One-page R: a survival guide to data science with R

This article comes from Togaware.

A Survival Guide to Data Science with R

These draft chapters weave together a collection of tools for the data scientist—tools that are all part of the R Statistical Software Suite.

Each chapter is a  collection of one (or more) pages that cover particular aspects of the topic. The chapters can be worked through as a hands-on guide to a specific task and then used as a reference guide. Each page aims to be a bite sized chunk for hands-on learning, building on what has gone before. Many chapters also have a lecture pack and a laboratory session where a number of tasks can be completed. The R code sitting behind each chapter is also provided and can be easily run standalone to replicate the material presented in the chapter.

The  material begins with an overview of how an organisation should go about setting up their Analytics capability and then introduce the Data Scientist to R.

Part 1: Data Science

1. Data Mining, Analytics, and Data Science
2. Rattle to R
   
3. An Introduction to R Programming
4. Literate Data Science with KnitR
   
5. More Basics of R 
   

Part 2: Dealing With Data

6. A Template for Preparing Data
   
7. Reading Data into R
   
8. Open Access Data via the CKAN API
   
9. Exploring and Summarising Data
   
10. Visualising Data with GGPlot2
    
11. Transforming Data
    
12. Case Study: Analysis of Sea Ports
    
13. Case Study: Web Log Analysis

Part 3: Building Models

14. A Template for Building Models
    
15. Cluster Analysis
    
16. Association Analysis
17. Decision Trees
    
18. Ensembles of Decision Trees
    
19. Support Vector Machines
20. Neural Networks
21. Naive Bayes
    
22. Multivariate Adaptive Regression Splines
    
23. Evaluating Models
    
24. Scoring (R)
25. PMML (R) Exporting Models for Deployment

Part 4: Advanced R and Analytics

26. Strings
    
27. Dates and Time
    
28. Spatial Data 
    
29. Big Data
    
30. Exploring Different Plots
    
31. Writing Functions
    
32. Parallel Processing
    
33. Environments
    
34. Text Mining
    
35. Social Network Analysis
36. Genetic Programming
37. Time Series Analysis
    

Part 5: Appendicies

38. Doing R with Style
    
39. Packaging (R) Pulling it Together into a Package
    

To check out all this information, click here. For other articles about R, click here. 

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