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Tony Fischetti - Data Analysis with R

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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
Book Description

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, its 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 anyones 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
About the Author

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
  1. RefresheR
  2. The Shape of Data
  3. Describing Relationships
  4. Probability
  5. Using Data to Reason About the World
  6. Testing Hypotheses
  7. Bayesian Methods
  8. Predicting Continuous Variables
  9. Predicting Categorical Variables
  10. Sources of Data
  11. Dealing with Messy Data
  12. Dealing with Large Data
  13. Reproducibility and Best Practices

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Data Analysis with R

Table of Contents
Data Analysis with R

Data Analysis with R

Copyright 2015 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

First published: December 2015

Production reference: 1171215

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham B3 2PB, UK.

ISBN 978-1-78528-814-2

www.packtpub.com

Credits

Author

Tony Fischetti

Reviewer

Dipanjan Sarkar

Commissioning Editor

Akram Hussain

Acquisition Editor

Meeta Rajani

Content Development Editor

Anish Dhurat

Technical Editor

Siddhesh Patil

Copy Editor

Sonia Mathur

Project Coordinator

Bijal Patel

Proofreader

Safis Editing

Indexer

Monica Ajmera Mehta

Graphics

Disha Haria

Production Coordinator

Conidon Miranda

Cover Work

Conidon Miranda

About the Author

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 contributing to open source software, blogging at http://www.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.

Because I'm aware of how incredibly lucky I am, it's really hard to express all the gratitude I have for everyone in my life that helped meeither directly, or indirectlyin completing this book. The following (partial) list is my best attempt at balancing thoroughness whilst also maximizing the number of people who will read this section by keeping it to a manageable length.

First, I'd like to thank all of my educators. In particular, I'd like to thank the Bronx High School of Science and Rensselaer Polytechnic Institute. More specifically, I'd like the Bronx Science Robotics Team, all it's members, it's team moms, the wonderful Dena Ford and Cherrie Fleisher-Strauss; and Justin Fox. From the latter institution, I'd like to thank all of my professors and advisors. Shout out to Mike Kalsher, Michael Schoelles, Wayne Gray, Bram van Heuveln, Larry Reid, and Keith Anderson (especially Keith Anderson).

I'd like to thank the New York Public Library, Wikipedia, and other freely available educational resources. On a related note, I need to thank the R community and, more generally, all of the authors of R packages and other open source software I use for spending their own personal time to benefit humanity. Shout out to GNU, the R core team, and Hadley Wickham (who wrote a majority of the R packages I use daily).

Next, I'd like to thank the company I work for, College Factual, and all of my brilliant co-workers from whom I've learned so much.

I also need to thank my support network of millions, and my many many friends that have all helped me more than they will likely ever realize.

I'd like to thank my partner, Bethany Wickham, who has been absolutely instrumental in providing much needed and appreciated emotional support during the writing of this book, and putting up with the mood swings that come along with working all day and writing all night.

Next, I'd like to express my gratitude for my sister, Andrea Fischetti, who means the world to me. Throughout my life, she's kept me warm and human in spite of the scientist in me that likes to get all reductionist and cerebral.

Finally, and most importantly, I'd like to thank my parents. This book is for my father, to whom I owe my love of learning and my interest in science and statistics; and to my mother for her love and unwavering support and, to whom I owe my work ethic and ability to handle anything and tackle any challenge.

About the Reviewer

Dipanjan Sarkar is an IT engineer at Intel, the world's largest silicon company, where he works on analytics, business intelligence, and application development. He received his master's degree in information technology from the International Institute of Information Technology, Bangalore. Dipanjan's area of specialization includes software engineering, data science, machine learning, and text analytics.

His interests include learning about new technologies, disruptive start-ups, and data science. In his spare time, he loves reading, playing games, and watching popular sitcoms. Dipanjan also reviewed Learning R for Geospatial Analysis and R Data Analysis Cookbook , both by Packt Publishing.

I would like to thank Bijal Patel, the project coordinator of this book, for making the reviewing experience really interactive and enjoyable.

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Preface

I'm going to shoot it to you straight: there are a lot of books about data analysis and the R programming language. I'll take it on faith that you already know why it's extremely helpful and fruitful to learn R and data analysis (if not, why are you reading this preface?!) but allow me to make a case for choosing

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