R

Shiny Server @CNR-IBBA. Contribute to cnr-ibba/shiny-server development by creating an account on GitHub.

This post has been cross-posted on the Epiverse-TRACE blog. In a previous post, we discussed a package dependency that goes slightly beyond the normal R package ecosystem dependency: R itself. Today, we step even further and discuss dependencies outside of R: system dependencies. This happens when packages rely on external software, such as how R packages integrating CUDA GPU computation in R require the CUDA library. In particular, we are going to talk about system dependencies in the context of automated testing: is there anything extra to do when setting continuous integration for your package with system dependencies?

Our founder Nicholas Yeo and Actuarial Analysts Debbie Min Jyeh Ooi and Nadia Suharto presented "Developing Actuarial Applications using R Shiny" at the event…

Creating custom classes and {dplyr} compatibility

The R task Force - R experts for all your needs

Here are some of the best IDEs for R programming that can help in complex data analysis and provide an easy to navigate UI. Also listed are some lightweight online R compilers to help you work on the go.

An introduction to using github actions with R.

A workflow for doing data science in the R language, using Agile principles.

This document is full of supplemental resources and content from the Shiny in Production Workshop delievered at rstudio::conf 2019.

A book created with bookdown.

Oui.

A book about engineering shiny application that will later be sent to production. This book cover project management, structuring your project, building a solid testing suite, and optimizing your codebase. We describe in this book a specific workflow: design, prototype, build, strengthen and deploy.

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

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

This is a workshop for RStudio::conf(2020) in San Francisco, California

A guide to R development.

Second edition of R Cookbook

This book will teach you how to use R to solve your statistical, data science and machine learning problems. Importing data, computing descriptive statistics, running regressions (or more complex machine learning models) and generating reports are some of the topics covered. No previous experience with R is needed.

This book covers R software development for building data science tools. This book provides rigorous training in the R language and covers modern software development practices for building tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers. (Printed copies coming soon!)

This guide aims to introduce the reader to the main elements of the R ecosystem.

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.