Mastering RStudio Develop Communicate and Collaborate with R

Author: Julian Hillebrand
Publisher: Packt Publishing Ltd
ISBN: 9781783982554
Release Date: 2015-12-04
Genre: Computers

Harness the power of RStudio to create web applications, R packages, markdown reports and pretty data visualizations About This Book Discover the multi-functional use of RStudio to support your daily work with R code Learn to create stunning, meaningful, and interactive graphs and learn to embed them into easy communicable reports using multiple R packages Develop your own R packages and Shiny web apps to share your knowledge and collaborate with others. Who This Book Is For This book is aimed at R developers and analysts who wish to do R statistical development while taking advantage of RStudio's functionality to ease their development efforts. R programming experience is assumed as well as being comfortable with R's basic structures and a number of functions. What You Will Learn Discover the RStudio IDE and details about the user interface Communicate your insights with R Markdown in static and interactive ways Learn how to use different graphic systems to visualize your data Build interactive web applications with the Shiny framework to present and share your results Understand the process of package development and assemble your own R packages Easily collaborate with other people on your projects by using Git and GitHub Manage the R environment for your organization with RStudio and Shiny server Apply your obtained knowledge about RStudio and R development to create a real-world dashboard solution In Detail RStudio helps you to manage small to large projects by giving you a multi-functional integrated development environment, combined with the power and flexibility of the R programming language, which is becoming the bridge language of data science for developers and analyst worldwide. Mastering the use of RStudio will help you to solve real-world data problems. This book begins by guiding you through the installation of RStudio and explaining the user interface step by step. From there, the next logical step is to use this knowledge to improve your data analysis workflow. We will do this by building up our toolbox to create interactive reports and graphs or even web applications with Shiny. To collaborate with others, we will explore how to use Git and GitHub with RStudio and how to build your own packages to ensure top quality results. Finally, we put it all together in an interactive dashboard written with R. Style and approach An easy-to-follow guide full of hands-on examples to master RStudio. Beginning from explaining the basics, each topic is explained with a lot of details for every feature.

Getting Started with RStudio

Author: John Verzani
Publisher: "O'Reilly Media, Inc."
ISBN: 9781449317072
Release Date: 2011-09-16
Genre: Computers

Dive into the RStudio Integrated Development Environment (IDE) for using and programming R, the popular open source software for statistical computing and graphics. This concise book provides new and experienced users with an overview of RStudio, as well as hands-on instructions for analyzing data, generating reports, and developing R software packages. The open source RStudio IDE brings many powerful coding tools together into an intuitive, easy-to-learn interface. With this guide, you’ll learn how to use its main components—including the console, source code editor, and data viewer—through descriptions and case studies. Getting Started with RStudio serves as both a reference and introduction to this unique IDE. Use RStudio to provide enhanced support for interactive R sessions Clean and format raw data quickly with several RStudio components Edit R commands with RStudio’s code editor, and combine them into functions Easily locate and use more than 3,000 add-on packages in R’s CRAN service Develop and document your own R packages with the code editor and related components Create one-click PDF reports in RStudio with a mix of text and R output

Learning RStudio for R Statistical Computing

Author: Mark P. J. Van der Loo
Publisher: Packt Publishing Ltd
ISBN: 9781782160618
Release Date: 2012-01-01
Genre: Computers

A practical tutorial covering how to leverage RStudio functionality to effectively perform R Development, analysis, and reporting with RStudio. The book is aimed at R developers and analysts who wish to do R statistical development while taking advantage of RStudio functionality to ease their development efforts. Familiarity with R is assumed. Those who want to get started with R development using RStudio will also find the book useful. Even if you already use R but want to create reproducible statistical analysis projects or extend R with self-written packages, this book shows how to quickly achieve this using RStudio.

RStudio for R Statistical Computing Cookbook

Author: Andrea Cirillo
Publisher: Packt Publishing Ltd
ISBN: 9781784396947
Release Date: 2016-04-29
Genre: Computers

