- Table View
- List View
Quicken 2009 For Dummies
by Stephen L. NelsonIf just thinking about financial management gives you a headache, personal finance software is better than aspirin. Quicken is tops, and Quicken 2009 For Dummies is the quickest and easiest way to put it to work.Here's the plain-English guide to using the newest update of the nation's leading personal finance software. A leading CPA shows you how to track your finances day to day, keep your checkbook and pay bills online, and even plan for tax time with Quicken. Learn as much or as little as you need to know -- just how to keep your checkbook straight, or even how Quicken helps manage stocks and the business end of rental property.Install and set up Quicken 2009, or update data files from a previous versionLearn to make a budget that's flexible enough to work for your family or your businessKeep your checkbook up to date, handle banking transactions online, and use Quicken calculatorsPrint Quicken reports to help you track cash flow, identify missing checks, summarize spending, and moreSet up tax-deferred or brokerage accounts and buy and sell securitiesTrack your credit cards and bank accounts as well as mortgages, loans, and other debtsUse Quicken's Home & Business or Rental Property Manager versions to keep your business booksHandle payroll for business or household employeesTrack deductions to make tax preparation easierWith Quicken 2009 For Dummies, you'll feel like a financial wizard!
Quicken 2011 For Dummies
by Stephen L. NelsonThe classic guide to the leading personal finance software—completely updated! As the number one personal finance software on the market, Quicken empowers you to take control of your personal finances quickly and effortlessly. Providing you with a thorough update of all the latest features and enhancements to the new release of Quicken 2011, Stephen Nelson shows you how to track your day-to-day finances, better manage your investments, evaluate the tax implications of your financial decisions, and much more. Veteran author Stephen Nelson provides a thorough update to his classic bestseller on the number one personal financial management planning program Shows you how to track your day-to-day finances, better manage your investments, boost your personal savings, be more responsible with your spending, tackle debt, and more Presents a fun and friendly approach to a topic that many people find intimidating or overwhelming and quickly and easily helps you take control of your personal finances Whether you're a first-time Quicken customer or looking to take advantage of the updates the latest release has to offer, Quicken 2011 For Dummies offers a straightforward-but-fun approach to this popular personal finance software.
Quickstart Apache Axis2
by Deepal JayasingheThis is a step by step practical guide for developing web services using Apache Axis2. There are lot of real-life examples, which makes this book a good learning material. This book is for Java developers who are interested in building web services using Apache Axis2. The book presumes that you are familiar with web standards like SOAP, WSDL and XML parsing.
Quickstart Python: An Introduction to Programming for STEM Students (essentials)
by Christoph SchäferChristoph Schäfer introduces the great world of programming with Python and provides a quick introduction to independent script development. He points out how the programming language Python has established itself in recent years alongside MATLAB and R as a standard at scientific workplaces in research and development, and shows that the great popularity of Python is based on its easy extensibility: It is very easy to use modules from other developers in your own scripts and programs. In particular, the author presents the modules NumPy, SciPy and Matplotlib, which offer scientists and engineers a perfect development environment for scientific and technical computing, for applications in physics, chemistry, biology and computer science. Python is also used in the latest applications in the highly topical fields of Big Data Science and Machine Learning. The author: Dr. Christoph Schäfer teaches and researches in the Department of Computational Physics at the Institute of Astronomy and Astrophysics at the Eberhard Karls University of Tübingen. This Springer essential is a translation of the original German 1st edition essentials, Schnellstart Python by Christoph Schäfer, published by Springer Fachmedien Wiesbaden GmbH, part of Springer Nature in 2019. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors.
QuickTime for Java: A Developer's Notebook
by Chris AdamsonJava developers who need to add audio, video, or interactive media creation and playback to their applications find that QuickTime Java is a powerful toolkit, but one that's not easy to get into. This book offers the first real look at this important software with an informal, code-intensive style that lets impatient early adopters focus on learning by doing. You get just the functionality you need.
