Download or read book Data Manipulation with R written by Jaynal Abedin and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is for all those who wish to learn about data manipulation from scratch and excel at aggregating data effectively. It is expected that you have basic knowledge of R and have previously done some basic administration work with R.

Download or read book Mastering RStudio Develop Communicate and Collaborate with R written by Julian Hillebrand and published by Packt Publishing Ltd. This book was released on 2015-12-04 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book R Data Analysis Cookbook written by Kuntal Ganguly and published by Packt Publishing Ltd. This book was released on 2017-09-20 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over 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

Download or read book Data Manipulation With R written by William Jayden and published by . This book was released on 2017-06-03 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book starts with the installation of R and how to go about using R and its libraries. We then discuss the mode of R objects and its classes and then highlight different R data types with their basic operations.The primary focus on group-wise data manipulation with the split-apply-combine strategy has been explained with specific examples. The book also contains coverage of some specific libraries such as lubridate, reshape2, plyr, dplyr, stringr, and sqldf. You will not only learn about group-wise data manipulation, but also learn how to efficiently handle date, string, and factor variables along with different layouts of datasets using the reshape2 package.By the end of this book, you will have learned about text manipulation using stringr, how to extract data from twitter using twitteR library, how to clean raw data, and how to structure your raw data for data mining.

Download or read book Ceph Cookbook written by Karan Singh and published by Packt Publishing Ltd. This book was released on 2016-02-29 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over 100 effective recipes to help you design, implement, and manage the software-defined and massively scalable Ceph storage system About This Book Implement a Ceph cluster successfully and gain deep insights into its best practices Harness the abilities of experienced storage administrators and architects, and run your own software-defined storage system This comprehensive, step-by-step guide will show you how to build and manage Ceph storage in production environment Who This Book Is For This book is aimed at storage and cloud system engineers, system administrators, and technical architects who are interested in building software-defined storage solutions to power their cloud and virtual infrastructure. If you have basic knowledge of GNU/Linux and storage systems, with no experience of software defined storage solutions and Ceph, but eager to learn this book is for you. What You Will Learn Understand, install, configure, and manage the Ceph storage system Get to grips with performance tuning and benchmarking, and gain practical tips to run Ceph in production Integrate Ceph with OpenStack Cinder, Glance, and nova components Deep dive into Ceph object storage, including s3, swift, and keystone integration Build a Dropbox-like file sync and share service and Ceph federated gateway setup Gain hands-on experience with Calamari and VSM for cluster monitoring Familiarize yourself with Ceph operations such as maintenance, monitoring, and troubleshooting Understand advanced topics including erasure coding, CRUSH map, cache pool, and system maintenance In Detail Ceph is a unified, distributed storage system designed for excellent performance, reliability, and scalability. This cutting-edge technology has been transforming the storage industry, and is evolving rapidly as a leader in software-defined storage space, extending full support to cloud platforms such as Openstack and Cloudstack, including virtualization platforms. It is the most popular storage backend for Openstack, public, and private clouds, so is the first choice for a storage solution. Ceph is backed by RedHat and is developed by a thriving open source community of individual developers as well as several companies across the globe. This book takes you from a basic knowledge of Ceph to an expert understanding of the most advanced features, walking you through building up a production-grade Ceph storage cluster and helping you develop all the skills you need to plan, deploy, and effectively manage your Ceph cluster. Beginning with the basics, you'll create a Ceph cluster, followed by block, object, and file storage provisioning. Next, you'll get a step-by-step tutorial on integrating it with OpenStack and building a Dropbox-like object storage solution. We'll also take a look at federated architecture and CephFS, and you'll dive into Calamari and VSM for monitoring the Ceph environment. You'll develop expert knowledge on troubleshooting and benchmarking your Ceph storage cluster. Finally, you'll get to grips with the best practices to operate Ceph in a production environment. Style and approach This step-by-step guide is filled with practical tutorials, making complex scenarios easy to understand.

Download or read book R Recipes for Analysis Visualization and Machine Learning written by Viswa Viswanathan and published by Packt Publishing Ltd. This book was released on 2016-11-24 with total page 958 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get 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.

