An introduction to trackr

R
richfitz/thor: R client for the Lightning Memory-Mapped Database
:zap::computer::zap: R client for the Lightning Memory-Mapped Database - richfitz/thor
richfitz/storr: Object cacher for R
:package: Object cacher for R. Contribute to richfitz/storr development by creating an account on GitHub.
RevolutionAnalytics/checkpoint: Install R packages from snapshots on checkpoint-server
Install R packages from snapshots on checkpoint-server - RevolutionAnalytics/checkpoint
gmbecker/switchr: An R package for managing and seamlessly switching between sets of installed R packages.
An R package for managing and seamlessly switching between sets of installed R packages. - gmbecker/switchr
CredibilityLab/groundhog: Reproducible R Scripts Via Version-Specific CRAN-Package Storing and Loading
Reproducible R Scripts Via Version-Specific CRAN-Package Storing and Loading - CredibilityLab/groundhog
CRAN - Package rbundler
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...
andrie/miniCRAN: R package to create internally consistent, mini version of CRAN
R package to create internally consistent, mini version of CRAN - andrie/miniCRAN
franapoli/repo: The Data-Centered Data Flow manager for R
The Data-Centered Data Flow manager for R. Contribute to franapoli/repo development by creating an account on GitHub.
repo.pdf
sahilseth/flowr: Robust and efficient workflows using a simple language agnostic approach
Robust and efficient workflows using a simple language agnostic approach - sahilseth/flowr
CRAN Task View: Reproducible Research
R Data Import/Export
Stash and Load Objects • 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.
R Markdown: 3 sources of reproducibility issues and options how to tackle them - Jozef's Rblog
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.
How to interactively examine any R code - 4 ways to not just read the code, but delve into it step-by-step - Jozef's Rblog
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.
Using environment variables and parametrized builds for automating R applications with Jenkins - Jozef's Rblog
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.
Using parallelization, multiple git repositories and setting permissions when automating R applications with Jenkins - Jozef's Rblog
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.
Package `parallel'
Connect to R-hub • rhub
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.
CRAN Task View: High-Performance and Parallel Computing with R
Setting up R with Visual Studio Code quickly and easily with the languageserversetup package - Jozef's Rblog
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
Automating R package checks across platforms with GitHub Actions and Docker in a portable way - Jozef's Rblog
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.
Caching in R | Joshua Cook
A brief look at the systems for caching in R.
'mustashe' Explained | Joshua Cook
An explanation of the caching system employed in 'mustashe'.
'mustashe' | Joshua Cook
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.
R Markdown animated GitHub corner | Joshua Cook
How to add an animated GitHub corner to an R Markdown page.
Creating a Streamlit web app, building with Docker + GitHub Actions, and hosting on Heroku | Joshua Cook
A step-by-step tutorial on creating a web application with Streamlit, building a Docker image with GitHub Actions, and hosting on Heroku.
Integrating React.js and Shiny
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...
Hosting a Shiny App using Docker
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.