This practical guide provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R’s graphing systems. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.\nMost of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you’re ready to get started.\n\nUse R’s default graphics for quick exploration of data\nCreate a variety of bar graphs, line graphs, and scatter plots\nSummarize data distributions with histograms, density curves, box plots, and other examples\nProvide annotations to help viewers interpret data\nControl the overall appearance of graphics\nRender data groups alongside each other for easy comparison\nUse colors in plots\nCreate network graphs, heat maps, and 3D scatter plots\nStructure data for graphing\n

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.\nAuthors 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.\nYou’ll learn how to:\n\n\nWrangle—transform your datasets into a form convenient for analysis\n\nProgram—learn powerful R tools for solving data problems with greater clarity and ease\n\nExplore—examine your data, generate hypotheses, and quickly test them\n\nModel—provide a low-dimensional summary that captures true "signals" in your dataset\n\nCommunicate—learn R Markdown for integrating prose, code, and results\n

Provides both rich theory and powerful applications Figures are accompanied by code required to produce them Full color figures

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The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis.You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You’ll even learn how to create impressive data visualizations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package.Dozens of hands-on exercises (with downloadable solutions) take you from theory to practice, as you learn:–The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops–Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R–How to access R’s thousands of functions, libraries, and data sets–How to draw valid and useful conclusions from your data–How to create publication-quality graphics of your resultsCombining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R’s functionality. Make The Book of R your doorway into the growing world of data analysis.

Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis is a unique introduction to data science for investment management that explores the three major R/finance coding paradigms, emphasizes data visualization, and explains how to build a cohesive suite of functioning Shiny applications. The full source code, asset price data and live Shiny applications are available at reproduciblefinance.com. The ideal reader works in finance or wants to work in finance and has a desire to learn R code and Shiny through simple, yet practical real-world examples. The book begins with the first step in data science: importing and wrangling data, which in the investment context means importing asset prices, converting to returns, and constructing a portfolio. The next section covers risk and tackles descriptive statistics such as standard deviation, skewness, kurtosis, and their rolling histories. The third section focuses on portfolio theory, analyzing the Sharpe Ratio, CAPM, and Fama French models. The book concludes with applications for finding individual asset contribution to risk and for running Monte Carlo simulations. For each of these tasks, the three major coding paradigms are explored and the work is wrapped into interactive Shiny dashboards.

With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression. Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you're a beginner, R Cookbook will help get you started. If you're an experienced data programmer, it will jog your memory and expand your horizons. You'll get the job done faster and learn more about R in the process. Create vectors, handle variables, and perform other basic functions Input and output data Tackle data structures such as matrices, lists, factors, and data frames Work with probability, probability distributions, and random variables Calculate statistics and confidence intervals, and perform statistical tests Create a variety of graphic displays Build statistical models with linear regressions and analysis of variance (Anova) Explore advanced statistical techniques, such as finding clusters in your data "Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language-one practical example at a time". -Jeffrey Ryan, software consultant and R package author

