R

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

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.

Use piped workflows for efficient data cleaning and visualization.

Best practices with R around select topics.

A package written for R focused on baseball analysis. Currently in development. - BillPetti/baseballr

Learn how to implement strong authentication and SSO in Shiny apps with Descope. This guide integrates both OIDC and SAML with Posit Connect for seamless login.

In order to facilitate parsing of http requests and creating appropriate responses this package provides two classes to handle a lot of the housekeeping involved in working with http exchanges. The infrastructure builds upon the rook specification and is thus well suited to be combined with httpuv based web servers.

Send error reports to the Google Error Reporting service and view errors and assign error status in the Google Error Reporting user interface.

Seamless SSO for R applications

The rcrd class extends vctr. A rcrd is composed of 1 or more fields, which must be vectors of the same length. Is designed specifically for classes that can naturally be decomposed into multiple vectors of the same length, like POSIXlt, but where the organisation should be considered an implementation detail invisible to the user (unlike a data.frame).

Reproducibility is a huge challenge in science, especially as datasets grow larger and workflows become more complex. Enter targets — an R package that helps

Provides tools for implementing Retrieval-Augmented Generation (RAG) workflows with Large Language Models (LLMs). Includes functions for document processing, text chunking, embedding generation, storage management, and content retrieval. Supports various document types and embedding providers (Ollama, OpenAI), with DuckDB as the default storage backend. Integrates with the ellmer package to equip chat objects with retrieval capabilities. Designed to offer both sensible defaults and customization options with transparent access to intermediate outputs.

📨 Create responsive emails with R. Contribute to JohnCoene/mjml development by creating an account on GitHub.

Awesome notifications for shiny. Contribute to JohnCoene/awn development by creating an account on GitHub.

Minimal busy indicator for Shiny apps. Contribute to dreamRs/shinybusy development by creating an account on GitHub.

Hierarchical tree input for Shiny apps. Contribute to dreamRs/shinytreeview development by creating an account on GitHub.

A data science blog