app1.R

library(shiny)
library(shinydashboard)
library(ggplot2)
library(forecast)
library(urca)
Dow_jones <- read.csv("C:\\Users\\prabh\\Desktop\\New folder\\DJ.csv")
Dow_jones1 <- ts(Dow_jones$Adj.Close, start=c(2000, 1), freq=12)

frow1 <- fluidRow(
  valueBoxOutput("value1")
  ,valueBoxOutput("value2")
  
  
)
frow1.a<-fluidRow(
  valueBoxOutput("value3")
 ,valueBoxOutput("value4")
                )
frow2 <- fluidRow( 
  
  box(
    title = "Bins",status = "primary", solidHeader = TRUE,collapsible = TRUE,
    sliderInput("Bin", "Slider input:", 1, 50, 20),height="360px"
    
  )
  ,box(
    title = "Histogram"
    ,status = "primary"
    ,solidHeader = TRUE 
    ,collapsible = TRUE 
    ,plotOutput("Histogram", height = "300px")
  )
)
  
  frow3 <- fluidRow( 
    
    box(
      title = "BOXplot"
      ,status = "primary"
      ,solidHeader = TRUE 
      ,collapsible = TRUE 
      ,plotOutput("BoxPlot", height = "300px")
      ,width="600px"
    )
    )
  frow4 <- fluidRow( 
    
    box(
      title = "Time Series Plot"
      ,status = "primary"
      ,solidHeader = TRUE 
      ,collapsible = TRUE 
      ,plotOutput("Timeseries", height = "300px")
      ,width = "400px"
    )

  )
  
  frow5<-fluidRow(
    box(
    title="ACF"
    ,status="primary"
    ,solidHeader=TRUE
    ,collapsible=TRUE
    ,plotOutput("ACF",height="300px")
    ),
      box(
      title="PACF"
      ,status="primary"
      ,solidHeader=TRUE
      ,collapsible=TRUE
      ,plotOutput("PACF",height="300px")
    )
  )
  
  frow6<-fluidRow( 
    box(title="Time Series Decomposition"
        ,status="primary"
        ,solidHeader=TRUE
        ,collapsible=TRUE
        ,plotOutput("TSD",height="300px")
        ,width='600px'
    ))
  frow7<-fluidRow( 
    box(title="Basic Forecasting techniques"
        ,status="primary"
        ,solidHeader=TRUE
        ,collapsible=TRUE
        ,plotOutput("BFT",height="300px")
        ,width='600px'
    ))
  
  frow8<-fluidRow( 
    box(title="MEAN"
        ,status="primary"
        ,solidHeader=TRUE
        ,collapsible=TRUE
        ,plotOutput("mean",height="300px")
        
    ),
  box(title="Drift"
      ,status="primary"
      ,solidHeader=TRUE
      ,collapsible=TRUE
      ,plotOutput("drift",height="300px")
      
  ))
  
  frow9<-fluidRow(
    box(title="Naive"
        ,status="primary"
        ,solidHeader=TRUE
        ,collapsible=TRUE
        ,plotOutput("naive",height="300px")
        
    ),
    
    box(title="Seasonal Naive"
        ,status="primary"
        ,solidHeader=TRUE
        ,collapsible=TRUE
        ,plotOutput("snaive",height="300px")
        
    ))
  frow10<-fluidRow(
    box(title="holt"
        ,status="primary"
        ,solidHeader=TRUE
        ,collapsible=TRUE
        ,plotOutput("holt",height="300px")
        ,width="600px"
        
    ))
  frow11<-fluidRow(
    box(title="holt Winter Multiplicative"
        ,status="primary"
        ,solidHeader=TRUE
        ,collapsible=TRUE
        ,plotOutput("hwm",height="300px")
        
        
    ),
    box(title="holt Winter Additive"
        ,status="primary"
        ,solidHeader=TRUE
        ,collapsible=TRUE
        ,plotOutput("hwa",height="300px")
        
    ))
  frow12<-fluidRow(
    box(title="Arima(2,1,1)"
        ,status="primary"
        ,solidHeader=TRUE
        ,collapsible=TRUE
        ,plotOutput("Arima",height="300px")
        
        
    ),
    box(title="Auto Regressive Model"
        ,status="primary"
        ,solidHeader=TRUE
        ,collapsible=TRUE
        ,plotOutput("ar",height="300px")
        
    ))
    
    
  
   
# combine the two fluid rows to make the body

ui <- dashboardPage(
  #Dashboard header carrying the title of the dashboard
  header <- dashboardHeader(title = "DOW JONES INDEX"),  
  #Sidebar content of the dashboard
  sidebar <- dashboardSidebar(
    sidebarMenu(menuItem("Exploratory Analysis", tabName = "dashboard", icon = icon("dashboard")),
      menuItem("Time Series", tabName = "dashboard2", icon = icon("dashboard")),menuItem("Basic Forecasting Tools", tabName = "dashboard3", icon = icon("dashboard")),menuItem("Advanced Forecasting Method", tabName = "dashboard4", icon = icon("dashboard")))),
    body <- dashboardBody(tabItems(tabItem(tabName= "dashboard",frow1,frow1.a,frow2,frow3),tabItem(tabName = "dashboard2",frow4,frow5,frow6),tabItem(tabName = "dashboard3",frow7,frow8,frow9),tabItem(tabName = "dashboard4",frow10,frow11,frow12))))
      
      

  
  # create the server functions for the dashboard  
  server <- function(input, output) { 
    #some data manipulation to derive the values of KPI boxes
    mean_Adj <- mean(Dow_jones1)
    median_Adj <- median(Dow_jones1)
    mode_Adj <- mode(Dow_jones1)
    
