# R, where should I start?

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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

### e-Books for Learning the R Language

### 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

### 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

### Interesting Blogs

### 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)

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