Detailed hands-on recipes for creating the most useful types of graphs in R - starting from the simplest versions to more advanced applications \n\nLearn to draw any type of graph or visual data representation in R Filled with practical tips and techniques for creating any type of graph you need; not just theoretical explanations All examples are accompanied with the corresponding graph images, so you know what the results look like Each recipe is independent and contains the complete explanation and code to perform the task as efficiently as possible \nIn Detail \nWith more than two million users worldwide, R is one of the most popular open source projects. It is a free and robust statistical programming environment with very powerful graphical capabilities. Analyzing and visualizing data with R is a necessary skill for anyone doing any kind of statistical analysis, and this book will help you do just that in the easiest and most efficient way possible. \nUnlike other books on R, this book takes a practical, hands-on approach and you dive straight into creating graphs in R right from the very first page. \nYou want to harness the power of this open source programming language to visually present and analyze your data in the best way possible - and this book will show you how. \nThe R Graph Cookbook takes a practical approach to teaching how to create effective and useful graphs using R. This practical guide begins by teaching you how to make basic graphs in R and progresses through subsequent dedicated chapters about each graph type in depth. It will demystify a lot of difficult and confusing R functions and parameters and enable you to construct and modify data graphics to suit your analysis, presentation, and publication needs. \nYou will learn all about making graphics such as scatter plots, line graphs, bar charts, pie charts, dot plots, heat maps, histograms and box plots. In addition, there are detailed recipes on making various combinations and advanced versions of these graphs. Dedicated chapters on polishing and finalizing graphs will enable you to produce professional-quality graphs for presentation and publication. With R Graph Cookbook in hand, making graphs in R has never been easier \nWhat you will learn from this book \n\nConstruct multiple graph matrix layouts Summarize multivariate datasets with a single graph Create custom graph functions to avoid code repetition Make and re-use visual themes for graphs Save and export graphs in various formats to print or publish Learn to use fonts and annotations in graphs on Windows, Mac, and Linux Combine different graph types to give a better visual summary of complex datasets Present geographical data on maps Use heatmaps to spot trends and anomalies in large datasets Add scientific annotations and formulae to label graphs Add text descriptions to create graph presentation handouts Create beautiful color palettes and apply them to graphs \nApproach \nThis hands-on guide cuts short the preamble and gets straight to the point - actually creating graphs, instead of just theoretical learning. Each recipe is specifically tailored to fulfill your appetite for visually representing you data in the best way possible. \nWho this book is written for \nThis book is for readers already familiar with the basics of R who want to learn the best techniques and code to create graphics in R in the best way possible. It will also serve as an invaluable reference book for expert R users. \nHrishi V. Mittal \n Hrishi Mittal has been working with R for a few years in different capacities. He was introduced to the exciting world of data analysis with R when he was working as Senior Air Quality Scientist at King's College London, where he used R extensively to analyze large amounts of air pollution and traffic data for informing the London Mayor's Air Quality Strategy. He has experience in various other programming languages, but prefers R for data analysis and visualization. He is actively involved in various R mailing lists, forums and the development of some R packages. \nIn early 2010, he started Pretty Graph Limited (prettygraph.com), a software company specializing in web-based data visualization products. The company's flagship product Pretty Graph uses R as the backend engine for helping researchers and businesses visualize and analyze data. The goal is to bring the power of R to a wider audience by providing a modern graphical user interface which can be accessed by anyone and from anywhere simply using a web browser.

An Essential Reference for Intermediate and Advanced R Programmers Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R. The book develops the necessary skills to produce quality code that can be used in a variety of circumstances. You will learn: \nThe fundamentals of R, including standard data types and functions Functional programming as a useful framework for solving wide classes of problems The positives and negatives of metaprogramming How to write fast, memory-efficient code \n This book not only helps current R users become R programmers but also shows existing programmers what’s special about R. Intermediate R programmers can dive deeper into R and learn new strategies for solving diverse problems while programmers from other languages can learn the details of R and understand why R works the way it does.

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Nominated for three 2010 Will Eisner Comic Industry Awards: From Creation to the death of Joseph, here are all 50 chapters of the Book of Genesis, revealingly illustrated as never before. Envisioning the first book of the bible like no one before him, R. Crumb, the legendary illustrator, reveals here the story of Genesis in a profoundly honest and deeply moving way. Originally thinking that we would do a take off of Adam and Eve, Crumb became so fascinated by the Bible’s language, “a text so great and so strange that it lends itself readily to graphic depictions,” that he decided instead to do a literal interpretation using the text word for word in a version primarily assembled from the translations of Robert Alter and the King James bible. Now, readers of every persuasion―Crumb fans, comic book lovers, and believers―can gain astonishing new insights from these harrowing, tragic, and even juicy stories. Crumb’s Book of Genesis reintroduces us to the bountiful tree lined garden of Adam and Eve, the massive ark of Noah with beasts of every kind, the cities of Sodom and Gomorrah destroyed by brimstone and fire that rained from the heavens, and the Egypt of the Pharaoh, where Joseph’s embalmed body is carried in a coffin, in a scene as elegiac as any in Genesis. Using clues from the text and peeling away the theological and scholarly interpretation that have often obscured the Bible’s most dramatic stories, Crumb fleshes out a parade of Biblical originals: from the serpent in Eden, the humanoid reptile appearing like an alien out of a science fiction movie, to Jacob, a “kind’ve depressed guy who doesn’t strike you as physically courageous,” and his bother, Esau, “a rough and kick ass guy,” to Abraham’s wife Sarah, more fetching than most woman at 90, to God himself, “a standard Charlton Heston-like figure with long white hair and a flowing beard.” As Crumb writes in his introduction, “the stories of these people, the Hebrews, were something more than just stories. They were the foundation, the source, in writing of religious and political power, handed down by God himself.” Crumb’s Book of Genesis, the culmination of 5 years of painstaking work, is a tapestry of masterly detail and storytelling which celebrates the astonishing diversity of the one of our greatest artistic geniuses. Nominated for three 2010 Will Eisner Comic Industry Awards: Best Adaptation from Another Work, Best Graphic Album, Best Writer/Artist.

