This package provides access to the Computational Geometry Algorithms Library (CGAL) in R. CGAL provides access to methods like KDtree, Hilbert sorting, convex hull calculation, and many more.
This package allows for the easy linking of the CGAL header files into R packages without having to download and manually add the appropriate CGAL header file into an R package.
Much like the BH package, the RcppCGAL package can be used via the LinkingTo: field in the DESCRIPTION file in R packages. This will allow access to the header files in C/C++ source code.
This package currently downloads the latest stable release from the CGAL GitHub.
To install this package, you can install the version from CRAN:
install.packages("RcppCGAL")Alternatively, you can download or clone the git repository. Then you can install using devtools
devtools::install("RcppCGAL")You may also install from github directly using the
devtools::install_github function.
By default, the package will try to download the header files from the
CGAL GitHub repository. If you already have one downloaded that you prefer to use,
you can specify the environmental variable CGAL_DIR and R will use that
instead:
Sys.setenv("CGAL_DIR" = "path/to/CGAL")Typically, the folder with all the header files is called CGAL. For example, on my Mac with a Homebrew install of CGAL, I would do
Sys.setenv("CGAL_DIR" = "/usr/local/Cellar/cgal/5.5.2/include/CGAL")Note: this must be done before the package is installed by R.
Alternatively, there is a function cgal_install() to download the header files after installation. This can be used to update to a new version of the header files or select an old version, if you wish. See the Installation vignette in the package.
We provide an example of how to perform Hilbert sorting using an R matrix:
// [[Rcpp::depends(RcppCGAL)]]
// [[Rcpp::depends(BH)]]
// [[Rcpp::depends(RcppEigen)]]
// [[Rcpp::plugins(cpp14)]]
#include <RcppEigen.h>
#include <CGAL/basic.h>
#include <CGAL/Cartesian_d.h>
#include <CGAL/spatial_sort.h>
#include <CGAL/Spatial_sort_traits_adapter_d.h>
#include <CGAL/boost/iterator/counting_iterator.hpp>
#include <CGAL/hilbert_sort.h>
#include <CGAL/Spatial_sort_traits_adapter_d.h>
typedef CGAL::Cartesian_d<double> Kernel;
typedef Kernel::Point_d Point_d;
typedef CGAL::Spatial_sort_traits_adapter_d<Kernel, Point_d*> Search_traits_d;
void hilbert_sort_cgal_fun(const double * A, int D, int N, int * idx)
{
std::vector<Point_d> v;
double * temp = new double[D];
for (int n = 0; n < N; n++ ) {
for (int d = 0; d < D; d ++) {
temp[d] = A[D * n + d];
}
v.push_back(Point_d(D, temp, temp+D));
}
std::vector<std::ptrdiff_t> temp_index;
temp_index.reserve(v.size());
std::copy(
boost::counting_iterator<std::ptrdiff_t>(0),
boost::counting_iterator<std::ptrdiff_t>(v.size()),
std::back_inserter(temp_index) );
CGAL::hilbert_sort (temp_index.begin(), temp_index.end(), Search_traits_d( &(v[0]) ) ) ;
for (int n = 0; n < N; n++) {
idx[n] = temp_index[n];
}
delete [] temp;
temp=NULL;
}
// [[Rcpp::export]]
Rcpp::IntegerVector hilbertSort(const Eigen::MatrixXd & A)
{
int K = A.rows();
int N = A.cols();
std::vector<int> idx(N);
hilbert_sort_cgal_fun(A.data(), K, N, &idx[0] );
return(Rcpp::wrap(idx));
}Saving this code as hilbertSort.cpp and sourcing with Rcpp Rcpp::sourceCpp("hilbertSort.cpp")
makes the function hilbertSort(). Be aware that this example
function example assumes that the observations are stored by
column rather than by row, that is as the transpose of the
usual R matrix or data.frame.
Eric Dunipace
This package is provided under the GPL-3. For the use of the header files outside this package, please see the information at the CGAL site: https://www.cgal.org/license.html