• Title: R Markdown Cookbook
• Author(s): Yihui Xie, Christophe Dervieux, Emily Riederer
• Publisher: Routledge; 1st edition (October 20, 2020); eBook (Creative Commons Licensed, 2024-02-29)
• License(s): Creative Commons License (CC)
• Paperback: 360 pages
• eBook: HTML and PDF
• Language: English
• ISBN-10: 0367563835
• ISBN-13: 978-0367563837
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Book Description

This book written by the developers of R Markdown is an essential reference that will help users learn and make full use of the software. It will be a popular addition on the desk of many professionals who regularly produce technical documents in the R language.

This book is useful for R stats users who, while using R Markdown, come across certain problems that can be solved. It also provides advanced users with additional options to further customize RMarkdown to their personal needs.

• Yihui Xie is a software engineer at RStudio. He has authored and co-authored several R packages, including knitr, rmarkdown, bookdown, blogdown, pagedown, tinytex, and xaringan.

• Amazon

• The R Programming Language
• Programming and Coding Cookbooks
• Statistics, and SAS Programming
• Data Analysis and Data Mining
• TeX, LaTeX, and AMS-LaTeX
• HTML (HTML5, XHTML, and DHTML, etc.)

• R Markdown Cookbook (Yihui Xie, et al)
• PDF Format

• R Markdown: The Definitive Guide (Yihui Xie, et al)

  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.

• blogdown: Creating Websites with R Markdown (Yihui Xie, et al)

  Provides a practical guide for creating websites using the blogdown package in R. Use dynamic R Markdown documents to build static websites with automatically rendered output such as graphics, tables, analysis results, and HTML widgets.

• bookdown: Authoring Books/Tech Documents with R Markdown

  This book presents a much easier way to write books and technical publications than traditional tools such as LaTeX and Microsoft Word. You can make better use of document elements such as figures, tables, equations, theorems, citations, and references.

• Hands-On 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. You’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 high-quality 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 real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R's functionality.