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Robert Kabacoff
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R in Action: Data analysis and graphics with R 2nd Edition, Kindle Edition

by Robert I. Kabacoff (Author) Format: Kindle Edition
4.6 4.6 out of 5 stars 264 ratings
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Summary

R in Action, Second Edition presents both the R language and the examples that make it so useful for business developers. Focusing on practical solutions, the book offers a crash course in statistics and covers elegant methods for dealing with messy and incomplete data that are difficult to analyze using traditional methods. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on time series analysis, cluster analysis, and classification methodologies, including decision trees, random forests, and support vector machines.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

Business pros and researchers thrive on data, and R speaks the language of data analysis. R is a powerful programming language for statistical computing. Unlike general-purpose tools, R provides thousands of modules for solving just about any data-crunching or presentation challenge you're likely to face. R runs on all important platforms and is used by thousands of major corporations and institutions worldwide.

About the Book

R in Action, Second Edition teaches you how to use the R language by presenting examples relevant to scientific, technical, and business developers. Focusing on practical solutions, the book offers a crash course in statistics, including elegant methods for dealing with messy and incomplete data. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on forecasting, data mining, and dynamic report writing.

What's Inside
  • Complete R language tutorial
  • Using R to manage, analyze, and visualize data
  • Techniques for debugging programs and creating packages
  • OOP in R
  • Over 160 graphs

About the Author

Dr. Rob Kabacoff is a seasoned researcher and teacher who specializes in data analysis. He also maintains the popular Quick-R website at statmethods.net.

Table of Contents
  1. PART 1 GETTING STARTED
  2. Introduction to R
  3. Creating a dataset
  4. Getting started with graphs
  5. Basic data management
  6. Advanced data management
  7. PART 2 BASIC METHODS
  8. Basic graphs
  9. Basic statistics
  10. PART 3 INTERMEDIATE METHODS
  11. Regression
  12. Analysis of variance
  13. Power analysis
  14. Intermediate graphs
  15. Resampling statistics and bootstrapping
  16. PART 4 ADVANCED METHODS
  17. Generalized linear models
  18. Principal components and factor analysis
  19. Time series
  20. Cluster analysis
  21. Classification
  22. Advanced methods for missing data
  23. PART 5 EXPANDING YOUR SKILLS
  24. Advanced graphics with ggplot2
  25. Advanced programming
  26. Creating a package
  27. Creating dynamic reports
  28. Advanced graphics with the lattice package available online only from manning.com/kabacoff2
Previous page
  1. ISBN-13
    978-1617291388
  2. Edition
    2nd
  3. Sticky notes
    Not Enabled
  4. Publisher
    Manning
  5. Publication date
    May 20, 2015
  6. Language
    English
  7. File size
    16026 KB
  8. See all details
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Editorial Reviews

About the Author

Dr. Rob Kabacoff is a seasoned researcher and teacher who specializes in data analysis. He also maintains the popular Quick-R website at statmethods.net.

Product details

  • ASIN ‏ : ‎ B09781BM38
  • Publisher ‏ : ‎ Manning; 2nd edition (May 20, 2015)
  • Publication date ‏ : ‎ May 20, 2015
  • Language ‏ : ‎ English
  • File size ‏ : ‎ 16026 KB
  • Text-to-Speech ‏ : ‎ Enabled
  • Enhanced typesetting ‏ : ‎ Enabled
  • X-Ray ‏ : ‎ Not Enabled
  • Word Wise ‏ : ‎ Not Enabled
  • Sticky notes ‏ : ‎ Not Enabled
  • Print length ‏ : ‎ 609 pages
  • Page numbers source ISBN ‏ : ‎ 1617291382
  • Customer Reviews:
    4.6 4.6 out of 5 stars 264 ratings

About the author

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Robert Kabacoff

Robert Kabacoff

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Rob Kabacoff is Vice President of Research for MRG, a global organizational development firm based in Portland Maine and Dublin Ireland. He has more than 20 years of experience providing research and statistical consultation to government, academia, and private industry. Prior to joining MRG, Dr. Kabacoff was a professor of psychology at Nova Southeastern University in Florida, where he taught graduate courses in clinical psychology, quantitative methods and statistical programming. He lives in Portland, Maine with his wife Carol Lynn and golden retriever Ella.

