No Clocks

Lots of change in the latest vcr release.

Materials for rstudio::conf. Contribute to rstudio/rstudio-conf development by creating an account on GitHub.

I'm struggling with the different R executables. What exactly is the difference between R.exe (with or without CMD BATCH option), Rcmd.exe, Rscript.exe and Rterm.exe when running command line in a ...

Running R scripts from the command line can be a powerful way to: Automate your R scripts Integrate R into production Call R through other tools or systems There are basically two Linux command...

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.

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

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.

Blog of the MZES Social Science Data Lab

Makes it easy to display descriptive information on a data set. Getting an easy overview of a data set by displaying and visualizing sample information in different tables (e.g., time and scope conditions). The package also provides publishable LaTeX code to present the sample information.

Easily Extracting Information About Your Data. Contribute to cosimameyer/overviewR development by creating an account on GitHub.

The goal of 'pak' is to make package installation faster and more reliable. In particular, it performs all HTTP operations in parallel, so metadata resolution and package downloads are fast. Metadata and package files are cached on the local disk as well. 'pak' has a dependency solver, so it finds version conflicts before performing the installation. This version of 'pak' supports CRAN, 'Bioconductor' and 'GitHub' packages as well.

A walkthrough of using the projmgr package for GitHub-based project management via R

Using controlled dictionaries for low-touch documentation, validation, and usability of tabular data

Using controlled vocabularies to encode contracts between data producers and consumers

Using the tidyverse's expressive data wrangling vocabulary as a preprocessor for elegant SQL scripts

ymlthis

Help yourself to these free books, tutorials, packages, cheat sheets, and many more materials for R programming. There’s a separate overview for handy R programming tricks. If you have additi…

Weekly News in R Community