argument "sidebar" is missing, with no default when creating shiny script - shiny

Question

I'm creating a shiny app for an app that predicts morphine consumption based on several variables. When I attempt to run the app I receive a sidebar error message stating I am missing script to create the sidebar ("argument "sidebar" is missing, with no default"). Here is my ui and server script.
#Load libraries
library(shiny)
library(shinydashboard)
library(ggplot2)
library(dplyr)
library(randomForest)
library(Metrics)
#R Shiny ui
ui <- dashboardPage(dashboardHeader(title = 'Morphine Consumption Explorer', titleWidth = 290))
#Sidebar layout
dashboardSidebar(sidebarMenu(id = "menu", sidebarMenuOutput("menu")))
sidebarMenu(menuItem("Plots", tabName = "plots", icon = icon('poll')),
menuItem("Dashboard", tabName = "dash", icon = icon('tachometer-alt')),
menuItem("Prediction", tabName = "pred", icon = icon('search')))
#pick variables
#Tabs layout
dashboardBody(tags$head(tags$style(HTML('.main-header .logo {font-weight: bold;}'))))
tabItems()
#Plots tab content
tabItem('plots',
#Histogram filter
box(status = 'primary', title = 'Filter for the histogram plot',
selectInput('num', "Numerical variables:", c("Age", "BMI", "IV_Fluids", "Operative_times", "Blood_loss", "Time_to_Aldrete_9", "morphine_consumption_24h1",
"VAS_basalR", "VAS_basalM", "VAS_2hrR", "VAS_2hrM", "VAS_4hrM", "VAS-4hrR",
"VAS_8hrR", "VAS_8hrM", "VAS_12hrR", "VAS_12hrM", "VAS_16hrR", "VAS_16hrM",
"VAS_24hrR", "VAS_24hrM", "QOR_psychological_support", "QOR_emotional_state",
"QOR_Physical_comfort", "QOR_physical_independence", "QOR_Pain", "Total")),
footer = 'Histogram plot for numerical variables'),
#Frequency plot filter
box(status = 'primary', title = 'Filter for the frequency plot',
selectInput('cat', 'Categorical variables:', c("ASA", "Postoperative_vomiting", "Sedation_0to8h", "Sedation_9to16h", "Sedation_17to24h")),
footer = 'Frequency plot for categorical variables'),
#Boxes to display the plots
box(plotOutput('histPlot')),
box(plotOutput('freqPlot')))
#Prediction tab content
tabItem('pred',
#Filters for categorical variables
box(title = 'Categorical variables',
status = 'primary', width = 12,
splitLayout(
tags$head(tags$style(HTML(".shiny-split-layout > div {overflow: visible;}"))),
cellWidths = c('0%', '19%', '4%', '19%', '4%', '19%', '4%', '19%', '4%', '8%'),
selectInput( 'p_group', 'group', c("0", "30", "60", "90")),
div(),
selectInput('p_ASA', 'ASA', c('1', '2', '3')),
div(),
selectInput( 'p_Sedation_17to24h', 'Ramsey Sedation at 17-24h', c('1', '2', '3', '4')),
div(),
radioButtons( 'p_Postoperative_vomiting', 'PONV', c('Yes', 'No')))),
#Filters for numeric variables
box(title = 'Numerical variables',
status = 'primary', width = 12,
splitLayout(cellWidths = c('22%', '4%','21%', '4%', '21%', '4%', '21%'),
sliderInput( 'p_Age', 'Age (year)', min = 0, max = 100, value = 0),
div(),
numericInput( 'p_BMI', 'BMI', 0),
div(),
numericInput( 'p_VAS_24hrM', 'VAS with Movement at 24hr', 0),
div(),
numericInput( 'p_QOR_psychological_support', 'QOR - Psychological Support', 0),
div(),
numericInput( 'p_QOR_Pain', 'QOR - Pain', 0),
numericInput( 'p_QOR_Physical_comfort', 'QOR - Physical Comfort', 0),
div(),
)),
#Box to display the prediction results
box(title = 'Prediction result',
status = 'success',
solidHeader = TRUE,
width = 4, height = 260,
div(h5('Morphine Consumption (mg):')),
verbatimTextOutput("value", placeholder = TRUE),
div(h5('Range of Morphine Consumption:')),
