Site icon R-bloggers

R, where should I start?

Jo-fai Chow

[This article was first published on Blend it like a Bayesian!, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
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

  1. Try R from Code School

e-Books for Learning the R Language

  1. R for Beginners by Emmanuel Paradis
  2. R Graphics by Paul Murrel
  3. ggplot2 (official documentation)

Online Courses for Data Mining with R

  1. Data Analysis by Jeff Leek (Coursera)
  2. Computing for Data Analysis by Roger Peng (Coursera)
  3. Data and Computing Fundamentals by Danny Kaplan and Libby Shoop (Macalester College)

e-Books for Data Mining with R

  1. R and Data Mining: Examples and Case Studies by Yanchang Zhao (Really useful worked examples!)
  2. Data Mining Algorithms in R (Wikibooks)
  3. The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman
  4. Introduction to Data Science by Jeffrey Stanton
  5. Forecasting: Principles and Practice by Rob Hyndman and George Athanasopoulous
  6. Bayesian Computation with R (Free Kindle Edition): UK Link, US Link

R Tutorials

  1. Twotorials by Anthony Damico (learning new tricks from short 2-min videos)
  2. Revolution Analytics Free Webinars
  3. ggplot2 Graphics Cheat Sheet
  4. 10 tips for making your R graphics look their best
  5. Making Maps with R
  6. Compiling R 3.0.1 with MKL support

Interesting Blogs

Useful R Packages

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

Interactive Development Environment

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

Other Useful Tips

  1. Re-installing packages in R version 3.0.0

Data

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

To leave a comment for the author, please follow the link and comment on their blog: Blend it like a Bayesian!.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.