¿Quién controla el futuro?
by Jaron LanierLúcido, original y provocador, ¿Quién controla el futuro? es una lectura necesaria para todos los que vivimos en un mundo parcialmente digital. Jaron Lanier, uno de los pensadores más influyentes de la actualidad, es autor de la obra fundamental sobre internet Contra el rebaño digital y padre de la realidad virtual. Desde hace décadas, Lanier ha aprovechado su experiencia para reflexionar acerca de cómo la tecnología transforma nuestra sociedad y nuestra cultura. ¿Quién controla el futuro? es la perspectiva de un pensador visionario sobre la cuestión económica y social más importante de la actualidad: la perniciosa concentración de dinero y poder en las redes digitales. Lanier piensa que el auge de las redes digitales ha conducido nuestras economías a la recesión y ha diezmado las clases medias. A medida que la tecnología destruye más y más sectores -desde medios de comunicación hasta la medicina o la industria-, afrontamos mayores desafíos al empleo y la prosperidad individual. Pero hay una alternativa a permitir que la tecnología se apropie de nuestro futuro. En esta obra tan ambiciosa como sensible al devenir humano, Lanier dibuja el camino hacia una nueva economía de la información que respetará a la sociedad y le permitirá crecer. Es hora de que la gente común sea recompensada por lo que crea y lo que comparte en red. La crítica ha dicho... «El libro más importante del año. Ideas provocadoras y heterodoxas para conseguir que la inevitable primacía del software en la sociedad sea provechosa en vez de dañina.» Joe Nocera, The New York Times «Son legión los que se quejan de internet, pero ni uno hace nada para remediarlo; excepto Jaron Lanier.»Neal Stephenson «Un libro inteligente y accesible que lanza una mirada crítica al mundo digital y pone de manifiesto su lamentable estado.»Carolyn Kellogg, Los Angeles Times «Atrevido, original y muy imaginativo. Lanier es tan entretenido y accesible como lúcido.»Janet Maslin, The New York Times «Provocador y polémico, una lectura esencial.»The Washington Post «Uno de los mejores libros escépticos acerca del mundo digital.»Salon «Este ambicioso libro busca ayudar a la gente corriente a sobrevivir y prosperar en una época en que los avances en informática reducen las posibilidades de encontrar empleo.»USA Today «Un correctivo muy útil. Si se produce un éxodo digital, Lanier será su Moisés.»San Francisco Chronicle
The Quiet Crypto Revolution: How Blockchain and Cryptocurrency Are Changing Our Lives
by Klaas JungCrypto is going to change the world, and for those tired of confusing financial jargon and complicated technical terminology, look no further. This book demystifies the world of cryptocurrencies and blockchain technology and explains in accessible language how it will affect your daily life. In The Quiet Crypto Revolution, Klaas Jung dives beneath the surface of Bitcoin to explore the engine that powers it - blockchain. Far surpassing the confines of cryptocurrencies, blockchain's potential for wide-ranging applications is enormous. It's crucial to understand that cryptocurrencies are merely a single manifestation of blockchain's capabilities. This book casts light on the broader spectrum of blockchain applications and the exciting future of this groundbreaking technology. With a focus on real-world applications, you'll gain a deeper understanding of the key concepts behind the innovative technology of blockchain, equipping you to make informed decisions. Whether you're a tech-savvy individual or a complete newcomer to the world of crypto and finance, this book will arm you with the knowledge and insights you need to understand the impact cryptocurrency and blockchain will have on your future.You WillLook at the future of blockchain technologyReview potential use cases for blockchain beyond cryptocurrencyStudy security strategies to avoid scams in the crypto spaceWho This Book Is ForBeginners who would like to gain a better understanding of cryptocurrency and the technology that supports it.
