Problem with my shiny app. Does not return a plot

[benjaminbech99]

library(shiny)
library(dplyr)
library(ggplot2)
library(shinythemes)

# Define the data source

# Define the price plot module
pricePlotModule <- function(id){
  moduleServer(id, function(input, output, session){
    # Define the asset IDs
    asset_ids <- reactive({
      input$asset_ids
    })
    
    # Define the date range
    date_range <- reactive({
      input$date_range
    })
    
    # Filter the data based on the user inputs
    filtered_data <- reactive({
      mInfo %>%
        filter(
          assetID %in% asset_ids(),
          TradeDate >= date_range()[1],
          TradeDate <= date_range()[2]
        )
    })
    
    # Create the plot
    output$price_plot <- renderPlot({
      ggplot(filtered_data(), aes(x = TradeDate, y = Price, color = assetID)) +
        geom_line() +
        labs(title = "Price",
             x = "",
             y = "") +
        PlotThemeShinyTS
    })
  })
}

oasPlotModule <- function(id){
  moduleServer(id, function(input, output, session){
    # Define the asset IDs
    asset_ids <- reactive({
      input$asset_ids
    })
    
    # Define the date range
    date_range <- reactive({
      input$date_range
    })
    
    # Filter the data based on the user inputs
    filtered_data <- reactive({
      mInfo %>%
        filter(
          assetID %in% asset_ids(),
          TradeDate >= date_range()[1],
          TradeDate <= date_range()[2]
        )
    })
    
    # Create the plot
    output$oas_spread_plot <- renderPlot({
      ggplot(filtered_data(), aes(x = TradeDate, y = OASpread, color = assetID)) +
        geom_line() +
        labs(title = "OAS Spread",
             x = "",
             y = "") +
        PlotThemeShinyTS
    })
  })
}

durPlotModule <- function(id){
  moduleServer(id, function(input, output, session){
    # Define the asset IDs
    asset_ids <- reactive({
      input$asset_ids
    })
    
    # Define the date range
    date_range <- reactive({
      input$date_range
    })
    
    # Filter the data based on the user inputs
    filtered_data <- reactive({
      mInfo %>%
        filter(
          assetID %in% asset_ids(),
          TradeDate >= date_range()[1],
          TradeDate <= date_range()[2]
        )
    })
    
    # Create the plot
    output$duration_plot <- renderPlot({
      ggplot(filtered_data(),aes(x = TradeDate, y = ModOAD, color = assetID)) +
        geom_line() +
        labs(title = "Duration",
             x = "",
             y = "") +
        PlotThemeShinyTS
    })
  })
}

convPlotModule <- function(id){
  moduleServer(id, function(input, output, session){
    # Define the asset IDs
    asset_ids <- reactive({
      input$asset_ids
    })
    
    # Define the date range
    date_range <- reactive({
      input$date_range
    })
    
    # Filter the data based on the user inputs
    filtered_data <- reactive({
      mInfo %>%
        filter(
          assetID %in% asset_ids(),
          TradeDate >= date_range()[1],
          TradeDate <= date_range()[2]
        )
    })
    
    # Create the plot
    output$convex_plot <- renderPlot({
      ggplot(filtered_data(),aes(x = TradeDate, y = ModOAC, color = assetID)) +
        geom_line() +
        labs(title = "Convexity",
             x = "",
             y = "") +
        PlotThemeShinyTS
    })
  })
}



outamountModule <- function(id){
  moduleServer(id, function(input, output, session){
  
    # Define the asset IDs
    asset_ids <- reactive({
      input$asset_ids
    })
    
    # Define the date range
    date_range <- reactive({
      input$date_range
    })
    
    # Filter the data based on the user inputs
    filtered_data <- reactive({
      mOutAmount %>%
        filter(
          assetID %in% asset_ids(),
          TradeDate >= date_range()[1],
          TradeDate <= date_range()[2]
        )
    })
    
    output$outamount <- renderPlot({
      ggplot(filtered_data(),aes(x = TradeDate, y = OutstandingAmountM, color = assetID)) +
        geom_line() +
        labs(title = "Outstanding amount",
             x = "",
             y = "") +
        PlotThemeShinyTS
    })
  })
}