Over 50 practical and useful recipes to help you perform data analysis with R by unleashing every native RStudio feature About This Book 54 useful and practical tasks to improve working systems Includes optimizing performance and reliability or uptime, reporting, system management tools, interfacing to standard data ports, and so on Offers 10-15 real-life, practical improvements for each user type Who This Book Is For This book is targeted at R statisticians, data scientists, and R programmers. Readers with R experience who are looking to take the plunge into statistical computing will find this Cookbook particularly indispensable. What You Will Learn Familiarize yourself with the latest advanced R console features Create advanced and interactive graphics Manage your R project and project files effectively Perform reproducible statistical analyses in your R projects Use RStudio to design predictive models for a specific domain-based application Use RStudio to effectively communicate your analyses results and even publish them to a blog Put yourself on the frontiers of data science and data monetization in R with all the tools that are needed to effectively communicate your results and even transform your work into a data product In Detail The requirement of handling complex datasets, performing unprecedented statistical analysis, and providing real-time visualizations to businesses has concerned statisticians and analysts across the globe. RStudio is a useful and powerful tool for statistical analysis that harnesses the power of R for computational statistics, visualization, and data science, in an integrated development environment. This book is a collection of recipes that will help you learn and understand RStudio features so that you can effectively perform statistical analysis and reporting, code editing, and R development. The first few chapters will teach you how to set up your own data analysis project in RStudio, acquire data from different data sources, and manipulate and clean data for analysis and visualization purposes. You'll get hands-on with various data visualization methods using ggplot2, and you will create interactive and multidimensional visualizations with D3.js. Additional recipes will help you optimize your code; implement various statistical models to manage large datasets; perform text analysis and predictive analysis; and master time series analysis, machine learning, forecasting; and so on. In the final few chapters, you'll learn how to create reports from your analytical application with the full range of static and dynamic reporting tools that are available in RStudio so that you can effectively communicate results and even transform them into interactive web applications. Style and approach RStudio is an open source Integrated Development Environment (IDE) for the R platform. The R programming language is used for statistical computing and graphics, which RStudio facilitates and enhances through its integrated environment. This Cookbook will help you learn to write better R code using the advanced features of the R programming language using RStudio. Readers will learn advanced R techniques to compute the language and control object evaluation within R functions. Some of the contents are: Accessing an API with R Substituting missing values by interpolation Performing data filtering activities R Statistical implementation for Geospatial data Developing shiny add-ins to expand RStudio functionalities Using GitHub with RStudio Modelling a recommendation engine with R Using R Markdown for static and dynamic reporting Curating a blog through RStudio Advanced statistical modelling with R and RStudio

Using R and RStudio for Data Management Statistical Analysis and Graphics Second Edition

Author: Nicholas J. Horton
Publisher: CRC Press
ISBN: 9781482237375
Release Date: 2015-03-10
Genre: Mathematics

Improve Your Analytical Skills Incorporating the latest R packages as well as new case studies and applications, Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition covers the aspects of R most often used by statistical analysts. New users of R will find the book’s simple approach easy to understand while more sophisticated users will appreciate the invaluable source of task-oriented information. New to the Second Edition The use of RStudio, which increases the productivity of R users and helps users avoid error-prone cut-and-paste workflows New chapter of case studies illustrating examples of useful data management tasks, reading complex files, making and annotating maps, "scraping" data from the web, mining text files, and generating dynamic graphics New chapter on special topics that describes key features, such as processing by group, and explores important areas of statistics, including Bayesian methods, propensity scores, and bootstrapping New chapter on simulation that includes examples of data generated from complex models and distributions A detailed discussion of the philosophy and use of the knitr and markdown packages for R New packages that extend the functionality of R and facilitate sophisticated analyses Reorganized and enhanced chapters on data input and output, data management, statistical and mathematical functions, programming, high-level graphics plots, and the customization of plots Easily Find Your Desired Task Conveniently organized by short, clear descriptive entries, this edition continues to show users how to easily perform an analytical task in R. Users can quickly find and implement the material they need through the extensive indexing, cross-referencing, and worked examples in the text. Datasets and code are available for download on a supplementary website.

Mastering Machine Learning with R

Author: Cory Lesmeister
Publisher: Packt Publishing Ltd
ISBN: 9781783984534
Release Date: 2015-10-28
Genre: Computers

