使用Shiny

Question

您好我在R中有预测模型，我需要客户在Web界面中输入他们的数据，以便分类器可以预测类。我使用Shiny包但没有输出。

第一步是让客户以csv格式输入他的数据。然后在后端使用该模型来预测输入文件的类。然后在Web界面中为客户提供每行的类。

    ########## Server
    bw <- read.csv("bw.csv", header = T)

    colnames(bw)[1] <- "class"

bw$class[bw$class=="1"] <- "A" 
bw$class[bw$class=="2"] <- "B" 
bw$class[bw$class=="3"] <- "C" 
bw$class[bw$class=="4"] <- "D" 
bw$class[bw$class=="5"] <- "E"

bw$class <- as.factor(bw$class) 
bw[ , 2:59]<- sapply(bw[ , 2:59], as.numeric)

control <- trainControl(method="repeatedcv", number=5, repeats=2)
# train the LDA model set.seed(7) 
modelLda <- train(class~., data=bw, method="lda", trControl=control)



library(shiny)

shinyServer(function(input, output) {   output$contents <- renderTable({

    # input$file1 will be NULL initially. After the user selects
    # and uploads a file, it will be a data frame with 'name',
    # 'size', 'type', and 'datapath' columns. The 'datapath'
    # column will contain the local filenames where the data can
    # be found.

    inFile <- input$file1

    if (is.null(inFile))
      return(NULL)

    head(read.csv(inFile$datapath, header=input$header, sep=input$sep, 
             quote=input$quote))    })
     # classification
     clusters <- reactive({
    predict(modelLda, input$file1(), input$clusters)   })
     # Show clusters:   

    output$table1 <- renderTable({
        input$clusters   })

    })

########### UI
library(shiny)

shinyUI(fluidPage(
  titlePanel("Uploading Files"),
  sidebarLayout(
    sidebarPanel(
      fileInput('file1', 'Choose CSV File',
                accept=c('text/csv', 
                         'text/comma-separated-values,text/plain', 
                         '.csv')),
      tags$hr(),
      checkboxInput('header', 'Header', TRUE),
      radioButtons('sep', 'Separator',
                   c(Comma=',',
                     Semicolon=';',
                     Tab='\t'),
                   ','),
      radioButtons('quote', 'Quote',
                   c(None='',
                     'Double Quote'='"',
                     'Single Quote'="'"),
                   '"')
    ),
      mainPanel(
        tableOutput("table1")
    )
  )
))

最后一个问题，分类器模型是否应该包含在server.r文件中，还是可以从另一个文件中调用？

感谢。

Answer 1

这里有一些东西可以帮助你开始（它对我有用）。我简化了它，但你会知道如何构建它。希望能帮助到你！

library(shiny)
library(caret)

    bw <- read.csv("bw_test.csv", header = T)

    colnames(bw)[1] <- "class"

bw$class[bw$class=="1"] <- "A" 
bw$class[bw$class=="2"] <- "B" 
bw$class[bw$class=="3"] <- "C" 
bw$class[bw$class=="4"] <- "D" 
bw$class[bw$class=="5"] <- "E"

bw$class <- as.factor(bw$class) 
bw[ , 2:58]<- sapply(bw[ , 2:58], as.numeric)

control <- trainControl(method="repeatedcv", number=5, repeats=2)
# train the LDA model set.seed(7) 
modelLda <- train(class~., data=bw, method="lda", trControl=control)



shinyServer(function(input, output) {  

    # input$file1 will be NULL initially. After the user selects
    # and uploads a file, it will be a data frame with 'name',
    # 'size', 'type', and 'datapath' columns. The 'datapath'
    # column will contain the local filenames where the data can
    # be found.

 #   inFile <- input$file1

  #  if (is.null(inFile))
   #   return(NULL)

    #head(read.csv(inFile$datapath, header=input$header, sep=input$sep, 
     #        quote=input$quote))    })
     # classification


          dInput = reactive({

    in.file = input$file1

    if (is.null(in.file))
      return(NULL)

read.csv(in.file$datapath, header=input$header, sep=input$sep, quote=input$quote)

  })


     clusters <- reactive({

        df <- dInput()

    tt <- as.data.frame(predict(modelLda))
    tt
    })
     # Show clusters:   



        output$table1 <- renderTable({
            toprint = clusters()
            head(toprint)
            })

        })

编辑：现在它会在打印表格之前等待下载文件，但现在你可以自己动手了：）

shinyServer(function(input, output) {  

    # input$file1 will be NULL initially. After the user selects
    # and uploads a file, it will be a data frame with 'name',
    # 'size', 'type', and 'datapath' columns. The 'datapath'
    # column will contain the local filenames where the data can
    # be found.

 #   inFile <- input$file1

  #  if (is.null(inFile))
   #   return(NULL)

    #head(read.csv(inFile$datapath, header=input$header, sep=input$sep, 
     #        quote=input$quote))    })
     # classification


          dInput = reactive({

    in.file = input$file1

    if (is.null(in.file))
      return(NULL)

 bw <- read.csv(in.file$datapath, header=input$header, sep=input$sep, quote=input$quote)

     colnames(bw)[1] <- "class"

bw$class[bw$class=="1"] <- "A" 
bw$class[bw$class=="2"] <- "B" 
bw$class[bw$class=="3"] <- "C" 
bw$class[bw$class=="4"] <- "D" 
bw$class[bw$class=="5"] <- "E"

bw$class <- as.factor(bw$class) 
bw[ , 2:58]<- sapply(bw[ , 2:58], as.numeric)
bw

  })


     clusters <- reactive({

        df <- dInput()

            if (is.null(df))
      return(NULL)
        control <- trainControl(method="repeatedcv", number=5, repeats=2)
        modelLda <- train(class~., data=df, method="lda", trControl=control)

    tt <- as.data.frame(predict(modelLda))
    tt
    })
     # Show clusters:   



        output$table1 <- renderTable({
            toprint = clusters()
            head(toprint)
            })

        })