• This book focuses on how to use advanced programming in R to speed up everyday tasks in data analysis and data science.
• This book is unique in its coverage of how to setup R in the cloud and generate dynamic reports for analyses that are regularly repeated, such as monthly analysis of company sales or student grades, enrollment, and dropout in schools each term with projections for future enrollment rates.

• R is quite popular and is relevant in MIS and in terms of revenue on book database engines like Safari, Books24x7.

  Autorentext
  Joshua F. Wiley is a lecturer in the Monash Institute for Cognitive and Clinical Neurosciences and School of Psychological Sciences at Monash University and a senior partner at Elkhart Group Limited, a statistical consultancy. He earned his PhD from the University of California, Los Angeles, and his research focuses on using advanced quantitative methods to understand the complex interplays of psychological, social, and physiological processes in relation to psychological and physical health. In statistics and data science, Joshua focuses on biostatistics and is interested in reproducible research and graphical displays of data and statistical models. Through consulting at Elkhart Group Limited and former work at the UCLA Statistical Consulting Group, he has supported a wide array of clients ranging from graduate students, to experienced researchers, and biotechnology companies. He also develops or co-develops a number of R packages including varian, a package to conduct Bayesian scale-location structural equation models, and MplusAutomation, a popular package that links R to the commercial Mplus software.
  Matt Wiley is a tenured, associate professor of mathematics with awards in both mathematics education and honour student engagement. He earned degrees in pure mathematics, computer science, and business administration through the University of California and Texas A&M systems. He serves as director for Victoria College's quality enhancement plan and managing partner at Elkhart Group Limited, a statistical consultancy. With programming experience in R, C++, Ruby, Fortran, and JavaScript, he has always found ways to meld his passion for writing with his joy of logical problem solving and data science. From the boardroom to the classroom, Matt enjoys finding dynamic ways to partner with interdisciplinary and diverse teams to make complex ideas and projects understandable and solvable.
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  Zusammenfassung

Program for data analysis using R and learn practical skills to make your work more efficient. This book covers how to automate running code and the creation of reports to share your results, as well as writing functions and packages. Advanced R is not designed to teach advanced R programming nor to teach the theory behind statistical procedures. Rather, it is designed to be a practical guide moving beyond merely using R to programming in R to automate tasks.

This book will show you how to manipulate data in modern R structures and includes connecting R to data bases such as SQLite, PostgeSQL, and MongoDB. The book closes with a hands-on section to get R running in the cloud. Each chapter also includes a detailed bibliography with references to research articles and other resources that cover relevant conceptual and theoretical topics.

What You Will Learn

• Write and document R functions
• Makean R package and share it via GitHub or privately
• Add tests to R code to insure it works as intended
• Build packages automatically with GitHub
• Use R to talk directly to databases and do complex data management
• Run R in the Amazon cloud

• Generate presentation-ready tables and reports using R
  Who This Book Is For

  Working professionals, researchers, or students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to take their R coding and programming to the next level.

  Inhalt
  1.Programming Basics.- 2.Programming Utilities.- 3.Loops, flow control, and *apply functions.- 4.Writing Functions.- 5.Writing Classes and Methods.- 6.Writing a Package.- 7.Data Management using data.table.- 8.Data Munging With data.table.- 9.Other Tools for Data Management.- 10.Reading Big Data(bases).- 11.Getting a Cloud.- 12.Ubuntu for Windows Users.- 13.Every Cloud has a Shiny lining.- 14.Shiny Dashboard Sampler.- 15.Dynamic Reports and the Cloud.- References.