No Clocks

This book covers R software development for building data science tools. This book provides rigorous training in the R language and covers modern software development practices for building tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers. (Printed copies coming soon!)

This book showcases a basic example of how to create an R package based on S3 classes.

A simple R package development best-practices-example · GitHub

This is a script that you can use to encrypt data using Rscript command line tool. It would be impossibly difficult to decrypt the data if you had not seen the code in this file. - GitHub - hacidur...

R code that allows you to encrypt and decrypt large sensitive files - GitHub - phildias/cryptR: R code that allows you to encrypt and decrypt large sensitive files

🔒 Encrypt/decrypt files of R packages. Contribute to gipso/encryptrpak development by creating an account on GitHub.

Small, opinionated package to manage encrypted credentials in R - encryptedCredentials/crypt.R at master · dirkschumacher/encryptedCredentials

13 votes and 12 comments so far on Reddit

How to write a well-structured and understandable R program

Illustrating with a Principal Component Analysis

Comprehensive set of tools for scaffolding R interfaces to modules, classes, functions, and documentations written in other programming languages, such as Python.

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…