Using R for scientific computing

R (R Development Core Team 2008) is the open-source (read: free-of-charge) version of the language S. It is best known as a package that performs statistical analysis and graphics. However, R is so much more: it is a high-level language in which one can perform complex calculations, implement new methods, and make high-quality figures. R has high-level functions to operate on matrices, perform numerical integration, advanced statistics,... which are easily triggered and which make it ideally suited for datavisualization, statistical analysis and mathematical modeling. It is the aim of these lecture notes to make you acquainted with the R language. The lecture notes are based on a book (Soetaert and Herman 2009) about ecological modelling in which R is extensively used for developing, applying and visualizing simulation models. There are many excellent sources for learning the R (or S) language. R comes with several manuals that can be consulted from the main R program (Help/Manuals). Rintro.pdf is a good start. Many other good introductions to R are available, some freely on the web, and accessible via the R web site (www.r-project.org). My favorite is the R introduction by Petra Kuhnert and Bill Venables (Kuhnert and Venables 2005), but beware: this ”introduction” comprises more than 300 pages!