i<invec.size(); i++) {
outvec[i] = log(invec[i]);
}
return outvec;
}x <- seq(1.0, 3.0, by=1)
cbind(x, logRcpp1(x)) x
[1,] 0.0000000 0.0000000
[2,] 0.6931472 0.6931472
[3,] 1.0986123 1.0986123Note: outvec and invec point to the same underlying R object.
Instead, Use clone to not modify original vector.
// [[Rcpp::export]]
NumericVector logRcpp2(NumericVector invec) {
NumericVector outvec=clone(invec);
for(int i=0; i<invec.size(); i++) {
outvec[i] = log(invec[i]);
}
return outvec;
}x <- seq(1.0, 3.0, by=1)
cbind(x, logRcpp2(x)) x
[1,] 1 0.0000000
[2,] 2 0.6931472
[3,] 3 1.0986123#include <RcppArmadillo.h>
// [[Rcpp::depends(RcppArmadillo)]]
// [[Rcpp::export]]
void showMatrix(arma::mat X) {
Rcout << "Armadillo matrix is" << std::endl << X << std::endl;
}
// [[Rcpp::export]]
List svd_arma(arma::mat x) {
arma::mat xtx = x.t() * x ;
arma::mat U ;
arma::vec s ;
arma::mat V ;
svd(U, s, V, xtx) ;
List ret ;
ret["U"] = U ;
ret["s"] = s ;
ret["V"] = V ;
return(ret) ;
}M <- matrix(1:9,3,3)
showMatrix(M)Armadillo matrix is
1.0000 4.0000 7.0000
2.0000 5.0000 8.0000
3.0000 6.0000 9.0000## SVD implemented in Armadillo
svd_arma(M)$U
[,1] [,2] [,3]
[1,] -0.2148372 0.8872307 0.4082483
[2,] -0.5205874 0.2496440 -0.8164966
[3,] -0.8263375 -0.3879428 0.4082483
$s
[,1]
[1,] 2.838586e+02
[2,] 1.141413e+00
[3,] 1.462429e-15
$V
[,1] [,2] [,3]
[1,] -0.2148372 0.8872307 -0.4082483
[2,] -0.5205874 0.2496440 0.8164966
[3,] -0.8263375 -0.3879428 -0.4082483Note above was an example of returning multiple objects to R in a list.
Before using Rcpp in your package, you should first consider if it’s necessary.
sessionInfo()R version 3.5.2 (2018-12-20)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.5
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] nvimcom_0.9-81
loaded via a namespace (and not attached):
[1] workflowr_1.6.0 Rcpp_1.0.3
[3] rprojroot_1.3-2 digest_0.6.21
[5] later_1.0.0 R6_2.4.0
[7] backports_1.1.5 git2r_0.26.1
[9] magrittr_1.5 evaluate_0.14
[11] stringi_1.4.3 rlang_0.4.0
[13] RcppArmadillo_0.9.850.1.0 fs_1.3.1
[15] promises_1.1.0 whisker_0.4
[17] rmarkdown_1.16 tools_3.5.2
[19] stringr_1.4.0 glue_1.3.1
[21] httpuv_1.5.2 xfun_0.10
[23] yaml_2.2.0 compiler_3.5.2
[25] htmltools_0.4.0 knitr_1.25