An Entertaining And Foundational Manual On How To Use R To Solve Statistical Problems. Discovering Statistics Using R Uses An Irreverent And Innovative Approach To Explain How Students Can Use R To Approach Statistical Problems. It Introduces Readers To The Software Environment Of R And Shows How It Can Be Used In The Field Of Statistics. The Authors Understand That Using R And Concepts Of Statistics Can Be Difficult To Access And So Use Humour And Extremely Informal And Conversational Language To Ease Comprehension. It Uses Multiple Engaging Examples As Well As Easy Problems To Ensure That The Concepts Of The Software As Well As The Statistical Concepts Can Be Easily Digested By The Readers. Given This Book's Accessibility, Fun Spirit, And Use Of Bizarre Real-world Research It Should Be Essential For Anyone Wanting To Learn About Statistics Using The Freely-available R Software. Key Features: Detailed Introduction To The Software Environment Of R Guides The Reader Through How To Use It. Relates Theory To The Real World To Help Students Think About How The Software Can Be Applied To Real Research Problems Humorous And Accessible Language That Simplify Complex Concepts And Processes Numerous Problems And Examples That Test The Readers Understanding Of The Subject--

Build machine learning algorithms, prepare data, and dig deep into data prediction techniques with RKey Features\nHarness the power of R for statistical computing and data science\nExplore, forecast, and classify data with R\nUse R to apply common machine learning algorithms to real-world scenarios\nBook DescriptionMachine learning, at its core, is concerned with transforming data into actionable knowledge. This makes machine learning well suited to the present-day era of big data. Given the growing prominence of R's cross-platform, zero-cost statistical programming environment, there has never been a better time to start applying machine learning to your data. Machine learning with R offers a powerful set of methods to quickly and easily gain insight from your data to both, veterans and beginners in data analytics.\nWant to turn your data into actionable knowledge, predict outcomes that make real impact, and have constantly developing insights? R gives you access to all the power you need to master exceptional machine learning techniques.\nThe second edition of Machine Learning with R provides you with an introduction to the essential skills required in data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms and crunching your data, with minimal previous experience.\nWith this book, you'll discover all the analytical tools you need to gain insights from complex data and learn to to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you'll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering. Transform the way you think about data; discover machine learning with R.What you will learn\nHarness the power of R to build common machine learning algorithms with real-world data science applications\nGet to grips with techniques in R to clean and prepare your data for analysis and visualize your results\nDiscover the different types of machine learning models and learn what is best to meet your data needs and solve data analysis problems\nClassify your data with Bayesian and nearest neighbour methods\nPredict values using R to build decision trees, rules, and support vector machines\nForecast numeric values with linear regression and model your data with neural networks\nEvaluate and improve the performance of machine learning models\nLearn specialized machine learning techniques for text mining, social network data, and big data\nWho This Book Is ForPerhaps you already know a bit about machine learning but have never used R, or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.Table of Contents\nIntroducing Machine Learning\nManaging and Understanding Data\nLazy Learning - Classification Using Nearest Neighbors\nProbabilistic Learning - Classification Using Naive Bayes\nDivide and Conquer - Classification Using Decision Trees and Rules\nForecasting Numeric Data - Regression Methods\nBlack Box Methods - Neural Networks and Support Vector Machines\nFinding Patterns - Market Basket Analysis Using Association Rules\nFinding Groups of Data - Clustering with K-means\nEvaluating Model Performance\nImproving Model Performance\n

Computer software is an essential tool for many statistical modelling and data analysis techniques, aiding in the implementation of large data sets in order to obtain useful results. R is one of the most powerful and flexible statistical software packages available, and enables the user to apply a wide variety of statistical methods ranging from simple regression to generalized linear modelling. Statistics: An Introduction using R is a clear and concise introductory textbook to statistical analysis using this powerful and free software, and follows on from the success of the author's previous best-selling title Statistical Computing. * Features step-by-step instructions that assume no mathematics, statistics or programming background, helping the non-statistician to fully understand the methodology. * Uses a series of realistic examples, developing step-wise from the simplest cases, with the emphasis on checking the assumptions (e.g. constancy of variance and normality of errors) and the adequacy of the model chosen to fit the data. * The emphasis throughout is on estimation of effect sizes and confidence intervals, rather than on hypothesis testing. * Covers the full range of statistical techniques likely to be need to analyse the data from research projects, including elementary material like t-tests and chi-squared tests, intermediate methods like regression and analysis of variance, and more advanced techniques like generalized linear modelling. * Includes numerous worked examples and exercises within each chapter. * Accompanied by a website featuring worked examples, data sets, exercises and solutions: http://www.imperial.ac.uk/bio/research/crawley/statistics Statistics: An Introduction using R is the first text to offer such a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to a broad range of disciplines. It is primarily aimed at undergraduate students in medicine, engineering, economics and biology - but will also appeal to postgraduates who have not previously covered this area, or wish to switch to using R.

