r - 为什么操作按钮无法正常工作，以及如何在我的应用程序中添加进度条？

Question

我闪亮的应用程序上的问题是操作按钮不起作用，当我单击它不运行的按钮时。我用神经网络创建了闪亮的应用程序来分类数据集。我还想在我的应用程序中添加 withprogress。我试过但失败了。谢谢，对不起我的英语不好

这是我的ui.R

library(shiny)
library(shinycssloaders)
library(shinyWidgets)
library(shinythemes)

# Define UI
shinyUI(fluidPage(
    theme = shinytheme("superhero"),

    tags$head(tags$style(".header{background-image:url('tes3.jpg')}
                     #title{
                         color: white;
                        text-align: center;
                     } ")),
    tags$div(class="header",
             titlePanel(
                 fluidRow(
                     column(3, img(height = 80, width = 80, src = "undip.png")),
                     column(5,   tags$div(id="title","Klasifikasi Neural Network Dengan Alghoritm Backpropagation")), 
                     column(2),
                     column(1, img(height = 80, width = 195, src = "statistika.png"))
                 )
             ),
             tags$style(HTML("
                    .dataTables_wrapper .dataTables_length, 
                    .dataTables_wrapper .dataTables_filter, 
                    .dataTables_wrapper .dataTables_info, 
                    .dataTables_wrapper .dataTables_processing, 
                    .dataTables_wrapper .dataTables_paginate {
                    color: #ffffff;
                    }

                    thead {
                    color: #ffffff;
                    }

                     tbody {
                    color: #000000;
                    }

                   "
             ))



             ),

    sidebarLayout(
        sidebarPanel(
            h4(strong("Tentang Aplikasi"),style="text-align:center"),
            div(
                p("Gui ini digunakan untuk melakukan klasifikasi data biner
                  dengan menggunakan jaringan syaraf tiruan dengan 
                  optimasi Backpropagation.",style="text-align:center")

            ),
            # Input: Select a file ----
            fileInput("file1", h1("Choose CSV File",align="center",
                                  style = "font-size:30px;"),
                      multiple = FALSE,
                      accept = c("text/csv",
                                 "text/comma-separated-values,text/plain",
                                 ".csv")),

            # Horizontal line ----
            tags$hr(),

            # Input: Checkbox if file has header ----
            checkboxInput("header", "Header", TRUE),

            # Input: Select separator ----
            radioButtons("sep", "Separator",
                         choices = c(Comma = ",",
                                     Semicolon = ";",
                                     Tab = "\t"),
                         selected = ","),

            # Input: Select quotes ----
            radioButtons("quote", "Quote",
                         choices = c(None = "",
                                     "Double Quote" = '"',
                                     "Single Quote" = "'"),
                         selected = '"'),

        ),

        mainPanel(
          tags$head(
            tags$style(type='text/css', 
                       ".nav-tabs {font-size: 20px} ")), 
            tabsetPanel(
                tabPanel("Data", span(style = "color:white", 
                                      DT::dataTableOutput("contents"))),
                tabPanel("Analisis", 
                         uiOutput("respon",),
                         conditionalPanel("Fungsi Aktivasi",
                                          selectInput("activhid", "Fungsi Aktivasi Hidden Layer:", 
                                                      choices= c("logistic", "tanh"))
                         ),
                         numericInput('train', 'Persentase Data Training (%) :',80),
                         numericInput('hidden', 'Jumlah Neuron Di Hidden Layer:',10),
                         numericInput('stepmax', 'Jumlah Iterasi Maksimal:',1e6),
                         numericInput('lrate', 'Nilai Learning Rate:',0.01),
                         numericInput('threshold', 'Nilai Threshold:',0.01),
                         numericInput("randseed", 
                                      "Random seed:",
                                      sample(1:1e5, size= 1)),
                         br(),

                         actionButton("action", "Predict!")

                ),
                tabPanel("Hasil",  withSpinner(verbatimTextOutput("hasil"))),
                tabPanel("Nilai Prediksi",  div(style = "color:black;font-size: 50%; width: 50%",withSpinner(DT::dataTableOutput("prediksi")))),
                tabPanel("Plot", 
                         h4(strong("Arsitektur Neural Network"),style="text-align:center"),
                         withSpinner(plotOutput("plot1", width = "100%"))
                )


            )


        )

    ),

    hr(),
    div(
        p("~~~Copyright@2019~~~",style="text-align:center"),
        p("By Gustiyan Islahuzaman ",style="text-align:center")

    )

    )


)

这是我的服务器.R

library(shiny)
library(neuralnet)
library(OneR)
library(caret)
library(DT)


shinyServer(function(input, output,session) {


    dInput <- reactive({
        in.file <- input$file1
        if (is.null(in.file)){
          return(NULL)

        }else
                df <- read.csv(input$file1$datapath,
                               header = input$header,
                               sep = input$sep,
                               quote = input$quote)

      })
    output$contents <- DT::renderDataTable({
      d.input <- datatable(dInput(),class = 'cell-border stripe')
      d.input
    })

    output$respon <- renderUI({
      df <- dInput()
      if (is.null(df)) return(NULL)
      items=names(df)
      names(items)=items
      selectInput("respon","Memilih Variabel Respon:",items)
    })


    observeEvent(input$action,{
        set.seed(input$randseed)
        f <- reactive({as.formula(paste(input$respon, "~ ."))})
        actfct = reactive({paste(input$activhid)})
        hid =reactive({as.numeric(paste(input$hidden))})
        lrate=reactive({as.numeric(paste(input$lrate))})
        d <-as.data.frame(req(dInput()))
        d[,input$respon] <- as.integer(d[,input$respon])
        d <- na.omit(d)

        normalize <- function(x) {
          return ((x - min(x)) / (max(x) - min(x)))
        }

        d<-as.data.frame(lapply(d, normalize))
        sampel<-reactive({ sample(1:nrow(d), (input$train/100) * nrow(d))})
        train<-d[sampel(),]
        test<- d[-sampel(),]
        fit <-reactive({ neuralnet(f(), data = train,algorithm = "backprop",learningrate =lrate() ,
                         hidden = hid(),act.fct =actfct(),lifesign = "full",linear.output=FALSE)
        })


      output$hasil<-renderPrint({
        pred<- round(predict(fit(), test))
        nn<-fit()
        print(nn$result.matrix)
        out=eval_model(pred, test)
      })

      output$plot1<-renderPlot({
        plot(fit(),rep="best")

        })


      output$prediksi<-DT::renderDataTable({
        pred<-round(predict(fit(), test))
        tabel<-cbind(Nilai_Aktual=test[,input$respon],NIlai_Prediksi=c(pred))

      })



      })

})

标签： rshiny