68 books like R in Action

By Robert I. Kabacoff,

Here are 68 books that R in Action fans have personally recommended if you like R in Action. Shepherd is a community of 11,000+ authors and super readers sharing their favorite books with the world.

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Book cover of R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

Tilman M. Davies Author Of The Book of R: A First Course in Programming and Statistics

From my list on intro to programming and data science with R.

Why am I passionate about this?

I’m an applied statistician and academic researcher/lecturer at New Zealand’s oldest university – the University of Otago. R facilitates everything I do – research, academic publication, and teaching. It’s the latter part of my job that motivated my own book on R. From first-year statistics students who have never seen R to my own Ph.D. students using R to implement novel and highly complex statistical methods and models, my experience is that all ultimately love the ease with which the R language permits exploration, visualisation, analysis, and inference of one’s data. The ever-growing need in today’s society for skilled statisticians and data scientists means there's never been a better time to learn this essential language.

Tilman's book list on intro to programming and data science with R

Tilman M. Davies Why did Tilman love this book?

For those intending to use R with an eye on the popular 'Tidyverse' suite of packages – which facilitate the handling, manipulation, and visualisation of data setsit's hard to go past this book. From the founding contributors of the RStudio/Tidyverse worlds, this is a great way to learn about this dialect of R against the overarching backdrop of statistical data analysis and data science.

By Hadley Wickham, Garrett Grolemund,

Why should I read it?

1 author picked R for Data Science as one of their favorite books, and they share why you should read it.

What is this book about?

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…


Book cover of The R Book

Tilman M. Davies Author Of The Book of R: A First Course in Programming and Statistics

From my list on intro to programming and data science with R.

Why am I passionate about this?

I’m an applied statistician and academic researcher/lecturer at New Zealand’s oldest university – the University of Otago. R facilitates everything I do – research, academic publication, and teaching. It’s the latter part of my job that motivated my own book on R. From first-year statistics students who have never seen R to my own Ph.D. students using R to implement novel and highly complex statistical methods and models, my experience is that all ultimately love the ease with which the R language permits exploration, visualisation, analysis, and inference of one’s data. The ever-growing need in today’s society for skilled statisticians and data scientists means there's never been a better time to learn this essential language.

Tilman's book list on intro to programming and data science with R

Tilman M. Davies Why did Tilman love this book?

An authoritative tome on R. This book is the ultimate reference guide, heavy on statistical methods from the simple to the advanced. Of the 29 chapters, only the first five chapters or so have R syntactical and programming skills as their main focus; the remaining content highlights the many and varied statistical techniques R is capable of. I think this is a fantastic book to have on the shelf for people who are likely to need R and its contributed packages for a variety of different statistical analyses, but might not know where to initially start for any given statistical method.

By Michael J. Crawley,

Why should I read it?

1 author picked The R Book as one of their favorite books, and they share why you should read it.

What is this book about?

Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition: * Features full colour text and extensive graphics throughout. * Introduces a clear structure with numbered section headings to help readers locate information more efficiently. * Looks at the evolution of R over the…


Book cover of R For Dummies

Tilman M. Davies Author Of The Book of R: A First Course in Programming and Statistics

From my list on intro to programming and data science with R.

Why am I passionate about this?

I’m an applied statistician and academic researcher/lecturer at New Zealand’s oldest university – the University of Otago. R facilitates everything I do – research, academic publication, and teaching. It’s the latter part of my job that motivated my own book on R. From first-year statistics students who have never seen R to my own Ph.D. students using R to implement novel and highly complex statistical methods and models, my experience is that all ultimately love the ease with which the R language permits exploration, visualisation, analysis, and inference of one’s data. The ever-growing need in today’s society for skilled statisticians and data scientists means there's never been a better time to learn this essential language.

Tilman's book list on intro to programming and data science with R

Tilman M. Davies Why did Tilman love this book?

A gentle yet detailed book for beginner programmers. A great book for those who know they'll be getting up to some programming in R but who are very new to programming in general. The book's chapters are filled with content on the syntax, usage, and 'best practice' guidelines. The examples guide the reader in a step-by-step fashion to maximise understanding. An especially unique chapter providing examples on things you can do in R that you might've otherwise done in Excel is one of its stand-out features.

By Andrie De Vries, Joris Meys,

Why should I read it?

1 author picked R For Dummies as one of their favorite books, and they share why you should read it.

What is this book about?

