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 is official open data science training for the Ocean Health Index.

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

Learn from Virtual Workshops that take you through the entire Data-Science-for-Business process of solving problems with data science, using machine learning to create interactive applications, and distributing solutions within an organization.

The R task Force - R experts for all your needs

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.

crosswalkr

I am working on a Shiny application which allows the user to upload data, do some analysis and processing on each variable in the data, and finally use the processed variables to build a statistical model. As there may be hundreds of variables in the data, the user may want ...

A book for Geospatial Health Analysis with R.

Business Analytics with R

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

Turn your analyses into high quality documents, reports, presentations and dashboards with R Markdown. Use a productive notebook interface to weave together narrative text and code to produce elegantly formatted output. Use multiple languages including R, Python, and SQL. R Markdown supports a reproducible workflow for dozens of static and dynamic output formats including HTML, PDF, MS Word, Beamer, HTML5 slides, Tufte-style handouts, books, dashboards, shiny applications, scientific articles, websites, and more.