**D**ata sciences relies on a strong foundation of mathematics, statistics, and results visualization, most of which are available through R statistical programming ecosystems. To master the data sciences, one needs to delve into some of the more important pieces of literature (spending 10,000 hours) . But what does one read and when?

**W**hile many have tried, it is impractical define the definitive list of R resources, given all the great blogs, texts, and videos available. Most attempt to create such a list are failures from the start. So, in many cases, one needs just to Google the phrase “R Resources” in order to find 80% of the good ones, while exerting less than 20% of your overall research effort.

**F**or my list, here are the texts and PDFs that I keep near or with me most of the time:

**General introductions to R**

1. An introduction to R. Venables and Smith (2009) – PDF

2. A beginner’s guide to R (Use R!). Zuur et al. (2009) – Text

3. R for Dummies. Meys and de Vries (2012) – Text

4. The R book. Crawley (2012)

5. R in a nutshell: A desktop quick reference. Adler (2012)

**Statistics books**

1. Statistics for Dummies. Gotelli and Ellison (2012) – Text

2. Statistical methods. Snedecor and Cochran (2014) – Text

3. Introduction to Statistics: Fundamental Concepts and Procedures of Data Analysis. Reid (2013) – Text

**Statistics books specifically using R**

1. Introductory statistics: a conceptual approach using R. Ware et al. (2012) – Text

2. Foundations and applications of statistics: an introduction using R. Pruim (2011) – Text

3. Probability and statistics with R, 2nd Edition. Ugarte et al. (2008) – Text

**Visualization using R**

1. ggplot2: elegant graphics for data analysis. Wickham (2009)

2. R graphics cookbook. Chang (2013)

**Programming using R**

1. The art of R programming. Matloff (2011)

2. Mastering Data Analysis with R. Daroczi (2015)

**Interesting predictive analytics books**

1. The Signal and the Noise: Why So Many Predictions Fail – But Some Don’t. Silver (2012)

2. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. Siegel (2013)

Categories: Predictive/Prescriptive, R, Reference Material, Visualization

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