R is a programming language developed by Ross Ihaka and Robert Gentleman in 1993. The language possesses an extensive catalogue of statistical and graphical methods. It includes machine learning algorithms, linear regression, time series, etc.
Here is a curated list of Top R Programing Books that should be part of any beginner to advanced R language Learners library.
R for Data Science is a book written by Hadley Wickham (Author), Garrett Grolemund. The book guides you through the steps of importing, exploring, and modeling your data.
This reference material also provides a complete, big-picture understanding of the data science cycle. You will also learn the basic tools you need to manage the details of R implementation. Each section included in this book is paired with exercises to help you practice what you've learned along the way.
Discovering Statistics Using R is a book written by Andy Field, Jeremy Miles, Zoe Field. The book is written in an unusual style, and it follows a ground-breaking structure and pedagogical approach.
This reference material is enhanced by a cast of characters to help the reader on their way. The book covers hundreds of examples, self-assessment, and additional website material for everyone who wants to learn more.
The Art of R Programming is a book written by Jared P. Lander. The book helps covers basic data types,data structures, closures, recursion, and anonymous functions.
In this book, you will also learn about functional and object-oriented programming, running mathematical simulations. You will also learn topics like rearranging complex data in simple and more useful formats.
R For Dummies is a book written by Andrie de Vries, Joris Meys. This book provides a quick method to master the R language. Moreover, to learn from this book, you don't need to have any earlier programming experience.
You'll also learn how to reshape and manipulate data, merge data sets, split and combine data, perform, etc. It also explains why R programming language of choice among statisticians and data analysts worldwide.
R in Action is a book written by Dr. Rob Kabacoff. The book presents both the R language and the examples that make it so useful for business developers. This book focuses on practical solutions and covers many important methods. It helps you to manage messy and incomplete data.
You'll also learn about R language's extensive graphical capabilities for exploring and presenting data visually. It also includes many chapters on time series analysis, cluster analysis, and classification methodologies, etc.
R for Everyone is a book written by Jared lander. This book provides extensive hands-on practice and sample code. You'll learn how to download and install R, navigate, and use the R environment. You will also learn basic program control, data import, manipulation, and visualization, etc.
The book also helps you to construct several complete models, both linear and nonlinear, and use some data mining techniques.
The book of R is written by Tilman M. Davies. It is a beginner-friendly guide to R. In this book, you will learn to require to begin using R effectively for statistical analysis.
The book also helps you to contributed packages, like ggplot2 and ggvis, interactive 3D visualizations using the rgl package.
Machine learning with R is a book is a readable guide to applying machine learning to real-world problems. This book is equally useful for an experienced R user or new to the language. The book also includes the detail that helps you to find key insights, make new predictions, and visualize your findings.
This R data science book offers newer and much improve libraries, advice on ethical issues in machine learning, and an introduction to deep learning.
Hand on programming with R is a book written by Garrett Grolemund. In this book, you'll learn about the method of loading data, assemble and disassemble data objects, navigate R's environment system, etc.
You'll able to gain valuable programming skills and support your work as a data scientist at the same time.
10) R Packages
R Packages is a book written by Hadley Wickham. This R programming book shows you how to bundle reusable R functions, sample data, and documentation with the package development philosophy.
In the process, you'll work with dev tools, roxygen, and test that. The book covers a set of R packages that help you to automate common development tasks. It is ideal for developers, data scientists, and programmers. The book starts you with the basics and shows you how to improve your package writing over time.
11) Learning R
Learning R is a book written by Richard Cotton. It shows you real data by covering everything from importing data to publishing your results. Every chapter in the R programming book includes a quiz on what you've learned and concludes with exercises. Many of it involves writing the R language code.
With the help of this reference book, you will learn how to write a simple R program and discover what the R language can do. You will also learn about the Apply R add-on packages, and package your work for others.
Advanced R is a book written by Richard Cotton. In this book, you will also learn how to perform data analysis with the R language, even if you don't have much programming experience.
This book teaches how to use the essential R tools you need to know to analyze data, including data types and programming concepts. The second half of this book provides real data analysis by covering everything from importing data to publishing your results.
Text Mining with R is a book written by Julia Silge and David Robinson. With this R programming book, you'll explore text-mining methods with tidytext, a package. In this book, writer Julia Silge and David Robinson developed using tidy principles like graph and dplyr.
You'll also learn how you can integrate NLP (natural language processing) into effective workflows. The book offers examples and data explorations will help you generate real insights from literature, news, and social media.
R, in a Nutshell, is a book written by Joseph Adler. In this book, you will learn how to write R functions and use R packages to prepare, visualize, and analyze data. Thie book illustrates each process with a wealth of examples from medicine, business, and sports.
This book includes topics like R performance, the ggplot2 data visualization package, and parallel R computing with Hadoop. In this book, you will also learn to use R to prepare data for analysis and visualize your data with R's graphics.
Software for Data Analysis is a book written by John M. Chambers. This book guides you about programming with R, beginning with simple interactive use and progressing by explaining some simple functions.
You will learn about many advanced programming techniques that can be added as needed, which benefits you to grow their careers and the community.
Practical Data Science with R is a book written by Nina Zumel and John Mount. The book explains the basic principles in the ever-growing field of data science.
This book helps you to get right to the real-world use cases as you apply the R programming language and statistical analysis techniques. This learning material also covers examples based on marketing, business intelligence, and decision support.
R Cookbook is a book written by JD Long and Paul Teetor. The book helps you to perform data analysis with R quickly and efficiently with more than 275 practical recipes. It also covers basic tasks of input and output, graphics, and linear regression.
Each topic cover in this book addresses a specific problem and includes a discussion that helps you to explain the solution and provides insight about how it works.