This is a dynamic post which I will continue to update whenever I find something new. Hope you will find the following links useful.

### Online Courses for Learning the R language

### Free Documentations for Learning the R Language

- R for Beginners by Emmanuel Paradis
- R Graphics by Paul Murrel
- ggplot2 (official documentation)
- Advanced R Programming by Hadley Wickham

### Online Courses for Data Mining with R

### e-Books for Data Mining with R

- R and Data Mining: Examples and Case Studies by Yanchang Zhao (Really useful worked examples!)
- Data Mining Algorithms in R (Wikibooks)
- The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman
- Introduction to Data Science by Jeffrey Stanton
- Forecasting: Principles and Practice by Rob Hyndman and George Athanasopoulous
- Bayesian Computation with R (Free Kindle Edition): UK Link, US Link (Aug 2013 Update: No longer free)
- 12 Free (as in beer) Data Mining Books

### R Tutorials

- Twotorials by Anthony Damico (learning new tricks from short 2-min videos)
- Revolution Analytics Free Webinars
- ggplot2 Graphics Cheat Sheet
- 10 tips for making your R graphics look their best
- Making Maps with R
- Compiling R 3.0.1 with MKL support
- Flowing Data - Tutorials
- Quick-R
- R-Uni (A List of Free R Tutorials and Resources in University Webpages)

### Interesting Blogs and Articles

- R-bloggers
- Statistics Blogs
- Whizage by Thia Kai Xin
- R Resources by Vivek Patil
- 100 most read R posts in 2012
- "R" you ready?
- The Angry Statistician
- VizWiz - Data Visualization Done Right
- FastML - Machine learning made easy
- ggplot2 Blog
- Spatial.ly - Visualisation, Analysis and Resources
- Vistat - a reproducible gallery of statistical graphics
- The Shape of Data
- Animated Graph for Data People
- is.R()
- 60+ R resources to improve your data skills

### Useful R Packages

- Ten R packages I wish I knew about earlier (Before you do anything, read this blog post first!!)
- caret (short for Classification And REgression Training) for a simple way to train and fine-tune model using different algorithms
- ff and bigmemory - two packages to solve memory issues with big datasets
- quantmod for financial modelling
- foreach and doSNOW for parallel computing in R

### Interactive Development Environment

- RStudio - a really nice IDE for R
- RStudio Server Amazon Machine Image by Louis Aslett (Wanna run RStudio on Amazon EC2? Try this!)

### Other Useful Tips

### Data

- UC Irvine Machine Learning Repository
- Time Series Data Library created by Rob Hyndman
- Quandl (like a Wikipedia for Time Series Data)