R

My lab investigates how blood pressure can be treated more effectively. Much of that work involves the painstaking development of new concepts and research methods to move forward the state of the art. For example, our work on urinary extracellular vesicles’ mRNA as an ex vivo assay of the ligand-activated transcription factor activity of mineralocorticoid receptors is challenging, fun, and rewarding. With a lot of work from Andrea Berrido and Pradeep Gunasekaran in my lab, we have been moving the ball forward on several key projects on that front.

{PrettyCols} is an R package containing aesthetically pleasing colour palettes that are compatible with {ggplot2}. Find out about new features and palettes contained in the latest release!

Securing your Shiny application is not just an added feature; it’s a fundamental necessity. Often, functionality and design are prioritized in development, but ensuring the security of your app is equally important, if not more so. Shiny security involves more than just adhering to general programming best practices like utilizing environment variables instead of hardcoding […] The post appeared first on appsilon.com/blog/.

You can read the original post in its original format on Rtask website by ThinkR here: Pimping your shiny app with a JavaScript library : an example using sweetalert2 You think that some of the components of {shiny} are not very functional or downright austere? Are you looking to implement some feature in your app but it is not available in the {shiny} toolbox? Take a look at JavaScript! JavaScript is a very popular programming language that is often used to add features to web pages. With HTML and This post is better presented on its original ThinkR website here: Pimping your shiny app with a JavaScript library : an example using sweetalert2

Master the art of Shiny app design: Boost aesthetics, improve UX, and refine code efficiency with our comprehensive guide.

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

Getting your app into an R package

A book about deeply customizing Shiny app for production.

I'm currently working on a project where I need to convert a nested JSON structure into a DataFrame using R. I'm facing some issues with the current approach, and I'd appreciate any help or guidance on how to properly handle this conversion. Json file looks like this : json_data - '{ "resourceType": "QuestionnaireResponse", "id": "example-questionnaireresponse", "questionnaire": "Questionnaire/example", "status": "completed", "subject": { "reference": "Patient/example" }, "a...

Weekly bite-sized tips on DataViz, Shiny and Stats/Machine Learning.

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

It's turtles all the way down...

Learn how to effortlessly deploy R Shiny apps on Google Cloud Platform using Docker and GitHub Actions for seamless integration and efficient application management.

“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

Learn how to supercharge your R Shiny applications with asynchronous programming. Explore top packages and best practices!

Shiny Server @CNR-IBBA. Contribute to cnr-ibba/shiny-server development by creating an account on GitHub.

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?

Our founder Nicholas Yeo and Actuarial Analysts Debbie Min Jyeh Ooi and Nadia Suharto presented "Developing Actuarial Applications using R Shiny" at the event…

Creating custom classes and {dplyr} compatibility

The R task Force - R experts for all your needs

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.

An introduction to using github actions with R.

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

This document is full of supplemental resources and content from the Shiny in Production Workshop delievered at rstudio::conf 2019.