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R for Data Science: Import, Tidy, Transform, Visualize, and Model Data 1st Edition

by Garrett Grolemund (Author), Hadley Wickham (Author)
4.7 4.7 out of 5 stars 1,592 ratings
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R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
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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.

You’ll learn how to:

  • Wrangle—transform your datasets into a form convenient for analysis
  • Program—learn powerful R tools for solving data problems with greater clarity and ease
  • Explore—examine your data, generate hypotheses, and quickly test them
  • Model—provide a low-dimensional summary that captures true "signals" in your dataset
  • Communicate—learn R Markdown for integrating prose, code, and results.
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  1. ISBN-10
    1491910399
  2. ISBN-13
    978-1491910399
  3. Edition
    1st
  4. Publisher
    O'Reilly Media
  5. Publication date
    January 31, 2017
  6. Language
    English
  7. Dimensions
    5.9 x 1.2 x 8.8 inches
  8. Print length
    518 pages
  9. See all details
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Editorial Reviews

About the Author

Hadley Wickham is an Assistant Professor and the Dobelman FamilyJunior Chair in Statistics at Rice University. He is an active memberof the R community, has written and contributed to over 30 R packages, and won the John Chambers Award for Statistical Computing for his work developing tools for data reshaping and visualization. His research focuses on how to make data analysis better, faster and easier, with a particular emphasis on the use of visualization to better understand data and models.

Garrett Grolemund is a statistician, teacher and R developer who currently works for RStudio. He sees data analysis as a largely untapped fountain of value for both industry and science. Garrett received his Ph.D at Rice University in Hadley Wickham's lab, where his research traced the origins of data analysis as a cognitive process and identified how attentional and epistemological concerns guide every data analysis.

Garrett is passionate about helping people avoid the frustration and unnecessary learning he went through while mastering data analysis. Even before he finished his dissertation, he started teaching corporate training in R and data analysis for Revolutions Analytics. He's taught at Google, eBay, Axciom and many other companies, and is currently developing a training curriculum for RStudio that will make useful know-how even more accessible.

Outside of teaching, Garrett spends time doing clinical trials research, legal research, and financial analysis. He also develops R software, he's co-authored the lubridate R package which provides methods to parse, manipulate, and do arithmetic with date-times and wrote the ggsubplot package, which extends the ggplot2 package.

Product details

  • Publisher ‏ : ‎ O'Reilly Media; 1st edition (January 31, 2017)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 518 pages
  • ISBN-10 ‏ : ‎ 1491910399
  • ISBN-13 ‏ : ‎ 978-1491910399
  • Item Weight ‏ : ‎ 1.64 pounds
  • Dimensions ‏ : ‎ 5.9 x 1.2 x 8.8 inches
  • Customer Reviews:
    4.7 4.7 out of 5 stars 1,592 ratings

About the authors

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  1. Hadley Wickham

    Hadley Wickham

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    Full content visible, double tap to read brief content.

    Hadley is Chief Scientist at RStudio and a member of the R Foundation. He builds tools (both computational and cognitive) that make data science easier, faster, and more fun. His work includes packages for data science (ggplot2, dplyr, tidyr), data ingest (readr, readxl, haven), and principled software development (roxygen2, testthat, devtools). He is also a writer, educator, and frequent speaker promoting the use of R for data science. Learn more on his homepage, http://hadley.nz.

    See more on the author's page
  2. Garrett Grolemund

    Garrett Grolemund

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Customer reviews

4.7 out of 5 stars
4.7 out of 5
1,592 global ratings
Good content, but print quality is so-so
4 Stars
Good content, but print quality is so-so
To anyone who may be unaware, all of the contents of the book are available for free online and are updated regularly. I purchased this book in October 2019 and it was already out of date compared the the version online. However, not enough out of date to deter me from using it.I prefer to read books in print versus online, which is the main reason I purchased this book. It also allows me to study while on a plane, which is very convenient. The book is great, easy to understand, and provides a lot of questions and examples to work through in each chapter.I’m halfway through the book now, and one complaint that I have is that the quality of the print leaves a bit to be desired. If you look at my photos, you’ll see that I drew boxes around certain words/letters. I had to do this because the printed lines were SO faint that I completely missed it the first time I read it. This made the chapter more confusing than it should have been. I had to reread it a few times before I made out the super faint boxes. I compared it to the online version, which had the lines/boxes clearly drawn. I also wish the solutions to the practice questions and exercises were printed in the book so that I could check my answers. However, the answers are available online...it just adds a bit of unnecessary hassle. Those are really my only complaints so far.Overall, good book for beginner R and data science learners.
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Top reviews from the United States