Solve challenging data science problems by mastering cutting-edge machine learning techniques in PythonAbout This Book\nResolve complex machine learning problems and explore deep learning\nLearn to use Python code for implementing a range of machine learning algorithms and techniques\nA practical tutorial that tackles real-world computing problems through a rigorous and effective approach\nWho This Book Is ForThis title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you’ve ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you!\nPrior experience of Python and grounding in some of the core concepts of machine learning would be helpful.What You Will Learn\nCompete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms\nApply your new found skills to solve real problems, through clearly-explained code for every technique and test\nAutomate large sets of complex data and overcome time-consuming practical challenges\nImprove the accuracy of models and your existing input data using powerful feature engineering techniques\nUse multiple learning techniques together to improve the consistency of results\nUnderstand the hidden structure of datasets using a range of unsupervised techniques\nGain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach\nImprove the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models together\nIn DetailDesigned to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data.\nThe machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce.\nThis book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano.\nBy this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering.Style and approachThis book focuses on clarifying the theory and code behind complex algorithms to make them practical, useable, and well-understood. Each topic is described with real-world applications, providing both broad contextual coverage and detailed guidance.

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Get more from your data through creating practical machine learning systems with Python About This Book\nBuild your own Python-based machine learning systems tailored to solve any problem\nDiscover how Python offers a multiple context solution for create machine learning systems\nPractical scenarios using the key Python machine learning libraries to successfully implement in your projects\nWho This Book Is ForThis book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems.What You Will Learn\nBuild a classification system that can be applied to text, images, or sounds Use NumPy, SciPy, scikit-learn a“ scientific Python open source libraries for scientific computing and machine learning Explore the mahotas library for image processing and computer vision Build a topic model for the whole of Wikipedia Employ Amazon Web Services to run analysis on the cloud Debug machine learning problems Get to grips with recommendations using basket analysis Recommend products to users based on past purchases In DetailUsing machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python is a wonderful language to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation. With its excellent collection of open source machine learning libraries you can focus on the task at hand while being able to quickly try out many ideas.\nThis book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and introducing libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling, creating recommendation systems. Later on, the book covers advanced topics such as topic modeling, basket analysis, and cloud computing. These will extend your abilities and enable you to create large complex systems.\nWith this book, you gain the tools and understanding required to build your own systems, tailored to solve your real-world data analysis problems.

Solve real-world data problems with R and machine learning Key Features \nThird edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 and beyond Harness the power of R to build flexible, effective, and transparent machine learning models Learn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett Lantz Book Description Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings. This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R. What you will learn \nDiscover the origins of machine learning and how exactly a computer learns by example Prepare your data for machine learning work with the R programming language Classify important outcomes using nearest neighbor and Bayesian methods Predict future events using decision trees, rules, and support vector machines Forecast numeric data and estimate financial values using regression methods Model complex processes with artificial neural networks ― the basis of deep learning Avoid bias in machine learning models Evaluate your models and improve their performance Connect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlow\n Who this book is for Data scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.Table of Contents \nIntroducing Machine Learning Managing and Understanding Data Lazy Learning – Classification Using Nearest Neighbors Probabilistic Learning – Classification Using Naive Bayes Divide and Conquer – Classification Using Decision Trees and Rules Forecasting Numeric Data – Regression Methods Black Box Methods – Neural Networks and Support Vector Machines Finding Patterns – Market Basket Analysis Using Association Rules Finding Groups of Data – Clustering with k-means Evaluating Model Performance Improving Model Performance Specialized Machine Learning Topics

This book presents an array of methods applicable for reading data into R, and efficiently manipulating that data. In addition to the built-in functions, a number of readily available packages from CRAN (the Comprehensive R Archive Network) are also covered.

