Code Details

Derek Friend

Last compiled on September 17, 2021

Vignette content

This vignette gives a brief overview of the code structure of the quadtree package.

Implementation details

The bulk of the code is written in C++ and interfaced with R via ‘Rcpp’.

The overall design philosophy was to keep the core C++ code completely independent from the R code (i.e. no ‘Rcpp’-related code in the core C++ files.) This results in a three-tiered organization of the code - core C++ code, ‘Rcpp’ C++ code, and R code.

Core C++ code

This consists of the following files (only the .h files are listed to avoid redundancy, but each of these files has a corresponding .cpp file):

As mentioned before, these files are completely independent of R and can be built and run independently of R.

‘Rcpp’ C++ code

These files are called ‘wrappers’ - essentially they each contain an instance of the relevant object and provide additional ‘Rcpp’-related functions that can be accessed from R. These essentially provide the “bridge” that allows the functionality in the core C++ files to be accessed from R.

R code

Almost all of the core functionality of the quadtree package is contained in the C++ code, and the R code serves primarily as an interface for working with the C++ quadtree data structure. A Quadtree S4 class is defined which consists only of one slot, which contains a CppQuadtree object. The methods for this class are often quite simple, merely consisting of calling one of the methods on the CppQuadtree object. Similarly, the LcpFinder class contains a CppLcpFinder object. Using this approach has a few benefits. First, wrapping the C++ class in an S4 class allows the quadtree functionality to be accessed in a way that is much more consistent with typical R syntax, which will hopefully be more intuitive to R users. Second, it allows for me to add R code to validate and make any necessary modifications to parameters before calling the C++ methods - this helps make the functions more robust. This also allows me to take advantage of existing R functionality (for example, resampling a raster from the ‘raster’ package).

I won’t discuss each R file/function here - see the the function help files for details on each R function.