No Clocks

hello, How can I clean the R environment both using RStudio and the R console? Thanks,

config package for R. Contribute to rstudio/config development by creating an account on GitHub.

Today we are excited to announce the pins package is available on CRAN! pins allows you to pin, discover and share remote resources, locally or in remote storage. If you find yourself using download.file() or asking others to download files before running your R code, use pin() to achieve fast, simple and reliable reproducible research over remote resources. Pins You can use the pins package to: Pin remote resources locally to work offline and cache results with ease, pin() stores resources in boards which you can then retrieve with pin_get().

This is a guide on how to conduct Meta-Analyses in R.

Data science is characterized among other things using open source tools. An advantage when working with open source languages such as R or Python is the large package world. This provides tools for numerous use cases and problems through the development within huge communities. The packages are organized in digital ...

HTTPS to Secure Your RStudio + Shiny App Work Environment Click any link in list below to jump to topic Creating a Friendly URL Route 53 to Host Domain and Create Subdomains AWS Certificate Manager for SSL keys AWS Elastic Load Balancers: HTTPS Redirection Installing Nginx & Creating Configuration Files I wanted to create this post as an addition to my previous post Running R on AWS EC2 and Logging into RStudio from Anywhere to show how to secure your AWS environment.

Fast and scalable R application deployment and orchestration - ixpantia/faucet

Recommended Packages Many useful R function come in packages, free libraries of code written by R's active user community. To install an R package, open an R session and type at the command line in...

This book will teach you how to use R to solve your statistical, data science and machine learning problems. Importing data, computing descriptive statistics, running regressions (or more complex machine learning models) and generating reports are some of the topics covered. No previous experience with R is needed.

The RStudio IDE reached version 1.0 this month. The IDE has come a long way since the initial release 5 and a half years ago. Many major features have been built: projects, package building tools, notebooks. During that same period, often hidden in the shadows, a growing list of smaller features has been changing lives. In celebration of version 1.0 this post hopes to spread fanfare for a few of these easy-to-miss tools.

Prototype implementation of an extension to S3 that provides explicit class definitions and a form of multiple dispatch. Represents the output of the Object-oriented Programming Working Group, sponsored by the R Consortium.

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