No Clocks

: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

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.

googlesheets4

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