• Title: R Graphics Cookbook: Practical Recipes for Visualizing Data
• Author(s) Winston Chang
• Publisher: O'Reilly Media; 2nd edition; eBook (2024-04-07; Read online here for free.)
• Permission: Read online here for free, or buy a physical copy on Amazon.
• Paperback: 444 pages
• eBook: HTML
• Language: English
• ISBN-10: 1491978600
• ISBN-13: 978-1491978603
• Share This:  

Book Description

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. 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.

Most of the recipes in this second edition use the updated version of the ggplot2 package, a powerful and flexible way to make graphs in R.

• Winston Chang is a software engineer at RStudio, where he works on data visualization and software development tools for R. He has a Ph.D. in Psychology from Northwestern University, and created the Cookbook for R website, which contains recipes for common tasks in R.

• Amazon

• Programming/Coding Cookbooks
• The R Programming Language
• Data Visualization
• Data Analysis and Data Mining
• Data Science

• R Graphics Cookbook: Practical Recipes for Visualizing Data (Winston Chang)
• The Mirror Site (1) - PDF

• Modern Data Visualization with R (Robert Kabacoff)

  This book describes the many ways that raw and summary data can be turned into visualizations that convey meaningful insights. It is written for those new to data analysis as well as the seasoned data scientist.

• Graphical Data Analysis with R Programming: A Handbook

  This book takes you through a comprehensive tour of graphical data analysis with R, explores the types of plots available in R and learn to create them with the help of functions and implementation examples.

• 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.

• Fundamentals of Data Visualization: Informative Figures

  This book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures, teaches you the elements most critical to successful data visualization.

• 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.

• 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.

• 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.

• 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.

• 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 much-needed, easy-to-follow 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.

• 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.