Looking for books to learn R programming? Then here are some best picked and recommendations of top programmers and experts.
No matter if you a beginner, don’t know the basics, or if your basics are clear and you want to be in the next level in R programming then you will also find your desire books from this list.
Good luck for your next reading list.
Table of Contents
The Art of R Programming by Norman Matloff
The Art of R Programming 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, and your programming skills can range from hobbyist to pro.
Along the way, you’ll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats.
You’ll also learn to:
Create artful graphs to visualize complex data sets and functions Write more efficient code using parallel R and vectorization Interface R with C/C++ and Python for increased speed or functionality.
Find new R packages for text analysis, image manipulation, and more Squash annoying bugs with advanced debugging techniques.
Whether you’re designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.
Practical Data Science with R by John Mount and Nina Zumel
Practical Data Science with R lives up to its name. It 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 on marketing, business intelligence, and decision support.
The purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
R Programming: A Step-By-Step Guide for Absolute Beginners by Daniel Bell
This book is about R programming. The following are the objectives of the author:
To familiarize you with the basics of R programming language.
To help you understand the various fields where R can be applied and its use cases in each field.
- To equip you with R programming skills, both beginner and advanced skills.
- To introduce you to R programming for data analysis.
- To introduce you to R programming for machine learning.
- To help you understand and appreciate the power of R in statistical computing, data analysis, and scientific research.
Who this Book is for?
Anybody who is a complete beginner to R Programming.
Anybody in need of advancing their R Programming skills.
Professionals in computer programming.
Professors, lecturers, or tutors who are looking to find better ways to explain R programming to their students in the simplest and easiest way.
Students and academicians, especially those focusing on R, Data Analysis, Machine Learning, computer science, and Database development.
R in Action by Rob Kabacoff
R in Action is the first book to present both the R system and the use cases that make it such a compelling package for business developers.
The book begins by introducing the R language, including the development environment.
Focusing on practical solutions, the book also offers a crash course in practical statistics and covers elegant methods for dealing with messy and incomplete data using features of R.
About the Technology
R is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task.
It runs on all important platforms and provides thousands of useful specialized modules and utilities. This makes R a great way to get meaningful information from mountains of raw data.
R for Everyone by Jared P. Lander
R for Everyone is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling.
Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code.
You’ll download and install R; navigate and use the R environment; master basic program control, data import, and manipulation; and walk through several essential tests.
Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques.
By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most.
R for dummies by Andrie de Vries and Joris Meys
R is the open-source programming language standard for data analysis and statistical modeling. R is packed with powerful programming capabilities, but learning to use R in the real world can be overwhelming for even the most seasoned statisticians.
This easy-to-follow guide explains how to use R for data processing and statistical analysis, and then, shows you how to present your data using compelling and informative graphics.
You’ll gain practical experience using R in a variety of settings and delve deeper into R’s feature-rich toolset.
- Includes tips for the initial installation of R
- Demonstrates how to easily perform calculations on vectors, arrays, and lists of data shown how to effectively visualize data using R’s powerful graphics packages
- Gives pointers on how to find, install, and use add-on packages created by the R community
- Provides tips on getting additional help from R mailing lists and websites
Machine Learning with R, the Tidyverse, and MLR by Hefin I. Rhys
Machine learning (ML) is a collection of programming techniques for discovering relationships in data. With ML algorithms, you can cluster and classify data for tasks like making recommendations or fraud detection and make predictions for sales trends, risk analysis, and other forecasts.
Once the domain of academic data scientists, machine learning has become a mainstream business process, and tools like the easy-to-learn R programming language put high-quality data analysis in the hands of any programmer.
Machine Learning with R, the tidyverse, and mlr teaches you widely used ML techniques and how to apply them to your own datasets using the R programming language and its powerful ecosystem of tools.
This book will get you started!
The purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Learn R Programming in 1 Day Complete Guide for Beginners by Krishna Rungta
R is a programming language developed is widely used for statistical and graphical analysis. It can execute advance machine learning algorithms including earning algorithm, linear regression, time series, statistical inference.
R programming language is used by Fortune 500 companies and tech bellwethers like Uber, Google, Airbnb, Facebook, Apple.
R Data Science Quick Reference by Thomas Mailund
In this handy, practical book you will cover each concept concisely, with many illustrative examples. You’ll be introduced to several R data science packages, with examples of how to use each of them.
Advanced R, Second Edition Hadley Wickham
Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R.
Advanced R Statistical Programming and Data Models by Joshua F. Wiley
Advanced R Statistical Programming and Data Models shows you how to conduct data analysis using the popular R language.
You’ll delve into the preconditions or hypothesis for various statistical tests and techniques and work through concrete examples using R for a variety of these next-level analytics. This is a must-have guide and reference on using and programming with the R language.
What You’ll Learn
Conduct advanced analyses in R including generalized linear models, generalized additive models, mixed-effects models, machine learning, and parallel processing.
Carry out regression modeling using R data visualization, linear and advanced regression, additive models, survival/time to event analysis
Handle machine learning using R including parallel processing, dimension reduction, and feature selection and classification
Address missing data using multiple imputations in R
Work on factor analysis, generalized linear mixed models, and modeling intraindividual variability
Beyond Spreadsheets with R by Dr. Jonathan Carroll
Beyond Spreadsheets with R shows you how to take raw data and transform it for use in computations, tables, graphs, and more. You’ll build on simple programming techniques like loops and conditionals to create your own custom functions.