Nc
5.0 out of 5 stars learning R
Reviewed in the United States on May 8, 2018
Verified Purchase
As a beginner to R, I bought this book at the recommendation from Data Science for Fundraising: Build Data-Driven Solutions Using R and am so glad that I did. R4DS provides useful content to get started with R. The book consists of 5 main sections:

1. Getting started (Explore)
2. Data manipulation (Wrangle)
3. Scripting (Program)
4. Build models
5. Presenting information (Communicate)

Here’s a brief overview of each section:

Explore
I was a bit puzzled as the book jumps directly into plotting using the ggplot library. Although the authors are clear that the purpose of introducing ggplot is to keep our motivation high, seeing the ggplot syntax without a whole lot of background can be confusing at first. But then the authors do a great job at explaining the various aspects of ggplot. So my advice to other R beginners is to just keep going forward!

After the intro to ggplot, you learn about the basics of R, such as variables and functions, as well as intermediate topics such as writing scripts, manipulating data using dplyr, and lastly EDA. This section ends with a good overview of how to use RStudio to saves script files.

Wrangle
This section starts with an intro to tibbles, which is a new concept for handling data in R. This section is important since most of the functions in later chapters use tibbles, such as reading data from various sources. There are many important concepts that seem useful such as “tidying up” your data and cleaning data points (string and dates).

Program
This is a function section that introduces data pipes. This section explores how to chain complicated data operations together. You also get to see some good practice in writing human readable code. Then the book jumps into using the power of R as a functional programming language. Once I feel more proficient, I will take a deeper look at purr.

Model
Now, depending on your situation, you may not be interested in this section, which is focuses on predictive modeling. There are quite a few recipes that explore “why” questions. Definitely planning to come back to this chapter later.

Communicate
This section is useful because it introduces how to create reusablereports.

Overall, this is my go-to book along with the book I previously mentioned. This book is a solid reference book for learning R. Another good thing is that the companion website is regularly updated and you can copy and paste the code examples directly into RStudio. I’m looking forward to further exploring R and learning how to create my own data visualizations.
10 people found this helpful
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Stephen Oates
5.0 out of 5 stars An excellent introduction to using R for Exploratory Analysis.
Reviewed in the United States on December 26, 2016
Verified Purchase
Wickham and Grolemund have produced an excellent book that would help a beginning R user become very efficient in explanatory analysis. Unsurprisingly the approach that they expound utilises the "hadleyverse" a collection of packages (ggplot2 for visualisation, tidyr for reshaping, dplyr for selecting and filtering, purrr for functional programming, broom for linear models etc) that dramatically speed up most of the common steps involved in an analysis. One benefit of Wickham's involvement in these packages has been a coherent philosophy that sits behind them. It can be a little tricky when learning this philosophy, but the long term benefits are enormous.

The book is broken up into a number of sections that effectively builds up the ability to ingest, transform, visualise and model datasets. A good portion of the book is available in an online version, to give you a taste of how it is written. Many have been following it as it was written. I have passed on copies of the book to a number of colleagues who were just starting out and the response has been uniformly positive. In my own case I was familiar with some of the these packages; ggplot2, dplyr, tidyr, but found the book taught me purrr and how to better use the packages together.

Probably my two biggest caveats to readers are that there are situations where packages from outside the "hadleyverse" maybe required. The authors do a great job of pointing this out, but it does pay in my experience to know data.table and lattice for example. Both because they can occasionally fit a problem better but also because you inevitably come across other people's code where these packages are used. The other caveat is that the modelling is a little rudimentary. Most of the examples are just fitting independent regression models, whereas it seems to me that a hierarchical model would be a better fit. Still these are small things and it would be silly to expect a single book to cover all of these areas.

