bookdown: Easy Book Publishing 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, visualizing, and exploring data.

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.

This is a minimal example of using the bookdown package to write a book. The output format for this example is bookdown::gitbook.

本书要写什么其实我也不太清楚。迷迷瞪瞪中，感觉写一些奇门遁甲之术会比较有趣吧，算是程序猿/媛的自娱自乐了，如果在自娱自乐之外，读者能学到一些有用的技能，那就更好了。

This is a minimal example of using the bookdown package to write a book. The output format for this example is bookdown::gitbook.

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.

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.