You’ll come away with a toolkit of strategies for analyzing and visualizing data of all sorts using R and RStudio.
The purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Mastering Social Media Mining with R by Vikram Garg
Explore the social media APIs in R to capture data and tame it.
Employ the machine learning capabilities of R to gain optimal business value. A hands-on guide with real-world examples to help you take advantage of the vast opportunities that come with social media data
Who This Book Is For?
If you have basic knowledge of R in terms of its libraries and are aware of different machine learning techniques, this book is for you.
Those with experience in data analysis who are interested in mining social media data will find this book useful.
Ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham
This new edition to the classic book by Ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. Ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. With ggplot2, It’s easy to:
Produce handsome, publication-quality plots with automatic legends created from the plot specification.
Superimpose multiple layers (points, lines, maps, tiles, box plots) from different data sources with automatically adjusted common scales.
- Add customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regression.
- Save any ggplot2 plot (or part thereof) for later modification or reuse.
- Create custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plots.
- Approach a graph from a visual perspective, thinking about how each component of the data is represented on the final plot.
R for Data Science by Garrett Grolemund and Hadley Wickham
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun.
Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.
Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results.
You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way.
Mastering Predictive Analytics with R by Rui Miguel Forte
Master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts.
About This Book
Grasp the major methods of predictive modeling and move beyond black box thinking to a deeper level of understanding.
Leverage the flexibility and modularity of R to experiment with a range of different techniques and data types.
Packed with practical advice and tips explaining important concepts and best practices to help you understand quickly and easily
Who This Book Is For?
This book is intended for the budding data scientist, predictive modeler, or quantitative analyst with only a basic exposure to R and statistics.
It is also designed to be a reference for experienced professionals wanting to brush up on the details of a particular type of predictive model.
Mastering Predictive Analytics with R assumes familiarity with only the fundamentals of R, such as the main data types, simple functions, and how to move data around.
No prior experience with machine learning or predictive modeling is assumed, however, you should have a basic understanding of statistics and calculus at a high school level.
Data Science with R by Andrew Oleksy
A Step By Step Guide with Visual Illustrations and Examples
The Data Science field is expected to continue growing rapidly over the next several years and Data Scientist is consistently rated as a top career.
Data Science with R gives you the necessary theoretical background to start your Data Science journey and shows you how to apply the R programming language through practical examples in order to extract valuable knowledge from data.
Professor Andrew Oleksy guides you through all important concepts of data science including the R programming language, Data Mining, Clustering, Classification and Prediction, the Hadoop framework, and more.
The Book of R by Tilman M. Davies
“The Book of R” is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis.
Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis.
You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling.
You’ll even learn how to create impressive data visualizations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package.
Efficient R Programming by Colin Gillespie and Robin Lovelace
“Efficient R Programming.” Drawing on years of experience teaching R courses, authors Colin Gillespie and Robin Lovelace give practical advice on a range of topics from optimizing set-up of RStudio to leveraging C++ that make this book a valuable asset for both experienced and novice programmers.
It’s suitable for academics, business users, and programmers from a wide range of backgrounds. Get practical, tried-and-true advice from longtime R instructors.
Dive into a wide range of topics, including RStudio set-up and leveraging C++, suitable for all skill levels.
Gain insight into RStudio s functionality to boost code-writing productivity.
Learn the necessary skills for team-based R programming work.
Save time, and energy, debugging code and searching online forums”
R Packages: Organize, Test, Document, and Share Your Code by Hadley Wickham
Learn about the most useful components of an R package, including vignettes and unit tests Automate anything you can, taking advantage of the years of development experience embodied in devtools.
Get tips on good style, such as organizing functions into files Streamline your development process with devtools.
Learn the best way to submit your package to the Comprehensive R Archive Network (CRAN) Learn from a well-respected member of the R community who created 30 R packages, including ggplot2, dplyr, and tidyr
R for Excel Users: An Introduction to R for Excel Analysts by John L. Taveras
R has a steep learning curve and, if taken in all at once, it can be overwhelming. But we can tame this curve by putting aside visualizations and analysis, and focusing on working with data.
This book is all about data manipulation: importing, creating, modifying, filtering, summarizing and reshaping data sets. You will also go deep on the building blocks of R: vectors and functions.
The language is simplified and technical lingo is kept to a minimum. You will see analogies to Excel where applicable, to ease your understanding of concepts.
R Cookbook by Paul Teetor
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.
Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you’re a beginner, R Cookbook will help get you started.
If you’re an experienced data programmer, it will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process.
R Graphics Cookbook: Practical Recipes for Visualizing Data by Winston Chang
Use R’s default graphics for quick exploration of data. Create a variety of bar graphs, line graphs, and scatter plots.
Summarize data distributions with histograms, density curves, box plots, and other examples
- Provide annotations to help viewers interpret data
- Control the overall appearance of graphics
- Render data groups alongside each other for easy comparison
- Use colors in plots
- Create network graphs, heat maps, and 3D scatter plots
- Structure data for graphing
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