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


 Title: R Markdown: The Definitive Guide
 Author(s): Yihui Xie, J. J. Allaire, Garrett Grolemund
 Publisher: Chapman and Hall/CRC (2018); eBook (Creative Commons Licensed, 2023)
 License(s): CC BYNCSA 4.0
 Paperback: 304 pages
 eBook: HTML
 Language: English
 ISBN10: 1138359335
 ISBN13: 9781138359338
 Share This:
Book Description
This is the first official book authored by the core R Markdown developers that provides a comprehensive and accurate reference to the R Markdown ecosystem.
With R Markdown, you can easily create reproducible data analysis reports, presentations, dashboards, interactive applications, books, dissertations, websites, and journal articles, while enjoying the simplicity of Markdown and the great power of R and other languages.
About the Authors Yihui Xie is a software engineer at RStudio.
 J.J. Allaire is the founder of RStudio and the creator of the RStudio IDE.
 Garrett Grolemund is the coauthor of R for Data Science and author of HandsOn Programming with R.
 The R Programming Language
 Statistics, and SAS Programming
 Data Analysis and Data Mining
 TeX, LaTeX, and AMSLaTeX
 HTML (HTML5, XHTML, and DHTML, etc.)

HandsOn Programming with R: Functions and Simulations
This book not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. Youâ€™ll gain valuable programming skills and support your work as a data scientist at the same time.

R Programming for Data Science (Roger D. Peng)
This book is about the fundamentals of R programming. Get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. You will have a solid foundation on data science toolbox.

R Graphics Cookbook: Practical Recipes for Visualizing Data
This cookbook provides more than 150 recipes to help scientists, engineers, programmers, and data analysts generate highquality graphs quickly  without having to comb through all the details of R's graphing systems.

An Introduction to R (Alex Douglas, et al.)
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.

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.

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.

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.

Efficient R Programming: Practical Guide to Smarter Programming
This book is about increasing the amount of work you can do with R in a given amount of time. It's about both computational and programmer efficiency. This book is for anyone who wants to make their use of R more reproducible, scalable, and faster.

Cookbook for R: Best R Programming TIPs (Winston Chang)
The goal of this cookbook is to provide solutions to common tasks and problems in analyzing data. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.

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.
:






















