R Programming is a powerful language with a wide range of applications. This makes it a great language to learn for people from all walks of life, including data scientists and software engineers alike.
With the wealth of books available on R programming, it can be difficult to find the right one for your needs. To help you out, here are some of the best books that will get you up and running with R programming in no time.
Beginning Data Science in R by Thomas Mailund
The book, Beginning Data Science in R by Thomas Mailund, is an essential resource for anyone interested in data science.
It provides comprehensive information on best practices and techniques related to the language of R to get you up and running quickly with data science.
Through hands-on instruction and examples, it will give you a solid foundation in everything from basic programming fundamentals to more complex topics like statistics and machine learning.
Throughout the book, Mailund provides detailed explanations of key concepts such as data manipulation, visualization, probability distributions, linear models, and more.
He also offers step-by-step instructions for writing code that will help you master each topic.
There are end-of-chapter exercises that reinforce what you have learned and can be used to test your understanding of the material presented.
The R Book by Michael J. Crawley
If you are an experienced programmer who wants to make robust applications in R, you need this book.
Much of the R code written is for temporary or expedient use. This book explains the vagaries of the language so that one can make reusable modules.
Beyond Spreadsheets with R by Dr. Jonathan Carroll
Dr. Jonathan Carroll’s book, Beyond Spreadsheets with R, is a comprehensive guide to the world of data analysis and statistical computing using the R programming language.
In this book, Carroll provides readers with a detailed overview of how to use the powerful and versatile R language to analyze data and create insightful visualizations.
He also lays out practical examples that demonstrate how to use R for data manipulation, regression modeling, hypothesis testing, and more.
By introducing readers to these concepts in an easy-to-understand manner, Carroll helps them become proficient in using this popular tool for research projects or everyday tasks.
R for Data Science by Garrett Grolemund, Hadley Wickham
Data science is an incredibly powerful tool used to uncover a wealth of insights from large datasets.
R for Data Science, by Garrett Grolemund and Hadley Wickham, is the perfect guidebook for those who want to take advantage of this powerful tool.
This book is an essential resource for anyone interested in learning how to effectively use R in data science tasks.
Authored by two experts in the field of data science, Grolemund and Wickham provide readers with an introduction to R, covering topics such as importing data into R, exploratory data analysis, visualizing data with ggplot2, and creating tidy datasets.
Readers will learn how to use the dplyr package for manipulating and transforming datasets, as well as advanced techniques such as writing functions in R and debugging code.
The authors also guide readers through strategies for communicating results through effective visualization techniques.
Discovering Statistics Using R by Andy Field, Jeremy Miles, and Zoë Field
Discovering Statistics Using R by Andy Field, Jeremy Miles, and Zo Field is an essential book for those interested in learning the fundamentals of statistical analysis.
The book provides readers with a comprehensive guide to understanding the basics of statistics using R software.
It covers a range of topics from basic concepts such as data distribution and descriptive measures to more complex methods like regression and multivariate analysis.
This book offers users step-by-step instructions on how to use the software with detailed explanations of every command used along the way.
Readers also get access to datasets that allow them to analyze real-world data using R. With its easy-to-follow approach and comprehensive coverage, this book is great for students who are new to statistics or those who have been studying it for some time but need help mastering the basics.
It is also useful for researchers looking for an accessible resource on statistical analysis techniques in R.
Hands-on Programming with R by Garrett Grolemund
Garrett Grolemund’s book, Hands-on Programming with R, is the perfect resource for anyone looking to learn how to use the R programming language.
This guide starts from the beginning, teaching readers all of the fundamental skills and concepts they need to understand before delving into more complicated topics.
The book covers everything from basic data structures and functions to statistical models and machine learning algorithms.
With clear instructions and plenty of code examples, this book will help readers quickly become proficient in using R for their own projects.
The chapters are ordered in a logical progression that builds upon previously learned concepts, making it easier for those without any prior programming experience to follow along.
Furthermore, each chapter includes practical exercises that allow readers to test out what they have just learned and apply it in their own work.
Hands-On Machine Learning with R by Brad Boehmke and Brandon M. Greenwell
Hands-On Machine Learning with R by Brad Boehmke and Brandon M. Greenwell is a comprehensive guide to getting started with machine learning algorithms in R.
This book dives deep into the fundamentals of machine learning, providing readers with the essential skills needed to use this powerful technology for predictive analytics.
From linear regression to deep learning, Hands-On Machine Learning with R presents an accessible introduction to many of today’s most popular techniques in a practical and engaging way.
Readers will learn how to apply the principles of machine learning, including feature engineering and model selection, using real-world data sets.
The book also covers best practices for optimizing models and deploying them in production systems, as well as troubleshooting common issues that arise when using machine learning algorithms.
Practical Machine Learning in R by Fred Nwanganga, Mike Chapple
You can’t believe the price of this book is so low. It’s a great overview of machine learning in R and even has color illustrations.
This is a great reference to have on hand or an introduction to machine learning. It covers all the major areas of machine learning with clear example code written in R to use as a guide.
This book is easy to follow with clear examples. Fred and Mike break down the material into an easy-to-follow format with practical examples and code.
Python and R for the Modern Data Scientist by Rick J. Scavetta, Boyan Angelov
Python and R for the Modern Data Scientist by Rick J. Scavetta, and Boyan Angelov is an essential guidebook for data scientists in today’s tech-driven world.
With the help of this book, readers can learn to use Python and R programming languages to work with data science projects more efficiently.
The authors explain all the concepts related to working with Python and R programming languages in a simple and easy-to-follow manner.