Download or read book Data Manipulation with R written by Phil Spector and published by Springer Science & Business Media. This book was released on 2008-03-27 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: The R language provides a rich environment for working with data, especially data to be used for statistical modeling or graphics. Coupled with the large variety of easily available packages, it allows access to both well-established and experimental statistical techniques. However techniques that might make sense in other languages are often very ine?cient in R, but, due to R’s ?- ibility, it is often possible to implement these techniques in R. Generally, the problem with such techniques is that they do not scale properly; that is, as the problem size grows, the methods slow down at a rate that might be unexpected. The goal of this book is to present a wide variety of data - nipulation techniques implemented in R to take advantage of the way that R works,ratherthandirectlyresemblingmethodsusedinotherlanguages. Since this requires a basic notion of how R stores data, the ?rst chapter of the book is devoted to the fundamentals of data in R. The material in this chapter is a prerequisite for understanding the ideas introduced in later chapters. Since one of the ?rst tasks in any project involving data and R is getting the data into R in a way that it will be usable, Chapter 2 covers reading data from a variety of sources (text ?les, spreadsheets, ?les from other programs, etc. ), as well as saving R objects both in native form and in formats that other programs will be able to work with.

Download or read book Growth Curve Analysis and Visualization Using R written by Daniel Mirman and published by CRC Press. This book was released on 2016-04-19 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn How to Use Growth Curve Analysis with Your Time Course Data An increasingly prominent statistical tool in the behavioral sciences, multilevel regression offers a statistical framework for analyzing longitudinal or time course data. It also provides a way to quantify and analyze individual differences, such as developmental and neuropsychological, in the context of a model of the overall group effects. To harness the practical aspects of this useful tool, behavioral science researchers need a concise, accessible resource that explains how to implement these analysis methods. Growth Curve Analysis and Visualization Using R provides a practical, easy-to-understand guide to carrying out multilevel regression/growth curve analysis (GCA) of time course or longitudinal data in the behavioral sciences, particularly cognitive science, cognitive neuroscience, and psychology. With a minimum of statistical theory and technical jargon, the author focuses on the concrete issue of applying GCA to behavioral science data and individual differences. The book begins with discussing problems encountered when analyzing time course data, how to visualize time course data using the ggplot2 package, and how to format data for GCA and plotting. It then presents a conceptual overview of GCA and the core analysis syntax using the lme4 package and demonstrates how to plot model fits. The book describes how to deal with change over time that is not linear, how to structure random effects, how GCA and regression use categorical predictors, and how to conduct multiple simultaneous comparisons among different levels of a factor. It also compares the advantages and disadvantages of approaches to implementing logistic and quasi-logistic GCA and discusses how to use GCA to analyze individual differences as both fixed and random effects. The final chapter presents the code for all of the key examples along with samples demonstrating how to report GCA results. Throughout the book, R code illustrates how to implement the analyses and generate the graphs. Each chapter ends with exercises to test your understanding. The example datasets, code for solutions to the exercises, and supplemental code and examples are available on the author’s website.

Download or read book A Computational Approach to Statistical Learning written by Taylor Arnold and published by CRC Press. This book was released on 2019-01-23 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset. The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models. Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the National Endowment for the Humanities (NEH) and the American Council of Learned Societies (ACLS). His first book, Humanities Data in R, was published in 2015. Michael Kane is an assistant professor of biostatistics at Yale University. He is the recipient of grants from the National Institutes of Health (NIH), DARPA, and the Bill and Melinda Gates Foundation. His R package bigmemory won the Chamber's prize for statistical software in 2010. Bryan Lewis is an applied mathematician and author of many popular R packages, including irlba, doRedis, and threejs.

