Analyzing Baseball Data With R Second Edition

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Analyzing Baseball Data with R, Second Edition

Author : Max Marchi
Publisher : CRC Press
ISBN 13 : 1351107070
Total Pages : 318 pages
Book Rating : 4.3/5 (511 download)

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Book Synopsis Analyzing Baseball Data with R, Second Edition by : Max Marchi

Download or read book Analyzing Baseball Data with R, Second Edition written by Max Marchi and published by CRC Press. This book was released on 2018-11-19 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analyzing Baseball Data with R Second Edition introduces R to sabermetricians, baseball enthusiasts, and students interested in exploring the richness of baseball data. It equips you with the necessary skills and software tools to perform all the analysis steps, from importing the data to transforming them into an appropriate format to visualizing the data via graphs to performing a statistical analysis. The authors first present an overview of publicly available baseball datasets and a gentle introduction to the type of data structures and exploratory and data management capabilities of R. They also cover the ggplot2 graphics functions and employ a tidyverse-friendly workflow throughout. Much of the book illustrates the use of R through popular sabermetrics topics, including the Pythagorean formula, runs expectancy, catcher framing, career trajectories, simulation of games and seasons, patterns of streaky behavior of players, and launch angles and exit velocities. All the datasets and R code used in the text are available online. New to the second edition are a systematic adoption of the tidyverse and incorporation of Statcast player tracking data (made available by Baseball Savant). All code from the first edition has been revised according to the principles of the tidyverse. Tidyverse packages, including dplyr, ggplot2, tidyr, purrr, and broom are emphasized throughout the book. Two entirely new chapters are made possible by the availability of Statcast data: one explores the notion of catcher framing ability, and the other uses launch angle and exit velocity to estimate the probability of a home run. Through the book’s various examples, you will learn about modern sabermetrics and how to conduct your own baseball analyses. Max Marchi is a Baseball Analytics Analyst for the Cleveland Indians. He was a regular contributor to The Hardball Times and Baseball Prospectus websites and previously consulted for other MLB clubs. Jim Albert is a Distinguished University Professor of statistics at Bowling Green State University. He has authored or coauthored several books including Curve Ball and Visualizing Baseball and was the editor of the Journal of Quantitative Analysis of Sports. Ben Baumer is an assistant professor of statistical & data sciences at Smith College. Previously a statistical analyst for the New York Mets, he is a co-author of The Sabermetric Revolution and Modern Data Science with R.

Analyzing Baseball Data with R

Author : Max Marchi
Publisher : CRC Press
ISBN 13 : 1466570237
Total Pages : 334 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis Analyzing Baseball Data with R by : Max Marchi

Download or read book Analyzing Baseball Data with R written by Max Marchi and published by CRC Press. This book was released on 2016-04-05 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: With its flexible capabilities and open-source platform, R has become a major tool for analyzing detailed, high-quality baseball data. Analyzing Baseball Data with R provides an introduction to R for sabermetricians, baseball enthusiasts, and students interested in exploring the rich sources of baseball data. It equips readers with the necessary skills and software tools to perform all of the analysis steps, from gathering the datasets and entering them in a convenient format to visualizing the data via graphs to performing a statistical analysis. The authors first present an overview of publicly available baseball datasets and a gentle introduction to the type of data structures and exploratory and data management capabilities of R. They also cover the traditional graphics functions in the base package and introduce more sophisticated graphical displays available through the lattice and ggplot2 packages. Much of the book illustrates the use of R through popular sabermetrics topics, including the Pythagorean formula, runs expectancy, career trajectories, simulation of games and seasons, patterns of streaky behavior of players, and fielding measures. Each chapter contains exercises that encourage readers to perform their own analyses using R. All of the datasets and R code used in the text are available online. This book helps readers answer questions about baseball teams, players, and strategy using large, publically available datasets. It offers detailed instructions on downloading the datasets and putting them into formats that simplify data exploration and analysis. Through the book’s various examples, readers will learn about modern sabermetrics and be able to conduct their own baseball analyses.