R: Data Analysis and Visualization
by Gergely Gabler Gergely Daroczi Péter Medvegyev Balázs Márkus Ágnes Vidovics-Dancs Edina Berlinger Ágnes Tuza Tony Fischetti Kata Váradi Bater Makhabel Hrishi V. Mittal Barbara Dömötör Tamás Vadász István Margitai Péter Juhász Brett Lantz Julia Molnár Dániel Havran Balázs Árpád Szucs Jaynal Abedin Milan Badics Adam Banai Ferenc IllesMaster the art of building analytical models using R About This Book * Load, wrangle, and analyze your data using the world's most powerful statistical programming language * Build and customize publication-quality visualizations of powerful and stunning R graphs * Develop key skills and techniques with R to create and customize data mining algorithms * Use R to optimize your trading strategy and build up your own risk management system * Discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R Who This Book Is For This course is for data scientist or quantitative analyst who are looking at learning R and take advantage of its powerful analytical design framework. It's a seamless journey in becoming a full-stack R developer What You Will Learn * Describe and visualize the behavior of data and relationships between data * Gain a thorough understanding of statistical reasoning and sampling * Handle missing data gracefully using multiple imputation * Create diverse types of bar charts using the default R functions * Produce and customize density plots and histograms with lattice and ggplot2 * Get to know the top classification algorithms written in R * Familiarize yourself with algorithms written in R for spatial data mining, text mining, and so on * Understand relationships between market factors and their impact on your portfolio * Harness the power of R to build machine learning algorithms with real-world data science applications * Learn specialized machine learning techniques for text mining, big data, and more In Detail The R learning path created for you has five connected modules,which are a mini-course in their own right.As you complete each one, you'll have gained key skills and be ready for the material in the next module! This course begins by looking at the Data Analysis with R module. This will help you navigate the R environment. You'll gain a thorough understanding of statistical reasoning and sampling. Finally, you'll be able to put best practices into effect to make your job easier and facilitate reproducibility. The second place to explore is R Graphs,which will help you leverage powerful default R graphics and utilize advanced graphics systems such as lattice and ggplot2, the grammar of graphics. Through inspecting large datasets using tableplot and stunning 3D visualizations, you will know how to produce, customize, and publish advanced visualizations using this popular and powerful framework. With the third module, Learning Data Mining with R, you will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs. You will finish this module feeling confident in your ability to know which data mining algorithm to apply in any situation. The Mastering R for Quantitative Finance module pragmatically introduces both the quantitative finance concepts and their modeling in R, enabling you to build a tailor-made trading system on your own. By the end of the module, you will be well-versed with various financial techniques using R and will be able to place good bets while making financial decisions. Finally, we'll look at the Machine Learning with R module. With this module, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. You'll also learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering. Style and approach Learn data analysis, data visualization techniques, data mining, and machine learning all using R and also learn to build models in quantitative finance using this powerful language
R: Recipes for Analysis, Visualization and Machine Learning
by Atmajitsinh Gohil Shanthi Viswanathan Viswa Viswanathan Yu-Wei ChiuGet savvy with R language and actualize projects aimed at analysis, visualization and machine learning About This Book * Proficiently analyze data and apply machine learning techniques * Generate visualizations, develop interactive visualizations and applications to understand various data exploratory functions in R * Construct a predictive model by using a variety of machine learning packages Who This Book Is For This Learning Path is ideal for those who have been exposed to R, but have not used it extensively yet. It covers the basics of using R and is written for new and intermediate R users interested in learning. This Learning Path also provides in-depth insights into professional techniques for analysis, visualization, and machine learning with R - it will help you increase your R expertise, regardless of your level of experience. What You Will Learn * Get data into your R environment and prepare it for analysis * Perform exploratory data analyses and generate meaningful visualizations of the data * Generate various plots in R using the basic R plotting techniques * Create presentations and learn the basics of creating apps in R for your audience * Create and inspect the transaction dataset, performing association analysis with the Apriori algorithm * Visualize associations in various graph formats and find frequent itemset using the ECLAT algorithm * Build, tune, and evaluate predictive models with different machine learning packages * Incorporate R and Hadoop to solve machine learning problems on big data In Detail The R language is a powerful, open source, functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. This Learning Path is chock-full of recipes. Literally! It aims to excite you with awesome projects focused on analysis, visualization, and machine learning. We'll start off with data analysis - this will show you ways to use R to generate professional analysis reports. We'll then move on to visualizing our data - this provides you with all the guidance needed to get comfortable with data visualization with R. Finally, we'll move into the world of machine learning - this introduces you to data classification, regression, clustering, association rule mining, and dimension reduction. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: * R Data Analysis Cookbook by Viswa Viswanathan and Shanthi Viswanathan * R Data Visualization Cookbook by Atmajitsinh Gohil * Machine Learning with R Cookbook by Yu-Wei, Chiu (David Chiu) Style and approach This course creates a smooth learning path that will teach you how to analyze data and create stunning visualizations. The step-by-step instructions provided for each recipe in this comprehensive Learning Path will show you how to create machine learning projects with R.
