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One Page R: A Survival Guide to Data Science with R

Graham Williams
International Visiting Professor
Chinese Academy of Sciences
Shenzhen Institutes of Advanced Technology

Welcome to One Page R, a compendium of modules weaving together a collection of tools for the data miner, data scientist, and decision scientist, based on R. Each module is a single e-document or PDF file made up of multiple one page recipes that cover many aspects of R. Each page aims to be a bite sized chunk for hands-on learning, building on what has gone before.

The material is always under development and some modules will change regularly. The collection is growing over time and we include planned topics as well as links to the already available material. Refer to the Data Mining Survival Guide or Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery (Use R) for related material.

Many of the documents have been developed and tested whilst visiting the Shenzhen Institutes of Technology as an International Visiting Professor of the Chinese Academy of Sciences. The data used is available for download as data.zip.

1. Getting Started
1. Intro - OnePageR, R - A Guide to the Data Mining
2. Start - OnePageR, Lecture - Getting Started with Rattle
3. StartR: (OnePageR, R, Lecture) Getting Started with R
4. BasicR: (OnePageR, R, Lecture) Writing R scripts
2. Dealing With Data
1. ReadR: (OnePageR, R, Lecture) Reading Data
2. Summarise (R)
3. Clean (R)
4. DateTime (PDF, R) Dates and Time
5. GGPlot2 (PDF, R) Visualising Data with ggplot2
6. Maps (PDF, R) Drawing and Annotating Maps
7. Spatial (R) Spatial Analysis
8. BigData (PDF, R) Handling Big Data
3. Models
1. Models (PDF, R) Template for Building Models
4. Descriptive Analytics
1. Clusters (PDF, R) Cluster Analysis
2. KMeans (R)
3. Associations (R)
4. ARules (R)
5. Predictive Analytics
1. DTreesR (PDF, R, Rattle) Decision Trees
2. Ensembles - Lecture - Decision Tree Ensembles
3. RF (R)
4. Ada (R)
5. SVM (R)
6. KernLab (R)
7. NeuralNetworks (R)
8. NNet (R)
6. Model Delivery
1. Evaluation (R)
2. Scoring (R)
3. PMML (R) Exporting Models for Deployment
7. Advanced Topics
1. TextMining (R)
8. Advanced R Topics
1. Plots (PDF, R) Miscellaneous Plots
2. Functions (PDF, R) Writing Functions in R
3. Parallel (PDF, R) Parallel Execution
4. Packaging (R) Pulling it Together into a Package
5. Style (PDF, R) Coding Style for programming R
6. KnitR (PDF, R) Literate Data Mining—Managing and Documenting the Project

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Other great resources for modular approaches to learning R include:

• Jared Knowles' R-Bootcamp

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The embedded Realistic Book format is generated using Realistic Books. Thanks to the team at the University of Waikato for this freely usable resource.


One Page R by Graham Williams is Copyright © 2012 – 2013 Togaware Pty Ltd and is
licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.

Last Modified 2013-05-20 15:14:26 Graham Williams

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