**Learning Path for R**

**1. Start with** Why R

**2. **Download R from CRAN also download R-Studio, install R-packages, some more important packages

**3. Start with R** basics from Datacamp or Code School for R or interactive swirl or the RGuide

**4. Few more important R packages**

- data.table tutorial, Cheat sheet for data.table
- dplyr tutorial
- dplyr slides
- Word cloud in R Part 1 and Part 2.
- Social Network Analysis
- For sentiment analysis using Twitter check here and here.
- For optimization through R read here and here
- Connect to databases through RODBC package
- Sql queries to data frames through sqldf package.

**5. Data Visualization** tutousing ggplot2

**6. Data Mining & Machine Learning in R **read data mining in R or togaware

- Book on Rattle
- A Little Book for Time Series in R
- Some more machine learning in R is here. free course here

**7. Practice **

- Compete with fellow Data Scientists on Kaggle
- More advanced Kaggle Analysis
- Subscribe to http://www.r-bloggers.com/
- Follow #rstats hashtag
- For quick help Quick-R

**8: Advanced Topics**

- For R on large datasets see RHadoop
- R with MongoDB
- R with NoSQL
- Make interactive web apps using Shiny from RStudio.
- R and Python syntax relate read this guide
- RevoScaleR package from Revolution Analytics sample here

**R Resources**

http://r-project.org

http://cran.r-project.org/

http://blog.revolutionanalytics.com

http://www.r-bloggers.com/

**R online (in browser)**

http://www.roncloud.com

http://www.compileonline.com/execute_r_online.php

http://www.r-fiddle.org

http://pbil.univ-lyon1.fr/Rweb/Rweb.general.html

**R Studio**

http://www.rstudio.com/

http://shiny.rstudio.com/

https://github.com/rstudio

**More Resources**

- Learn R via interactive tutorial
- R reference card (more Reference Cards here)
- Quick-R: quick online reference for data input, basic statistics and plots
- Thomas Girke’s R & Bioconductor manuals
- https://cran.r-project.org/doc/contrib/Owen-TheRGuide.pdf
- https://cran.r-project.org/doc/contrib/Seefeld_StatsRBio.pdf
- https://cran.r-project.org/doc/contrib/Krijnen-IntroBioInfStatistics.pdf
- http://www.statmethods.net/
- R programming class on Coursera, taught by Roger Peng, Jeff Leek and Brian Caffo
- The free “try R” class from Code School is also a good place to start: http://tryr.codeschool.com/
- swirl: learn R interactively from within the R console.
- Data structures summary by Hadley Whickham
- https://www.datacamp.com/
- Two minute tutorials for R
- http://ropengov.github.io/
- Building Predictive Models in R Using the caret Package
- Using R for Introductory Statistics
- Using R for Data Analysis and Graphics
- An Introduction to R: Software for Statistical Modeling & Computing:
- An Introduction to R
- R and Data Mining: Examples and Case Studies
- Introduction to Statistical Thinking With R, without calculus
- University of Auckland Lecture Slides on R
- R programming for those coming from other languages
- R tips by Paul E. Johnson
- R for Beginners by Emmanuel Paradis
- R Graphics by Paul Murrel
- Rob Kabacoff: Quick R
- Free Course on Statistics using R
- Jarrett Byrnes: A Quick and Dirty Intro to doing statistics with R
- Mark Gardener: Using R for statistical analysis
- Chi Yau: Elementary Statistics with R
- Robert Muenchen: R for statistics (author of how R can be used by SAS/SPSS users)
- Winston Chang: Cookbook for R
- Venables, Smith, and the R Core team: Book/Manual – Intro to R
- Jonathan Baron: Notes on R for the use of psychological experiments and questionnaires
- William Revelle: Psychometric Theory with applications in R
- Tuimela and Greco: A presentation of basic statistics using R
- R Graphical Manual
- Book Recommendations for learning R
- R Search Engine 1
- R Search Engine 2
- R Search Engine 3
- Data Science Book
- Practical Regression and Anova using R
- The R Inferno
- A Little Book of R for Multivariate Analysis
- A Little Book of R for Biomedical Statistics
- A Little Book of R for Time Series
- R and Data Mining: Examples and Case Studies by Yanchang Zhao
- Data Mining Algorithms in R (Wikibooks)
- Forecasting: Principles and Practice by Rob Hyndman and George Athanasopoulous
- Bayesian Computation with R
- Introduction to Probability and Statistics Using R.
- Revolution Analytics Free Webinars
- 10 R packages I wish I knew about earlier
- 10 tips for making your R graphics look their best
- ggplot2 Graphics Cheat Sheet
- RStudio Server Amazon Machine Image by Louis Aslett
- Professor Vivek Patil
- Jo-fai Chow
- Overview and Data visualization
- Data Preparation
- Generalized Linear Regression
- NeuralNet, Bayesian, SVM, KNN
- Decision Tree and Ensembles
- Evaluate Model Performance
- http://google-styleguide.googlecode.com/svn/trunk/Rguide.xml
- http://msenux.redwoods.edu/math/R/boxplot.php
- 50 functions to help you clear a job interview in R here
- Read about split, apply, combine approach for data analysis from Journal of Statisical Software.
- Try learning about tidy data approach for data analysis.
- For connecting to a RDBMS- a MySQL database through R
- You really should do a data quality exercise.
- Bored with analyzing numbers alone. Try sports analysis with a cricket analysis using R.
- If you need a book on R for Business Analytics , “R for Business Analytics by Ajay Ohri.
- If you need a book on learning R quickly , see http://statmethods.net
- Read about Edward Tufte and his principles on how to make (and not make) data visualizations here . Especially read on data-ink, lie factor and data density.
- Read about the common pitfalls on dashboard design by Stephen Few.
- For learning grammar of graphics and a practical way to do it in R. Go through this link from Dr Hadley Wickham creator of ggplot2 and one of the most brilliant R package creators in the world today.
- Are you interested in visualzing data on spatial analsysis. Go through the amazing ggmap package.
- Interested in making animations thorugh R. Look through these examples. Animate package will help youhere.
- Slidify will help supercharge your graphics with HTML5.
- https://rpubs.com/
- http://bioconductor.org/

### R books

- Software for Data Analysis: Programming with R (Statistics and Computing) by John M. Chambers (Springer)
- S Programming (Statistics and Computing) by Brian D. Ripley and William N. Venables (Springer)
- Programming with Data: A Guide to the S Language by John M. Chambers (Springer)