Mastering R has never been easier Picking up R can be tough, even for seasoned statisticians and data analysts. R For Dummies, 2nd Edition provides a quick and painless way to master all the R you'll ever need. Requiring no prior programming experience and packed with tons of practical examples, step-by-step exercises, and sample code, this friendly and accessible guide shows you how to know your way around lists, data frames, and other R data structures, while learning to interact with other programs, such as Microsoft Excel. You'll learn how to reshape and manipulate data, merge data sets, split and…


Book cover of A First Course in Statistical Programming with R

Tilman M. Davies Author Of The Book of R: A First Course in Programming and Statistics

From my list on intro to programming and data science with R.

Why am I passionate about this?

I’m an applied statistician and academic researcher/lecturer at New Zealand’s oldest university – the University of Otago. R facilitates everything I do – research, academic publication, and teaching. It’s the latter part of my job that motivated my own book on R. From first-year statistics students who have never seen R to my own Ph.D. students using R to implement novel and highly complex statistical methods and models, my experience is that all ultimately love the ease with which the R language permits exploration, visualisation, analysis, and inference of one’s data. The ever-growing need in today’s society for skilled statisticians and data scientists means there's never been a better time to learn this essential language.

Tilman's book list on intro to programming and data science with R

Tilman M. Davies Why did Tilman love this book?

From well-known authorities in the R-sphere (including a former R Core Team member), this is a long-standing text whose first edition was one of the early books intended to teach R to beginners. It provides concise instructions and examples on how R is used as a programming language before focusing on 'number-crunching' statistical methods that are typically seen as computationally intensive. One of the notable features of this book is the statistical methods at hand are not just illustrated using 'black-box' code--the reader is provided with the necessary mathematical detail to understand what's going on behind the scenes for those that are so inclined.

By W. John Braun, Duncan J. Murdoch,

Why should I read it?

1 author picked A First Course in Statistical Programming with R as one of their favorite books, and they share why you should read it.

What is this book about?

This third edition of Braun and Murdoch's bestselling textbook now includes discussion of the use and design principles of the tidyverse packages in R, including expanded coverage of ggplot2, and R Markdown. The expanded simulation chapter introduces the Box-Muller and Metropolis-Hastings algorithms. New examples and exercises have been added throughout. This is the only introduction you'll need to start programming in R, the computing standard for analyzing data. This book comes with real R code that teaches the standards of the language. Unlike other introductory books on the R system, this book emphasizes portable programming skills that apply to most…


Book cover of Predict and Surveil: Data, Discretion, and the Future of Policing

Luke Hunt Author Of Police Deception and Dishonesty: The Logic of Lying

From my list on the cluster-f*ck we call policing.

Why am I passionate about this?

I’m an Associate Professor in the University of Alabama’s Department of Philosophy. I worked as an FBI Special Agent before making the natural transition to academic philosophy. Being a professor was always a close second to Quantico, but that scene in Point Break in which Keanu Reeves and Patrick Swayze fight Anthony Kiedis on the beach made it seem like the FBI would be more fun than academia. In my current position as a professor at the University of Alabama, I teach in my department’s Jurisprudence Specialization. My primary research interests are at the intersection of philosophy of law, political philosophy, and criminal justice. I’ve written three books on policing.

Luke's book list on the cluster-f*ck we call policing

Luke Hunt Why did Luke love this book?

I love this book because it reminds us of the many ways that technology can affect justice.

It is tempting to think sophisticated tactics such as “predictive policing” can solve all problems relating to human bias. However, Brayne shows that data and algorithms do not eliminate bias and discretion. Instead, high-tech police tools simply make bias less overt and visible, which erodes the public’s ability to hold the police accountable.

I especially enjoyed how the book flips the script, considering diverse ways to use these tools to help the public. For example, how can municipalities use technology to analyze the underlying factors that contribute to policing problems in the first place?

By Sarah Brayne,

Why should I read it?

1 author picked Predict and Surveil as one of their favorite books, and they share why you should read it.

What is this book about?

The scope of criminal justice surveillance, from the police to the prisons, has expanded rapidly in recent decades. At the same time, the use of big data has spread across a range of fields, including finance, politics, health, and marketing. While law enforcement's use of big data is hotly contested, very little is known about how the police actually use it in daily operations and with what consequences.