Modern Data Science with R

Author : Benjamin S. Baumer
Publisher : CRC Press
ISBN 13 : 0429575394
Total Pages : 830 pages
Book Rating : 4.4/5 (295 download)

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Book Synopsis Modern Data Science with R by : Benjamin S. Baumer

Download or read book Modern Data Science with R written by Benjamin S. Baumer and published by CRC Press. This book was released on 2021-03-31 with total page 830 pages. Available in PDF, EPUB and Kindle. Book excerpt: From a review of the first edition: "Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics" (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.

Baseball Hacks

Author : Joseph Adler
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491949422
Total Pages : 472 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Baseball Hacks by : Joseph Adler

Download or read book Baseball Hacks written by Joseph Adler and published by "O'Reilly Media, Inc.". This book was released on 2006-01-31 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Baseball Hacks isn't your typical baseball book--it's a book about how to watch, research, and understand baseball. It's an instruction manual for the free baseball databases. It's a cookbook for baseball research. Every part of this book is designed to teach baseball fans how to do something. In short, it's a how-to book--one that will increase your enjoyment and knowledge of the game. So much of the way baseball is played today hinges upon interpreting statistical data. Players are acquired based on their performance in statistical categories that ownership deems most important. Managers make in-game decisions based not on instincts, but on probability - how a particular batter might fare against left-handedpitching, for instance. The goal of this unique book is to show fans all the baseball-related stuff that they can do for free (or close to free). Just as open source projects have made great software freely available, collaborative projects such as Retrosheet and Baseball DataBank have made great data freely available. You can use these data sources to research your favorite players, win your fantasy league, or appreciate the game of baseball even more than you do now. Baseball Hacks shows how easy it is to get data, process it, and use it to truly understand baseball. The book lists a number of sources for current and historical baseball data, and explains how to load it into a database for analysis. It then introduces several powerful statistical tools for understanding data and forecasting results. For the uninitiated baseball fan, author Joseph Adler walks readers through the core statistical categories for hitters (batting average, on-base percentage, etc.), pitchers (earned run average, strikeout-to-walk ratio, etc.), and fielders (putouts, errors, etc.). He then extrapolates upon these numbers to examine more advanced data groups like career averages, team stats, season-by-season comparisons, and more. Whether you're a mathematician, scientist, or season-ticket holder to your favorite team, Baseball Hacks is sure to have something for you. Advance praise for Baseball Hacks: "Baseball Hacks is the best book ever written for understanding and practicing baseball analytics. A must-read for baseball professionals and enthusiasts alike." -- Ari Kaplan, database consultant to the Montreal Expos, San Diego Padres, and Baltimore Orioles "The game was born in the 19th century, but the passion for its analysis continues to grow into the 21st. In Baseball Hacks, Joe Adler not only demonstrates thatthe latest data-mining technologies have useful application to the study of baseball statistics, he also teaches the reader how to do the analysis himself, arming the dedicated baseball fan with tools to take his understanding of the game to a higher level." -- Mark E. Johnson, Ph.D., Founder, SportMetrika, Inc. and Baseball Analyst for the 2004 St. Louis Cardinals

Teaching Statistics Using Baseball

Author : Jim Albert
Publisher : American Mathematical Society
ISBN 13 : 1470469383
Total Pages : 257 pages
Book Rating : 4.4/5 (74 download)

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Book Synopsis Teaching Statistics Using Baseball by : Jim Albert