verbatimTextOutput("range", placeholder = TRUE),
actionButton('cal','Calculate', icon = icon('calculator'))),
#Box to display information about the model
box(title = 'Model explanation',
status = 'success',
width = 8, height = 260,
helpText('The following model will predict the total amount of morphine consumed by age, BMI, Visual Analog Scale at 24 hours with movement, and Quality of Recovery.'),
helpText('The name of the dataset used to train the model is "Short-term efficacy of preoperative Duloxetine for patients subjected to modified radical mastectomy A dose ranging randomized controlled trial", taken from the UCI Machine Learning Repository website. The data contains 17,379 observations and 16 attributes related to time and weather conditions.'),
helpText(sprintf('The prediction is based on a random forest supervised machine learning model. Furthermore, the models deliver a mean absolute error (MAE) of %s morphine consumed, and a root mean squared error (RMSE) of %s total number of morphine consumed.', round(mae_rf, digits = 0), round(rmse_rf, digits = 0)))))
# R Shiny server
server <- shinyServer(function(input, output) {
#Univariate analysis
output$histPlot <- renderPlot({...})
output$freqPlot <- renderPlot({...})
#Dashboard analysis
output$linePlot <- renderPlot({...})
output$barPlot <- renderPlot({...})
#Prediction model
#React value when using the action button
a <- reactiveValues(result = NULL)
observeEvent(input$cal, {
#Copy of the test data without the dependent variable
test_pred <- test_set[-10]
#Dataframe for the single prediction
values = data.frame(mnth = input$p_mnth,
Group = input$p_group,
ASA = input$p_ASA,
Sedation_17to24hr = input$p_Sedation_17to24h,
PONV = input$p_Postoperative_vomiting)
#Include the values into the new data
test_pred <- rbind(test_pred,values)
#Single preiction using the randomforest model
a$result <- round(predict(model_rf,
newdata = test_pred[nrow(test_pred),]),
digits = 0)
})
output$value <- renderText({
#Display the prediction value
paste(a$result)
})
output$range <- renderText({
#Display the range of prediction value using the MAE value
input$cal
isolate(sprintf('(%s) - (%s)',
round(a$result - mae_rf, digits = 0),
round(a$result + mae_rf, digits = 0)))
})
})
shinyApp(ui, server)
I appreciate any feedback.
Thank you. A
I tried manipulating the sidebar script after I ran the app. I'm expecting a shiny app that allows me to picture variables and estimate morphine consumption.

Answer 1

The header, sidebar and body functions need to passed as parameters to the dashboardPage(header, sidebar, body, title = NULL) function. Please check the following:
# Load libraries
library(shiny)
library(shinydashboard)
library(shinydashboardPlus)
library(ggplot2)
library(dplyr)
library(randomForest)
library(Metrics)
# R Shiny ui
ui <- dashboardPage(header = dashboardHeader(title = 'Morphine Consumption Explorer', titleWidth = 290),
sidebar = dashboardSidebar(sidebarMenu(menuItem("Plots", tabName = "plots", icon = icon('poll')),
menuItem("Dashboard", tabName = "dash", icon = icon('tachometer-alt')),
menuItem("Prediction", tabName = "pred", icon = icon('search')), id = "menu")),
body = dashboardBody(tags$head(tags$style(HTML('.main-header .logo {font-weight: bold;}'))),
tabItems(
#Plots tab content
tabItem('plots',
#Histogram filter
box(status = 'primary', title = 'Filter for the histogram plot',
