Here are 49 books that R for Data Science fans have personally recommended if you like
R for Data Science.
Shepherd is a community of 12,000+ authors and super readers sharing their favorite books with the world.
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.
This provides a superb balance between technical aspects of R coding and the statistical methods that motivate its use. It's rare to find a book on topics like this that are written with Kabacoff's easygoing yet precise style, which makes it ideal for beginners. From my own experience, it is obvious the author has spent many years teaching this type of content, knowing where things deserve extra explanation up front and where other more technical details can be relegated to more advanced texts.
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 in Action, Second Edition is language tutorial focused on practical problems. Written by a research methodologist, it takes a direct and modular approach to quickly give readers the information they need to produce useful results. Focusing on realistic data analyses and a comprehensive integration of graphics, it follows the steps that…
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.
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.
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…
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.
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.
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…
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.
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.
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…
Colin Koopman researches and teaches about technology ethics at the University of Oregon, where he is a Professor of Philosophy and Director of the interdisciplinary certificate program in New Media & Culture. His research pursuits have spanned from the history of efforts in the early twentieth century to standardize birth certificates to our understanding of ourselves as effects of the code inscribed into our genes. Koopman is currently at work on a book that will develop our understanding of what it takes to achieve equality and fairness in data systems, tentatively titled Data Equals.
W.E.B. Du Bois is widely acknowledged as the leading activist for racial equality of his generation. But until very recently little had been known of his deep commitment to the pursuit of equality within and through data technology. As Du Bois was preparing notes for his famous 1903 book The Souls of Black Folk, he was also preparing an exposition of what we would today call “infographics” (or what the editors of this volume aptly call “data portraits”) for exhibition at the 1900 Paris Exposition world’s fair. This volume handsomely reproduces for the first time a full-color complete set of Du Bois’s charts, graphs, maps, and ingenious spirals. A beautiful book to live with, it also subtly transforms one’s understanding of the history of racial progress and inequality in America.
"As visually arresting as it is informative."-The Boston Globe
"Du Bois's bold colors and geometric shapes were decades ahead of modernist graphic design in America."-Fast Company's Co.Design
W.E.B. Du Bois's Data Portraits is the first complete publication of W.E.B. Du Bois's groundbreaking charts, graphs, and maps presented at the 1900 Paris Exposition.
Famed sociologist, writer, and Black rights activist W.E.B. Du Bois fundamentally changed the representation of Black Americans with his exhibition of data visualizations at the 1900 Paris Exposition. Beautiful in design and powerful in content, these data portraits make visible a wide spectrum of African American culture, from…
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.
Data scientists and analytics specialists are great at building models and algorithms, but often wrap them in a presentation or dashboard that diminishes their value and reduces the likelihood of their work being adopted. This book encourages practitioners to always consider the last mile and to pay as much attention to presentation and aesthetics as we do to the model itself.
Master the art and science of data storytelling-with frameworks and techniques to help you craft compelling stories with data.
The ability to effectively communicate with data is no longer a luxury in today's economy; it is a necessity. Transforming data into visual communication is only one part of the picture. It is equally important to engage your audience with a narrative-to tell a story with the numbers. Effective Data Storytelling will teach you the essential skills necessary to communicate your insights through persuasive and memorable data stories.
Narratives are more powerful than raw statistics, more enduring than pretty charts. When…
In sixth grade, my teacher tried to teach the class how to read line charts – and something fell into place for me. Ever since then, I’ve tried to sort data into forms that we can use to make sense of it. As a researcher at Microsoft, I consulted with teams across the organization – from sales to legal; and from Excel to XBox – to help them understand their data. At Honeycomb, I design tools for software operations teams to diagnose their complex systems. These books each gave me an “ah-hah” moment that made me think differently about the craft of creating visualization. They now sit on my shelf in easy reach – I hope you find them fascinating too.
