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Following the Rcpp 0.10.0 release, I had written about

simulating pi easily by using the wonderful new

*Rcpp Attributes* feature.

Now with Rcpp 0.10.1 released a good week ago, it is time to look

at how *Rcpp Attributes* can help with external libraries. As this posts aims to show, it is a breeze!

One key aspect is the use of the *plugins* for the inline package. They provide

something akin to a callback mechanism so that compilation and linking steps can be informed about header and library locations and names.

We are going to illustrate this with an example from GNU Scientific Library (GSL). The example I picked

uses B-Spline estimation from the GSL. This is a little redundant as R has its own spline routines and package, but serves well as a simple

illustration—and, by reproducing an existing example, followed an established path. So we will look at

Section 39.7 of the GSL manual which has a

complete example as a standalone C program, generating both the data and the fit via cubic B-splines.

We can decompose this two parts: data generation, and fitting. We will provide one function each, and then use both from R. These two

function will follow the

aforementioned example from Section 39.7

somewhat closely.

We start with the first function to generate the data.

// [[Rcpp::depends(RcppGSL)]] #include#include #include #include #include #include const int N = 200; // number of data points to fit const int NCOEFFS = 12; // number of fit coefficients */ const int NBREAK = (NCOEFFS - 2); // nbreak = ncoeffs + 2 - k = ncoeffs - 2 since k = 4 */ // [[Rcpp::export]] Rcpp::List genData() { const size_t n = N; size_t i; double dy; gsl_rng *r; RcppGSL::vector<double> w(n), x(n), y(n); gsl_rng_env_setup(); r = gsl_rng_alloc(gsl_rng_default); //printf("#m=0,S=0\n"); /* this is the data to be fitted */ for (i = 0; i < n; ++i) { double sigma; double xi = (15.0 / (N - 1)) * i; double yi = cos(xi) * exp(-0.1 * xi); sigma = 0.1 * yi; dy = gsl_ran_gaussian(r, sigma); yi += dy; gsl_vector_set(x, i, xi); gsl_vector_set(y, i, yi); gsl_vector_set(w, i, 1.0 / (sigma * sigma)); //printf("%f %f\n", xi, yi); } Rcpp::DataFrame res = Rcpp::DataFrame::create(Rcpp::Named("x") = x, Rcpp::Named("y") = y, Rcpp::Named("w") = w); x.free(); y.free(); w.free(); gsl_rng_free(r); return(res); }

We include a few header files, define (in what is common for C programs) a few constants and then define a single function

`genData()`

which returns and `Rcpp::List`

as a list object to R. A primary importance here are the two attributes:

one to declare a dependence on the RcppGSL package, and one to declare the

export of the data generator function. *That is all it takes!* The plugin of the

RcppGSL will provide information about the headers and library, and *Rcpp
Attributes* will do the rest.

The core of the function is fairly self-explanatory, and closely follows the

original example. Space gets

allocated, the RNG is setup and a simple functional form generates some data plus noise (see below). In the original, the data is written to

the standard output; here we return it to R as three columns in a `data.frame`

object familiar to R users. We then free the GSL

vectors; this manual step is needed as they are implemented as C vectors which do not have a destructor.

Next, we can turn the fitting function.

// [[Rcpp::export]] Rcpp::List fitData(Rcpp::DataFrame ds) { const size_t ncoeffs = NCOEFFS; const size_t nbreak = NBREAK; const size_t n = N; size_t i, j; Rcpp::DataFrame D(ds); // construct the data.frame object RcppGSL::vector<double> y = D["y"]; // access columns by name, RcppGSL::vector<double> x = D["x"]; // assigning to GSL vectors RcppGSL::vector<double> w = D["w"]; gsl_bspline_workspace *bw; gsl_vector *B; gsl_vector *c; gsl_matrix *X, *cov; gsl_multifit_linear_workspace *mw; double chisq, Rsq, dof, tss; bw = gsl_bspline_alloc(4, nbreak); // allocate a cubic bspline workspace (k = 4) B = gsl_vector_alloc(ncoeffs); X = gsl_matrix_alloc(n, ncoeffs); c = gsl_vector_alloc(ncoeffs); cov = gsl_matrix_alloc(ncoeffs, ncoeffs); mw = gsl_multifit_linear_alloc(n, ncoeffs); gsl_bspline_knots_uniform(0.0, 15.0, bw); // use uniform breakpoints on [0, 15] for (i = 0; i < n; ++i) { // construct the fit matrix X double xi = gsl_vector_get(x, i); gsl_bspline_eval(xi, B, bw); // compute B_j(xi) for all j for (j = 0; j < ncoeffs; ++j) { // fill in row i of X double Bj = gsl_vector_get(B, j); gsl_matrix_set(X, i, j, Bj); } } gsl_multifit_wlinear(X, w, y, c, cov, &chisq, mw); // do the fit dof = n - ncoeffs; tss = gsl_stats_wtss(w->data, 1, y->data, 1, y->size); Rsq = 1.0 - chisq / tss; Rcpp::NumericVector FX(151), FY(151); // output the smoothed curve double xi, yi, yerr; for (xi = 0.0, i=0; xi < 15.0; xi += 0.1, i++) { gsl_bspline_eval(xi, B, bw); gsl_multifit_linear_est(B, c, cov, &yi, &yerr); FX[i] = xi; FY[i] = yi; } Rcpp::List res = Rcpp::List::create(Rcpp::Named("X")=FX, Rcpp::Named("Y")=FY, Rcpp::Named("chisqdof")=Rcpp::wrap(chisq/dof), Rcpp::Named("rsq")=Rcpp::wrap(Rsq)); gsl_bspline_free(bw); gsl_vector_free(B); gsl_matrix_free(X); gsl_vector_free(c); gsl_matrix_free(cov); gsl_multifit_linear_free(mw); y.free(); x.free(); w.free(); return(res); }

The second function closely follows the second part of the GSL example and, given the input data, fits the output data. Data structures are

setup, the spline basis is created, data is fit and then the fit is evaluated at a number of points. These two vectors are returned along

with two goodness of fit measures.

We only need to load the Rcpp package and source a file containing the two

snippets shown above, and we are ready to deploy this:

library(Rcpp) sourceCpp("bSpline.cpp") # compile two functions dat <- genData() # generate the data fit <- fitData(dat) # fit the model, returns matrix and gof measures

And with that, we generate a chart such as

via a simple four lines, or as much as it took to create the C++ functions, generate the data and fit it!

op <- par(mar=c(3,3,1,1)) plot(dat[,"x"], dat[,"y"], pch=19, col="#00000044") lines(fit[[1]], fit[[2]], col="orange", lwd=2) par(op)

The RcppArmadillo and RcppEigen package support plugin use in the same way. Add an attribute to export a function, and an attribute for the

depends — and you’re done. Extending R with (potentially much faster) C++ code has never been easier, and opens a whole new set of doors.

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