This Fascinating Book Demonstrates How You Can Build Web Applications To Mine The Enormous Amount Of Data Created By People On The Internet. With The Sophisticated Algorithms In This Book, You Can Write Smart Programs To Access Interesting Datasets From Other Web Sites, Collect Data From Users Of Your Own Applications, And Analyze And Understand The Data Once You've Found It.

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.\nSee What’s New in the Second Edition: \nIncreased 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.

Perform data analysis with R quickly and efficiently with more than 275 practical recipes in this expanded second edition. The R language provides everything you need to do statistical work, but its structure can be difficult to master. These task-oriented recipes make you productive with R immediately. Solutions range from basic tasks to input and output, general statistics, graphics, and linear regression.\nEach recipe addresses a specific problem and includes a discussion that explains the solution and provides insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an intermediate user, this book will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process.\n\nCreate vectors, handle variables, and perform basic functions\nSimplify data input and output\nTackle data structures such as matrices, lists, factors, and data frames\nWork with probability, probability distributions, and random variables\nCalculate statistics and confidence intervals and perform statistical tests\nCreate a variety of graphic displays\nBuild statistical models with linear regressions and analysis of variance (ANOVA)\nExplore advanced statistical techniques, such as finding clusters in your data\n

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsAbout This Book\nLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization\nLearn effective strategies and best practices to improve and optimize machine learning systems and algorithms\nAsk – and answer – tough questions of your data with robust statistical models, built for a range of datasets\nWho This Book Is ForIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.What You Will Learn\nExplore how to use different machine learning models to ask different questions of your data\nLearn how to build neural networks using Pylearn 2 and Theano\nFind out how to write clean and elegant Python code that will optimize the strength of your algorithms\nDiscover how to embed your machine learning model in a web application for increased accessibility\nPredict continuous target outcomes using regression analysis\nUncover hidden patterns and structures in data with clustering\nOrganize data using effective pre-processing techniques\nGet to grips with sentiment analysis to delve deeper into textual and social media data\nIn DetailMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.\nPython Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.Style and approachPython Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.

Summary\nR in Action is the first book to present both the R system and the use cases that make it such a compelling package for business developers. The book begins by introducing the R language, including the development environment. Focusing on practical solutions, the book also offers a crash course in practical statistics and covers elegant methods for dealing with messy and incomplete data using features of R.\nAbout the TechnologyR is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task. It runs on all important platforms and provides thousands of useful specialized modules and utilities. This makes R a great way to get meaningful information from mountains of raw data.\nAbout the BookR in Action is a language tutorial focused on practical problems. It presents useful statistics examples and includes elegant methods for handling messy, incomplete, and non-normal data that are difficult to analyze using traditional methods. And statistical analysis is only part of the story. You'll also master R's extensive graphical capabilities for exploring and presenting data visually.\n Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. \nWhat's Inside\nPractical data analysis, step by step\nInterfacing R with other software\nUsing R to visualize data\nOver 130 graphs\nEight reference appendixes\n\n================================\nTable of ContentsPart I Getting startedIntroduction to R\nCreating a dataset\nGetting started with graphs\nBasic data management\nAdvanced data managementPart II Basic methodsBasic graphs\nBasic statisticsPart III Intermediate methodsRegression\nAnalysis of variance\nPower analysis\nIntermediate graphs\nRe-sampling statistics and bootstrappingPart IV Advanced methodsGeneralized linear models\nPrincipal components and factor analysis\nAdvanced methods for missing data\nAdvanced graphics\n

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.

There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and ‘rusty’ calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs.The textbook bridges the students from their undergraduate training into modern Bayesian methods.\n\nAccessible, including the basics of essential concepts of probability and random sampling\nExamples with R programming language and BUGS software\nComprehensive coverage of all scenarios addressed by non bayesian textbooks t tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi square (contingency table analysis).\nCoverage of experiment planning\nR and BUGS computer programming code on website\nExercises have explicit purposes and guidelines for accomplishment\n

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills.