In Predict and Surveil, Sarah Brayne offers an unprecedented, inside look at how police use big data and new surveillance technologies, leveraging on-the-ground fieldwork with one of the most technologically advanced law…


Book cover of How to Lie with Statistics

Bastiaan C. van Fraassen Author Of Philosophy and Science of Risk: An Introduction

From my list on exploring the meaning of probability and risk.

Why am I passionate about this?

I’ve wanted to be a philosopher since I read Plato’s Phaedo when I was 17, a new immigrant in Canada. Since then, I’ve been fascinated with time, space, and quantum mechanics and involved in the great debates about their mysteries. I saw probability coming into play more and more in curious roles both in the sciences and in practical life. These five books led me on an exciting journey into the history of probability, the meaning of risk, and the use of probability to assess the possibility of harm. I was gripped, entertained, illuminated, and often amazed at what I was discovering. 

Bastiaan's book list on exploring the meaning of probability and risk

Bastiaan C. van Fraassen Why did Bastiaan love this book?

I am laughing out loud, even now that I am rereading this book for the umpteenth time. Fraudsters are so clever, and so is advertising. And then there is sloppy journalism with its “wow” statistics.

I like his book enormously, not least because of its witty illustrations. It is subversive, comic, and provocative, and it makes me wise to seductive, misleading practices–and it does so with a light touch.

By Darrell Huff, Irving Geis (illustrator),

Why should I read it?

3 authors picked How to Lie with Statistics as one of their favorite books, and they share why you should read it.

What is this book about?

From distorted graphs and biased samples to misleading averages, there are countless statistical dodges that lend cover to anyone with an ax to grind or a product to sell. With abundant examples and illustrations, Darrell Huff's lively and engaging primer clarifies the basic principles of statistics and explains how they're used to present information in honest and not-so-honest ways. Now even more indispensable in our data-driven world than it was when first published, How to Lie with Statistics is the book that generations of readers have relied on to keep from being fooled.


Book cover of Competing on Analytics: The New Science of Winning

Jeremy Adamson Author Of Minding the Machines: Building and Leading Data Science and Analytics Teams

From my list on for data science and analytics leaders.

Why am I passionate about this?

I am a leader in analytics and AI strategy, and have a broad range of experience in aviation, energy, financial services, and the public sector.  I have worked with several major organizations to help them establish a leadership position in data science and to unlock real business value using advanced analytics. 

Jeremy's book list on for data science and analytics leaders

Jeremy Adamson Why did Jeremy love this book?

This is a foundational book on analytics and data science as a business function and helped to shape the development of the practice. It provides a view of the discipline through a business lens and avoids deep technical examinations. Though much has changed in the 15 years since it was originally published, it is still essential reading for a leader in the field. No book since has captured as well the competitive differentiation that analytics provides.

By Thomas H. Davenport, Jeanne G. Harris,

Why should I read it?

1 author picked Competing on Analytics as one of their favorite books, and they share why you should read it.

What is this book about?

You have more information at hand about your business environment than ever before. But are you using it to "out-think" your rivals? If not, you may be missing out on a potent competitive tool. In Competing on Analytics: The New Science of Winning, Thomas H. Davenport and Jeanne G. Harris argue that the frontier for using data to make decisions has shifted dramatically. Certain high-performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. Their secret weapon? Analytics: sophisticated quantitative and statistical analysis and predictive modeling. Exemplars of analytics are using new…


Book cover of Statistics and Data Analysis for Financial Engineering: With R Examples

Ernest P. Chan Author Of Quantitative Trading: How to Build Your Own Algorithmic Trading Business

From my list on quantitative trading for beginners.

Why am I passionate about this?

A noted quantitative hedge fund manager and quant finance author, Ernie is the founder of QTS Capital Management and Predictnow.ai. Previously he has applied his expertise in machine learning at IBM T.J. Watson Research Center’s Human Language Technologies group, at Morgan Stanley’s Data Mining and Artificial Intelligence Group, and at Credit Suisse’s Horizon Trading Group. Ernie was quoted by Bloomberg, the Wall Street Journal, New York Times, Forbes, and the CIO magazine, and interviewed on CNBC’s Closing Bell program. He is an adjunct faculty at Northwestern University’s Master’s in Data Science program and supervises student theses there. Ernie holds a Ph.D. in theoretical physics from Cornell University.

Ernest's book list on quantitative trading for beginners

Ernest P. Chan Why did Ernest love this book?