Download or read book Teaching Statistics Using Baseball written by Jim Albert and published by American Mathematical Society. This book was released on 2022-02-04 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: Teaching Statistics Using Baseball is a collection of case studies and exercises applying statistical and probabilistic thinking to the game of baseball. Baseball is the most statistical of all sports since players are identified and evaluated by their corresponding hitting and pitching statistics. There is an active effort by people in the baseball community to learn more about baseball performance and strategy by the use of statistics. This book illustrates basic methods of data analysis and probability models by means of baseball statistics collected on players and teams. Students often have difficulty learning statistics ideas since they are explained using examples that are foreign to the students. The idea of the book is to describe statistical thinking in a context (that is, baseball) that will be familiar and interesting to students. The book is organized using a same structure as most introductory statistics texts. There are chapters on the analysis on a single batch of data, followed with chapters on comparing batches of data and relationships. There are chapters on probability models and on statistical inference. The book can be used as the framework for a one-semester introductory statistics class focused on baseball or sports. This type of class has been taught at Bowling Green State University. It may be very suitable for a statistics class for students with sports-related majors, such as sports management or sports medicine. Alternately, the book can be used as a resource for instructors who wish to infuse their present course in probability or statistics with applications from baseball. The second edition of Teaching Statistics follows the same structure as the first edition, where the case studies and exercises have been replaced by modern players and teams, and the new types of baseball data from the PitchFX system and fangraphs.com are incorporated into the text.

Analyzing Baseball Data with R, Second Edition

Author : Jim Albert
Publisher : Chapman & Hall/CRC
ISBN 13 : 9780367024864
Total Pages : 0 pages
Book Rating : 4.0/5 (248 download)

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Book Synopsis Analyzing Baseball Data with R, Second Edition by : Jim Albert

Download or read book Analyzing Baseball Data with R, Second Edition written by Jim Albert and published by Chapman & Hall/CRC. This book was released on 2018-11-22 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book will be of interest to basefall fans who want to learn some sabermetrics, and also people who know sabermetrics but would like to use R in their data exploration. Many students do not work on baseball data because the datasets are very large. By learning R through our book, they will be encouraged to do more baseball research on their own.

Introduction to Data Science

Author : Rafael A. Irizarry
Publisher : CRC Press
ISBN 13 : 1000708039
Total Pages : 794 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Introduction to Data Science by : Rafael A. Irizarry

Download or read book Introduction to Data Science written by Rafael A. Irizarry and published by CRC Press. This book was released on 2019-11-20 with total page 794 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.

Basketball Data Science

Author : Paola Zuccolotto
Publisher : CRC Press
ISBN 13 : 0429894260
Total Pages : 219 pages
Book Rating : 4.4/5 (298 download)

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Book Synopsis Basketball Data Science by : Paola Zuccolotto

Download or read book Basketball Data Science written by Paola Zuccolotto and published by CRC Press. This book was released on 2020-01-03 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using data from one season of NBA games, Basketball Data Science: With Applications in R is the perfect book for anyone interested in learning and applying data analytics in basketball. Whether assessing the spatial performance of an MBA player’s shots or doing an analysis of the impact of high pressure game situations on the probability of scoring, this book discusses a variety of case studies and hands-on examples using a custom R package. The codes are supplied so readers can reproduce the analyses themselves or create their own. Assuming a basic statistical knowledge, Basketball Data Science with R is suitable for students, technicians, coaches, data analysts and applied researchers. Features: · One of the first books to provide statistical and data mining methods for the growing field of analytics in basketball. · Presents tools for modelling graphs and figures to visualize the data. · Includes real world case studies and examples, such as estimations of scoring probability using the Golden State Warriors as a test case. · Provides the source code and data so readers can do their own analyses on NBA teams and players.

The Sabermetric Revolution

Author : Benjamin Baumer
Publisher : University of Pennsylvania Press
ISBN 13 : 0812245725
Total Pages : 208 pages
Book Rating : 4.8/5 (122 download)

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Book Synopsis The Sabermetric Revolution by : Benjamin Baumer

Download or read book The Sabermetric Revolution written by Benjamin Baumer and published by University of Pennsylvania Press. This book was released on 2014-01-23 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: The authors look at the history of statistical analysis in baseball, how it can best be used today and how its it must evolve for the future.

