Programming Languages - R
R is a free, open-source software environment for statistical computing and graphics. It is able to compile and run across a wide variety of UNIX platforms, Windows, and MacOS. If after running through the examples today, you would like to learn more about R or complete additional examples, you can download and install R on your own computer.
Why use R?
- R is open-source and available across OS platforms, therefore the code used and produced here can be re-run at home.
- R is easily extended by users.
- R is a popular and professional choice for many statisticians, data scientists, and machine learning researchers.
R Documentation
- "Official" introduction manual R-intro.html | R-intro.pdf
- "Official" FAQ http://cran.r-project.org/doc/FAQ/R-FAQ.html
- MacOSX FAQ http://cran.r-project.org/bin/macosx/RMacOSX-FAQ.html
- Windows FAQ http://cran.r-project.org/bin/windows/base/rw-FAQ.html
- List of Packages HTML
- Quick Reference Card PDF
RStudio
RStudio is an integrated development environment (IDE) for R. The RStudio IDE is free and open-source that works across Windows, Mac, and Linux platforms.
Other IDEs, Editors, and Tools
- Rattle, gui for Data Mining using R Rattle
- ESS, Emacs Speaks Statistics ESS
- Vim-R plugin Vim
- Revolution Analytics RA
- Try R Online with R-Fiddle
Reproducible Research
Integrate publishing directly into code. R Markdown builds on Markdown with the ability to embed R code. Sweave also for combinging R with LaTeX. Pandoc is a universal mark-up converter.
- R Markdown Link
- Rpubs, public repository of RMarkdown in RStudio Rpubs
- Information on R Markdown with RStudio Link
Online Resources
- Cookbook for R Cookbook
- SimpleR by John Verzani, Using R for Introductory Statistics PDF
- The R Inferno by Patrick Burns, "If you are using R and you think you’re in hell, this is a map for you." PDF
- R Fundamental and Programming Techniques, Thomas Lumly - slides as PDF
- Learn R programming CodeSchool
- R Coding conventions Link
Online Courses
- Coursera Learn R Programming
part of a 9 course series on Data Science - edX Intro to R for Data Science
Tutorials
- Quick-R Tutorial
- R Tutorial, Kelly Black Tutorial
- Twotorials, video tutorials Tutorial-videos
- Di Cook, ACAS 2008, Tutorial, slides & code
Blogs
- Revolution R Blog
- R Bloggers
- Top 100 posts on R-bloggers Top posts