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The R Programming Language
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  • 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.

  • Bootstrap Methods and Applications to R (A. C. Davison, et al.)

    This book provides a compact introduction to the Bootstrap Method. It is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form.

  • Modern Statistics with R: Wrangling, Inference and Predicting

    The aim of the book is to introduce you to key parts of the modern statistical toolkit. It teaches you: - Data wrangling - importing, formatting, reshaping, merging, and filtering data in R.

  • Data Mining with R: Learning with Case Studies (Luis Torgo)

    Introduce the reader to the use of R as a tool for performing data mining and statistical computing and graphics. The large set of available packages make this tool an excellent alternative to the existing (and expensive!) data mining tools.

  • An Introduction to R and Python for Data Analysis

    This book helps teach students to code in both R and Python simultaneously. The book is written in an engaging, collaborative style that makes it enjoyable to read. It maintains its formality without creating a barrier between the reader and the content.

  • Big Book of R (Oscar Baruffa)

    This book covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results.

  • R Cookbook: Recipes for Data Analysis, Statistics, and Graphics

    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.

  • 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.

  • Mastering Software Development in R (Roger D. Peng, et al.)

    The book covers R software development for building data science tools. As the field of data science evolves, it has become clear that software development skills are essential for producing useful data science results and products.

  • 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 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.

  • The R Inferno (Patrick Burns)

    This book is an essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks. If you are using spreadsheets to understand data, switch to R.

  • Using R for Introductory Econometrics (Florian Heiss)

    This book introduces the popular, powerful and free programming language and software package R, focuses on implementation of standard tools and methods used in econometrics.

  • 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.

  • Spatial Statistics for Data Science with R (Paula Moraga)

    Spatial data is crucial to improve decision-making in a wide range of fields. This book describes statistical methods, modeling approaches, and visualization techniques to analyze spatial data using R.

  • Hands-On Spatial Data Science with R (Luc Anselin, et al.)

    This is a textbook accessible and reader-friendly 'how to' for anyone using R for the first time, for use in spatial statistical analysis, geocomputation and digital mapping.

  • R for Multivariate Analysis (Avril Coghlan)

    This book explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software.

  • Developing Data Products in R (Brian Caffo, et al.)

    This book introduces the topic of Developing Data Products in R. A data product is the ideal output of a Data Science experiment. Particular emphasis is paid to developing Shiny apps and interactive graphics.

  • R for Time Series (Avril Coghlan)

    Build efficient forecasting models using traditional time series models and machine learning algorithms. This book explores the basics of time series analysis with R and lays the foundations you need to build forecasting models.

  • Data Analysis and Graphics Using R: An Example-Based Approach

    Discover what you can do with R! This book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data.

  • Data Mining Algorithms in R (WikiBooks)

    This book presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R programming language.

  • Doing Data Science in R: An Introduction for Social Scientists

    This approachable introduction to doing data science in R provides step-by-step advice on using the tools and statistical methods to carry out data analysis. It builds knowledge and skills gradually.

  • Beyond Multiple Linear Regression: Linear & Multilevel Models in R

    This book is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure.

  • Generalized Linear Models With Examples In R

    This book is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure.

  • R Markdown Cookbook (Yihui Xie, et al)

    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.

  • Mastering Spark with R: Large-Scale Analysis and Modeling

    With this practical book, data scientists and professionals working with large-scale data applications will learn how to use Spark from R to tackle big data and big compute problems.

  • Using Spark from R for Performance with Arbitrary Code

    This short publication attempts to provide practical insights into using the sparklyr interface to gain the benefits of Apache Spark while still retaining the ability to use R code organized in custom-built functions and packages.

  • Analyzing US Census Data: Methods, Maps, and Models in R

    This book is an introduction to geographic data science using R, covers the necessary skills in basic programming, data wrangling and reproducible research to tackle sophisticated but non-spatial data analyses.

  • R for Geographic Data Science (Stefano De Sabbata)

    This book introduces readers to tools in the R programming language for accessing and analyzing Census data from the United States Census Bureau and shows how to carry out demographic analyses in a single computing environment.

