Discover and read the best of Twitter Threads about #shinyapps
Most recents (3)
[1/10] 🚀 Advanced R Debugging: Debugging & error handling are essential skills for every R programmer. In this thread, we'll explore powerful tools & techniques like traceback(), browser(), & conditional breakpoints to make debugging in R a breeze. #rstats #datascience 
[2/10] 📝 traceback(): When your code throws an error, use traceback() to get a detailed call stack. This function helps you identify the exact location of the error in your code, making it easier to pinpoint the issue. #rstats #debugging #datascience
[3/10] 🔍 browser(): With browser(), you can pause the execution of your code & step through it one line at a time. This interactive debugging tool allows you to inspect the values of variables and expressions, which can be a game-changer when diagnosing complex issues. #rstats
1/ 🧪 Advanced Shiny App Development: Tips for Creating Engaging, Scalable, and Robust Apps 🌐 Dive into this thread to learn how to build powerful, interactive web apps with Shiny! We'll cover advanced features, optimization, and deployment. #rstats #datascience #ShinyApps
2/ 🎛️ Advanced Features: Enhance your Shiny apps with:
•Custom input controls and UI elements
•Dynamic UI generation with renderUI()
•Incorporating JavaScript and CSS for added interactivity and styling
•Integrating with APIs for external data
#rstats #datascience
•Custom input controls and UI elements
•Dynamic UI generation with renderUI()
•Incorporating JavaScript and CSS for added interactivity and styling
•Integrating with APIs for external data
#rstats #datascience
3/ 🚀 Optimization: Improve app's performance and responsiveness by:
•Using reactive programming for efficient data manipulation
•Profiling and benchmarking with profvis
•Caching data and results to reduce computation time
•Employing async and future for nonblocking tasks
•Using reactive programming for efficient data manipulation
•Profiling and benchmarking with profvis
•Caching data and results to reduce computation time
•Employing async and future for nonblocking tasks
“Brain charts for the human lifespan”! The result of 200 people putting their 🧠🧠🧠together (120,000+ of them!) to generate growth curves from mid-gestation to 100 years. brainchart.io & biorxiv.org/content/10.110… @jakob_seidlitz @SimonWhite83 @edbullmore @Aaron_A_B
How did we get here? Through countless zoom hours, DUAs and emails, we aggregated data from over 100,000 individuals across 6 continents, comprising over 120,000 structural brain scans and racked up 1,400,000 @FreeSurferMRI computing hours processing them @CambridgeRcs.
Next we planned to run these derived variables through @WHO recommended GAMLSS models and quickly found we had to go a little beyond the standard functionalities if we were to account for the complexity posed by multi-site imaging data.