Master machine learning techniques with R to deliver insights for complex projects About This Book Get to grips with the application of Machine Learning methods using an extensive set of R packages Understand the benefits and potential pitfalls of using machine learning methods Implement the numerous powerful features offered by R with this comprehensive guide to building an independent R-based ML system Who This Book Is For If you want to learn how to use R's machine learning capabilities to solve complex business problems, then this book is for you. Some experience with R and a working knowledge of basic statistical or machine learning will prove helpful. What You Will Learn Gain deep insights to learn the applications of machine learning tools to the industry Manipulate data in R efficiently to prepare it for analysis Master the skill of recognizing techniques for effective visualization of data Understand why and how to create test and training data sets for analysis Familiarize yourself with fundamental learning methods such as linear and logistic regression Comprehend advanced learning methods such as support vector machines Realize why and how to apply unsupervised learning methods In Detail Machine learning is a field of Artificial Intelligence to build systems that learn from data. Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning to your data. The book starts with introduction to Cross-Industry Standard Process for Data Mining. It takes you through Multivariate Regression in detail. Moving on, you will also address Classification and Regression trees. You will learn a couple of “Unsupervised techniques”. Finally, the book will walk you through text analysis and time series. The book will deliver practical and real-world solutions to problems and variety of tasks such as complex recommendation systems. By the end of this book, you will gain expertise in performing R machine learning and will be able to build complex ML projects using R and its packages. Style and approach This is a book explains complicated concepts with easy to follow theory and real-world, practical applications. It demonstrates the power of R and machine learning extensively while highlighting the constraints.

Unsupervised Learning with R

Author: Erik Rodriguez Pacheco
Publisher: Packt Publishing Ltd
ISBN: 9781785885815
Release Date: 2015-12-03
Genre: Computers

Work with over 40 packages to draw inferences from complex datasets and find hidden patterns in raw unstructured data About This Book Unlock and discover how to tackle clusters of raw data through practical examples in R Explore your data and create your own models from scratch Analyze the main aspects of unsupervised learning with this comprehensive, practical step-by-step guide Who This Book Is For This book is intended for professionals who are interested in data analysis using unsupervised learning techniques, as well as data analysts, statisticians, and data scientists seeking to learn to use R to apply data mining techniques. Knowledge of R, machine learning, and mathematics would help, but are not a strict requirement. What You Will Learn Load, manipulate, and explore your data in R using techniques for exploratory data analysis such as summarization, manipulation, correlation, and data visualization Transform your data by using approaches such as scaling, re-centering, scale [0-1], median/MAD, natural log, and imputation data Build and interpret clustering models using K-Means algorithms in R Build and interpret clustering models by Hierarchical Clustering Algorithm's in R Understand and apply dimensionality reduction techniques Create and use learning association rules models, such as recommendation algorithms Use and learn about the techniques of feature selection Install and use end-user tools as an alternative to programming directly in the R console In Detail The R Project for Statistical Computing provides an excellent platform to tackle data processing, data manipulation, modeling, and presentation. The capabilities of this language, its freedom of use, and a very active community of users makes R one of the best tools to learn and implement unsupervised learning. If you are new to R or want to learn about unsupervised learning, this book is for you. Packed with critical information, this book will guide you through a conceptual explanation and practical examples programmed directly into the R console. Starting from the beginning, this book introduces you to unsupervised learning and provides a high-level introduction to the topic. We quickly move on to discuss the application of key concepts and techniques for exploratory data analysis. The book then teaches you to identify groups with the help of clustering methods or building association rules. Finally, it provides alternatives for the treatment of high-dimensional datasets, as well as using dimensionality reduction techniques and feature selection techniques. By the end of this book, you will be able to implement unsupervised learning and various approaches associated with it in real-world projects. Style and approach This book takes a step-by-step approach to unsupervised learning concepts and tools, explained in a conversational and easy-to-follow style. Each topic is explained sequentially, explaining the theory and then putting it into practice by using specialized R packages for each topic.

The R Book

Author: Michael J. Crawley
Publisher: John Wiley & Sons
ISBN: 9781118448960
Release Date: 2012-11-07
Genre: Mathematics

Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition: Features full colour text and extensive graphics throughout. Introduces a clear structure with numbered section headings to help readers locate information more efficiently. Looks at the evolution of R over the past five years. Features a new chapter on Bayesian Analysis and Meta-Analysis. Presents a fully revised and updated bibliography and reference section. Is supported by an accompanying website allowing examples from the text to be run by the user. Praise for the first edition: ‘…if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.’ (The American Statistician, August 2008) ‘The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book…’ (Professional Pensions, July 2007)

Git in Practice

Author: Mike McQuaid
Publisher: Manning Publications
ISBN: 1617291978
Release Date: 2014-10
Genre: Computers

