bookdown: Easy Book Publishing with R Markdown
Write HTML, PDF, ePub, and Kindle books with R Markdown
A guide to authoring books with R Markdown, including how to generate figures and tables, and insert cross-references, citations, HTML widgets, and Shiny apps in R Markdown. The book can be exported to HTML, PDF, and e-books (e.g. EPUB). The book style is customizable. You can easily write and preview the book in RStudio IDE or other editors, and host the book wherever you want (e.g. bookdown.org).
This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.
The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox.
Efficient R Programming is about increasing the amount of work you can do with R in a given amount of time. It’s about both computational and programmer efficiency.
A guide for analisis of germination variables and usage of GerminaQuant web App.
Scalable Machine Learning and Data Science with Microsoft R Server and Spark
These are (tentatively) rough notes showcasing some tips on conducting large scale data analysis with R, Spark, and Microsoft R Server. The focus is primarily on machine learning with Azure HDInsight platform, but review other in-memory, large-scale data analysis platforms, such as R Services with SQL Server 2016, and discuss how to utilize BI tools such as PowerBI and Shiny for dynamic reporting, and report generation.
Piemēri darbā ar programmu R, lai risinātu statistikas problēmas bioloģijā.
A Minimal Book Example
This is a minimal example of using the bookdown package to write a book. The output format for this example is bookdown::gitbook.
Block Relaxation Methods in Statistics
The book discusses block relaxation, alternating least squares, augmentation, and majorization algorithms to minimize loss functions, with applications in statistics, multivariate analysis, and multidimensional scaling.
APL in R
R versions of the array manipulation functions of APL are presented. We do not translate the system functions or other parts of the runtime. Also, the current version has does not have the nested arrays of APL2.
Backtesting Strategies with R
Backtesting strategies with R