No Clocks

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.

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

Unlocking code assistance with local LLMs & GitHub Copilot! Discover R optimization & streamline your workflow.

gander is an in-editor AI tool that describes R objects to improve coding efficiency.

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

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

Best practices with R around select topics.

A data science blog

Do you need to add LLM capabilities to your R scripts and applications? Here are three tools you'll want to know.

Natural language processing has come a long way since its foundations were laid in the 1940s and 50s (for an introduction see, e.g., Jurafsky and Martin (2008, 2009, 2022 draft third edition): Speech and Language Processing, Pearson Prentice Hall). This CRAN task view collects relevant R packages that support computational linguists in conducting analysis of speech and language on a variety of levels - setting focus on words, syntax, semantics, and pragmatics.

If you are looking to use Natural Language Processing in R applications, these are some of the best NLP packages you must know.

The R task Force - R experts for all your needs

Tools for theming Shiny and R Markdown via Bootstrap 3, 4, or 5. - rstudio/bslib

Whether you're building data visualisations or generative art, at some point you will likely need to consider which colours to use in R. This blog post describes different ways to define colours, how to make good choices about colour palettes, and ways to generate your own colour schemes.

A collection of tables produced with R. Reproducible code and explanation provided using up-to-date libraries.

S7: a new OO system for R.

Fast JSON package for R.

The R task Force - R experts for all your needs

In this page you can find various blogs and articles that are related to this topic: Working With Data In Shiny Apps

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

Support chat with Large Language Models. Contribute to choonghyunryu/chat2doc development by creating an account on GitHub.

testthat-style LLM evaluation for R. Contribute to simonpcouch/evalthat development by creating an account on GitHub.