I worked on a refactor of an R package at work the other day. Here’s some notes about that after doing the work. This IS NOT a best practices post - it’s just a collection of thoughts. For context, the package is an API client. It made sense to break the work for any given exported function into the following components, as applicable depending on the endpoint being handled (some endpoints needed just a few lines of code, so those funtions were left unchanged):

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.

Purposefully simple helper functions, tools, and a framework for creating reusable applications fit for production in clinical systems.

As part of our work documenting R-Universe, we’re adding screenshots of the interface to the documentation website. Taking screenshots manually could quickly become very cumbersome, especially as we expect they’ll need updating in future: we might want to change the universes we feature, the interface might improve yet again and therefore look slightly different. Therefore, we decided to opt for a programmatic approach. In this post we shall present our learnings from using the R packages chromote and magick to produce screenshots.

Prints code that can be used to recreate R objects. In a sense it is similar to base::dput() or base::deparse() but constructive strives to use idiomatic constructors.

This note describes a useful replyr tool we call a "join controller" (and is part of our "R and Big Data" series, please see here for the introduction, and here for one our big …

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.

Info: This is a draft for discussion purposes. It's not a polished PR and currently includes minimal error handling and documentation. It may be big enough to warrant a separate package, bu...

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

Shiny development helpers.

The R graph gallery displays hundreds of charts made with R, always providing the reproducible code.

The ultimate tool for finding the perfect color palette for data visualization with R and paletteer. Explore over 2000 palettes, see them in action on various charts, simulate color blindness, and export ready-to-use R code snippets.

Create an interactive table of Shiny inputs by providing data and a table definition.

How to fix error & warning messages in R - List of most common errors & warnings - Tutorials & examples on how to fix in RStudio

httr2

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

a small data filter module for shiny.

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 modern APIs you will often have to wrangle with data in JSON format. This article presents some tools and recipes for working with JSON data with R in the tidyverse. We’ll use purrr::map functions to extract and transform our JSON data. And we’ll provide intuitive examples of the cross-overs and differences between purrr ... Read more

R - JSON Files - JSON file stores data as text in human-readable format. Json stands for JavaScript Object Notation. R can read JSON files using the rjson package.

this is exactly the data i have when i load data from mongolite no i need to remove the list with string concatenation

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.

You can read the original post in its original format on Rtask website by ThinkR here: Pimping your shiny app with a JavaScript library : an example using sweetalert2 You think that some of the components of {shiny} are not very functional or downright austere? Are you looking to implement some feature in your app but it is not available in the {shiny} toolbox? Take a look at JavaScript! JavaScript is a very popular programming language that is often used to add features to web pages. With HTML and This post is better presented on its original ThinkR website here: Pimping your shiny app with a JavaScript library : an example using sweetalert2