02-AREAS

Robust and efficient workflows using a simple language agnostic approach - sahilseth/flowr

A curated list of awesome pipeline toolkits inspired by Awesome Sysadmin - pditommaso/awesome-pipeline

Harrell Miscellaneous. Contribute to harrelfe/Hmisc development by creating an account on GitHub.

A simple system for saving and loading objects in R. Long running computations can be stashed after the first run and then reloaded the next time. Dependencies can be added to ensure that a computation is re-run if any of its dependencies or inputs have changed.

In this post, I will list a few sources of reproducibility issues I came across working with R Markdown and how I tried to solve them.

In this post, we provide tips on how to interactively debug R code step-by-step and investigate the values of objects in the middle of function execution. We will look at doing this for both exported and non-exported functions from different packages.

In this post we examine using environment variables needed for R applications with Jenkins builds and how to retrieve build parameters set via Jenkins from R.

In this post, we look at various tips that can be useful when automating R application testing and continuous integration, with regards to orchestrating parallelization, combining sources from multiple git repositories and ensuring proper access right to the Jenkins agent.

Run R CMD check on any of the R-hub () architectures, from the command line. The current architectures include Windows, macOS, Solaris and various Linux distributions.

In this post, we will look at the `languageserversetup` package that aims to make the setup of the R Language Server robust and easy to use by installing it into a separate, independent library and adjusting R startup in a way that initializes the language server when relevant

In this post, we will examine using GitHub actions and Docker to test our R packages across platforms in a portable way and show how this setup works for the CRAN package languageserversetup.

A brief look at the systems for caching in R.

An explanation of the caching system employed in 'mustashe'.

A simple system for saving and loading objects in R. Long running computations can be stashed after the first run and then reloaded the next time. Dependencies can be added to ensure that a computation is re-run if any of its dependencies or inputs have changed.

How to add an animated GitHub corner to an R Markdown page.

A step-by-step tutorial on creating a web application with Streamlit, building a Docker image with GitHub Actions, and hosting on Heroku.

Get into serverless computing with Google App Engine, Google Cloud Run, Google Cloud Functions

React.js is a thriving JavaScript library that eases encapsulating and sharing sophisticated component libraries. The React.js ecosystem is filled with components for doing everything from...

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.

Yes, Shiny apps can be fast and scalable. But only if you build them in the right way and use the proper tools.

Aaaand... because I couldn't help myself. A simple little example using shinyjs that seems to work for my not-very-thorough testing 😄 The two important bits: In the UI: shinyjs::useShinyjs(), In the Server (10 seconds for testing): shinyjs::runjs( "function reload_page() { window.location.reload(); setTimeout(reload_page, 10000); } setTimeout(reload_page, 10000); ") You definitely don't need the shinyjs package to make this work, but it does make things a little easier :s...