I have a Shiny app twhere the user selects a date and metric, and shows a line graph.

But if I select a metric, it will pop0ulate that values and then quickly rever to the original plot.

After some research, I found info on these update event functions but have had no luck implementing it.

Any ideas what is going wrong?

server <- function(input, output, session) {

  
  #===============================================================================
  
  message("app: Create Query n")
  
  AbrQuery <- "

      SELECT 
          * 
      FROM SQ_ML_MODEL.dbo.DataSci_ABR_Preds_Vs_Actuals_updated
 
    "

  #===============================================================================
  
  message("app: Set Secret Values n")
  
  SecretValuesLst <- GetSecret(Debug = TRUE)
  
  print(SecretValuesLst)
  
  print(SecretValuesLst$SERVER)
 
  #===============================================================================
  
  ConnectionString <- CreateConnectionString(SecretValuesLst, Debug = TRUE)
  
  print(ConnectionString)
  
  #===============================================================================
  
  message("app: Connect to Database n")
  
  # DbCon <- RODBC::odbcDriverConnect(connectionString)
  # 
  # print(DbCon)
  
  Conn <- GetSQLServerConnection(ConnectionString, Debug = TRUE)
  
  print(Conn)
  
  #===============================================================================
  
  message("app: Pull Data From Database n")
  
  DataOutput <- RODBC::sqlQuery(Conn[["DbCon"]], query = AbrQuery)
  
  setDT(DataOutput)
  
  DataOutput
  
  #===============================================================================
  ### 
  
  message("app: Prepare Data n")
  
  # RawAbrData
  
  # str(RawAbrData)
  
  VizAbrData <- melt.data.table(DataOutput[, .(MinuteOfDay,
                                               AgentCategory,
                                               Queue,
                                               Login,
                                               Tier,
                                               Abr_Actuals,
                                               Abr_Preds,
                                               Abr_Diff)],
                                id.vars = c("MinuteOfDay",
                                            "AgentCategory"))
  
  VizAbrData
  
  # VizAbrData[, .N, by = variable]
  
  # VizAbrData[variable == "Login"]
  
  VizAbrData[, AsOfDate := as_date(MinuteOfDay)]
  
  # VizAbrData[, .N, by = AsOfDate]
  
  # WhichDates <- VizAbrData[order(-AsOfDate)][, unique(AsOfDate)][1:14]
  
  # WhichDates
  
  # VizAbrData <- VizAbrData[AsOfDate %in% WhichDates]
  
  # VizAbrData
  
  VizAbrData$value[is.na(VizAbrData$value)] <- 0
  
  # VizAbrData
  
  library(hms)
  
  VizAbrData[, MinuteOfDay_TIME := as_hms(MinuteOfDay)]
  
  MyDT <- VizAbrData
  
  MyDT[, AsOfDate := as.Date(AsOfDate)]
  
  MyDT[, variable := as.character(variable)]
  
  #WhichDates <- mydat[, .N, by = AsOfDate][order(-AsOfDate)][1:10, AsOfDate]
  
  #WhichVariables <- mydat[, .N, by = variable][order(-variable)][1:10, variable]
  
  #===============================================================================


  
  
  
  output$distPlot1 <- renderPlot({
    
    filtered_data_1 <- reactive({
      subset(MyDT, 
             as.Date(MyDT$AsOfDate) %in% as.Date(input$Date1) & 
               as.character(MyDT$variable) %in% as.character(input$Metric1))
    })
    
    
    observeEvent(filtered_data_1(), {
      updateSelectizeInput(session, 'filtered_data_1', 
                           choices = names(filtered_data_1()))
    })
    
    updateSelectInput(session, "Date1", choices = MyDT$AsOfDate)
    updateSelectInput(session, "Metric1", choices = MyDT$variable)
    
    ggplot(data = filtered_data_1(),
           aes(x = MinuteOfDay,
               y = value)) +
      geom_point(stat = "identity", col = "black") +
      geom_line(stat = "identity", linewidth = 1.6, col = "red") + 
      labs(x = "MinuteOfDay", y = input$Metric1) +
      ggtitle(paste0("Time Series: ", input$Metric1, sep = "")) +
      theme(
        plot.title = element_text(size=16, face= "bold", colour= "black", hjust = 0.5, margin = margin(t=10,b=-20)),
        axis.title.x = element_text(size=16, face="bold", colour = "black"),
        axis.title.y = element_text(size=14, face="bold", colour = "black"),
        axis.text.x = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=14, face="bold", colour = "black"), # bold
        strip.text.x = element_text(size = 14, face="bold", colour = "black" ),
        strip.text.y = element_text(size = 14, face="bold", colour = "black"),
        axis.line.x = element_line(color="black", size = 0.3),
        axis.line.y = element_line(color="black", size = 0.3),
        panel.border = element_rect(colour = "black", fill=NA)
      )  +
      theme_classic()  
    
  })
  
  
}


#===============================================================================

# x <- seq(("2010/01/01"), as.Date("2016/12/11"), by = "day")

message("app: Create UI  n")

ui <- fluidPage(
  
  titlePanel("ABR Dashboard"),
  
  tabsetPanel(      
    
    tabPanel("Time Series",
             sidebarLayout(
               selectInput(inputId = "Date1", 
                           label = "Select Date", 
                           choices = ""
               ),
               selectInput(inputId = "Metric1",
                           label = "Which Metric",
                           choices = ""
               )
             ),
             
             mainPanel(
               plotOutput("distPlot1")
             )
    ) 
)

#===============================================================================

message("app: Create App n")

# Run the application 
shinyApp(ui = ui, server = server)

#===============================================================================