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How to Wrangle JSON Data in R with jsonlite, purr and dplyr - Robot Wealth
How to Wrangle JSON Data in R with jsonlite, purr and dplyr - Robot Wealth
Working with modern APIs you will often have to wrangle with data in JSON format. This article presents some tools and recipes for working with JSON data with R in the tidyverse. We’ll use purrr::map functions to extract and transform our JSON data. And we’ll provide intuitive examples of the cross-overs and differences between purrr ... Read more
·robotwealth.com·
How to Wrangle JSON Data in R with jsonlite, purr and dplyr - Robot Wealth
R - JSON Files
R - JSON Files
R - JSON Files - JSON file stores data as text in human-readable format. Json stands for JavaScript Object Notation. R can read JSON files using the rjson package.
·tutorialspoint.com·
R - JSON Files
hendrikvanb
hendrikvanb
Working with complex, hierarchically nested JSON data in R can be a bit of a pain. In this post, I illustrate how you can convert JSON data into tidy tibbles with particular emphasis on what I’ve found to be a reasonably good, general approach for converting nested JSON into nested tibbles. I use three illustrative examples of increasing complexity to help highlight some pitfalls and build up the logic underlying the approach before applying it in the context of some real-world rock climbing competition data.
·hendrikvanb.gitlab.io·
hendrikvanb
JSON files & tidy data | The Byrd Lab
JSON files & tidy data | The Byrd Lab
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.
·byrdlab.org·
JSON files & tidy data | The Byrd Lab
R Shiny Security: How to Make Your Shiny Apps Secured – R-Craft
R Shiny Security: How to Make Your Shiny Apps Secured – R-Craft
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/.
·r-craft.org·
R Shiny Security: How to Make Your Shiny Apps Secured – R-Craft
Pimping your shiny app with a JavaScript library : an example using sweetalert2 – R-Craft
Pimping your shiny app with a JavaScript library : an example using sweetalert2 – R-Craft
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
·r-craft.org·
Pimping your shiny app with a JavaScript library : an example using sweetalert2 – R-Craft
Pack YouR Code
Pack YouR Code
This book showcases a basic example of how to create an R package based on S3 classes.
·gastonsanchez.com·
Pack YouR Code
Converting Nested JSON to DataFrame in R? - General - Posit Community
Converting Nested JSON to DataFrame in R? - General - Posit Community
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...
·forum.posit.co·
Converting Nested JSON to DataFrame in R? - General - Posit Community
Explain R environments like I’m five
Explain R environments like I’m five
“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
·colinfay.me·
Explain R environments like I’m five
Introduction
Introduction
Shiny Server @CNR-IBBA. Contribute to cnr-ibba/shiny-server development by creating an account on GitHub.
·github.com·
Introduction
System Dependencies in R Packages & Automatic Testing - R-hub blog
System Dependencies in R Packages & Automatic Testing - R-hub blog
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?
·blog.r-hub.io·
System Dependencies in R Packages & Automatic Testing - R-hub blog
5 Best IDEs for R Programming in 2023
5 Best IDEs for R Programming in 2023
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.
·geekflare.com·
5 Best IDEs for R Programming in 2023