No Clocks

Overview Code snippets are text macros that are used for quickly inserting common snippets of code. For example, the fun snippet inserts an R function definition: If you select the snippet from th...

Master the art of data wrangling with the R programming language.

Customizing Package Build Options Overview There are three R package build commands used by the RStudio package development tools: R CMD check R CMD build R CMD INSTALL It's possible to c...

Interact with basecamp api in R. Contribute to yonicd/basecamper development by creating an account on GitHub.

If you are interested in learning Data Science with R, but not interested in spending money on books, you are definitely in a very good space. There are a number of fantastic R/Data Science books and resources available online for free from top most creators and scientists. Here are such 13 free 20 free (so […]

Both R and distributed programming rank highly on my list of “good things”, so imagine my delight when two new... The post The Evolution of Distributed Programming in R appeared first on Mango Solutions.

In this article we present our R package rsync, which serves as an interface between R and the popular Linux command line tool rsync. Rsync allows users of Unix systems to synchronize local and remote files between two locations.

As an update to this post, here's a list of the major events in R history since its creation: 1992: R development begins as a research project in Auckland, NZ by Robert Gentleman and Ross Ihaka 1993: First binary versions of R published at Statlib 1995: R first distributed as open-source software, under GPL2 license 1997: R core group formed 1997: CRAN founded (by Kurt Hornik and Fritz Leisch) 1999: The R website, r-project.org, founded 1999: First in-person meeting of R Core team, at inaugural Directions in Statistical Computing conference, Vienna 2000: R 1.0.0 released (February 29) 2000:...

Introduction Starting the viewer Sorting Filtering Searching Advanced topics Auto-refreshing Labels Restrictions and Performance Saving filters Introduction RStudio includes a data viewer th...

A "work bench" to manage projects from within R.

When you write code, you’re sure to run into problems from time to time. Here are some advanced tips and tricks for handling these errors, explained accessibly.

(Update 4: 2016-08-19) I made many significant updates / improvements to this deployment method which are documented in a more recent blog p...

‎Write R code directly on your iPhone, iPad and iPod Touch! This app is ideal for learning and testing code snippets! R is a programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used am…

R is a free software environment for statistical computing and graphics, available from The R Project for Statistical Computing. At Indiana University, R is ...

R Engineering, training, and consulting. ThinkR has 87 repositories available. Follow their code on GitHub.

I was my great pleasure yesterday to be a presenter in the "Why R Webinar" series, on the topic R at Microsoft. In the talk (which you can watch below) I recounted the history of Microsoft's acquisition of Revolution Analytics, and the various way the Microsoft supports R: its membership of the R Consortium, integration with many products (including demos of Azure ML Service with GitHub Actions, and Azure Functions), and how Microsoft has driven adoption of R in large organizations by making R "IT approved". Many thanks to the Why R Foundation for hosting the talk, and to everyone...

This post has been cross-posted on the Epiverse-TRACE blog. In a previous post, we discussed a package dependency that goes slightly beyond the normal R package ecosystem dependency: R itself. Today, we step even further and discuss dependencies outside of R: system dependencies. This happens when packages rely on external software, such as how R packages integrating CUDA GPU computation in R require the CUDA library. In particular, we are going to talk about system dependencies in the context of automated testing: is there anything extra to do when setting continuous integration for your package with system dependencies?

Creating custom classes and {dplyr} compatibility

Here is how to create a progress bar in R that is versatile when working with loops, for example, by using package purrr.

Simple mechanisms for defining and interpreting package options. Provides helpers for interpreting environment variables, global options, defining default values and more.

Ever heard the phrase “Read the source, Luke”? It’s a play on “Use the force, Luke” from Star Wars, with no definite source 😉 that we could find^[We erroneously first linked to a rather recent blog post but Robert Link corrected us in a comment that we reproduce here in case the post gets separated from its comments: ““Use the Source, Luke” goes way back before 2012, and probably even before blogs were a thing.

When developing a package it is essential to track the changes you make to your code. This is especially vital if they are breaking changes which have implications for any code written that depends on your package, i.e. a major version bump. Although you can always look back at your version control history in git, it is also convenient to have documentation which summarises the changes. This is where the NEWS file comes in.

rrapply

There are many community projects out there that provide binary R packages for various distributions. You may know Michael Rutter’s legendary c2d4u.team/c2d4u4.0+ PPA, but this situation has …

How to build a CLI for R, with npm.BackgroundThis blog post was triggered by a discussion on Twitter with MartinSkarzynski,who was looking for a way to build a CLI that launches an RScript.Here’s a way to do this using npm.Please note that this blog post won’t teach you how to build the commandline tool, it will quickly go over the way to create a system-widecommand line interface, using npm.If you want to learn more about building the utility, see thisfantastic series of blogpostsby Mark Sellor.Now, the idea is to have a CLI, i.e. a way to launch your utility with:$ mytoolAnd that, system-wide.What you’ll need An R script (script.R) with in it, for example:#!/usr/bin/env Rscript --vanillacli::cat_rule("yeay")cli::cat_bullet(Sys.time()) npm, which you can get fromthere.Let’s goCreate a new folder, and go inside it.mkdir cli && cd cliCreate the R Script there.echo '#!/usr/bin/env Rscript --vanilla' script.Recho 'cli::cat_rule("yeay")' script.Recho 'cli::cat_bullet(Sys.time())' script.RTry your script to see if it works:Rscript script.RNow launch an npm project:npm init -y(You can also run it without the -y to interactively add informationto the package.json.)Now the important part: add a "bin" value in the package.json, withthe name of the bin you want to create, and the path to the script,relatively to the package file. Here is an example of a package.json(I removed some elements).{ "name": "cli", "version": "1.0.0", "description": "CLI example with npm", "bin" : { "clir" : "./script.R" }, "author": "Colin Fay", "license": "MIT"}Install it globally (need sudo rights):sudo npm linkAnd, voilà! Open your terminal, and you’re done!clirOther way to go See the {littler}implementation