R

httr2 1.2.0 improves security for redacted headers, improves URL parsing and building, enhances debugging, and includes a bunch of other quality of life improvements.

In this chapter we aim at adding HTTP testing infrastructure to exemplighratia2 using httptest2. For this, we start from the initial state of exemplighratia2 again. Back to square one!...

This is a compilation of lecture notes that accompany my Intro to GIS and Spatial Analysis course.

Welcome | Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data.

Themeable HTML components use Sass to generate CSS rules from Bootstrap Sass variables, functions, and/or mixins (i.e., stuff inside of theme). bs_dependencies() makes it a bit easier to create themeable components by compiling sass::sass() (input) together with Bootstrap Sass inside of a theme, and packaging up the result into an htmltools::htmlDependency(). Themable components can also be dynamically themed inside of Shiny (i.e., they may be themed in 'real-time' via bs_themer(), and more generally, update their styles in response to shiny::session's setCurrentTheme() method). Dynamically themeable components provide a "recipe" (i.e., a function) to bs_dependency_defer(), describing how to generate new CSS stylesheet(s) from a new theme. This function is called when the HTML page is first rendered, and may be invoked again with a new theme whenever shiny::session's setCurrentTheme() is called.

Audit your Shiny apps at each commit. Multiple levels of testings are offered: startup and crash tests, performance tests (load test and global code profiling), reactivity audit as well as output tests. All results are gathered in an HTML report uploaded and available to everyone on any CI/CD plaform or RStudio Connect.

Master Shiny development with our comprehensive learning path covering fundamentals, UI design, server logic, advanced concepts, and production deployment. Transform from R user to professional web app developer through hands-on tutorials and real-world projects.

Master Shiny’s reactive programming model with comprehensive coverage of reactive expressions, observers, event handling, and advanced patterns. Learn to build efficient, dynamic applications with proper reactive design.

The unicode character U+1F4C1 (📁) is named "File Folder" and belongs to the Miscellaneous Symbols and Pictographs block. It is HTML encoded as 📁.

Learn how to manage state in Shiny modules using a React-inspired approach with event handlers for better control and flexibility.

Shiny is a package that makes it easy to create interactive web apps using R and Python.

Shiny is a package that makes it easy to create interactive web apps using R and Python.

Shiny is a package that makes it easy to create interactive web apps using R and Python.

Shiny is a package that makes it easy to create interactive web apps using R and Python.

Nowadays, more and more government organisations subscribe to the open data movement and some have done so in France, in the hopes that new services or insights would come from the analysis of this data.

Adding acceptance tests first makes refactoring safer.

Master enterprise-grade UI/UX design for Shiny applications using Bootstrap 5, bslib theming, and professional design systems. Learn to create accessible, responsive interfaces that meet corporate standards for biostatistics and clinical research applications.

Simon Couch demonstrates how the btw package provides context to LLMs through system prompts and tool calls.

The R package shinymgr provides a unifying framework that allows Shiny developers to create, manage, and deploy a master Shiny application comprised of one or more "apps", where an "app" is a tab-based workflow that guides end-users through a step-by-step analysis. Each tab in a given "app" consists of one or more Shiny modules. The shinymgr app builder allows developers to "stitch" Shiny modules together so that outputs from one module serve as inputs to the next, creating an analysis pipeline that is easy to implement and maintain. Apps developed using shinymgr can be incorporated into R packages or deployed on a server, where they are accessible to end-users. Users of shinymgr apps can save analyses as an RDS file that fully reproduces the analytic steps and can be ingested into an RMarkdown or Quarto report for rapid reporting. In short, developers use the shinymgr framework to write Shiny modules and seamlessly combine them into Shiny apps, and end-users of these apps can execute reproducible analyses that can be incorporated into reports for rapid dissemination. A comprehensive overview of the package is provided by 12 learnr tutorials.

A Unifying Parallelization Framework in R for Everyone