Shiny CheckFunc Not Triggering ValueFunc: A Deep Dive into reactivePoll()

When building a Shiny application, it’s not uncommon to encounter issues with the reactivePoll() function. In this article, we’ll explore one such issue where the checkFunc is not triggering the valueFunc, and provide a step-by-step guide on how to resolve it.

Understanding reactivePoll()

reactivePoll() is a Shiny function that allows you to create an infinite loop of updates based on user input. It consists of two main parts: checkFunc and valueFunc.

• The checkFunc is a function that checks if the data has changed since the last update.
• The valueFunc is a function that updates the output when the data changes.

When reactivePoll() is called, it schedules an event to check the checkFunc at regular intervals. If the checkFunc returns TRUE, indicating that the data has changed, the event triggers the valueFunc to update the output.

The Issue

In the provided Shiny app code, the checkUpdateData() function checks if the total income of the current month (decFat) has increased. If it has, then the valueFunc is called. However, in this case, the print("************************ check") statement is never triggered, indicating that the checkFunc is not being executed.

Debugging the Issue

To debug this issue, we need to understand why the checkFunc is not being executed. There could be several reasons for this:

• The checkUpdateData() function is not returning TRUE when the data has changed.
• The reactivePoll() function is not scheduled correctly.
• The valueFunc is not updating the output correctly.

Resolving the Issue

To resolve this issue, we need to ensure that the checkUpdateData() function returns TRUE when the data has changed and that the reactivePoll() function is scheduled correctly. We also need to verify that the valueFunc is updating the output correctly.

Here’s an updated version of the Shiny app code with some modifications:

server <- function(input, output, session) {
  # Define the checkFunc
  checkUpdateData <- function() {
    decFatOld <- pDecFat[-1]
    decFatNew <- pDecFat[1]
    
    if (decFatNew > decFatOld) {
      print("************************ check")
      return(TRUE)
    } else {
      return(FALSE)
    }
  }

  # Define the valueFunc
  etl <- function(input = getData()) {
    # Perform ETL process and output a cleaned list object with data frame attached to it.
    listObj <- lapply(input, function(x) x %>% 
                         filter(Mes == format(Sys.Date(), format="%m")) %>% 
                         select(" ", emoji, Representante, Faturamento2, Regiao, "% Met Bat.", "% Fat")
                        )
    
    return(listObj)
  }

  # Create a reactivePoll object
  pollData <- reactivePoll(
    intervalMillis = 15000,
    session = NULL,
    checkFunc = checkUpdateData,
    valueFunc = function() {
      etl()
    }
  )

  # Output data table
  output$dataReq <- renderTable({
    DT::datatable(pollData())
  })

  # Output update time
  output$updateTime <- renderText({
    str_c(pollData(), collapse = " ")
  })
}

In this updated version, the checkUpdateData() function returns TRUE when the data has changed. The reactivePoll() function is scheduled correctly, and the valueFunc updates the output correctly.

Conclusion

The shiny reactivePoll() function can be finicky to work with, but by understanding how it works and how to debug issues, you can create powerful and dynamic Shiny apps that update in real-time. Remember to check your checkFunc and valueFunc functions carefully, as small mistakes can cause the app to malfunction.

Additionally, make sure to test your app thoroughly to ensure that it’s working as expected. This may involve adding additional debug statements or using tools like Shiny Server or RStudio Server to monitor the app’s performance.

By following these steps and tips, you should be able to create a Shiny app that uses reactivePoll() effectively and updates in real-time without any issues.

Last modified on 2024-12-22