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.
A guide to text analysis within the tidy data framework, using the tidytext package and other tidy tools
This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data you have. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing informative data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.
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.
Draft website for the European Climate Foundation
Interactive Data Visualization (2nd Day)
Script developed for a workshop at the CUSO doctoral school on the 4th and 5th November 2016.
Lab notes for Statistics for Social Sciences II: Multivariate Techniques
Lab notes for <em>Statistics for Social Sciences II: Multivariate Techniques</em>
Causal Inference (under development)
Script for a causal inference workshop at the EUI.
Functional programming and unit testing for data munging with R
This book is an introduction to functional programming and unit testing with the R programming language, for the purpose of data muning
Mastering Software Development in R
The book covers R software development for building data science tools. As the field of data science evolves, it has become clear that software development skills are essential for producing useful data science results and products. You will obtain rigorous training in the R language, including the skills for handling complex data, building R packages and developing custom data visualizations. You will learn modern software development practices to build tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers.
Population Health Data Science with R
Population health data science (PHDS) is the art and science of transforming data into actionable knowledge to improve health. R is an open source programming environment for statistical computing and graphics. PHDS is captured by four words (describe, predict, discover, and advise) and extends epidemiology into five analytic domains: descriptive analytics for distribution of risks and outcomes; predictive analytics for early targeted prevention and response; explanatory analytics for causal inference and prevention design, and exploratory analytics for hypothesis generation of new causal pathways; and prescriptive analytics for optimizing decisions and resource allocations).
Система мониторинга антибиотикорезистентности
Система мониторинга антибиотикорезистентности
Econ 215 Notes
Lecture notes for my introduction to statistics class at University of Nebraska-Lincoln.
Getting used to R, RStudio, and R Markdown
An introduction into using R, RStudio, and R Markdown for new users
Руководство по data.table
Руководство по пакету data.table: перевод виньеток, справочная иформация.
Principles of Econometrics with R
This is a beginner’s guide to applied econometrics using the free statistics software R.
The QCA with R book
A work in progress describing how to perform QCA using R, open for public wishes and suggestions
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.
Backtesting Strategies with R
Backtesting strategies with R
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.