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Categories Free Downloadable R Programming eBooks! Bookmark and Share
Introduction to Data Science, with Introduction to R, Version 3
This book provides non-technical readers with a gentle introduction to essential concepts and activities of data science. For more technical readers, the book provides explanations and code for a range of interesting applications using the open source R language for statistical computing and graphics.

It also addresses the various skills required, the key steps in the Data Science process, software technology related to the effective practice of Data Science, and the best rising academic programs for training in the field.
R Succinctly
Begin developing your mastery of the powerful R programming language. Become comfortable with the R environment and learn how to find ways for R to fulfill your data needs. With author Barton Poulson as your guide, you will soon be making use of this flexible, unparalleled tool. Unprecedented control of your data is within your grasp!
Statistics with R
This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics.

Using R for Data Analysis and Graphics
This book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is done and why. The companion website has code and datasets, allowing readers to reproduce all analyses, along with solutions to selected exercises and updates.

Advanced R Programming
The book is designed primarily for R users who want to improve their programming skills and understanding of the language. It should also be useful for programmers coming to R from other languages, as it explains some of R's quirks and shows how some parts that seem horrible do have a positive side.
Introduction to Probability and Statistics Using R
This is a textbook for an undergraduate course in probability and statistics. The approximate prerequisites are two or three semesters of calculus and some linear algebra. Students attending the class include mathematics, engineering, and computer science majors.
The Art of R Programming: A Tour of Statistical Software Design
R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly.
Learning Statistics with R
R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly.
A Handbook of Statistical Analyses Using R
Doing for R what Everitt's other Handbooks have done for S-PLUS, STATA, SPSS, and SAS, A Handbook of Statistical Analyses Using R presents straightforward, self-contained descriptions of how to perform a variety of statistical analyses in the R environment. From simple inference to recursive partitioning and cluster analysis, eminent experts Everitt and Hothorn lead you methodically through the steps, commands, and interpretation of the results, addressing theory and statistical background only when useful or necessary.
An Introduction to R: A Programming Environment for Data Analysis and Graphics
This tutorial manual provides a comprehensive introduction to R, a software package for statistical computing and graphics.

The R Inferno
An essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks. R is free, open-source, and has thousands of contributed packages.




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