Books about the R programming language fall in different categories:

• Learning R
• Reference books for the professional R programmer
• Books about data science or visualization, using R to illustrate the concepts

Books are a great way to learn a new programming language. Code samples is another great tool to start learning R, especially if you already use a different programming language. How to turn on mic for discord on mac. You might also want to check our DSC articles about R: they also include cheat sheets. If you are unsure about learning R, read about R versus Python.

Explore, forecast, and classify data with R; Use R to apply common machine learning algorithms to real-world scenarios; Book Description. Machine learning, at its core, is concerned with transforming data into actionable knowledge. This makes machine learning well suited to the present-day era of big data. We've had some great threads on books for learning statistics and other topics recently! What are the goto resources for Machine learning in R.

Example of chart produced with R

Books lo learn R

• Learning R - Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, you’ll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts.
• R in a Nutshell - If you’re considering R for statistical computing and data visualization, this book provides a quick and practical guide to just about everything you can do with the open source R language and software environment. You’ll learn how to write R functions and use R packages to help you prepare, visualize, and analyze data. Author Joseph Adler illustrates each process with a wealth of examples from medicine, business, and sports.
• Introduction to Data Science with R - Learn practical skills for visualizing, transforming, and modeling data in R. This comprehensive video course shows you how to explore and understand data, as well as how to build linear and non-linear models in the R language and environment. It’s ideal whether you’re a non-programmer with no data science experience, or a data scientist switching to R from other software such as SAS or Excel.

Reference books

• R Cookbook - With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.
• R Graphics Cookbook - This practical guide provides more than 150 recipes to help you 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 use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you’re ready to get started.
• R Packages - 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 Hadley Wickham’s package development philosophy. In the process, you’ll work with devtools, roxygen, and testthat, a set of R packages that automate common development tasks. Devtools encapsulates best practices that Hadley has learned from years of working with this programming language.

Data science books using R for illustration purposes

• A Handbook of Statistical Analyses Using R - Provides a guide to data analysis using the R system for statistical computing. Each chapter includes a brief account of the relevant statistical background, along with appropriate references.
• An Introduction to Statistical Learning: with Applications in R - 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. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.
• Practical Data Science with R - Explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.
  
• Other Books - 154 books listed on R-Project.org, covering many different fields.

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