    
    #creating the valueBoxOutput content
    output$value1 <- renderValueBox({
      valueBox(
        formatC(mean_Adj, format="d", big.mark=',')
        ,paste('Mean')
        ,icon = icon("stats",lib='glyphicon')
        ,color = "purple")  
    })
    output$value2 <- renderValueBox({ 
      valueBox(
        formatC(median_Adj, format="d", big.mark=',')
        ,'Median'
        ,icon = icon("",lib='glyphicon')
        ,color = "green")  
    })
    output$value3 <- renderValueBox({
      valueBox(
        formatC(max(Dow_jones1), format="d", big.mark=',')
        ,paste('Maximum')
        ,icon = icon("menu-hamburger",lib='glyphicon')
        ,color = "yellow")
      
    })
    output$value4 <- renderValueBox({
      valueBox(
        formatC(min(Dow_jones1), format="d", big.mark=',')
        ,paste('Minimum')
        ,icon = icon("menu-hamburger",lib='glyphicon')
        ,color = "blue")  
    })
    
    fit_stl <- stl(Dow_jones1, s.window=10)
    
    
    output$TSD <- renderPlot({autoplot(fit_stl)})
    output$Timeseries<-renderPlot({autoplot(Dow_jones1)})
    output$ACF<-renderPlot({ggAcf(Dow_jones1)})
    output$PACF<-renderPlot({ggPacf(Dow_jones1)})
    #creating the plotOutput content
  
    output$Histogram <- renderPlot({
      x<-Dow_jones1
      bins <- seq(min(x), max(x), length.out = input$Bin + 1)
      hist(x, breaks = bins, col = "#75AADB", border = "white",
           xlab = "Adj Close in $",
           main = "Histogram of Adj Close")
    })
    output$BoxPlot<- renderPlot({
      boxplot(Dow_jones1,horizontal=TRUE)
    })
    
    
    dowjones_train<-window(Dow_jones1, start=c(2000, 1), end=c(2017, 12), freq=12)
    dowjones_test <- window(Dow_jones1, start=c(2018, 1), end=c(2018, 12), freq=12)
    
    
    dow_mean <-meanf(dowjones_train,h=12)
    dow_mean
    output$mean<-renderPlot({autoplot(dow_mean)+xlab("Year") + ylab("Adjusted Closing Price")})
      
    
    
    dow_naive <- naive(dowjones_train, h=12)
    dow_naive
    output$naive<-renderPlot({autoplot(dow_naive)+ xlab("Year") + ylab("Adjusted Closing Price")})
    
    dow_snaive <- snaive(dowjones_train, h=12)
    output$snaive<-renderPlot({autoplot(dow_snaive)+xlab("Year") + ylab("Adjusted Closing Price") })
    
    dow_drift<-rwf(dowjones_train,h=12,drift=TRUE)
    output$drift<-renderPlot({autoplot(dow_drift)+xlab("Year") + ylab("Adjusted Closing Price")})
    
    output$BFT<-renderPlot({autoplot(dowjones_train) +
        autolayer(dow_mean, series="Mean", PI=FALSE) +
        autolayer(dow_naive, series="Naive", PI=FALSE) +
        autolayer(dow_snaive, series="Seasonal Naive", PI=FALSE) +
        autolayer(dow_drift, series="Drift", PI=FALSE) +
        xlab("Year") + ylab("Adjusted Closing Price") +
        ggtitle("DOW JONES INEDEX") +
        guides(colour=guide_legend(title="Forecast"))})
    
    holt_fit<-holt(dowjones_train,h=12)
    fit_holt<-forecast(holt_fit,h=12)
    output$holt<-renderPlot({autoplot(dowjones_train) +
        autolayer(fit_holt, series="Holt", PI=FALSE) })
    
    hwa<- hw(dowjones_train,seasonal="additive")
    fit_hwa<-forecast(hwa,h=12)
    
    output$hwa<-renderPlot({
    autoplot(dowjones_train) +
      autolayer(fit_hwa, series="Holt Winter Additive", PI=FALSE) })
    
    hwm<- hw(dowjones_train,seasonal="multiplicative")
    fit_hwm<-forecast(hwm,h=12)
    
    output$hwm<-renderPlot({
      autoplot(dowjones_train) +
        autolayer(fit_hwm, series="Holt Winter Multiplicative", PI=FALSE) })
    
    ndiffs(Dow_jones1)
    dowjones_stationary<-diff(Dow_jones1)
    
    plot(dowjones_stationary)
    plot(acf(dowjones_stationary))
    plot(pacf(dowjones_stationary))
    
    dowjones_train_stationary<-window(dowjones_stationary, start=c(2000, 1), end=c(2017, 12), freq=12)
    dowjones_test_stationary <- window(dowjones_stationary, start=c(2018, 1), end=c(2018, 12), freq=12)
    
    fitteda<-Arima(dowjones_train_stationary,order=c(1,0,0))
    val<-forecast(fitteda,h=12)
    output$ar<-renderPlot({autoplot(dowjones_train_stationary) +
        autolayer(val, series="AR model", PI=FALSE) 
    })
    
    arima_model <- Arima(dowjones_train, order = c(2,1,1),include.drift = TRUE)
    fit1<-forecast(arima_model,h=12)
   
    output$Arima<-renderPlot({ autoplot(dowjones_train) +
      autolayer(fit1, series="Arima model", PI=FALSE) })
    
   
    
  }
  ui <- dashboardPage(title = 'This is my Page title', header, sidebar, body, skin='red')

shinyApp(ui, server)