02-AREAS

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.

How to work with context in Cursor

PostGIS is an extension to the PostgreSQL object-relational database system which allows GIS (Geographic Information Systems) objects to be stored in the database. PostGIS includes support for GiST-based R-Tree spatial indexes, and functions for analysis and processing of GIS objects. This is the manual for version 3.5.4dev This work is licensed under a Creative Commons Attribution-Share Alike 3.0 License. Feel free to use this material any way you like, but we ask that you attribute credit to the PostGIS Project and wherever possible, a link back to https://postgis.net.

Learn about Cursor and how to get started

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.

Mount file shares in the cloud or on-premises on Windows, Linux, and macOS. Cache Azure file shares on Windows Servers with Azure File Sync for local access.

Learn to use temporary and permanent storage mounts in Azure Container Apps

Get instant answers about anything you've seen, heard or said. Download free: highlightai.com

Scalable communication between agents

Documentation for your agent.

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

Build LIVING software. Your guide to mastering prompts, ai coding, ai agents, and agentic workflows.

Deploy your first application to Azure Container Apps using the Azure CLI containerapp up command.

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

Build production-ready Copilots and Agents effortlessly.

Unable to find practical examples of idempotent data pipelines? Then, this post is for you. In this post, we go over a technique that you can use to make your data pipelines professional and data reprocessing a breeze.

In this extensive lecture, I, Steven Bucher, a product manager on the PowerShell team, discuss the integration of AI into the shell environment. Over the pas...

This is a book that covers the standard shiny app workflow.

Automatic normalisation of a data frame to third normal form, with the intention of easing the process of data cleaning. (Usage to design your actual database for you is not advised.) Originally inspired by the 'AutoNormalize' library for 'Python' by 'Alteryx' (<a href="https://github.com/alteryx/autonormalize" target="_top"https://github.com/alteryx/autonormalize/a>), with various changes and improvements. Automatic discovery of functional or approximate dependencies, normalisation based on those, and plotting of the resulting "database" via 'Graphviz', with options to exclude some attributes at discovery time, or remove discovered dependencies at normalisation time.