Customer reviews

4.6 out of 5 stars
4.6 out of 5
264 global ratings
Incorrect page sizes made it hard to read
2 Stars
Incorrect page sizes made it hard to read
The content of the book itself was good. However the quality of the book was lacking. As you can see in the pictures a few of the pages were not cut correctly and the corners are longer than they should be. This made the examples in those sections very hard to follow. They came folded up so I would not notice until I got to those chapters in class, very dissapointed.
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Top reviews from the United States

Philip
5.0 out of 5 stars Excellent Book
Reviewed in the United States on March 14, 2024
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This book emerges as a critical resource for data scientists and analysts, providing a deep dive into the capabilities and applications of the R language. R, renowned for its proficiency in analyzing large datasets, boasts compatibility with nearly every popular architectural style currently available. The book demystifies R—a programming environment that interprets the R language to execute commands and process data—highlighting its significance in the data science realm.

Furthermore, the book introduces R Studio, a software package celebrated for its user-friendly graphical interface. This feature significantly simplifies the process of inputting R language commands for execution, making it accessible to a wide range of users.

The author(s)’s engaging writing style effectively conveys the intricacies of R and its practical usage, making this book an invaluable asset not just for scientists and data analysts, but also for business executives, IT managers, and analysts. It offers insights that are beneficial even to those who may not directly work with R, providing a competitive edge in the fast-paced business world by fostering an understanding of R’s powerful capabilities.

Highlighting the syntactical similarities between R and Python, yet emphasizing R's unique identity as a programming language, the book serves as a comprehensive guide. It is highly recommended for anyone seeking to enhance their analytical skills, understand data processing more deeply, or gain a competitive advantage by familiarizing themselves with the functionalities of R and R Studio.
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nascanio
5.0 out of 5 stars Good book
Reviewed in the United States on December 18, 2023
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Book in good condition, excellent price and contents.
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William P Ross
5.0 out of 5 stars Excellent Learning Resource For R And Statistics
Reviewed in the United States on December 16, 2016
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R in Action does an excellent job showing the power of the R language. The book also serves a way to learn statistics and data science through using the R language.

At the beginning the examples are practical, showing how to make graphs based off data sets that come packaged with R. Sometimes packages are required to install to get the examples running, and the author is clear about how to do this.

In chapter eight there was an excellent description about Regression analysis. I knew what regressions were, but this explanation helped clear up some confusion I had. The author says (p.167): "It's [regression analysis] a broad term for a set of methodologies used to predict a response variable (also called a dependent, criterion, or outcome variable) from one or more predictor variables (also called independent or explanatory variables).

Following this explanation is a concrete example given showing how to predict the number of calories a person burns on a treadmill based off different factors. I liked that the author defined all the terms, and then gave a concrete example. It is a pattern throughout the book that worked very well.

The later parts of the book briefly dive in machine learning and more advanced data science concepts.

The main focus of the book is on statistics, graphing, and plotting. This worked well, because it shows the areas where R shines compared with other languages. Luckily for the reader the author knows which techniques work well, and states when certain data analysis techniques work better than others. The author remains humble throughout, stating when he finds a topic complex.

R in Action is a great resource for learning R and learning statistics. Explanations transition from easy to hard in a logical manner and this book will be useful for data scientists of all experience levels.
12 people found this helpful
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TechnoTron
4.0 out of 5 stars Great Reference Book
Reviewed in the United States on February 8, 2016
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Excellent book . If you are new to Data Analysis it will more than meet your needs. The first few chapters copy basic R , input , output , data types. If you already know R , you will find that a quick review will show you ways to upload most any type of data including Excel spreadsheets.
Chapters 7 on actually cover college level statistics including one-way and two-way tables, t-tests, regression . For statistical reference and data modeling the graphics that are included use the latest library's so you won't be wasting time trying to write your own functions. Chapters 12 on are pretty advanced statistics with real world examples.
10 people found this helpful
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Ravindra Arya
5.0 out of 5 stars Required reading for R users!
Reviewed in the United States on November 22, 2017
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I have read quite a few books on R, and will rate this as perhaps one of the best if not the best. The author has explained the material with excellent clarity, the examples are relevant to real-world applications, and the code executes well using either base installation of R (with added packages) or R studio. Also, the author has managed to concurrently develop both data analysis and data visualization (graphing), which helps the reader, and exposes the capabilities of R very well. The range of topics covered in this book is very broad and gives a good overview of the field. As a clinician with interest in epidemiology, I found almost everything needed for beginner or intermediate level applications. Also, if one works through this book diligently, s/he will be in a very good position to follow more advanced topics (like multilevel models) from other resources.
I have only one issue with the book (which is a generic issue with most statistics or data science books centered around a software). That is, it contains very little, if any, mathematical basis or details of the methods being discussed. These days, statistics is taught "thorough" a software. Hence, it is artificial to separate mathematical statistics from computational aspects. I am waiting for a book which will start with the mathematical aspects including derivations of the theorems/equations, give examples of real world applications, and illustrate the application in R.
One minor issue is related to very poor quality of the binding of the book. In the copy that I received, perhaps the pasting was defective, and as soon as I first opened it, the first page tore off. Soon, the binding opened up and pages/sections started falling off. I am not sure if this is just limited to the copy that I got or is a systemic issue.
22 people found this helpful
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Top reviews from other countries

