Engineering Production-Grade Shiny Apps — To be published in the R Series in 2020. - ThinkR-open/engineering-shiny-book

Writing R Extensions covers how to create your own packages, write R help files, and the foreign language (C, C++, Fortran, …) interfaces.

DASE Data Analysis in Software Engineering

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).

A guide to making visualizations that accurately reflect the data, tell a story, and look professional.

This is a guide on how to conduct Meta-Analyses in R.

The primary goal of this book is to get you up to speed with tidy evaluation and how to write functions around tidyverse pipelines and grammars.

A modest and very incomplete listing of resources for tackling data science problems in R.

This book is an introduction to functional programming and unit testing with the R programming language, for the purpose of data muning

An introductory book to R written by, and for, R pirates

Using Git and GitHub with R, Rstudio, and R Markdown

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.

An intuitive and practical approach to data analysis, data preparation and machine learning, suitable for all ages!

Business Analytics with R

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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.