R: Unleash Machine Learning Techniques
by Dipanjan Sarkar Brett Lantz Raghav Bali Cory LesmeisterFind out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner. About This Book * Build your confidence with R and find out how to solve a huge range of data-related problems * Get to grips with some of the most important machine learning techniques being used by data scientists and analysts across industries today * Don't just learn - apply your knowledge by following featured practical projects covering everything from financial modeling to social media analysis Who This Book Is For Aimed for intermediate-to-advanced people (especially data scientist) who are already into the field of data science What You Will Learn * Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results * Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action * Solve interesting real-world problems using machine learning and R as the journey unfolds * Write reusable code and build complete machine learning systems from the ground up * Learn specialized machine learning techniques for text mining, social network data, big data, and more * Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems * Evaluate and improve the performance of machine learning models * Learn specialized machine learning techniques for text mining, social network data, big data, and more In Detail R is the established language of data analysts and statisticians around the world. And you shouldn't be afraid to use it... This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R. In the first module you'll get to grips with the fundamentals of R. This means you'll be taking a look at some of the details of how the language works, before seeing how to put your knowledge into practice to build some simple machine learning projects that could prove useful for a range of real world problems. For the following two modules we'll begin to investigate machine learning algorithms in more detail. To build upon the basics, you'll get to work on three different projects that will test your skills. Covering some of the most important algorithms and featuring some of the most popular R packages, they're all focused on solving real problems in different areas, ranging from finance to social media. This Learning Path has been curated from three Packt products: * R Machine Learning By Example By Raghav Bali, Dipanjan Sarkar * Machine Learning with R Learning - Second Edition By Brett Lantz * Mastering Machine Learning with R By Cory Lesmeister Style and approach This is an enticing learning path that starts from the very basics to gradually pick up pace as the story unfolds. Each concept is first defined in the larger context of things succinctly, followed by a detailed explanation of their application. Each topic is explained with the help of a project that solves a real-world problem involving hands-on work thus giving you a deep insight into the world of machine learning.
R 4 Data Science Quick Reference: A Pocket Guide to APIs, Libraries, and Packages
by Thomas MailundIn this handy, quick reference book you'll be introduced to several R data science packages, with examples of how to use each of them. All concepts will be covered concisely, with many illustrative examples using the following APIs: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more.With R 4 Data Science Quick Reference, you'll have the code, APIs, and insights to write data science-based applications in the R programming language. You'll also be able to carry out data analysis. All source code used in the book is freely available on GitHub.. What You'll LearnImplement applicable R 4 programming language specification featuresImport data with readrWork with categories using forcats, time and dates with lubridate, and strings with stringrFormat data using tidyr and then transform that data using magrittr and dplyrWrite functions with R for data science, data mining, and analytics-based applicationsVisualize data with ggplot2 and fit data to models using modelrWho This Book Is ForProgrammers new to R's data science, data mining, and analytics packages. Some prior coding experience with R in general is recommended.
R 4 Quick Syntax Reference: A Pocket Guide to the Language, API's and Library
by Margot TollefsonThis handy reference book detailing the intricacies of R covers version 4.x features, including numerous and significant changes to syntax, strings, reference counting, grid units, and more.Starting with the basic structure of R, the book takes you on a journey through the terminology used in R and the syntax required to make R work. You will find looking up the correct form for an expression quick and easy. Some of the new material includes information on RStudio, S4 syntax, working with character strings, and an example using the Twitter API.With a copy of the R 4 Quick Syntax Reference in hand, you will find that you are able to use the multitude of functions available in R and are even able to write your own functions to explore and analyze data.What You Will LearnDiscover the modes and classes of R objects and how to use themUse both packaged and user-created functions in R Import/export data and create new data objects in RCreate descriptive functions and manipulate objects in RTake advantage of flow control and conditional statementsWork with packages such as base, stats, and graphicsWho This Book Is ForThose with programming experience, either new to R, or those with at least some exposure to R but who are new to the latest version.
R All-in-One For Dummies
by Joseph SchmullerA deep dive into the programming language of choice for statistics and data With R All-in-One For Dummies, you get five mini-books in one, offering a complete and thorough resource on the R programming language and a road map for making sense of the sea of data we’re all swimming in. Maybe you’re pursuing a career in data science, maybe you’re looking to infuse a little statistics know-how into your existing career, or maybe you’re just R-curious. This book has your back. Along with providing an overview of coding in R and how to work with the language, this book delves into the types of projects and applications R programmers tend to tackle the most. You’ll find coverage of statistical analysis, machine learning, and data management with R. Grasp the basics of the R programming language and write your first lines of code Understand how R programmers use code to analyze data and perform statistical analysis Use R to create data visualizations and machine learning programs Work through sample projects to hone your R coding skill This is an excellent all-in-one resource for beginning coders who'd like to move into the data space by knowing more about R.
