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Data Analysis with R, by Tony Fischetti
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Key Features
- Load, manipulate and analyze data from different sources
- Gain a deeper understanding of fundamentals of applied statistics
- A practical guide to performing data analysis in practice
Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. With over 7,000 user contributed packages, it's easy to find support for the latest and greatest algorithms and techniques.
Starting with the basics of R and statistical reasoning, Data Analysis with R dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples.
Packed with engaging problems and exercises, this book begins with a review of R and its syntax. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data , large data, communicating results, and facilitating reproducibility.
This book is engineered to be an invaluable resource through many stages of anyone's career as a data analyst.
What you will learn- Navigate the R environment
- Describe and visualize the behavior of data and relationships between data
- Gain a thorough understanding of statistical reasoning and sampling
- Employ hypothesis tests to draw inferences from your data
- Learn Bayesian methods for estimating parameters
- Perform regression to predict continuous variables
- Apply powerful classification methods to predict categorical data
- Handle missing data gracefully using multiple imputation
- Identify and manage problematic data points
- Employ parallelization and Rcpp to scale your analyses to larger data
- Put best practices into effect to make your job easier and facilitate reproducibility
Tony Fischetti is a data scientist at College Factual, where he gets to use R everyday to build personalized rankings and recommender systems. He graduated in cognitive science from Rensselaer Polytechnic Institute, and his thesis was strongly focused on using statistics to study visual short-term memory.
Tony enjoys writing and and contributing to open source software, blogging at onthelambda.com, writing about himself in third person, and sharing his knowledge using simple, approachable language and engaging examples.
The more traditionally exciting of his daily activities include listening to records, playing the guitar and bass (poorly), weight training, and helping others.
Table of Contents- Sales Rank: #158748 in Books
- Published on: 2015-12-22
- Released on: 2015-12-22
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .88" w x 7.50" l, 1.46 pounds
- Binding: Paperback
- 446 pages
About the Author
Tony Fischetti
Tony Fischetti is a data scientist at College Factual, where he gets to use R everyday to build personalized rankings and recommender systems. He graduated in cognitive science from Rensselaer Polytechnic Institute, and his thesis was strongly focused on using statistics to study visual short-term memory. Tony enjoys writing and and contributing to open source software, blogging at onthelambda.com, writing about himself in third person, and sharing his knowledge using simple, approachable language and engaging examples. The more traditionally exciting of his daily activities include listening to records, playing the guitar and bass (poorly), weight training, and helping others.
Most helpful customer reviews
5 of 5 people found the following review helpful.
Well done
By Dimitri Shvorob
Packt's conveyor is not slowing down: only three months ago I surveyed their fresh crop of "data science with R" offerings
"Mastering Predictive Analytics with R" by Forte, £32.99
"Mastering Machine Learning with R" by Lesmeister, £34.99
"R Data Analysis Cookbook" by Viswanathan and Viswanathan, £29.99
"Machine Learning with R Cookbook" by Yu-Wei, £30.99
and now there are four more:
"Unsupervised Learning with R" by Pacheco, £25.99
"Data Analysis with R" by Fischetti, £34.99
"Learning Predictive Analytics with R" by Mayor, £31.99
"Mastering Data Analysis with R" by Daroczi, £34.99
So far I have gone through the first two "new" titles, and had a peek at the other two. Pacheco's book is a clear "pass", Mayor's and Daroczi's look promising [UPD: I was wrong] - and finally, Fischetti's is ... a pleasant surprise, a rare careful, original book in Pack't sea of low-quality, low-value-added quickies. It is so nice to see an author (a) thinking about what he wants to present and (b) crafting his text - as opposed to walking through an oh-so-familiar checklist of machine-learning algorithms and regaling readers with half-competent, shallow digests of "theory" found in better books. Now, since this book, surprisingly, covers "proper" statistics, not the machine-learning algorithms, you may need a book on those - I would recommend "Introduction to Statistical Learning" by Witten et al. - but "Data Analysis with R" definitely gets my endorsement, as a friendly yet substantial introduction.
UPD. With the benefit of a little more life experience, I would say: don't spend your time on *any* R book. Python is the way to go.
3 of 3 people found the following review helpful.
Entertaining Introduction - Recommended
By Amazon Customer
Based on co-worker's recommendation, I purchased Tony Fischetti's book "Data Analysis with R". I read most of it over the weekend and I can say it is an entertaining read.
Depending on one's approach to statistics, the book can be praised or criticized for being intentionally light on theory and heavy on practical application. In my opinion the world is full of heavy theoretical tomes, so I consider this assessment praise. That said, a background in calculus will make the integration discussion seem less like magic and more like the obvious application of mathematics that it is. I think the book is arguably aimed at CS majors, given how it is written and who published it, so I think assuming that most readers will have had at least some exposure to integral calculus is fine.
The book is full of graphics, which I like, and I appreciate that the author takes the time to tell the reader how to make these graphs, plots, etc. for him/her self. The code itself is also both informative and amusing. The book is published in black and white, and all the graphics look perfectly normal. However, the instructions included in the book would give you some really fun colored graphs if followed to the letter. Pink probability density functions? - Oh yeah!
I also enjoyed the well-constructed humor in the Exercises. I will use the exercises at the end of Chapter 3 to highlight his style of humor. Chapter 3 is about "describing relationships" between variables and includes correlation, etc. All perfectly logical things for the third chapter of a book on data analysis. Here are two of the exercises at the end of the chapter. One is normal. One is . . . not.
- Look at the documentation on cor with help("cor"). You can see, in addition to "pearson" and "spearman", there is an option for "kendall". Learn about Kendall's tau. Why, and under what conditions is it considered better than Spearman's rho?
- Gustave Flaubert is well understood to be a classist misogynist and this, of course, influenced how he developed the character of Emma Bovary. However, it is not uncommon for the readers to identify and empathize with her, and they are often devastated by trhe book's conclusion. In fact, translator Geoffrey Wall asserts that Emma dies in a pain that is exactly adjusted to the intensity of our preceding identification. How can the fact that some sympathize with Emma be reconciled with Flaubert's apparent intention? In your response, assume a post-structuralist approach to authorial intent.
Fortunately, I am married to an English major. I confess, I was drinking bourbon when I read this question and I nearly sprayed perfectly good bourbon all over an unsuspecting cat who was sitting in my lap at that moment. Fortunately, for the cat (and the book), I kept the bourbon in my mouth.
0 of 0 people found the following review helpful.
Head start to R...........
By Sudhir Chawla
The book starts with ‘Refresher’ section which nicely explains the fundamentals of R . It helps clear the basics for the beginners like me, and provide a quick recap to people well-versed with it. As it progresses It deep dives into the application of advanced and effective analytic methodologies and shows how to apply those techniques to real-world data though with real-world examples. The last chapter gives an insight on how to put best practices into effect to make our job easier.
The contents of the book are well categorized and filled with many simple tips, tricks and small notes. Each chapter has engaging problems and exercises. The author also added humor into the book which makes it more interesting and entertaining. Each chapter ends with a comprehensive summary. The generous use of examples and illustrations in form of charts helped me grasp things faster. The author has taken efforts to show step-by-step resolution for his examples. However, disappointed to see goof-up with the page numbers, if possible resolve it quickly. Also, it would have been better had the author provided few pointers against the problems given in the exercises. That way it would have matched his view and the reader's view and ensured that the reader has understood the concepts completely or not.
To summarize, this book is for those who are looking out for actual implementation of different prediction techniques and not just theory about R. I would highly recommend this book to everyone who wants to work on R.
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