FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World

 Title An Introduction to R
 Author(s) Alex Douglas, Deon Roos, Francesca Mancini, Ana Couto, and David Lusseau
 Publisher: Bookdown (July 22, 2022)
 Hardcover/Paperback N/A
 eBook: HTML and PDF
 Language: English
 ISBN10: N/A
 ISBN13: N/A
 Share This:
Book Description
The aim of this book is to introduce you to using R, a powerful and flexible interactive environment for statistical computing and research. R in itself is not difficult to learn, but as with learning any new language (spoken or computer) the initial learning curve can be a little steep and somewhat daunting.
Neither is it intended to be an introductory statistics course, although you will be using some simple statistics to highlight some of R's capabilities. The main aim of this book is to help you climb the initial learning curve and provide you with the basic skills and experience (and confidence!) to enable you to further your experience in using R.
Although R is not new, it's popularity has increased rapidly over the last 10 years or so (see here for some interesting data). It was originally created and developed by Ross Ihaka and Robert Gentleman during the 1990’s with the first stable version released in 2000. Nowadays R is maintained by the R Development Core Team. So, why has R become so popular and why should you learn how to use it? Some reasons include:
About the Authors N/A
 The R Programming Language
 Statistics, and SAS Programming
 Data Analysis and Data Mining
 Geographic Information System (GIS) and Web Mapping

The R Inferno (Patrick Burns)
This book is 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. If you are using spreadsheets to understand data, switch to R.

R Packages: Organize, Test, Document, and Share Your Code
Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author's package development philosophy.

R for Data Science: Visualize, Model, Transform, Tidy, Import
This book teaches you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it, how data science can help you work with the uncertainty and capture the opportunities.

Regression Models for Data Science in R (Brian Caffo)
The book gives a rigorous treatment of the elementary concepts of regression models from a practical perspective. The ideal reader for this book will be quantitatively literate and has a basic understanding of statistical concepts and R programming.

Advanced R (Florian Prive)
This book aims at giving a wide understanding of many aspects of R. Combining detailed explanations with realworld examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R's functionality.

Applied Statistics with R (David Dalpiaz)
This book provides an integrated treatment of statistical inference techniques in data science using the R Statistical Software. It provides a muchneeded, easytofollow introduction to statistics and the R programming language.

Advanced R, Second Edition (Hadley Wickham)
This book helps you understand how R works at a fundamental level. Designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages to understand what makes R different and special.

Advanced R Solutions (Malte Grosser, et al)
This book offers solutions to the exercises from Advanced R, 2nd Edition by Hadley Wickham. It is work in progress and under active development. The 2nd edition of Advanced R is in print now and we hope to provide most of the answers.

An Introduction to Statistical Learning: with Applications in R
It provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.

An Introduction to Bayesian Thinking (Merlise Clyde, et al.)
It provides an introduction to Bayesian Inference in decision making without requiring calculus. It may be used on its own as an openaccess introduction to Bayesian inference using R Programming for anyone interested in learning about Bayesian statistics.

Advanced Data Analysis using R (Cosma R. Shalizi)
This is a textbook on data analysis methods, intended for advance undergraduate students who have already taken classes in probability, mathematical statistics, and linear regression. All examples implemented using R.
:






















