Joseph Rickert, a Data Scientist and R Community Manager for Revolution Analytics recently created a ‘Meta’ book about ‘R’ which he posits might be called, “** An R Based Introduction to Probability and Statistics with Applications**”. ‘Meta’ in this context means this R Book is virtual-made up of references to its content; various other books, papers and articles.

What follows below is a snippet of a blog post by Joe discussing this ‘Meta’ book and a table which describes the various chapters and topics of the book with the associated reference (as clickable links) to the material Joe has identified to fill out the contents of his hypothetical book.

At the Revolutions Analytics Blog, Joseph Rickert wrote:

[…] it occurred to me that there is a tremendous amount of high quality material on a wide range of topics in the Contributed Documentation page (of CRAN) that would make a perfect introduction to all sorts of people coming to R. […] from among this treasure cache (and a few other online sources), I have assembled an R “meta” book in the following table that might be called:

.An R Based Introduction to Probability and Statistics with Applications[…]

The content column lists the topics that I think ought to be included in a good introductory probability and statistics textbook. With a little searching, you will be able to find a discussion of each topic in the document listed to its right. Obviously, there is a lot overlap among the documents listed, since most of them are substantial works that cover much more than the few topics that I have listed.

Content | Document | Author | |
---|---|---|---|

1. | Basic Probability and Statistics | Introduction to Probability and Statistics Using R | G. Jay Kerns |

2. | Fitting Probability Distributions | Fitting Distributions with R | Vito Ricci |

3. | • Regression | Practical Regression and Anova using R | Julian J. Faraway |

– Inference | |||

– Diagnostics | |||

– Stepwise Regression | |||

– Ridge Regression | |||

• ANOVA | |||

4. | Experimental Design | An R companion to Experimental Design | Vikneswaran |

5. | Survival Analysis | Cox Proportional-Hazards Regression for Survival Data | John Fox |

6. | Generalized Linear Models | Analysis of epidemiological data using R and Epicalc | Virasakdi Chongsuvivatwong |

7. | • Bootstrap | icebreakeR | Andrew Robinson |

• Hierarchical Models | |||

• Nonlinear Mixed Effects | |||

8. | Time Series | Time Series Analysis with R | McLeod, Yu and Mahdi |

9. | • Bayesian Statistics | Statistics Using R with Biological Examples | Kim Seefeld and Ernst Linder |

• Gibbs Sampler | |||

10. | Machine Learning | R and Data Mining: Examples and Case Studies | Yanchang Zhao |

• Decision Trees and Random Forest | |||

• Clustering | |||

• Outlier Detection | |||

• Time Series Analysis and Mining | |||

• Text Mining | |||

• Social Network Analysis | |||

11. | Bioinformatics | Applied Statistics for Bioinformatics using R | Wim P. Krijnen |

• Cluster Analysis | |||

• Classification Methods | |||

• Markov Models | |||

• Micro Array Analysis | |||

12. | Forecasting | Forecasting: principles and practice | Hyndman and Athanasopoulos |

13. | Structural Equation Models | Structural Equation Models | John Fox |

14. | Credit Scoring | Guide to Credit Scoring in R | Dhruv Sharma |