Download or read book Data Science with R for Psychologists and Healthcare Professionals written by Christian Ryan and published by CRC Press. This book was released on 2021-12-23 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: This introduction to R for students of psychology and health sciences aims to fast-track the reader through some of the most difficult aspects of learning to do data analysis and statistics. It demonstrates the benefits for reproducibility and reliability of using a programming language over commercial software packages such as SPSS. The early chapters build at a gentle pace, to give the reader confidence in moving from a point-and-click software environment, to the more robust and reliable world of statistical coding. This is a thoroughly modern and up-to-date approach using RStudio and the tidyverse. A range of R packages relevant to psychological research are discussed in detail. A great deal of research in the health sciences concerns questionnaire data, which may require recoding, aggregation and transformation before quantitative techniques and statistical analysis can be applied. R offers many useful and transparent functions to process data and check psychometric properties. These are illustrated in detail, along with a wide range of tools R affords for data visualisation. Many introductory statistics books for the health sciences rely on toy examples - in contrast, this book benefits from utilising open datasets from published psychological studies, to both motivate and demonstrate the transition from data manipulation and analysis to published report. R Markdown is becoming the preferred method for communicating in the open science community. This book also covers the detail of how to integrate the use of R Markdown documents into the research workflow and how to use these in preparing manuscripts for publication, adhering to the latest APA style guidelines.

Download or read book The relation between potassium nutrition and water use efficiency of crop plants written by Bálint Jákli and published by Cuvillier Verlag. This book was released on 2016-12-12 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: The world’s population presently experiences a period of unprecedented growth, increasing the need to further intensify the agricultural production (Garnett et al.2013). At the same time, recent climate trends and model predictions suggest shifting temperature, precipitation and circulation patterns on global and regional scales (Shepherd 2014). In this context, the risk of temporal or unseasonal drought is increasing in many regions worldwide (Pachauri et al. 2014). Agriculture accounts for about 75 % of human water use (Wallace 2000). However, the availability of water is the most limiting abiotic factor for plant production (Boyer 1996). Therefore, agricultural production and food security are highly susceptible to increased incidences of drought. Improving the water-use efficiency (WUE) of crop plants and cropping systems is therefore an important strategy to face the current challenges of global change (Pinstrup-Andersen et al. 1999).

Download or read book Data Manipulation with R and SQL written by Michael Grogan and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "R is the tool of choice for the business analyst faced with the daunting task of analyzing huge datasets. Unlike traditional analysis tools like Excel, R offers the flexibility to structure large datasets in a manner suitable for robust analysis. It also provides the speed and capacity necessary to run computations in a fast and efficient manner. This video teaches you how to use R and SQL to manipulate densely populated datasets for business analytics. You'll learn how to connect R to a SQL database, import data, commit queries from R, and how to replicate Excel functions like VLOOKUP and SUMIF to conduct advanced manipulation methods. Participants should be familiar with SQL databases and have some prior experience manipulating databases using Excel type software."--Resource description page.

Download or read book A Primer for Computational Biology written by Shawn T. O'Neil and published by . This book was released on 2017-12-21 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Primer for Computational Biology aims to provide life scientists and students the skills necessary for research in a data-rich world. The text covers accessing and using remote servers via the command-line, writing programs and pipelines for data analysis, and provides useful vocabulary for interdisciplinary work. The book is broken into three parts: Introduction to Unix/Linux: The command-line is the "natural environment" of scientific computing, and this part covers a wide range of topics, including logging in, working with files and directories, installing programs and writing scripts, and the powerful "pipe" operator for file and data manipulation. Programming in Python: Python is both a premier language for learning and a common choice in scientific software development. This part covers the basic concepts in programming (data types, if-statements and loops, functions) via examples of DNA-sequence analysis. This part also covers more complex subjects in software development such as objects and classes, modules, and APIs. Programming in R: The R language specializes in statistical data analysis, and is also quite useful for visualizing large datasets. This third part covers the basics of R as a programming language (data types, if-statements, functions, loops and when to use them) as well as techniques for large-scale, multi-test analyses. Other topics include S3 classes and data visualization with ggplot2.

Download or read book Data Manipulation in R written by Stephanie Locke and published by Createspace Independent Publishing Platform. This book was released on 2017-12-14 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: Continue your data science journey today. Discover how to systematically process and analyse data - a vital skill for a data scientist. This series of books takes you through everything you need to know and starts off with the very basics. The second book gives you a thorough grounding in analysing data. From preparing it so you can go on to applying machine learning algorithms, to producing high-level analysis, this book gives you what you need. You'll be equipped to work with common data sources like spreadsheets and databases, process data, and pass it on to others. "Great stuff. I learned lots of new things, including some advanced wrangling that I had not seen covered elsewhere. This book packs a lot in - covering all the essential requirements for day to day working with R. This book will definitely help those transitioning from spreadsheets to become proficient with R." - John MacKintosh Whether you're looking to become more productive with data analysis, or you'd like to learn machine learning and statistics, this book gives you a rock-solid foundation in wrangling data that will enable you to grow and achieve your goals.