# Define the UI
OSCUI <- tabItem("osc",
                 fluidPage(shinythemes::themeSelector(),
      
                    
      # Add the picker input for asset IDs
      pickerInput(inputId = "asset_ids",
                  label = "Select Asset IDs",
                  choices = unique(mInfo$assetID),
                  multiple = TRUE,
                  options = list(`actions-box` = TRUE,
                                 `selected-text-format` = "count > 3")),
      
      # Add the date range input
      dateRangeInput(inputId = "date_range",
                     label = "Select Date Range",
                     start = Sys.Date() - 365,
                     end = Sys.Date()),
      
      fluidRow(
        column(width = 4,
               plotOutput("priceplot", height = "400px")),
        column(width = 4,
               plotOutput("oasplot", height = "400px")),
        column(width = 4,
               plotOutput("durplot", height = "400px"))
      ),
      
      fluidRow(
        column(width = 4,
               plotOutput("convplot", height = "400px")),
        column(width = 4,
               plotOutput("outplot", height = "400px"))
      )
  )
)


server1 <- function(id){
  pricePlotModule("priceplot")
  oasPlotModule("oasplot")
  durPlotModule("durplot")
  convPlotModule("convplot")
  outamountModule("outplot")
}




#####Customize app

#Add logo
title_logo <- tags$a(href = 'https://www.jyskebank.dk/presse/foto/logoerfoto',
                     tags$img(src = 'https://www.jyskebank.dk/wps/wcm/connect/jfo/0df5fc4a-b754-4b54-aad4-6b2f851d50ef/JB-logo_web.png?MOD=AJPERES&CVID=mAGm.6G', height= 25,width= 160))

#Specify the header
HeaderSize <-tags$head(tags$style(HTML(
  '.myClass {
line-height: 50px;
text-align: center;
font-family: "Effra";
padding: 0 15px;
color: white;
font-size: 1.2vw;
    }
    ')))

#Text in header
HeaderText <-tags$script(HTML('$(document).ready(function() {
                      $("header").find("nav").append(\'<div class="myClass"> Fixed Income Research - Key Figures </div>\');
                      })'))

#Colours in header and backgrounds
header_col <- tags$head(tags$style(HTML('
.skin-blue .main-header .logo { background-color: #FFFFFF;}
.skin-blue .main-header .navbar {background-color: #005c3c}
.content-wrapper {background-color: #ededed;}'))) # White background color


ui <- dashboardPage(
  dashboardHeader(
    title = title_logo , titleWidth = 300),
  
  dashboardSidebar(width = 300,
                   sidebarMenu(
                     #Key figures tab
                     menuItem("OSC", tabName = "osc", icon = icon("file")
                     )
                   )),
  dashboardBody(
    #Add CSS Style to Dashboard
    HeaderSize,
    HeaderText,
    header_col,
    tabItems(
      
      ##Front page
      OSCUI
      
    )
  )
)



server <- function(input, output, session){
  server1("osc")
}

# Define the app
shinyApp(ui, server)

The above returns the app with no problem and it returns the pickerInput and dateRangeinput. However, it does not show any plots. I think the communication between module and server is wrong.

[nirgrahamuk]

Could you format this into a reproducible example? That is a set of code that folks can easily get up and running to replicate your issue? Currently, this is only part of a shiny app there is no data.

IF you aren't familiar with best practices for shiny reprexes, check out

Shiny debugging and reprex guide

Shiny issues can be challenging to resolve relative to other problems with your code or statistical methods. Shiny apps are often large, complex projects with interacting files. When seeking help from others it is considered polite to: First, do your best to work through RStudio's debugging tools to diagnose your issue on your own. Often those shiny logs and tracebacks are useful to others trying to help out. Second, strive to minimize the effort required to replicate your issue. You can do this with a reproducible example ("reprex"). Shiny Debugging Errors in Shiny code can be difficult to track down. If you don't know where your problem is coming from, you can track it down with some o…

This will make it easier for folks to replicate your issue and offer suggestions to solve it.