Advanced R helps you understand how R works at a fundamental level. It is designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages who want to understand what makes R different and special. This book will teach you the foundations of R; three fundamental programming paradigms (functional, object-oriented, and metaprogramming); and powerful techniques for debugging and optimisingyour code.By reading this book, you will learn: \nThe difference between an object and its name, and why the distinction is important The important vector data structures, how they fit together, and how you can pull them apart using subsetting The fine details of functions and environments The condition system, which powers messages, warnings, and errors The powerful functional programming paradigm, which can replace many for loops The three most important OO systems: S3, S4, and R6 The tidy eval toolkit for metaprogramming, which allows you to manipulate code and control evaluation Effective debugging techniques that you can deploy, regardless of how your code is run How to find and remove performance bottlenecks \n\nThe second edition is a comprehensive update: \nNew foundational chapters: "Names and values," "Control flow," and "Conditions" comprehensive coverage of object oriented programming with chapters on S3, S4, R6, and how to choose between them Much deeper coverage of metaprogramming, including the new tidy evaluation framework use of new package like rlang (rlang.r-lib.org), which provides a clean interface to low-level operations, and purr (purrr.tidyverse.org/) for functional programming Use of color in code chunks and figures \n

John Chambers turns his attention to R, the enormously successful open-source system based on the S language. His book guides the reader through programming with R, beginning with simple interactive use and progressing by gradual stages, starting with simple functions. More advanced programming techniques can be added as needed, allowing users to grow into software contributors, benefiting their careers and the community. R packages provide a powerful mechanism for contributions to be organized and communicated. This is the only advanced programming book on R, written by the author of the S language from which R evolved.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.\nTwo of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

Explore the world of data science through Python and learn how to make sense of dataAbout This Book\nMaster data science methods using Python and its libraries\nCreate data visualizations and mine for patterns\nAdvanced techniques for the four fundamentals of Data Science with Python - data mining, data analysis, data visualization, and machine learning\nWho This Book Is ForIf you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed.What You Will Learn\nManage data and perform linear algebra in Python\nDerive inferences from the analysis by performing inferential statistics\nSolve data science problems in Python\nCreate high-end visualizations using Python\nEvaluate and apply the linear regression technique to estimate the relationships among variables.\nBuild recommendation engines with the various collaborative filtering algorithms\nApply the ensemble methods to improve your predictions\nWork with big data technologies to handle data at scale\nIn DetailData science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving.\nThis comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science.\nBeginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods.\nFinally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics.Style and approachThis book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios.

Design and Analysis of Experiments with R presents a unified treatment of experimental designs and design concepts commonly used in practice. It connects the objectives of research to the type of experimental design required, describes the process of creating the design and collecting the data, shows how to perform the proper analysis of the data, and illustrates the interpretation of results. Drawing on his many years of working in the pharmaceutical, agricultural, industrial chemicals, and machinery industries, the author teaches students how to: \nMake an appropriate design choice based on the objectives of a research project Create a design and perform an experiment Interpret the results of computer data analysis The book emphasizes the connection among the experimental units, the way treatments are randomized to experimental units, and the proper error term for data analysis. R code is used to create and analyze all the example experiments. The code examples from the text are available for download on the author’s website, enabling students to duplicate all the designs and data analysis. Intended for a one-semester or two-quarter course on experimental design, this text covers classical ideas in experimental design as well as the latest research topics. It gives students practical guidance on using R to analyze experimental data.

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web ResourceThe book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

Shaken by the dramatic inroads made by Japanese competitors into high-tech electronics, a number of U.S. electronic firms joined forces in 1982 to form the first U.S. for-profit research consortium: MCC (Microelectronics and Computer Technology Corp.). Since then more than 200 other consortia have been formed in a variety of industries. The authors describe MCC's formation, the problems it encountered, and its progress from its rocky inception under Admiral Bobby Inman through its recent past under Dr. Craig Fields. At the same time, they examine the crucial role that public/private alliances at the local level played in the choice of Austin, Texas as the site for MCC and, more generally, in the rise of Texas high-tech industry and the emergence of Austin as a computer and technology center. The authors also address the important management issues that this very new kind of business organization raises. These include questions about the ability of competing companies to work together successfully; about their ability to transfer R&D findings to members; and about the implications of these consortia for national and international competitiveness.

If you’re curious about how things work, this fun and intriguing guide will help you find real answers to everyday problems. By using fundamental math and doing simple programming with the Ruby and R languages, you’ll learn how to model a problem and work toward a solution.\nAll you need is a basic understanding of programming. After a quick introduction to Ruby and R, you’ll explore a wide range of questions by learning how to assemble, process, simulate, and analyze the available data. You’ll learn to see everyday things in a different perspective through simple programs and common sense logic. Once you finish this book, you can begin your own journey of exploration and discovery.\nHere are some of the questions you’ll explore:\n\nDetermine how many restroom stalls can accommodate an office with 70 employees\nMine your email to understand your particular emailing habits\nUse simple audio and video recording devices to calculate your heart rate\nCreate an artificial society—and analyze its behavioral patterns to learn how specific factors affect our real society\n