Download or read book A Data Scientist s Guide to Acquiring Cleaning and Managing Data in R written by Samuel E. Buttrey and published by John Wiley & Sons. This book was released on 2017-10-24 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: The only how-to guide offering a unified, systemic approach to acquiring, cleaning, and managing data in R Every experienced practitioner knows that preparing data for modeling is a painstaking, time-consuming process. Adding to the difficulty is that most modelers learn the steps involved in cleaning and managing data piecemeal, often on the fly, or they develop their own ad hoc methods. This book helps simplify their task by providing a unified, systematic approach to acquiring, modeling, manipulating, cleaning, and maintaining data in R. Starting with the very basics, data scientists Samuel E. Buttrey and Lyn R. Whitaker walk readers through the entire process. From what data looks like and what it should look like, they progress through all the steps involved in getting data ready for modeling. They describe best practices for acquiring data from numerous sources; explore key issues in data handling, including text/regular expressions, big data, parallel processing, merging, matching, and checking for duplicates; and outline highly efficient and reliable techniques for documenting data and recordkeeping, including audit trails, getting data back out of R, and more. The only single-source guide to R data and its preparation, it describes best practices for acquiring, manipulating, cleaning, and maintaining data Begins with the basics and walks readers through all the steps necessary to get data ready for the modeling process Provides expert guidance on how to document the processes described so that they are reproducible Written by seasoned professionals, it provides both introductory and advanced techniques Features case studies with supporting data and R code, hosted on a companion website A Data Scientist's Guide to Acquiring, Cleaning and Managing Data in R is a valuable working resource/bench manual for practitioners who collect and analyze data, lab scientists and research associates of all levels of experience, and graduate-level data mining students.

Download or read book Data Analysis and Graphics Using R written by John Maindonald and published by Cambridge University Press. This book was released on 2010-05-06 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is done and why. The companion website has code and datasets, allowing readers to reproduce all analyses, along with solutions to selected exercises and updates. Assuming basic statistical knowledge and some experience with data analysis (but not R), the book is ideal for research scientists, final-year undergraduate or graduate-level students of applied statistics, and practising statisticians. It is both for learning and for reference. This third edition expands upon topics such as Bayesian inference for regression, errors in variables, generalized linear mixed models, and random forests.

Download or read book Genetic Analysis of Complex Disease written by William K. Scott and published by John Wiley & Sons. This book was released on 2021-11-11 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic Analysis of Complex Diseases An up-to-date and complete treatment of the strategies, designs and analysis methods for studying complex genetic disease in human beings In the newly revised Third Edition of Genetic Analysis of Complex Diseases, a team of distinguished geneticists delivers a comprehensive introduction to the most relevant strategies, designs and methods of analysis for the study of complex genetic disease in humans. The book focuses on concepts and designs, thereby offering readers a broad understanding of common problems and solutions in the field based on successful applications in the design and execution of genetic studies. This edited volume contains contributions from some of the leading voices in the area and presents new chapters on high-throughput genomic sequencing, copy-number variant analysis and epigenetic studies. Providing clear and easily referenced overviews of the considerations involved in genetic analysis of complex human genetic disease, including sampling, design, data collection, linkage and association studies and social, legal and ethical issues. Genetic Analysis of Complex Diseases also provides: A thorough introduction to study design for the identification of genes in complex traits Comprehensive explorations of basic concepts in genetics, disease phenotype definition and the determination of the genetic components of disease Practical discussions of modern bioinformatics tools for analysis of genetic data Reflecting on responsible conduct of research in genetic studies, as well as linkage analysis and data management New expanded chapter on complex genetic interactions This latest edition of Genetic Analysis of Complex Diseases is a must-read resource for molecular biologists, human geneticists, genetic epidemiologists and pharmaceutical researchers. It is also invaluable for graduate students taking courses in statistical genetics or genetic epidemiology.