R Alles-in-einem-Band für Dummies (Für Dummies)
by Joseph SchmullerWenn Sie R von Grund auf kennenlernen und auch die fortgeschrittenen Techniken zur Lösung gängiger Aufgaben bei der Datenanalyse mit R beherrschen möchten, dann liegen Sie mit diesem Buch goldrichtig. Es bietet Ihnen nicht nur einen Überblick über die Programmierung in R und die Arbeit mit der Sprache, sondern geht auch auf die Arten von Projekten und Anwendungen ein, die R-Entwicklerinnen und -Entwickler häufig in Angriff nehmen müssen. Statistische Analysen, Datenvisualisierungen, maschinelles Lernen und Datenmanagement mit R: All das lernen Sie mit diesem Buch intensiv kennen.
R Bioinformatics Cookbook: Use R and Bioconductor to perform RNAseq, genomics, data visualization, and bioinformatic analysis
by Dan MacLeanOver 60 recipes to model and handle real-life biological data using modern libraries from the R ecosystem Key Features Apply modern R packages to handle biological data using real-world examples Represent biological data with advanced visualizations suitable for research and publications Handle real-world problems in bioinformatics such as next-generation sequencing, metagenomics, and automating analyses Book Description Handling biological data effectively requires an in-depth knowledge of machine learning techniques and computational skills, along with an understanding of how to use tools such as edgeR and DESeq. With the R Bioinformatics Cookbook, you'll explore all this and more, tackling common and not-so-common challenges in the bioinformatics domain using real-world examples. This book will use a recipe-based approach to show you how to perform practical research and analysis in computational biology with R. You will learn how to effectively analyze your data with the latest tools in Bioconductor, ggplot, and tidyverse. The book will guide you through the essential tools in Bioconductor to help you understand and carry out protocols in RNAseq, phylogenetics, genomics, and sequence analysis. As you progress, you will get up to speed with how machine learning techniques can be used in the bioinformatics domain. You will gradually develop key computational skills such as creating reusable workflows in R Markdown and packages for code reuse. By the end of this book, you'll have gained a solid understanding of the most important and widely used techniques in bioinformatic analysis and the tools you need to work with real biological data. What you will learn Employ Bioconductor to determine differential expressions in RNAseq data Run SAMtools and develop pipelines to find single nucleotide polymorphisms (SNPs) and Indels Use ggplot to create and annotate a range of visualizations Query external databases with Ensembl to find functional genomics information Execute large-scale multiple sequence alignment with DECIPHER to perform comparative genomics Use d3.js and Plotly to create dynamic and interactive web graphics Use k-nearest neighbors, support vector machines and random forests to find groups and classify data Who this book is for This book is for bioinformaticians, data analysts, researchers, and R developers who want to address intermediate-to-advanced biological and bioinformatics problems by learning through a recipe-based approach. Working knowledge of R programming language and basic knowledge of bioinformatics are prerequisites.
The R Book
by Elinor Jones Simon Harden Michael J. CrawleyA start-to-finish guide to one of the most useful programming languages for researchers in a variety of fields In the newly revised Third Edition of The R Book, a team of distinguished teachers and researchers delivers a user-friendly and comprehensive discussion of foundational and advanced topics in the R software language, which is used widely in science, engineering, medicine, economics, and other fields. The book is designed to be used as both a complete text—readable from cover to cover—and as a reference manual for practitioners seeking authoritative guidance on particular topics. This latest edition offers instruction on the use of the RStudio GUI, an easy-to-use environment for those new to R. It provides readers with a complete walkthrough of the R language, beginning at a point that assumes no prior knowledge of R and very little previous knowledge of statistics. Readers will also find: A thorough introduction to fundamental concepts in statistics and step-by-step roadmaps to their implementation in R; Comprehensive explorations of worked examples in R; A complementary companion website with downloadable datasets that are used in the book; In-depth examination of essential R packages. Perfect for undergraduate and postgraduate students of science, engineering, medicine economics, and geography, The R Book will also earn a place in the libraries of social sciences professionals.