Summary Git in Practice is a collection of 66 tested techniques that will optimize the way you and your team manage your development projects. The book begins with a brief reminder of the core version control concepts you need when using Git and moves on to the high-value features you may not have explored yet. Then, you'll dig into cookbook-style techniques like history visualization, advanced branching and rewriting history each presented in a problem-solution-discussion format. Finally you'll work out how to use Git to its full potential through configuration, team workflows, submodules and using GitHub pull requests effectively. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Git is a source control system, but it's a lot more than just that. For teams working in today's agile, continuous delivery environments, Git is a strategic advantage. Built with a decentralized structure that's perfect for a distributed team, Git manages branching, committing, complex merges, and task switching with minimal ceremony so you can concentrate on your code. About the Book Git in Practice is a collection of battle-tested techniques designed to optimize the way you and your team manage development projects. After a brief overview of Git's core features, this practical guide moves quickly to high-value topics like history visualization, advanced branching and rewriting, optimized configuration, team workflows, submodules, and how to use GitHub pull requests. Written in an easy-to-follow Problem/Solution/Discussion format with numerous diagrams and examples, it skips the theory and gets right to the nitty-gritty tasks that will transform the way you work. Written for developers familiar with version control and ready for the good stuff in Git. What's Inside Team interaction strategies and techniques Replacing bad habits with good practices Juggling complex configurations Rewriting history and disaster recovery About the Author Mike McQuaid is a software engineer at GitHub. He's contributed to Qt and the Linux kernel, and he maintains the Git-based Homebrew project. Table of Contents PART 1 INTRODUCTION TO GIT Local Git Remote Git PART 2 GIT ESSENTIALS Filesystem interactions History visualization Advanced branching Rewriting history and disaster recovery PART 3 ADVANCED GIT Personalizing Git Vendoring dependencies as submodules Working with Subversion GitHub pull requests Hosting a repository PART 4 GIT BEST PRACTICES Creating a clean history Merging vs. rebasing Recommended team workflows

Git for Teams

Author: Emma Jane Hogbin Westby
Publisher: "O'Reilly Media, Inc."
ISBN: 9781491911228
Release Date: 2015-08-24
Genre: Computers

You can do more with Git than just build software. This practical guide delivers a unique people-first approach to version control that also explains how using Git as a focal point can help your team work better together. You’ll learn how to plan and pursue a Git workflow that not only ensures that you accomplish project goals, but also fits the immediate needs and future growth of your team. The first part of the book on structuring workflow is useful for project managers, technical team leads, and CTOs. The second part provides hands-on exercises to help developers gain a better understanding of Git commands. Explore the dynamics of team building Walk through the process of creating and deploying software with Git Structure workflow to influence the way your team collaborates Learn a useful process for conducting code reviews Set up a shared repository and identify specific team members as contributors, consumers, or maintainers Know the why behind the Git commands your teammates use Use branching strategies to separate different approaches to your project Examine popular collaboration platforms: GitHub, Bitbucket, and GitLab

R For Dummies

Author: Andrie de Vries
Publisher: John Wiley & Sons
ISBN: 9781119963134
Release Date: 2012-06-06
Genre: Computers

Master the programming language of choice among statisticians and data analysts worldwide Coming to grips with R can be tough, even for seasoned statisticians and data analysts. Enter R For Dummies, the quick, easy way to master all the R you'll ever need. Requiring no prior programming experience and packed with practical examples, easy, step-by-step exercises, and sample code, this extremely accessible guide is the ideal introduction to R for complete beginners. It also covers many concepts that intermediate-level programmers will find extremely useful. Master your R ABCs ? get up to speed in no time with the basics, from installing and configuring R to writing simple scripts and performing simultaneous calculations on many variables Put data in its place ? get to know your way around lists, data frames, and other R data structures while learning to interact with other programs, such as Microsoft Excel Make data dance to your tune ? learn how to reshape and manipulate data, merge data sets, split and combine data, perform calculations on vectors and arrays, and much more Visualize it ? learn to use R's powerful data visualization features to create beautiful and informative graphical presentations of your data Get statistical ? find out how to do simple statistical analysis, summarize your variables, and conduct classic statistical tests, such as t-tests Expand and customize R ? get the lowdown on how to find, install, and make the most of add-on packages created by the global R community for a wide variety of purposes Open the book and find: Help downloading, installing, and configuring R Tips for getting data in and out of R Ways to use data frames and lists to organize data How to manipulate and process data Advice on fitting regression models and ANOVA Helpful hints for working with graphics How to code in R What R mailing lists and forums can do for you

Bioinformatics Data Skills

Author: Vince Buffalo
Publisher: "O'Reilly Media, Inc."
ISBN: 9781449367510
Release Date: 2015-07-01
Genre: Computers

