In my example, I created a package called ‘finaljarvismarch’, and write the path to my cpp file in ‘cpp_files’. It is very simple to create: in R, run the code: package.skeleton(“The name of package”, cpp_files=”path to your c++ file”, example_code=FALSE) Skeleton folder just like the skeleton, containing all the main programs here. It allows users to terminal algorithm when it runs too long.Īfter obtaining our ‘cpp’ algorithm, we have to package it as a ‘zip’ file so it could be easily downloaded by anyone who wants to implement it in R.

Add user interrupt through Rcpp::checkUserInterrupt().

And next, we have to add some code in C++ to make sure it could be translated by R: So, I will write it in 3 steps and using an Example.įirstly, you have to write your own algorithm in C++ in a Linux system. It is useful when statisticians want to develop their own R package. With Rcpp, it could easily transfer the algorithm or functions between R and C++, providing high-performance statistical computing to most R users. Rcpp is an R Package that combines C++ and R. It is well known that the computing speed of R is slower than C++. This semester we have been trained to use C++ and Rcpp to write the R package.

Here we assume our readers are confident of C++, Linux and R. This blog is to give ideas how to build R package through Rcpp and C++.

Create your own r package