(formerly CSE 8091: Advanced Scientific Computing with R)

Scientific computing applies computational methods to scientific and engineering problems. This course will help you exploit the power of R, a freely available language and environment for statistical computing and graphics, to boost your research with state of the art data analysis and visualization. R is currently the 2nd most widely used environment for data analysis/mining beating the well-known commercial tools IBM/SPSS, SAS and Matlab (2011 KDDnuggets Survey) and the most used programing language for data mining and analytics (another 2011 KDDnuggets Survey).

The material collection of this page was created for the course CSE 8091 Advanced Scientific Computing with R held in Fall 2011 (see course syllabus). The material is suitable for learning R by yourself. Here are the steps you need to follow:

- Install R (see Tools and Software below). You probably also want to install RStudio.
- Work through "An Introduction to R" (see Readings Section below). The presentation slides and videos on this page cover a large part of the introduction.
- Check out the rest of the sections Readings and the Useful Links section below.

- Overview: A first R session - Slides / Video
- Objects, arrays and lists - Slides / Video
- Loops, apply and functions - Slides / Video
- Plots and Visualization - Slides / Video
- Creating simulated data - Slides
- Object-oriented programing - S3 object system / S4 object system
- Anatomy of packages - Example package TSP
- Basic regression and classification models - Code examples
- Computation using multiple-cores or a cluster - multi-core example, cluster example
- Visualizing Graphs - igraph examples (map.R)

- Getting started: An Introduction to R, R Reference Card
- How to find packages: CRAN Task Views, Search the R archives.
- Creating your own packages: Writing R Extensions Manual, Creating R Packages: A Tutorial
- Object-Oriented Programming: Object-oriented programming (R Language Definition), How S4 methods work
- Learn about the details of the R language (objects, expressions, debugging, etc.): R Language Definition
- Including "alive" R code into (Latex) documents: Sweave User Manual
- Creating GUIs and web applications with Shiny.

This is the preferred installation! You can do one of the following:

- Install Ubuntu on a spare partition to create a dual boot.
- Install Virtual Box and install Ubuntu on a virtual machine.

You can download R for Windows from CRAN.

RStudio makes working with R much easier.

- R-project home page
- The Comprehensive R Archive Network: Downloads, packages, documentation, etc.
- CRAN Task Views: Lists and discussed various R packages available sorted by application area.
- Search the R archives for answers
- Good Sources for R with examples: R Wiki, Quick-R, RDataMining.com, R-Tutor.com, R-bloggers
- Revolution Analytics: A commercial high-performance, scalable, enterprise-capable analytics platform using R.
- Cloud Computing: Running R on Amazon EC2
- Presentation on large data with R
- Presentation on parallelization with R

Michael Hahsler

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