![]() The resulting data object, daily_data, is reactive and can be used in downstream functions like render***. If the data has changed, the file is re-read using readFunc. #Update r studio update#The function checks the shared data file’s update timestamp every intervalMillis to see if the data has changed. (It wouldn’t be a very good dashboard if users had to refresh a page to see new data.) In Shiny, this behavior is accomplished with the reactiveFileReader function: daily_data <- reactiveFileReader( The dashboard needs to look for updates to the underlying shared data and automatically update when the data changes. Make sure the data, written to shared storage, is readable by the user running the Shiny application - typically a service account like rstudio-connect or shiny can be set as the run-as user to ensure consistent behavior.Īlternatively, instead of writing results to the file system, prepped data can be saved to a view in a database. (Connect can optionally send the successfully rendered report, as well.) However, the same scheduling could be accomplished with a workflow tool or even CRON. If the job fails, Connect also sends us an email containing stdout from the render, which helps us stay on top of errors. We use RStudio Connect to easily schedule the document, view past historical renderings, and ultimately to host the application. Using R Markdown also forces us to properly document the scheduled process. However, our team has found it incredibly useful to be able to look back through historical renderings of the “report” to gut-check the process. It may seem odd at first to use a R Markdown document as the scheduled task. The entire R Markdown document is scheduled for execution. The R Markdown document ends by saving the cleansed data into a shared data directory. #Update r studio code#The code for this process is written into an R Markdown document, alongside process documentation and a few simple graphs that help validate the new data. #Update r studio series#In this case, the data preparation requires a series of API requests and then basic data cleansing. The first challenge is preparing the daily data. The backend for the dashboard looks like: ![]()
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