R by Example (Use R!)
by Jim Albert Maria RizzoNow in its second edition, R by Example is an example-based introduction to the statistical computing environment that does not assume any previous familiarity with R or other software packages. R functions are presented in the context of interesting applications with real data. The purpose of this book is to illustrate a range of statistical and probability computations using R for people who are learning, teaching, or using statistics. Specifically, it is written for users who have covered at least the equivalent of (or are currently studying) undergraduate level calculus-based courses in statistics. These users are learning or applying exploratory and inferential methods for analyzing data, and this book is intended to be a useful resource for learning how to implement these procedures in R. The new edition includes expanded coverage of ggplot2 graphics, as well as new chapters on importing data and multivariate data methods.
R-Calculus, V: Description Logics (Perspectives in Formal Induction, Revision and Evolution)
by Wei Li Yuefei SuiThis book series consists of two parts, decidable description logics and undecidable description logics. It gives the R-calculi for description logics. This book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners in the field of logic.
R-Calculus, VI: Finite Injury Priority Method (Perspectives in Formal Induction, Revision and Evolution)
by Wei Li Yuefei SuiThis sixth volume of the book series applies finite injury priority method to R-calculi and obtain (in)completeness theorem for binary-valued, Post three-valued, B2^2-valued and L4-valued first-order logics, and extend the method to infinite injury priority method and 0"-method for default logic to produce pseudo-extensions of a default theory, corresponding to different R-calculi. Finite injury priority method and tree constructions are discussed in this book. This book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners in the field of logic.
R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics (Cookbook Ser.)
by Jd Long Paul TeetorPerform 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.Each 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.Create vectors, handle variables, and perform basic functionsSimplify data input and outputTackle data structures such as matrices, lists, factors, and data framesWork with probability, probability distributions, and random variablesCalculate statistics and confidence intervals and perform statistical testsCreate a variety of graphic displaysBuild statistical models with linear regressions and analysis of variance (ANOVA)Explore advanced statistical techniques, such as finding clusters in your data
R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics (Cookbook Ser.)
by Paul TeetorWith 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
R Data Analysis Cookbook
by Viswa Viswanathan Shanthi ViswanathanThis book is ideal for those who are already exposed to R, but have not yet used it extensively for data analytics and are seeking to get up and running quickly for analytics tasks. This book will help people who aspire to enhance their skills in any of the following ways: * perform advanced analyses and create informative and professional charts * become proficient in acquiring data from many sources * apply supervised and unsupervised data mining techniques * use R's features to present analyses professionally
R Data Analysis Cookbook - Second Edition
by Kuntal GangulyOver 80 recipes to help you breeze through your data analysis projects using R About This Book • Analyse your data using the popular R packages like ggplot2 with ready-to-use and customizable recipes • Find meaningful insights from your data and generate dynamic reports • A practical guide to help you put your data analysis skills in R to practical use Who This Book Is For This book is for data scientists, analysts and even enthusiasts who want to learn and implement the various data analysis techniques using R in a practical way. Those looking for quick, handy solutions to common tasks and challenges in data analysis will find this book to be very useful. Basic knowledge of statistics and R programming is assumed. What You Will Learn • Acquire, format and visualize your data using R • Using R to perform an Exploratory data analysis • Introduction to machine learning algorithms such as classification and regression • Get started with social network analysis • Generate dynamic reporting with Shiny • Get started with geospatial analysis • Handling large data with R using Spark and MongoDB • Build Recommendation system- Collaborative Filtering, Content based and Hybrid • Learn real world dataset examples- Fraud Detection and Image Recognition In Detail Data analytics with R has emerged as a very important focus for organizations of all kinds. R enables even those with only an intuitive grasp of the underlying concepts, without a deep mathematical background, to unleash powerful and detailed examinations of their data. This book will show you how you can put your data analysis skills in R to practical use, with recipes catering to the basic as well as advanced data analysis tasks. Right from acquiring your data and preparing it for analysis to the more complex data analysis techniques, the book will show you how you can implement each technique in the best possible manner. You will also visualize your data using the popular R packages like ggplot2 and gain hidden insights from it. Starting with implementing the basic data analysis concepts like handling your data to creating basic plots, you will master the more advanced data analysis techniques like performing cluster analysis, and generating effective analysis reports and visualizations. Throughout the book, you will get to know the common problems and obstacles you might encounter while implementing each of the data analysis techniques in R, with ways to overcoming them in the easiest possible way. By the end of this book, you will have all the knowledge you need to become an expert in data analysis with R, and put your skills to test in real-world scenarios. Style and Approach • Hands-on recipes to walk through data science challenges using R • Your one-stop solution for common and not-so-common pain points while performing real-world problems to execute a series of tasks. • Addressing your common and not-so-common pain points, this is a book that you must have on the shelf