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Sagnikdas
5.0 out of 5 stars A must have for R programming beginners.
Reviewed in India on April 30, 2024
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Got the book in good condition. The author via this book actually gives a captivating learning journey for the aspirant.
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Dr. Franco Arda
5.0 out of 5 stars A Masterpiece ( a Python user )
Reviewed in Germany on May 13, 2020
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Nowadays, if you add “statistics” and “data science” in the book title, it’s trendy. But Dr. Kabacoff was “trendy” a long time ago.

Today’s challenge for Data Scientists
In fact, I would argue that today’s “statistics books for data science” are not as broad and deep as this book. Of course, there’s a reason for it. Today, data scientists “must” have such a broad range of skills that it’s literally impossible to dive deep in many subjects. There is simply not enough time to learn it all, let alone master it.

Back to this outstanding book. The title “R in Action” basically means “statistics with code.” It doesn’t teach you statistics but rather shows you how to code it in R. And the range is really wide. As a practicing data scientists, it’s almost impossible to have a deep knowledge of all topics.

I would argue, this is a fantastic book if you want to go deeper where your interests lie.

Python user
I’m a Python user and are not willing to learn R. Some experts recommend learning several programming languages, I don’t. I battle to improve my current skills.

I deeply love Python, but for advanced statistics, the libraries, literature and courses are thin. The new book “Statistics for Data Scientists” doesn’t change it. It’s very basic. If you’re like me who wants to stick to Python while doing advanced statistics, here’s my tip: read this book to understand the ideas and try to implement them in Python. Some advanced courses on DataCamp can help (e.g. Statistical Simulation in Python).

My personal highlights:

Power Analysis (Chapter 10)
I have never seen a more lucid explanation. Probably because many don’t understand it (including me). In the real-world, I would argue, we don’t power analysis often. But when we do, it’s extremely important. Power analysis answers the question for parametric tests “how many samples do we need” with a certain confidence interval.

Advanced methods for missing data (Chapter 18)
An often-neglected topic which can quickly become very difficult. In the real-world, we often take short cuts such as creating a new column and labeling missing data (often seen in Kaggle competitions).

Resampling statistics and bootstrapping (Chapter 12)
At first, one might think this is overkill, but if you have a non-parametric dataset (i.e. you don’t know the distribution or variance) and need to be 95% sure that you have enough samples, then this technique is super powerful. Bootstrapping can become extremely hard when you have different bins and weights.
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Marc-Andre Chenier
5.0 out of 5 stars It's a great self-learning book and
Reviewed in Canada on March 28, 2018
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It's a great self-learning book and, after two years, it still serves me well as a reference. Its focus is on how to do analyses in R so it's always useful to check before I delve fully into a new project. Unlike other books, it doesn't really cover the tidyverse, which leaves space for the really useful stuff. It's a book for people that want to do statistics in R and not so much aspiring data scientists. I liked it so much that I bought a copy for my girlfriend and it's pretty useful in her research.
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R. Magallanes Quintanar
5.0 out of 5 stars Doblemente valioso!
Reviewed in Mexico on May 22, 2017
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El texto de Kabacoff es una excelente introducción al lenguaje R. El enfoque por ejemplo prácticos hace que se comprendan todos los temas sin dificultad . Incluye temas que van desde la estadística descriptiva hasta temas más avanzados como la gráficación y el modelo lineal entre muchos otros temas. En esta segunda edición se incluyen temas adicionales como el análisis de series de tiempo y el análisis de conglomerados. Adicionalmente los libros de la editorial Manning incluyen la descarga de la versión digital del libro, lo que lo hace doblemente valioso. Es un libro que puede ser de utilidad para el novato en R así como para al experto en estadística. Totalmente recomendable.
One person found this helpful
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Jas
5.0 out of 5 stars Clear and Engaging
Reviewed in the United Kingdom on January 10, 2018
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My daughter (studying economics) had to learn R in a short period from a background of no coding whatsoever. I helped her identify this book which has been immensely helpful to her. The author very kindly lets you read the whole text as a free e-book first, which we did. We found it so helpful that we’ve now done the right thing and bought the book. If you’re faced with learning R I really recommend this book.
2 people found this helpful
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