selectInput('num', "Numerical variables:", c("Age", "BMI", "IV_Fluids", "Operative_times", "Blood_loss", "Time_to_Aldrete_9", "morphine_consumption_24h1",
"VAS_basalR", "VAS_basalM", "VAS_2hrR", "VAS_2hrM", "VAS_4hrM", "VAS-4hrR",
"VAS_8hrR", "VAS_8hrM", "VAS_12hrR", "VAS_12hrM", "VAS_16hrR", "VAS_16hrM",
"VAS_24hrR", "VAS_24hrM", "QOR_psychological_support", "QOR_emotional_state",
"QOR_Physical_comfort", "QOR_physical_independence", "QOR_Pain", "Total")),
footer = 'Histogram plot for numerical variables'),
#Frequency plot filter
box(status = 'primary', title = 'Filter for the frequency plot',
selectInput('cat', 'Categorical variables:', c("ASA", "Postoperative_vomiting", "Sedation_0to8h", "Sedation_9to16h", "Sedation_17to24h")),
footer = 'Frequency plot for categorical variables'),
#Boxes to display the plots
box(plotOutput('histPlot')),
box(plotOutput('freqPlot'))),
#Prediction tab content
tabItem('pred',
#Filters for categorical variables
box(title = 'Categorical variables',
status = 'primary', width = 12,
splitLayout(
tags$head(tags$style(HTML(".shiny-split-layout > div {overflow: visible;}"))),
cellWidths = c('0%', '19%', '4%', '19%', '4%', '19%', '4%', '19%', '4%', '8%'),
selectInput( 'p_group', 'group', c("0", "30", "60", "90")),
div(),
selectInput('p_ASA', 'ASA', c('1', '2', '3')),
div(),
selectInput( 'p_Sedation_17to24h', 'Ramsey Sedation at 17-24h', c('1', '2', '3', '4')),
div(),
radioButtons( 'p_Postoperative_vomiting', 'PONV', c('Yes', 'No')))),
#Filters for numeric variables
box(title = 'Numerical variables',
status = 'primary', width = 12,
splitLayout(cellWidths = c('22%', '4%','21%', '4%', '21%', '4%', '21%'),
sliderInput( 'p_Age', 'Age (year)', min = 0, max = 100, value = 0),
div(),
numericInput( 'p_BMI', 'BMI', 0),
div(),
numericInput( 'p_VAS_24hrM', 'VAS with Movement at 24hr', 0),
div(),
numericInput( 'p_QOR_psychological_support', 'QOR - Psychological Support', 0),
div(),
numericInput( 'p_QOR_Pain', 'QOR - Pain', 0),
numericInput( 'p_QOR_Physical_comfort', 'QOR - Physical Comfort', 0),
div(),
)),
#Box to display the prediction results
box(title = 'Prediction result',
status = 'success',
solidHeader = TRUE,
width = 4, height = 260,
div(h5('Morphine Consumption (mg):')),
verbatimTextOutput("value", placeholder = TRUE),
div(h5('Range of Morphine Consumption:')),
verbatimTextOutput("range", placeholder = TRUE),
actionButton('cal','Calculate', icon = icon('calculator'))),
#Box to display information about the model
box(title = 'Model explanation',
status = 'success',
width = 8, height = 260,
helpText('The following model will predict the total amount of morphine consumed by age, BMI, Visual Analog Scale at 24 hours with movement, and Quality of Recovery.'),
helpText('The name of the dataset used to train the model is "Short-term efficacy of preoperative Duloxetine for patients subjected to modified radical mastectomy A dose ranging randomized controlled trial", taken from the UCI Machine Learning Repository website. The data contains 17,379 observations and 16 attributes related to time and weather conditions.'),
helpText(sprintf('The prediction is based on a random forest supervised machine learning model. Furthermore, the models deliver a mean absolute error (MAE) of %s morphine consumed, and a root mean squared error (RMSE) of %s total number of morphine consumed.', round(mae_rf, digits = 0), round(rmse_rf, digits = 0)))
))
)
),
title = 'Morphine Consumption Explorer',
skin = "blue")
server <- function(input, output, session) {}
shinyApp(ui, server)