• Title: R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics
• Author(s) James (JD) Long, Paul Teetor
• Publisher: O'Reilly Media; 2nd edition (July 30, 2019); eBook (Online Edition)
• Paperback: 598 pages
• eBook: HTML
• Language: English
• ISBN-10: 1492040681
• ISBN-13: 978-1492040682
• Share This:  

Book Description

This book is full of how-to recipes, each of which solves a specific problem. Each recipe includes a quick introduction to the solution followed by a discussion that aims to unpack the solution and give you some insight into how it works.

We know these recipes are useful and we know they work, because we use them ourselves.

• J.D. Long is a misplaced southern agricultural economist currently working for Renaissance Re in New York City. He is an avid user of Python, R, AWS and colorful metaphors, and is a frequent presenter at R conferences as well as the founder of the Chicago R User Group.

• Amazon

• Programming/Coding Cookbooks
• The R Programming Language
• Data Analysis and Data Mining
• Data Science
• Statistics, and SAS Programming

• R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics (James Long, et al)
• The Mirror Site (1) - PDF
• Source Code Repository

• 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.

• 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.

• 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.