Learn the data skills necessary for turning large sequencing datasets into reproducible and robust biological findings. With this practical guide, you’ll learn how to use freely available open source tools to extract meaning from large complex biological data sets. At no other point in human history has our ability to understand life’s complexities been so dependent on our skills to work with and analyze data. This intermediate-level book teaches the general computational and data skills you need to analyze biological data. If you have experience with a scripting language like Python, you’re ready to get started. Go from handling small problems with messy scripts to tackling large problems with clever methods and tools Process bioinformatics data with powerful Unix pipelines and data tools Learn how to use exploratory data analysis techniques in the R language Use efficient methods to work with genomic range data and range operations Work with common genomics data file formats like FASTA, FASTQ, SAM, and BAM Manage your bioinformatics project with the Git version control system Tackle tedious data processing tasks with with Bash scripts and Makefiles

C for Financial Markets

Author: Daniel J. Duffy
Publisher: John Wiley & Sons
ISBN: 9781118502839
Release Date: 2013-01-14
Genre: Business & Economics

A practice-oriented guide to using C# to design and program pricing and trading models In this step-by-step guide to software development for financial analysts, traders, developers and quants, the authors show both novice and experienced practitioners how to develop robust and accurate pricing models and employ them in real environments. Traders will learn how to design and implement applications for curve and surface modeling, fixed income products, hedging strategies, plain and exotic option modeling, interest rate options, structured bonds, unfunded structured products, and more. A unique mix of modern software technology and quantitative finance, this book is both timely and practical. The approach is thorough and comprehensive and the authors use a combination of C# language features, design patterns, mathematics and finance to produce efficient and maintainable software. Designed for quant developers, traders and MSc/MFE students, each chapter has numerous exercises and the book is accompanied by a dedicated companion website, http://www.datasimfinancial.com/forum/viewforum.php?f=196&sid=f30022095850dee48c7db5ff62192b34, providing all source code, alongside audio, support and discussion forums for readers to comment on the code and obtain new versions of the software.

Web Application Development with R Using Shiny Second Edition

Author: Chris Beeley
Publisher: Packt Publishing
ISBN: 1782174346
Release Date: 2016-01-28
Genre: Computers

Integrate the power of R with the simplicity of Shiny to deliver cutting-edge analytics over the WebAbout This Book- Use Shiny's built-in functions to produce engaging user interfaces, and integrate them into your own web pages- Implement powerful user-contributed packages to access graphics from the web, make your own dashboards, use interactive maps, and more- Extend Shiny using JavaScript and jQuery with minimal coding using this handy, step-by-step guideWho This Book Is ForThis book is for anybody who wants to produce interactive data summaries over the web, whether you want to share them with a few colleagues or the whole world. No previous experience with R, Shiny, HTML, or CSS is required to begin using this book, although you should possess some previous experience with programming in a different language.What You Will Learn- Build interactive applications using Shiny's built-in widgets- Use the built-in layout functions in Shiny to produce user-friendly applications- Integrate Shiny applications with web pages and customize them using HTML and CSS- Harness the power of JavaScript and jQuery to customize your applications- Engage your users and build better analytics using interactive plots- Debug your applications using Shiny's built-in functions- Deliver simple and powerful analytics across your organization using Shiny dashboards- Share your applications with colleagues or over the Internet using cloud services or your own serverIn DetailR is a highly flexible and powerful tool for analyzing and visualizing data. Most of the applications built using various libraries with R are desktop-based. But what if you want to go on the web? Here comes Shiny to your rescue!Shiny allows you to create interactive web applications using the excellent analytical and graphical capabilities of R. This book will guide you through basic data management and analysis with R through your first Shiny application, and then show you how to integrate Shiny applications with your own web pages. Finally, you will learn how to finely control the inputs and outputs of your application, along with using other packages to build state-of-the-art applications, including dashboards.Style and approachLearn by doing! Each chapter includes code and examples to use and adapt for your own applications. As the chapters progress, the code and examples are built upon until you have all the materials required to build a large, complex, real-world analytics application.

Practical Data Science Cookbook

Author: Tony Ojeda
Publisher: Packt Publishing Ltd
ISBN: 9781783980253
Release Date: 2014-09-25
Genre: Computers

If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of data science projects, the steps in the data science pipeline, and the programming examples presented in this book. Since the book is formatted to walk you through the projects with examples and explanations along the way, no prior programming experience is required.