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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

  1. A Template for Preparing Data
  2. Reading Data into R
  3. Open Access Data via the CKAN API
  4. Exploring and Summarising Data
  5. Visualising Data with GGPlot2
  6. Transforming Data
  7. Case Study: Analysis of Sea Ports
  8. Case Study: Web Log Analysis

Part 3: Building Models

  1. A Template for Building Models
  2. Cluster Analysis
  3. Association Analysis
  4. Decision Trees
  5. Ensembles of Decision Trees
  6. Support Vector Machines
  7. Neural Networks
  8. Naive Bayes
  9. Multivariate Adaptive Regression Splines
  10. Evaluating Models
  11. Scoring (R)
  12. PMML (R) Exporting Models for Deployment

Part 4: Advanced R and Analytics

  1. Strings
  2. Dates and Time
  3. Spatial Data 
  4. Big Data
  5. Exploring Different Plots
  6. Writing Functions
  7. Parallel Processing
  8. Environments
  9. Text Mining
  10. Social Network Analysis
  11. Genetic Programming
  12. Time Series Analysis

Part 5: Appendicies

  1. Doing R with Style
  2. 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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