A blog about RStats by David Schoch

Explore building clinical data apps with {teal}, featuring insightful demos, code reproducibility, and customizable reporting tools.

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):

tl;dr I implemented a todo backend in R with plumber. Use Rocker with PPAs for fast container builds. Dirk Eddelbuettel demonstrates how at this link. todo backend Todo-Backend is “a shared example to showcase backend tech stacks”, inspired by the front-end todomvc. It’s a good way to get a sense for how you might implement similar functionality in other languages. I’d read some other posts on setting up plumber apis so I decided to give it a shot.

A conversational AI system that listens, learns, and challenges

A conversational AI system that listens, learns, and challenges

A blog about RStats by David Schoch

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

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

library(shiny) is better with library(bslib), so let’s make it official and update our shinyapp snippet together.

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.

Weekly bite-sized tips on DataViz, Shiny and Stats/Machine Learning.

150+ Free R programming books

Extended version of the rOpenSci packaging guide. This book is a guide for authors, maintainers, reviewers and editors of rOpenSci. The first section of the book contains our guidelines for creating and testing R packages. The second section is dedicated to rOpenSci’s software peer review process: what it is, our policies, and specific guides for authors, editors and reviewers throughout the process. The third and last section features our best practice for nurturing your package once it has been onboarded: how to collaborate with other developers, how to document releases, how to promote y...