No Clocks

When you’re designing, testing, or releasing a new Web API, you’re building a new system on top of an existing complex and sophisticated system. At a minimum, you’re building upon HTTP, which is built upon TCP/IP, which is built upon a series of tubes. You’re also building upon a web...

Learn how to design REST APIs to be easy to understand for anyone, future-proof, secure, and fast since they serve data to clients that may be confidential.

If you have a REST API accessible on the internet, you're going to need to secure it. Here's the best practices on how to do that.

Learn how to use OpenAPI and JSON Schema, two popular standards for describing and validating RESTful APIs and JSON data, in this article for back-end web developers.

Get a comprehensive GA4 audit with an actionable plan using GA4 Auditor - the ultimate Google Analytics 4 audit tool with a detaileda checklist for enhancing your website's tracking

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.

Book on using JavaScript with R for visualisations, web development, and computations.

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.

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