  • Introduction to Statistical Data Analysis with R (Matthias Kohl)

    The book offers an introduction to statistical data analysis applying the free statistical software R, probably the most powerful statistical software today. The analyses are performed and discussed using real data.

  • The Fundamentals of People Analytics: With Applications in R

    Human capital is an organization’s most important asset. Address this need by curating key concepts spanning the entire analytics lifecycle, along with step-by-step instructions for their applications to real-world problems, using open-source software.

  • 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.

  • Engineering Production-Grade Shiny Apps (Colin Fay, et al)

    This book helps people build production-grade Shiny applications, by providing advice, tools, and a methodology to work on web applications with R, with a series of approaches and advice about optimizations for production.

  • Report Writing for Data Science in R (Roger D. Peng)

    Teaches the fundamental concepts and tools behind reporting modern data analyses in a reproducible manner. As data analyses become increasingly complex, the need for clear and reproducible report writing is greater than ever.

  • Mastering Shiny: Apps, Reports, and Dashboards Powered by R

    Master the Shiny web framework - and take your R skills to a whole new level. By letting you move beyond static reports, Shiny helps you create fully interactive web apps for data analyses.

  • Tidy Modeling with R: A Framework for Modeling in the Tidyverse

    This book shows data analysts, business analysts, and data scientists how the tidymodels framework offers a consistent, flexible approach for your work. It demonstrate ways to create models by focusing on an R dialect called the Tidyverse.

  • 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.

  • 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.

  • 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.

  • Text Mining with R: A Tidy Approach (Julia Silge, et al)

    You'll explore text-mining techniques with tidytext, a package that authors developed using the tidy principles behind R packages like ggraph and dplyr. You'll learn how tidytext and other tidy tools in R can make text analysis easier and more effective.

  • Advanced R Programming (Hadley Wickham)

    This book presents useful tools and techniques for attacking many types of R programming problems. It not only helps current R users become R programmers but also shows existing programmers what's special about R.

  • R for Beginners (Sasha D. Hafner)

    The objective of this book is to introduce participants to the use of R for data manipulation and analysis. It is intented for individuals with little or no prior experience in R. The topics that are covered are the most important for getting started with R.

  • Just Enough R: Learn Data Analysis with R in a Day (S. Raman)

    Learn R programming for data analysis in a single day. The book aims to teach data analysis using R within a single day to anyone who already knows some programming in any other language.

  • A Step-by-Step R Tutorial: Applications and Programming

    This book has a two-fold aim: to learn the basics of R and to acquire basic skills for programming efficiently in R. Emphasis is on converting ideas about analysing data into useful R programs.

  • 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.

  • Introduction to Statistical Thought using R (Michael Lavine)

    The book is intended as an upper level undergraduate or introductory graduate textbook in statistical thinking with a likelihood emphasis for students with a good knowledge of calculus and the ability to think abstractly.

  • The Art of R Programming: A Tour of Statistical Software Design

    A guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro.

  • Statistical Inference via Data Science: ModernDive, R, Tidyverse

    This book provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, etc.

  • 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.

  • Geocomputation with R (Robin Lovelace, et al.)

    This book is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities.

  • Geographic Data Science with R: Visualizing and Analyzing

    This book provides a series of tutorials aimed at teaching good practices for using Time Series and geospatial data to address topics related to environmental change. It is based on the R language and aims at data scientists.

  • Spatial Data Science: With Applications in R (Edzer Pebesma, et al)

    Introduces and explains the concepts underlying Spatial Data, gives a detailed explanation of the core spatial software packages for R: sf for simple feature access, and stars for raster and vector data cubes – array data with spatial and temporal dimensions.

  • Forecasting, Principles and Practice, Using R (R. J. Hyndman)

    This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.

  • Introduction to Data Science: Data Analysis and Algorithms with R

    Introduces concepts and skills that can help tackling real-world data analysis challenges. Covers concepts from probability, statistical inference, linear regression, and machine learning. Helps developing skills such as R programming, data wrangling, etc.

