No Clocks

Best practice and tips for testing packages interfacing web resources.

It's turtles all the way down...

“Can you explain me what are environments in R?”The beginning of a series of blogpost about R concepts, explained to mydaughter. Side note: no, my daughter is not five, and she’s not named Alice. Andshe doesn’t speak english either ¯\(ツ)/¯.“Daddy, I’ve seen you reading this book with a weird chapter named‘Environments in R’. What does it mean?”“Alice darling, just sit down for a minute.Let’s say the world is a big computer, and everyone living in it is apiece of information we call ‘data’. Right now, we are at home, and homeis a small piece from the whole world. In R, these smaller places arecalled environments, and they are used just as our home: they cancontain data, and we can refer to these data with names which arespecific to the environment.For example, when we are at home, there are five pieces of data: you,me, mommy, and the two cats. At home, I can say ‘Darling’, and as we arein this small subset of the whole world where ‘darling’ refers to you,I’m pretty sure I will find you. But if I go in another home, that isto say in another environment with other data, another dad is callinghis daughter ‘Darling’. In this small other environment, different fromours, ‘Darling’ does not refer to the same piece of data. And the samegoes for “Mommy” and “Daddy”: in another home, they refer to otherpersons.If I go out in the wild world and try to use the word ‘Mommy’, thiswon’t specifically refer to your mum, as there are not one single‘Mommy’ in this world, and because this word refers to someonespecific to the home we are using it in. In the wild world, if I want torefer to your mum, I’ll need to specify from which home the ‘Mommy’ I’mlooking for is coming from.”“So why don’t we use the full name every time then? It seems simpler.”“Environments allow us to use the same word to refer to different data,depending on where we are using the word. It also allows to giveinformation about a piece of data: it’s quite normal to think that afather uses ‘Darling’ to refer to someone he loves very very much. Evenif, strictly speaking, nothing prevents the contrary from happening.Also, it wouldn’t be fair to only allow only one ‘Darling’ in the wholeworld. Thanks to environment, there won’t be any problem if every fatherin the world use this word, as it refers, in each home, to a specificlittle girl.”“Ok, thanks dadddy!”“You’re welcome, Darling”The R code behindAbout environments# Creating two houseshome

An introduction to using github actions with R.

A workflow for doing data science in the R language, using Agile principles.

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.