I have used this book to teach my Financial Risk Analytics course at Northwestern University for many years. As a textbook, it is surprisingly easy to read, and the abundant exercises are great. This would be a foundational text to read after you have read my own books. It puts you on solid ground to understand all the financial babble that you may read elsewhere. It includes extensive coverage of most basic topics important to a serious quantitative trader, while not being overly mathematical. Easily understandable if you have basic programming and math background from first year of university.

Everything is practical in this book, which isn’t what you would expect from a textbook! There is no math for math’s sake. I have used the techniques discussed in this book for real trading, and for creating features at my machine learning SaaS predictnow.ai. Examples: What’s the difference between net…

By David Ruppert, David S. Matteson,

Why should I read it?

1 author picked Statistics and Data Analysis for Financial Engineering as one of their favorite books, and they share why you should read it.

What is this book about?

The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code…


Book cover of The Cartoon Guide to Statistics

Martin Sternstein Author Of Barron's AP Statistics

From my list on statistical insights into social issues.

Why am I passionate about this?

I taught for 45 years at Ithaca College broken by two years as Fulbright Professor in West Africa at the University of Liberia. During my years in academia, I developed several new courses including a popular “Math in Africa” class and the first U.S. course for college credit in chess theory. I’ve always had a passion for and continue to have strong interests in (1) national educational and social issues concerning equal access to math education for all and (2) teaching others about the power of mathematics and statistics to help one more deeply understand social issues.

Martin's book list on statistical insights into social issues

Martin Sternstein Why did Martin love this book?

This book is kind of a fun crash course in statistics which covers all the basic concepts at an introductory level.

The cartoons are a little bit dated, but still entertaining. There are lots of pictures and graphs which are a pleasure if you are a visual learner. The reader will come away with many useful tools to help understand real world problems.

I’m a retired math professor, but still got a real kick out of this book and especially appreciated the many good examples referenced such as gender discrimination in salaries and racial discrimination in jury selection. I recommended it to many of my struggling students.

By Larry Gonick, Woollcott Smith,

Why should I read it?

1 author picked The Cartoon Guide to Statistics as one of their favorite books, and they share why you should read it.

What is this book about?

Updated version featuring all new material. If you have ever looked for P-values by shopping at P mart, tried to watch the Bernoulli Trails on "People's Court," or think that the standard deviation is a criminal offense in six states, then you need The Cartoon Guide to Statistics to put you on the road to statistical literacy. The Cartoon Guide to Statistics covers all the central ideas of modern statistics: the summary and display of data, probability in gambling and medicine, random variables, Bernoulli Trails, the Central Limit Theorem, hypothesis testing, confidence interval estimation, and much more-all explained in simple,…


Book cover of Thinking About Statistics: The Philosophical Foundations

Michael Anthony Lewis Author Of Social Workers Count: Numbers and Social Issues

From my list on quant geeks.

Why am I passionate about this?

I've had a long-time interest in two things: mathematics and social issues. This is why I got degrees in social work (Masters) and sociology (PhD) and eventually focused on the quantitative aspects of these two areas. Social Workers Count gave me the chance to marry these two interests by showing the role mathematics can play in illuminating a number of pressing social issues.

Michael's book list on quant geeks

Michael Anthony Lewis Why did Michael love this book?

Jun Otsuka, a philosopher who also has training in statistics, zooms in on their philosophical foundations.

His book discusses the metaphysical, epistemological, and semantic assumptions on which Classical statistics, Bayesian statistics, predictive/classification AI models, and causal inference are based.

For those interested in these disciplines but who're also sensitive to the philosophical issues they raise, Otsuka's book is simply amazing. Run out and get a copy as soon as possible.   

By Jun Otsuka,

Why should I read it?

1 author picked Thinking About Statistics as one of their favorite books, and they share why you should read it.

What is this book about?

Simply stated, this book bridges the gap between statistics and philosophy. It does this by delineating the conceptual cores of various statistical methodologies (Bayesian/frequentist statistics, model selection, machine learning, causal inference, etc.) and drawing out their philosophical implications. Portraying statistical inference as an epistemic endeavor to justify hypotheses about a probabilistic model of a given empirical problem, the book explains the role of ontological, semantic, and epistemological assumptions that make such inductive inference possible. From this perspective, various statistical methodologies are characterized by their epistemological nature: Bayesian statistics by internalist epistemology, classical statistics by externalist epistemology, model selection by pragmatist…


5 book lists we think you will like!

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