R

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.

This book explains the most important things you need to know while you are writing production level R code.

It’s often beneficial to have a variable shared between all the functions in an R package. One obvious example would be the maintenance of a package-wide cache for all of your functions. I’ve encountered this situation multiple times and always forget at least one important step in the process, so I thought I’d document it [...]

Provides a toolkit for manipulating arrays in a consistent, powerful, and intuitive manner through the use of broadcasting and a new array class, the rray.

1      1Share This article describes the essentials of R coding style best practices. It’s based on the tidyverse style guide. Google’s current guide is also derived from the tidyverse style guide. […]

Convert code from one programming language to another in just a click of a button.

Explore a curated collection of thought-provoking chatbot prompts designed for R-programming enthusiasts. Ignite your coding creativity!

Write more robust R code

Web framework for the R programming language, inspired by express.js, allows building applications as well as APIs.

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.

Learn how to webscrape in R and use it to gather real data on the Internet.

Streamlines the process of updating changelogs (NEWS.md) and versioning R packages developed in git repositories.

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.

An R Interface to the IUROPA API.

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...

R package to implement development stages for package development - openpharma/staged.dependencies

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

Shiny development helpers.

1 Using R in the back-end .. but not with R-Shiny LIBData Portal

Cloud Run Websocket support now allows you to deploy a R Shiny Server - a dashboard hosting tool to host R Shiny dashhboards - as a serverless app to GCP Cloud Run

Showcase your R shiny application and grow your user base. Real time usage stats and reviews for all your apps. Contribute today.

Although the concealment of Javascript is by design and makes Shiny in the first instance easy to use, in the long run when you want to build serious and more visual appealing apps, you most likely need to utilize javascript to make most of the web framework

Tidy your JSON data in R with tidyjson.

Helpers to work with JSON in R.