The R Programming Language
Reference documents in The R Programming Language.
Documents in The R Programming Language
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Master the Shiny web framework - and take your R skills to a whole new level. By letting you move beyond static reports, Shiny helps you create fully interactive web apps for data analyses. Users will be able to jump between datasets, explore different subsets or facets of the data, run models with parameter values of their choosing, customize visualizations, and much more.
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This book teaches the fundamental concepts and tools behind reporting modern data analyses in a reproducible manner. As data analyses become increasingly complex, the need for clear and reproducible report writing is greater than ever.
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This book helps people build production-grade Shiny applications, by providing advice, tools, and a methodology to work on web applications with R, with a series of approaches and advice about optimizations for production.
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This book written by the developers of R Markdown is an essential reference that will help users learn and make full use of the software. It will be a popular addition on the desk of many professionals who regularly produce technical documents in the R language.
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Discover what you can do with R! 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.
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Build efficient forecasting models using traditional time series models and machine learning algorithms. This book explores the basics of time series analysis with R and lays the foundations you need to build forecasting models.
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This book explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software.
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This book takes you through a comprehensive tour of graphical data analysis with R, explores the types of plots available in R and learn to create them with the help of functions and implementation examples.
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This book introduces the popular, powerful and free programming language and software package R, focuses on implementation of standard tools and methods used in econometrics.
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This book covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results.
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This book provides a compact introduction to the Bootstrap Method. It is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form.
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An essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks. R is free, open-source, and has thousands of contributed packages. It is used in such diverse fields as ecology, finance, genomics and music. If you are using spreadsheets to understand data, switch to R. You will have safer - and ultimately, more convenient - computations.
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Become a more productive programmer with Efficient R Programming. Drawing on years of experience teaching R courses, authors Colin Gillespie and Robin Lovelace give practical advice on a range of topics - from optimizing set-up of RStudio to leveraging C++ - that make this book a valuable asset for both experienced and novice programmers. It's suitable for academics, business users, and programmers from a wide range of backgrounds.
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The goal of this book is to teach you how to develop packages so that you can write your own, not just use other people's.
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Learn how to program by diving into the R language, and then use your newfound skills to solve practical data science problems. With this book, you’ll learn how to load data, assemble and disassemble data objects, navigate R's environment system, write your own functions, and use all of R’s programming tools.
