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 bundles the 6.0.1 stable release.
It’s important to note that the version number of the package roughly corresponds (as best I’m able to) with the version of the CGAL header files.
This can be important to make note of as future versions of the CGAL header files may cause breaking changes in your packages. This last happened with the switch from 5.x to 6.x when the flag for ignoring GMP changed!
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 use the header files bundled with the
package. However, if you already have a version of the CGAL headers 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")or, if the function is already installed, you can use the
set_cgal() function in the package
set_cgal("path/to/CGAL")and then re-install.
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/5.6/include/CGAL")Note: this must be done before the package is installed by
R.
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