They have also included numerous practical examples throughout the book, which helps readers apply their new knowledge in real-life scenarios.
Furthermore, this comprehensive guidebook also covers topics such as machine learning algorithms, data visualization techniques, natural language processing techniques, and much more.
This book is perfect for beginners as well as experienced professionals who wish to learn how to use Python and R effectively for data science projects.
R Cookbook by Paul Teetor
Paul Teetor’s R Cookbook is an essential resource for anyone looking to expand their knowledge of the popular statistical programming language.
This book provides readers with over 200 practical recipes, which allow them to quickly and easily learn how to use R in data analysis.
With clear explanations and examples of code, Teetor covers topics ranging from basic summary statistics and graphs to more advanced topics such as simulation studies and web scraping.
He also includes coverage on data manipulation, data visualization, model fitting, and more.
Written by a seasoned R programmer with years of experience working with the language, the book provides readers with an easy-to-follow guide to understanding the complexities of R programming.
R Cookbook is perfect for beginners who are just getting started using R or experienced users looking for new ways to optimize their code.
R for Everyone by Jared P. Lander
R for Everyone by Jared P. Lander is a comprehensive guide to the R programming language and its many uses.
Written in an easy-to-digest format, this book provides readers with a grounding in R syntax, data types, algorithms, and tools.
It also helps readers understand how to use the language for complex tasks such as generating graphs and creating predictive analytics models.
With step-by-step instructions, this book will help even those with no prior experience in R become proficient quickly.
This book is suitable for both experienced coders and complete beginners looking to learn more about data science or expand their coding skillset.
It offers detailed guidance on how to effectively manipulate data sets, work with statistical packages, produce appealing visualizations of results and create effective machine learning models.
R For Dummies by Andrie de Vries, Joris Meys
R For Dummies by Andrie de Vries, Joris Meys is the ultimate guide to using the R programming language.
Written in a simple manner, this book offers an easy-to-follow approach to learning R. Aimed at those who are new to coding and data science, this book provides readers with a comprehensive introduction to the fundamentals of R.
The authors provide step-by-step instructions on how to install, use, and write code in R as well as teach readers important concepts such as data structures and functions.
This allows users to develop their own scripts which can be used for data exploration and analysis.
Furthermore, there are plenty of examples included throughout the text so readers can understand how each concept works in practice.
This book is essential for anyone looking for an introduction to the world of R programming.
R for Excel Users by John L Taveras
R for Excel Users by John L Taveras is a great book for people who think in spreadsheets and struggle learning programming.
This is a pure beginner’s book to get you started in R and helps you understand basic concepts and uses Excel as a base knowledge to teach the concepts.
The layout and design of the book are very simple and easy to read without being overwhelming.
R Graphics Cookbook by Winston Chang
If you’re looking for an easy-to-use guide to visualizing data with R, Winston Chang’s “R Graphics Cookbook” is the perfect book for you.
This book features over 100 practical recipes for creating stunning graphics with R, the popular open-source statistical computing language.
The recipes are aimed at helping individuals and researchers to create high-quality graphs quickly and easily.
Chang walks readers through all the basics of R graphics, including aesthetics, color palettes, and themes.
He also provides detailed step-by-step instructions on how to generate a wide range of charts and plots such as scatterplots, histograms, boxplots, bar charts, pie charts, and more.
Additionally, he covers more advanced topics like working with maps and 3D graphics using packages like ggplot2 and lattice.
R in Action by Rob Kabacoff
R in Action, Written by a veteran data scientist and statistician, the book provides a comprehensive yet accessible introduction to programming with R.
With this book, readers gain confidence as they gain skills in working with one of the most widely used languages for statistical analysis.
The author explains key concepts and techniques step by step using examples from both business and academic settings.
The book offers an overview of basic topics such as loading data into R and manipulating it before getting more advanced with creating graphics, running tests on the data, performing machine learning tasks, and more.
In addition to providing detailed instructions on how to use R commands effectively, Kabacoff also covers important topics such as debugging code and writing user-friendly functions.
R Programming for Beginners by Nathan Metzler
Do you want to get started learning how to program, but don’t know where to begin?
R Programming for Beginners by Nathan Metzler (Book) is a comprehensive guide for anyone looking to learn the basics of R programming.
This book provides an easy-to-follow tutorial that walks readers through the fundamentals of coding in the R language.
It covers topics such as data structures, functions, algorithms, and more. The book also contains hands-on examples throughout each chapter so readers can build their knowledge as they go along.
The Art of R Programming by Norman Matloff
Norman Matloff’s “The Art of R Programming” is a comprehensive guide to the popular statistical software, R.
This book is a valuable resource for anyone who wants to learn more about this powerful statistical tool and how it can be applied to data analysis.
Matloff provides an overview of the features of R programming, as well as detailed instructions on how to use them effectively.
He also presents several examples that demonstrate the application of these features in real-world settings.
Readers will benefit from his expertise in designing and building robust software programs and can gain insight into the best practices for working with large datasets.
Additionally, Matloff takes readers through a complete tour of the various packages available within R that enable efficient data manipulation and analysis.
The Book of R by Tilman M. Davies
The Book of R by Tilman M. Davies is a comprehensive introductory guide to programming and statistics for the budding data scientist.
With its easy-to-follow style, The Book of R provides a step-by-step introduction to the fundamentals of programming and statistical analysis through real-world examples.
Through clear instruction, detailed examples, and exercises, this book gives readers an essential overview of the power of R as an analytical tool.
The Book of R takes users on a journey from learning basic principles such as functions, packages, loops, and data structures to more advanced topics like data visualization, machine learning algorithms, and web scraping.
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