  • Linear Regression Using R: An Introduction to Data Modeling

    This book presents one of the fundamental data modeling techniques in an informal tutorial style. Learn how to predict system outputs from measured data using a detailed step-by-step process to develop, train, and test reliable regression models.

  • Regression Models for Data Science in R (Brian Caffo)

    The book gives a rigorous treatment of the elementary concepts of regression models from a practical perspective. The ideal reader for this book will be quantitatively literate and has a basic understanding of statistical concepts and R programming.

  • Practical Regression and Anova Using R (Julian J. Faraway)

    The emphasis of this book is on the practice of regression and analysis of variance. The objective is to learn what methods are available and more importantly, when they should be applied.

  • Hyperparameter Tuning for Machine and Deep Learning with R

    This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods.

  • 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.

  • Using R With Multivariate Statistics (Randall E. Schumacker)

    This book is a quick guide to using R, free-access software available for Windows and Mac operating systems that allows users to customize statistical analysis, provides data analysis examples, R code, computer output, and explanation of results.

  • Introduction to Statistical Learning: with Applications in R

    It 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.

  • An Introduction to Bayesian Thinking using R

    It provides an introduction to Bayesian Inference in decision making without requiring calculus. It may be used on its own as an open-access introduction to Bayesian inference using R Programming for anyone interested in learning about Bayesian statistics.

  • Bayes Rules! An Introduction to Applied Bayesian Modeling using R

    An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, it brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. Integrates R code, including RStan modeling tools, bayesrules package.

  • Bayes Factors for Forensic Decision Analyses with R

    This book provides a self-contained introduction to computational Bayesian statistics using R programming language. With its primary focus on Bayes factors supported by data sets, this book features an operational perspective, practical relevance, and applicability.

  • 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.

  • Introduction to Statistical Thinking, with R, without Calculus

    This is an introduction to statistics, with R, without calculus, for students who are required to learn statistics, students with little background in mathematics and often no motivation to learn more.

  • Exploratory Data Analysis with R (Roger D. Peng)

    This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models.

  • Learning Statistics with R (Daniel Navarro)

    This book takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required.

  • Analyzing Linguistic Data: Introduction to Statistics using R

    A straightforward introduction to the statistical analysis of language, designed for those with a non-mathematical background. Using the leading statistics programme 'R'. Suitable for all those working with quantitative language data.

  • Statistical Foundations of Machine Learning using R

    This book aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. All the examples are implemented in the statistical programming language R.

  • Statistics with R (Vincent Zoonekynd)

    Provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint.

  • Introduction to Probability and Statistics Using R (G. Jay Kerns)

    This is a textbook for an undergraduate course in probability and statistics, covers various basic and advanced statistical procedures, from correlation and graph creation to analysis of variance, regression, non-parametric tests, and more.

  • R Succinctly (Barton Poulson)

    Begin developing your mastery of the powerful R programming language. Become comfortable with the R environment and learn how to find ways for R to fulfill your data needs.

  • Using R for Data Analysis and Graphics (J H Maindonald)

    This book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. There are many worked examples from real-world.

  • An Introduction to R: A Programming Environment for Data Analysis

    This tutorial manual provides a comprehensive introduction to R, an open source software package for statistical computing and graphics

  • Applied Spatial Data Analysis with R (Roger S. Bivand, et al)

    It presents R packages, functions, classes and methods for handling spatial data, and showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping.

  • A Handbook of Statistical Analyses Using R (Brian S. Everitt, et al)

    This book is the perfect guide for newcomers as well as seasoned users of R who want concrete, step-by-step guidance on how to use the software easily and effectively for nearly any statistical analysis.

  • Using R for Introductory Statistics (John Verzani)

    This book guides students through the basics of R, helping them overcome the sometimes steep learning curve, lays the foundation for further study and development in statistics using R - integrating the study of statistics with a powerful computational tool.

  • Exploring Data Science (Nina Zumel, et al)

    This book introduces readers to various areas in data science and explains which methodologies work best for each, with practical examples in R, Python, and other languages.

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