No Clocks

This is a post I’ve been meaning to write for some time now about {capsule}. {capsule} provides alternative workflows to {renv} for establishing and working with controlled package libraries in R. It also uses an renv.lock so it is compatible with {renv} - you can switch between doing things the {capsule} way and the {renv} or vice versa at any time. Introducing an R package I wrote nearly three years ago Carefully curating a controlled package environment the {renv} way can be kind of a chore.

Learn R, R tutorials, R resources, blog posts and the latest updates about the statistical programming R language.

Here are some of the best IDEs for R programming that can help in complex data analysis and provide an easy to navigate UI. Also listed are some lightweight online R compilers to help you work on the go.

In this guide, we'll learn to install R in Ubuntu. R is an open-source programming language used extensively for statistical and scientific computing. Due to

Tools and guides for putting R in production

Locking down R package dependencies and versions is a solved problem, thanks to the easy-to-use renv package. System dependencies — those Linux packages that need to be installed to make certain R packages work — are a bit harder to manage. Option 1: Hard-coding The easiest option is to hard-code the system dependencies. I did this recently when I was creating a Dockerfile for a very simple Plumber API: RUN apt-get update -qq && apt-get -y --no-install-recommends install \ make \ libsodium-dev \ libicu-dev \ libcurl4-openssl-dev \ libssl-dev My Dockerfile used only three R packages and so its system dependencies were not complicated.

Determine system requirements from R packages using the RStudio Package Manager. This is a weekend project, not a real package, so please think twice before using it for anything serious. - getsys...

Overview This walkthrough of the fundamentals of shell programming with Z shell (Zsh) and Bourne Again SHell (BASH) includes a comparison of similar components and features in R and RStudio. An alt…

Tiny things can separate life into “before” and “after”. Here is one. For almost a year I’ve been daily sending mental “thank you” to Ugo (@ugobas) who showed me how to re-organize panes in RStudio. Since then I’ve been spreading this tiny improvement so many times that I thought the tiny advise deserved a separate tiny post. Please note, below is an opinionated view of a comfortable UI improvement; feel free to ignore it if you don’t like. This advise is highly subjective, though, I really believe it is useful.

This is a guest post by Colin Gillespie from Jumping Rivers answering your questions from their most recent series of webinars, Practical Advice for Putting R in Production.

We deliver software development and ML solutions for Fortune 500 companies. We are an RStudio Full Service Certified Partner and global leaders in R Shiny.

Does your R package work best with some configuration? You probably want it to be easily found by your package. Does your R package download huge datasets that don’t change much on the provider side? Maybe you want to save the corresponding data somewhere persistent so that things will go faster during the next R session. In this blog post we shall explain how an R package developer can go about using and setting persistent configuration and data on the user’s machine.

Safely Deploy R to Production

Design a computing infrastructure and choose packages that can set you and your learners on the happy path.

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...

RStudio's webinars offer helpful perspective and advice to data scientists, data science leaders, DevOps engineers and IT Admins. Presenters come from companies around the globe, as well as the RStudio staff.

Below we list the tools and innovations from the RStudio Conference in Austin, Texas that we found the most useful and exciting. As with last year’s conference, the event is well organized an…

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.