R Data Analysis Projects
by Gopi SubramanianGet valuable insights from your data by building data analysis systems from scratch with R. About This Book • A handy guide to take your understanding of data analysis with R to the next level • Real-world projects that focus on problems in finance, network analysis, social media, and more • From data manipulation to analysis to visualization in R, this book will teach you everything you need to know about building end-to-end data analysis pipelines using R Who This Book Is For If you are looking for a book that takes you all the way through the practical application of advanced and effective analytics methodologies in R, then this is the book for you. A fundamental understanding of R and the basic concepts of data analysis is all you need to get started with this book. What You Will Learn • Build end-to-end predictive analytics systems in R • Build an experimental design to gather your own data and conduct analysis • Build a recommender system from scratch using different approaches • Use and leverage RShiny to build reactive programming applications • Build systems for varied domains including market research, network analysis, social media analysis, and more • Explore various R Packages such as RShiny, ggplot, recommenderlab, dplyr, and find out how to use them effectively • Communicate modeling results using Shiny Dashboards • Perform multi-variate time-series analysis prediction, supplemented with sensitivity analysis and risk modeling In Detail R offers a large variety of packages and libraries for fast and accurate data analysis and visualization. As a result, it's one of the most popularly used languages by data scientists and analysts, or anyone who wants to perform data analysis. This book will demonstrate how you can put to use your existing knowledge of data analysis in R to build highly efficient, end-to-end data analysis pipelines without any hassle. You'll start by building a content-based recommendation system, followed by building a project on sentiment analysis with tweets. You'll implement time-series modeling for anomaly detection, and understand cluster analysis of streaming data. You'll work through projects on performing efficient market data research, building recommendation systems, and analyzing networks accurately, all provided with easy to follow codes. With the help of these real-world projects, you'll get a better understanding of the challenges faced when building data analysis pipelines, and see how you can overcome them without compromising on the efficiency or accuracy of your systems. The book covers some popularly used R packages such as dplyr, ggplot2, RShiny, and others, and includes tips on using them effectively. By the end of this book, you'll have a better understanding of data analysis with R, and be able to put your knowledge to practical use without any hassle. Style and approach This book takes a unique, learn-as-you-do approach, as you build on your understanding of data analysis progressively with each project. This book is designed in a way that implementing each project will empower you with a unique skill set, and enable you to implement the next project more confidently.
R Data Mining
by Andrea CirilloMine valuable insights from your data using popular tools and techniques in R About This Book • Understand the basics of data mining and why R is a perfect tool for it. • Manipulate your data using popular R packages such as ggplot2, dplyr, and so on to gather valuable business insights from it. • Apply effective data mining models to perform regression and classification tasks. Who This Book Is For If you are a budding data scientist, or a data analyst with a basic knowledge of R, and want to get into the intricacies of data mining in a practical manner, this is the book for you. No previous experience of data mining is required. What You Will Learn • Master relevant packages such as dplyr, ggplot2 and so on for data mining • Learn how to effectively organize a data mining project through the CRISP-DM methodology • Implement data cleaning and validation tasks to get your data ready for data mining activities • Execute Exploratory Data Analysis both the numerical and the graphical way • Develop simple and multiple regression models along with logistic regression • Apply basic ensemble learning techniques to join together results from different data mining models • Perform text mining analysis from unstructured pdf files and textual data • Produce reports to effectively communicate objectives, methods, and insights of your analyses In Detail R is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R. It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques. While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data. Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets. Style and approach This book takes a practical, step-by-step approach to explain the concepts of data mining. Practical use-cases involving real-world datasets are used throughout the book to clearly explain theoretical concepts.