RCBL project:Presentation
M. E. Macherki · Figshare · 2016
description R [1] [2] is one of the most widely used programming languages and it is very simple to use .Firstly, the R language is used to deal with statistic tools and using their high levels graphical tools. After years of package development, R is extended to the entire science subject. In fact of expensive documentation, R is easy to learn and may be few hours can lead user to make advanced tasks using this very simple tools. The negative point is that R is not fast if it is derailed from vectorization which uses internal or primitive functions writing in C or in FORTRAN. Normally, if we use R, iterative function had a long running time. R use the virtual machine such JAVA and consider as high level programming language. The memory access is automatically controlled and the data structures are checked every time are called, respecting then the R purpose to be interactive and efficacy. Thus, user had to use compiled code to overcome the problem, using for example the byte code [3] provided by the compiler package. The 'Writing R Extensions' [4] manual notes that it is possible to run functions using foreign low level language such C or FORTRAN. However, this operation is not easy to handle even with the help of advanced packages such inline. The C++ language is considered as low level programming and compiled language. That is mean, before program execution, script had to be compiled to the binary coding which provide faster running time. The Rcpp [5] is a package used to integrate the C++ code into R. This package provides the ability to use the C++ style to program function, create class and modules. 1. Team, R.C., R: A language and environment for statistical computing. R Foundation for Statistical Computing, 2016.2. Gentleman, R.C., et al., Bioconductor: open software development for computational biology and bioinformatics. Genome biology, 2004. 5(10): p. R80.3. Tierney, L., A byte code compiler for R. system, 2014. 6: p. 0.010.4. Team, R.C., Writing R Extensions. R Foundation for Statistical Computing, 1999. 5. Eddelbuettel, D., et al., Rcpp: Seamless R and C++ integration. Journal of Statistical Software, 2011. 40(8): p. 1-18. Purpose: This project aims to create a library of Rcpp functions can be used directly from R after compiled with the Rcpp package or used as basics for Rcpp programming. Each function will be explicated in detail and provides examples and applications in the computational biology purpose. The RCBL is in facts an application tools from a side and in the other a source for learning Rcpp API. This library uses the function programming paradigm (no class) because it is more simply to understand for an R user.