A new edition of Bertin’s 1963 Semiology was released a few years ago, and my heart swelled with joy. For years, I’d worked off of bad photocopies of an inter-library loan book that had long since gone out of print. In this new edition, I could see how Bertin works through different dimensions and axes – when you want to plot two different quantitative axes over a map, what are your choices? What if you want to plot them over a graph, instead? What changes? I loved exploring these choices with Bertin, as he explores how different color mappings, iconic representations, and design choices change the way the reader interprets the graph.
Originally published in French in 1967, Semiology of Graphics is internationally recognized as a foundational work in the fields of design and cartography. Based on Jacques Bertin's practical experience as a cartographer, part one of this work is an unprecedented attempt to synthesize principles of graphic communication with the logic of standard rules applied to writing and topography. Part two brings Bertin's theory to life, presenting a close study of graphic techniques, including shape, orientation, colour, texture, volume, and size, in an array of more than 1,000 maps and diagrams.
I started my career as a research scientist building machine learning algorithms for weather forecasting. Twenty years later, I found myself at a precision agriculture startup creating models that provided guidance to farmers on when to plant, what to plant, etc. So, I am part of the movement from academia to industry. Now, at Google Cloud, my team builds cross-industry solutions and I see firsthand what our customers need in their data science teams. This set of books is what I suggest when a CTO asks how to upskill their workforce, or when a graduate student asks me how to break into the industry.
It is not enough for a data scientist to be able to analyze data and build ML models. You have to be able to communicate the insights to decision-makers concisely and accurately. This book shows you bad and good visualizations — you’ll be surprised by how often you would have defaulted to the bad way without the guidance provided by this book!
Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options.
This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke…
I am not very good at making things. I am good enough to appreciate the craftsmanship of those much better than me. I am more of an ideas person, perhaps why I ended up with a PhD in Philosophy of Science. But I have always held a secret admiration—with a tinge of envy—for people who are makers. As I went deeper into my career as a philosopher of science, I became aware that the material/making aspect of science—and technology—was largely ignored by ideas-obsessed philosophers. So, this is where I focused my attention, and I’ve loved vicariously being able to be part of making the world.
When I was a kid, one of my favorite books was The Way Things Work, not the more recent David Macaulay book—which is also good—but the earlier 1967 book by T. Lodewijk. With great diagrams, it showed how complicated machines work.
Randall Munroe's Thing Explainer, while less comprehensive, similarly captures this magic for me. It has great diagrams and simple clarifying text—self-consciously limited to the 1,000 words people use the most. I could stare at the diagrams for hours, learning about everything from cameras (“picture takers”) to submarines (“boats that go under the sea”).
From the No. 1 bestselling author of What If? - the man who created xkcd and explained the laws of science with cartoons - comes a series of brilliantly simple diagrams ('blueprints' if you want to be complicated about it) that show how important things work: from the nuclear bomb to the biro.
It's good to know what the parts of a thing are called, but it's much more interesting to know what they do. Richard Feynman once said that if you can't explain something to a first-year student, you don't really get it. In Thing Explainer, Randall Munroe takes…
I am motivated by working on products that many people use. I've been a part of companies that deliver products impacting millions of people. To achieve it, I am working in the Big Data ecosystem and striving to simplify it by contributing to Dremio's Data LakeHouse solution. I worked on projects using Spark, HDFS, Cassandra, and Kafka technologies. I have been working in the software engineering industry for ten years now, and I've tried to share my experience and lessons learned in the Software Mistakes and Tradeoffs book, hoping that it will allow current and the next generation of engineers to create better software, leading to more happy users.
Apache Spark has a very high point of entry for newcomers to the Big Data ecosystem.
However, it is a key tool that almost everyone is using for running distributed processing. I recommend everyone to read this book before delving into production solutions based on Apache Spark.
This book will allow you to alleviate many spark problems, such as serialization, memory utilization, and parallelization of processing.
In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. You'll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques-classification, collaborative filtering, and anomaly detection among others-to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you'll find these patterns useful for…
