Simplified R caching for reproducible big data projects - databio/simpleCache

:recycle: R package: R.cache - Fast and Light-weight Caching (Memoization) of Objects and Results to Speed Up Computations - HenrikBengtsson/R.cache

A set of tools for datasets and plots archiving. Contribute to pbiecek/archivist development by creating an account on GitHub.

Parallel Processing in R using a Thread Pool. Contribute to rdpeng/threadpool development by creating an account on GitHub.

Simple Bootstrap Routines. Contribute to rdpeng/simpleboot development by creating an account on GitHub.

:zap::computer::zap: R client for the Lightning Memory-Mapped Database - richfitz/thor

:package: Object cacher for R. Contribute to richfitz/storr development by creating an account on GitHub.

Install R packages from snapshots on checkpoint-server - RevolutionAnalytics/checkpoint

An R package for managing and seamlessly switching between sets of installed R packages. - gmbecker/switchr

Reproducible R Scripts Via Version-Specific CRAN-Package Storing and Loading - CredibilityLab/groundhog

Rbundler manages a project-specific library for dependency package installation. By specifying dependencies in a DESCRIPTION file in a project's root directory, one may install and use dependencies in a repeatable fashion without requiring manual maintenance. rbundler creates a project-specific R library in 'PROJECT_ROOT/.Rbundle' (by default) and a project-specific 'R_LIBS_USER' value, set in 'PROJECT_ROOT/.Renviron'. It supports dependency management for R standard "Depends", "Imports", "Suggests", and "LinkingTo" package dependencies. rbundler also attempts to validate and install versio...

R package to create internally consistent, mini version of CRAN - andrie/miniCRAN

The Data-Centered Data Flow manager for R. Contribute to franapoli/repo development by creating an account on GitHub.

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

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.

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

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...

interface with DHIS2 to get listing of malaria-relevant data elements - jpainter/dataDictionary

Contribute to ekstroem/dataReporter development by creating an account on GitHub.