In short this is the book I would give to someone who was keen to learn about how to use R for data science. It reads really well building up the different components whilst still being a valuable reference if you just need a reminder of a particular package (what is the difference between tibbles and data frames again?). Even though a good portion of the book is available online, it is well worth it to have the full thing on your bookshelf (digital or otherwise). On a broader note with Max Kuhn (author of the excellent "Applied Predictive Modelling" with Kjell Johnson) joining Wickham and Grolemund at RStudio, it is a great time to start your R journey.
122 people found this helpful
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XV123
5.0 out of 5 stars Great book - Very useful!
Reviewed in the United States on June 12, 2018
Verified Purchase
I am very happy with this book. Its easy to read and the exercises/code work and are applicable. The text is clearly written and the supporting graphics are well constructed. I have started working through some of the exercises in R and find them very helpful. I bought this book after completing a graduate course in data analysis using R. Much of the course was constructed around Hadley Wickham's work. We used ggplot2 and tidyverse extensively. We did not use this book in the course and I now wish we had used it as a desk reference. This book is answering many of the questions I had. It is also providing an overview of many basic concepts and tools in R and would have been helpful to have at my fingertips during my graduate course. Google is a great resource, but a well written and easily sourced desktop resource like this is indispensable. I am learning something new about R constantly...I would consider myself a late beginner to early intermediate user of R for data science/data analysis. I often find books that cover the basics extremely useful, because its easy to forget. So for me, this was a good buy - regardless of my skill level. Others will have to make their own decision. This book does not cover mapping in R. In fact, there is a sentence in Chapter 1 that states - this book does not cover mapping. I would like to have had more on that topic or a follow-up book committed to mapping and integration with other tools like Leaflet or ArcGIS.
One person found this helpful
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Top reviews from other countries

Conwyn
5.0 out of 5 stars Good introduction to R
Reviewed in the United Kingdom on March 31, 2023
Verified Purchase
Although my apprenticeship has free on-line books I like a physical book so you can flip back and fore. It is important you practice with R Studio but this book is well written and a pleasant read. Please note there is a new edition August 2023 so it might be worth waiting.
2 people found this helpful
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Serge
2.0 out of 5 stars Very basic
Reviewed in the Netherlands on March 19, 2023
Verified Purchase
This book is mostly about ggplot2 and not the core language
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Joe Rat
5.0 out of 5 stars “R for Data Science” is an exceptional book for Data Scientists.
Reviewed in Italy on September 11, 2022
Verified Purchase
The authors, Hadley Wickham and Garrett Grolemund, are probably among the greatest authorities in their field. An important part of the book is devoted to the Tidyverse collection of packages for R. Once one has mastered the basics of the R language this book greatly enhances the capability of what one can do in this language, coupled to the Tidyverse language. In my opinion this book is the logical continuation of one’s learning experience once one has learned R. O’Reilly, the publisher of this book has lavished beautiful illustrations that facilitates greatly the comprehension of the clearly written text. A veritable pleasure to see such typographical masterpiece.
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Diego Santiago Alarcon
5.0 out of 5 stars Best way to learn data manipulation and creating graphs in R
Reviewed in Mexico on July 19, 2020
Verified Purchase
I think this is the best book to learn R for data science, it clearly teaches what it claims it will teach. In particular, it is a very strong book to learn how to manipulate data and create nice plots in R. The only place where I think it can improve is in relation to R Markdown, the treatment of that package is very shallow and it is even shallower in terms of some other packages such as shiny and web pages. Of course, now we have the book by Xie et al. 2019 R Markdown the definitive guide. If you do not know R and you are just learning a computer language then I recommend starting with Grolemund's book Hands-On Programming With R, then with this one.
One person found this helpful
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Amin
5.0 out of 5 stars Great book for R, RStudio, and learning about data
Reviewed in Canada on May 9, 2020
Verified Purchase
Thank you Hadley Wickham and the RStudio team for making data analysis so much easier!

As a graduate student this book provided me with a foundation for using R and Rstudio using the tidyverse package. It makes you a generalist in R where you can do most things afterwards. It could take a few months with meaningful projects on hand to get the hang of most things. The writers are good teachers as they take you through the nicely structured concepts without confusing or overwhelming you. If you know a little you can enter different chapters without necessarily knowing the previous ones. The book exposes you to a lot of snippets of good workflows and you learn how to do things the right way. You also see what is possible generally and this seeds new ideas in one's head. Most of the best practices you learn can be taken to other languages and workflows as well. Learning R and data analysis this way will not only let you be a good data analyst but but it also prepares you to navigate any programming language with the foundation and framework for understanding data you get here. You can always combine R with another language where you think R, and the tidyverse package in particular, will get the job done better.

This book is available online for free but I bought the physical copy as I wanted to read it seriously from cover to cover. In 6 months I have read about 1/2 to all the content in each chapter of the book and used its code in my own workflow. I started using R sporadically for the first time two years prior and Rstudio two months prior to purchasing this book.

In the end the book has become my go to for learning and coding, as a learning tool and a handy reference. I use it with the Oxford Handbook of Medical Statistics by Peacock, Janet L. for statistical methods and the STDHA website for other sample workflows and codes.
2 people found this helpful
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