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

Best practices with R around select topics.

The basics of roxygen2 tags and how to use them for documenting functions.

How to include tables in your {ggiraph} tooltips.

An R package created to make the styling and customization of {reactable} tables easier.

Build robust and maintainable software with object-oriented design patterns in R. Design patterns abstract and present in neat, well-defined components and interfaces the experience of many software designers and architects over many years of solving similar problems. These are solutions that have withstood the test of time with respect to re-usability, flexibility, and maintainability. R6P provides abstract base classes with examples for a few known design patterns. The patterns were selected by their applicability to analytic projects in R. Using these patterns in R projects have proven effective in dealing with the complexity that data-driven applications possess.

A fast JSON parser, generator and validator which converts JSON, NDJSON (Newline Delimited JSON) and GeoJSON (Geographic JSON) data to/from R objects. The standard R data types are supported (e.g. logical, numeric, integer) with configurable handling of NULL and NA values. Data frames, atomic vectors and lists are all supported as data containers translated to/from JSON. GeoJSON data is read in as simple features objects. This implementation wraps the yyjson C library which is available from .

The tidyprompt package allows users to prompt and empower their large language models (LLMs) in a tidy way. It provides a framework to construct LLM prompts using tidyverse-inspired piping syntax, with a library of pre-built prompt wrappers and the option to build custom ones. Additionally, it supports structured LLM output extraction and validation, with automatic feedback and retries if necessary. Moreover, it enables specific LLM reasoning modes, autonomous R function calling for LLMs, and compatibility with any LLM provider.

Data Wrangling and Query Generating Operators for R. Distributed under choice of GPL-2 or GPL-3 license. - WinVector/rquery

httpOnly Cookies are crucial for security, protecting against cross-site scripting attacks in R Shiny apps. Read more about them here.

mlr3: Machine Learning in R - next generation. Contribute to mlr-org/mlr3 development by creating an account on GitHub.

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

Rectangling is the art and craft of taking a deeply nested list (often sourced from wild caught JSON or XML) and taming it into a tidy data set of rows and columns. This vignette introduces you to the main rectangling tools provided by tidyr: `unnest_longer()`, `unnest_wider()`, and `hoist()`.

Working with complex, hierarchically nested JSON data in R can be a bit of a pain. In this post, I illustrate how you can convert JSON data into tidy tibbles with particular emphasis on what I’ve found to be a reasonably good, general approach for converting nested JSON into nested tibbles. I use three illustrative examples of increasing complexity to help highlight some pitfalls and build up the logic underlying the approach before applying it in the context of some real-world rock climbing competition data.

My lab investigates how blood pressure can be treated more effectively. Much of that work involves the painstaking development of new concepts and research methods to move forward the state of the art. For example, our work on urinary extracellular vesicles’ mRNA as an ex vivo assay of the ligand-activated transcription factor activity of mineralocorticoid receptors is challenging, fun, and rewarding. With a lot of work from Andrea Berrido and Pradeep Gunasekaran in my lab, we have been moving the ball forward on several key projects on that front.

Getting your app into an R package

A book about deeply customizing Shiny app for production.

A guide to R development.

This work is intended to foster best practices in reproducible data documentation and manipulation, statistical analysis, graphics, and reporting.

R configurations for Docker. Contribute to rocker-org/rocker development by creating an account on GitHub.

Shiny modules to import data into an application or addin - dreamRs/datamods

I used shinyapps.io for my own shiny app. It’s a great service. You can deploy your app for free, test it and show it to other people. But there’s also a downside: The memory an app can use is limited. So I was looking for another way to deploy my app. So I took a look at Docker.

This post provides a guide to use ShinyProxy, an open-source tool with enterprise features, to deploy R Shiny apps.

This post provides a guide to secure ShinyProxy with Nginx, Certbot and AWS Cognito, and monitor usage statistics with InfluxDB, Telegraf and Grafana.

A fresh approach to package installation. Contribute to r-lib/pak development by creating an account on GitHub.

Environment Management with Docker