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  • Any R programmers here that have experience with Shiny?

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    Answer it

    I keep seeing 'Not Found' when trying to load my Shiny application, it seems to be that R cannot find the Shiny server, I have tried reinstalling all packages and still no luck.

    The strange thing is that it works the first time I open R Studio, then it doesn't hereafter.. spooky

    ---
    title: "Benchmarking Algorithms for Credit Card Fraud Detection"
    output: 
      flexdashboard::flex_dashboard:
        orientation: columns
        vertical_layout: fill
        social: ["menu"]
        runtime: shiny
        #options: (shiny.maxRequestSize = 1500*1024^2)
    ---
    ```{r setup, include=FALSE}
    suppressPackageStartupMessages({
    library(shiny)
    library(flexdashboard)
    library(datasets)
    library(caTools)
    library(hydroGOF)
    })
    
    ```
    
    Column {.tabset}
    -------------------------------------
    
    ### Linear Regression Model
    
    ```{r}
    ui <- fluidPage(
    
      titlePanel("Upload Transaction Data Set"),
    
      sidebarLayout(
    
        sidebarPanel(
    
          fileInput("file1", "Choose CSV File",
                    multiple = FALSE,
                    accept = c("text/csv",
                               "text/comma-separated-values,text/plain",
                               ".csv")),
    
          p("The transaction data file must contain a 'Class' column that specifies the number 1 for fraudulent transactions, and 0 otherwise"),
    
          p("The 'Actual' graph exhibits the real calculations of the uploaded data, and the 'predicted' graph exhibits the predicted 'Class' column based on the other features of data included in your upload.")
    
        ),
    
        mainPanel(
        h1("Models"),
        plotOutput("actual"),
        plotOutput("prediction"),
        h1("Root Mean Square Error (RMSE) Accuracy %"),
        verbatimTextOutput("accuracy"),
        h1("Summary"),
        verbatimTextOutput("summary")
        
        )
      )
    )
    
    server = function(input,output){
      options(shiny.maxRequestSize=500*1024^2)
      thedata = reactive({
      req(input$file1)
      read.csv(file = input$file1$datapath)
      })
    
       output$prediction = renderPlot({
       req(thedata())
    
       set.seed(2)
    
       #Split data
       split <- sample.split(thedata(), SplitRatio=0.7)
       thedata <- subset(thedata(), split=TRUE)
       actual <- subset(thedata(), split=FALSE)
    
       #Create the model
        model <- lm(Class ~.,data = thedata())
    
       #Prediction
       prediction <- predict(model, actual)
       distPred <- predict(model, actual)
    
       #Comparing predicted vs actual model
       plot(prediction,type = "l",lty= 1.8,col = "blue")
       lines(prediction, type = "l", col = "blue")
    
       })
    
       output$actual = renderPlot({
       req(thedata())
       set.seed(2)
    
       split <- sample.split(thedata()$Class,0.7)
       
       thedata <- subset(thedata(),split)
       actual <- subset(thedata(),!split)
    
       model <- lm(Class ~.,data = thedata())
    
       prediction <- predict(model, actual)
       
       plot(actual$Class,type = "l",lty= 1.8,col = "blue")
    
    output$accuracy <- renderPrint({
        rmse <- sqrt(mean(prediction-thedata$Class)^2)/diff(range(thedata$Class))
        rmse
      })
    })
       
    output$summary <- renderPrint({
        model <- lm(Class ~.,data = thedata())
        summary (model)
        })
    }
    
    shinyApp(ui, server)
    
    ```
    
    -------------------------------------
    
    ### Logistic Regression Model
    
    ```{r}
    ui <- fluidPage(
    
      titlePanel("Upload Transaction Data Set"),
    
      sidebarLayout(
    
        sidebarPanel(
    
          fileInput("file1", "Choose CSV File",
                    multiple = FALSE,
                    accept = c("text/csv",
                               "text/comma-separated-values,text/plain",
                               ".csv")),
    
      p("The transaction data file must contain a 'Class' column that specifies the number 1 for fraudulent transactions, and 0 otherwise"),
    
      p("The 'Actual' graph exhibits the real calculations of the uploaded data, and the 'predicted' graph exhibits the predicted 'Class' column based on the other features of data included in your upload.")
    
        ),
    
      mainPanel(
        h1("Models"),
        plotOutput("actual"),
        plotOutput("prediction"),
        h1("Confusion Matrix"),
        verbatimTextOutput("confMatrix"),
        h1("Accuracy (%)"),
        verbatimTextOutput("accuracy"),
        h1("Summary"),
        verbatimTextOutput("summary")
        
        )
      )
    )
    
    server = function(input,output){
      options(shiny.maxRequestSize=500*1024^2) #Max File Size
      thedata = reactive({
      req(input$file1)
      read.csv(file = input$file1$datapath)
      })
    
       output$prediction = renderPlot({
       req(thedata())
    
       #Split Data
       split <- sample.split(thedata(), SplitRatio=0.7)
       train <- subset(thedata(), split=TRUE)
       Actual <- subset(thedata(), split=FALSE)
    
       #Create Model
       mymodel <- glm(Class ~., data = thedata(), family = binomial, control = list(maxit = 50))
    
       #Prediction
       Prediction <- predict(mymodel, Actual, type="response")
    
       #Predicted Model
       plot(Prediction,type = "l",lty= 1.8,col = "blue")
    
      })
    
       output$actual = renderPlot({
       req(thedata())
       
       # Split Data
       split <- sample.split(thedata(), SplitRatio=0.7)
       train <- subset(thedata(), split=TRUE)
       Actual <- subset(thedata(), split=FALSE)
    
       #Create Model
       mymodel <- glm(Class ~., data = thedata(), family = binomial, control = list(maxit = 50))
    
       #Prediction
       Prediction <- predict(mymodel, Actual, type="response")
    
       #Actual Model
       plot(Actual$Class,type = "l",lty= 1.8,col = "blue")
       
      output$confMatrix <- renderPrint({
        confusionMatrix <- table(Actual_Value=train$Class, Predicted_Values=Prediction > 0.5)
        confusionMatrix
        })
       
      output$accuracy <- renderPrint({
        confusionMatrix <- table(Actual_Value=train$Class, Predicted_Values=Prediction > 0.5)
        (confusionMatrix[[1,1]] + confusionMatrix[[2,2]]) / sum(confusionMatrix) *100
        })
      })
    
       output$summary <- renderPrint({
        mymodel <- glm(Class ~., data = thedata(), family = binomial, control = list(maxit = 50))
        summary (mymodel)
        })
    }
    
    shinyApp(ui, server)
    
    ```   
    
    -------------------------------------
    
    ### Bayesian Model
    
    ```{r}
    
    ```
    
    

    Message:

    Loading required package: shiny
    
    Listening on http://127.0.0.1:4616

     

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