The Book

Author :
Publisher : Potomac Books, Inc.
ISBN 13 : 1597973653
Total Pages : 458 pages
Book Rating : 4.5/5 (979 download)

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Book Synopsis The Book by :

Download or read book The Book written by and published by Potomac Books, Inc.. This book was released on 2007 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: Baseball "by The Book."

Using R for Introductory Statistics

Author : John Verzani
Publisher : CRC Press
ISBN 13 : 1315360306
Total Pages : 522 pages
Book Rating : 4.3/5 (153 download)

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Book Synopsis Using R for Introductory Statistics by : John Verzani

Download or read book Using R for Introductory Statistics written by John Verzani and published by CRC Press. This book was released on 2018-10-03 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version. See What’s New in the Second Edition: Increased emphasis on more idiomatic R provides a grounding in the functionality of base R. Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible. Use of knitr package makes code easier to read and therefore easier to reason about. Additional information on computer-intensive approaches motivates the traditional approach. Updated examples and data make the information current and topical. The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package="UsingR")), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text. The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing.

Analysis of Categorical Data with R

Author : Christopher R. Bilder
Publisher : CRC Press
ISBN 13 : 1439855676
Total Pages : 549 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Analysis of Categorical Data with R by : Christopher R. Bilder

Download or read book Analysis of Categorical Data with R written by Christopher R. Bilder and published by CRC Press. This book was released on 2014-08-11 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn How to Properly Analyze Categorical Data Analysis of Categorical Data with R presents a modern account of categorical data analysis using the popular R software. It covers recent techniques of model building and assessment for binary, multicategory, and count response variables and discusses fundamentals, such as odds ratio and probability estimation. The authors give detailed advice and guidelines on which procedures to use and why to use them. The Use of R as Both a Data Analysis Method and a Learning Tool Requiring no prior experience with R, the text offers an introduction to the essential features and functions of R. It incorporates numerous examples from medicine, psychology, sports, ecology, and other areas, along with extensive R code and output. The authors use data simulation in R to help readers understand the underlying assumptions of a procedure and then to evaluate the procedure’s performance. They also present many graphical demonstrations of the features and properties of various analysis methods. Web Resource The data sets and R programs from each example are available at www.chrisbilder.com/categorical. The programs include code used to create every plot and piece of output. Many of these programs contain code to demonstrate additional features or to perform more detailed analyses than what is in the text. Designed to be used in tandem with the book, the website also uniquely provides videos of the authors teaching a course on the subject. These videos include live, in-class recordings, which instructors may find useful in a blended or flipped classroom setting. The videos are also suitable as a substitute for a short course.

Analytic Methods in Sports

Author : Thomas A. Severini
Publisher : CRC Press
ISBN 13 : 1000050947
Total Pages : 327 pages
Book Rating : 4.0/5 ( download)

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Book Synopsis Analytic Methods in Sports by : Thomas A. Severini

Download or read book Analytic Methods in Sports written by Thomas A. Severini and published by CRC Press. This book was released on 2020-04-15 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the greatest changes in the sports world in the past 20 years has been the use of mathematical methods to analyze performances, recognize trends and patterns, and predict results. Analytic Methods in Sports: Using Mathematics and Statistics to Understand Data from Baseball, Football, Basketball, and Other Sports, Second Edition provides a concise yet thorough introduction to the analytic and statistical methods that are useful in studying sports. The book gives you all the tools necessary to answer key questions in sports analysis. It explains how to apply the methods to sports data and interpret the results, demonstrating that the analysis of sports data is often different from standard statistical analyses. The book integrates a large number of motivating sports examples throughout and offers guidance on computation and suggestions for further reading in each chapter. Features Covers numerous statistical procedures for analyzing data based on sports results Presents fundamental methods for describing and summarizing data Describes aspects of probability theory and basic statistical concepts that are necessary to understand and deal with the randomness inherent in sports data Explains the statistical reasoning underlying the methods Illustrates the methods using real data drawn from a wide variety of sports Offers many of the datasets on the author’s website, enabling you to replicate the analyses or conduct related analyses New to the Second Edition R code included for all calculations A new chapter discussing several more advanced methods, such as binary response models, random effects, multilevel models, spline methods, and principal components analysis, and more Exercises added to the end of each chapter, to enable use for courses and self-study

