An R Meta Book

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:

Captured by An R "meta" Book

[…] 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
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

Infographic: How travel marketers can benefit from Big Data analytics

According to a Big Data-focused infographic by McKinsey,

42% of European consumers carry out web searches on their mobile devices in stores.

61% of consumers worldwide use digital channels as part of their purchase journey.

78% of mobile activity is data, and not voice.

72% of CEOs report that marketers can rarely explain the incremental business that marketing spend can generate.

via How travel marketers can benefit from Big Data analytics [INFOGRAPHIC].

Infographic: Why Marketers Should a Learn to a Stop Worrying and Love the Data - McKinsey and Company

Infographic: Why Marketers Should a Learn to a Stop Worrying and Love the Data – McKinsey and Company


29 Dumb Things Finance People Say

Motley Fool LogoThis article by Morgan Housel of the Motley Fool, posted at Business Insider is a hilarious, absolute must-read.

From the article, a few of my favorites:

“They don’t have any debt except for a mortgage and student loans.”

OK. And I’m vegan except for bacon-wrapped steak.

“Earnings missed estimates.”

No. Earnings don’t miss estimates; estimates miss earnings. No one ever says “the weather missed estimates.” They blame the weatherman for getting it wrong. Finance is the only industry where people blame their poor forecasting skills on reality.

“More buyers than sellers.”

This is the equivalent of saying someone has more mothers than fathers. There’s one buyer and one seller for every trade. Every single one.

“We’re trying to maximize returns and minimize risks.”

Unlike everyone else, who are just dying to set their money ablaze.

“The Dow is down 50 points as investors react to news of [X].”

Stop it, you’re just making stuff up. “Stocks are down and no one knows why” is the only honest headline in this category.

“Investors are fleeing the market.”

Every stock is owned by someone all the time.

And, IMO, the best one:

“He was tired of throwing his money away renting, so he bought a house.”

He knows a mortgage is renting money from a bank, right?

The article can be found and read at 29 Dumb Things Finance People Say – Business Insider.