R for Everyone

Author : Jared P. Lander
Publisher : Addison-Wesley Professional
ISBN 13 : 0134546997
Total Pages : 1454 pages
Book Rating : 4.1/5 (345 download)

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Book Synopsis R for Everyone by : Jared P. Lander

Download or read book R for Everyone written by Jared P. Lander and published by Addison-Wesley Professional. This book was released on 2017-06-13 with total page 1454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques. After all this you’ll make your code reproducible with LaTeX, RMarkdown, and Shiny. By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most. Coverage includes Explore R, RStudio, and R packages Use R for math: variable types, vectors, calling functions, and more Exploit data structures, including data.frames, matrices, and lists Read many different types of data Create attractive, intuitive statistical graphics Write user-defined functions Control program flow with if, ifelse, and complex checks Improve program efficiency with group manipulations Combine and reshape multiple datasets Manipulate strings using R’s facilities and regular expressions Create normal, binomial, and Poisson probability distributions Build linear, generalized linear, and nonlinear models Program basic statistics: mean, standard deviation, and t-tests Train machine learning models Assess the quality of models and variable selection Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods Analyze univariate and multivariate time series data Group data via K-means and hierarchical clustering Prepare reports, slideshows, and web pages with knitr Display interactive data with RMarkdown and htmlwidgets Implement dashboards with Shiny Build reusable R packages with devtools and Rcpp Register your product at informit.com/register for convenient access to downloads, updates, and corrections as they become available.

The New Bill James Historical Baseball Abstract

Author : Bill James
Publisher : Simon and Schuster
ISBN 13 : 1439106932
Total Pages : 1026 pages
Book Rating : 4.4/5 (391 download)

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Book Synopsis The New Bill James Historical Baseball Abstract by : Bill James

Download or read book The New Bill James Historical Baseball Abstract written by Bill James and published by Simon and Schuster. This book was released on 2010-05-11 with total page 1026 pages. Available in PDF, EPUB and Kindle. Book excerpt: When Bill James published his original Historical Baseball Abstract in 1985, he produced an immediate classic, hailed by the Chicago Tribune as the “holy book of baseball.” Now, baseball's beloved “Sultan of Stats” (The Boston Globe) is back with a fully revised and updated edition for the new millennium. Like the original, The New Bill James Historical Baseball Abstract is really several books in one. The Game provides a century's worth of American baseball history, told one decade at a time, with energetic facts and figures about How, Where, and by Whom the game was played. In The Players, you'll find listings of the top 100 players at each position in the major leagues, along with James's signature stats-based ratings method called “Win Shares,” a way of quantifying individual performance and calculating the offensive and defensive contributions of catchers, pitchers, infielders, and outfielders. And there's more: the Reference section covers Win Shares for each season and each player, and even offers a Win Share team comparison. A must-have for baseball fans and historians alike, The New Bill James Historical Baseball Abstract is as essential, entertaining, and enlightening as the sport itself.

Practical Statistics for Data Scientists

Author : Peter Bruce
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491952911
Total Pages : 395 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Practical Statistics for Data Scientists by : Peter Bruce

Download or read book Practical Statistics for Data Scientists written by Peter Bruce and published by "O'Reilly Media, Inc.". This book was released on 2017-05-10 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data

R for Data Science

Author : Hadley Wickham
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491910364
Total Pages : 521 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis R for Data Science by : Hadley Wickham

Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2016-12-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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