我使用R Shiny相对较新，我正在尝试为预测建模构建Shiny应用程序。 我已准备好R代码并将它们加载到R Shiny中。
请参阅我准备的ui.r和server.r。
shinyUI(
fluidPage(
titlePanel("Prediction"),
sidebarLayout(
sidebarPanel(
fileInput('file1', 'Choose Past CSV File',
accept=c('text/csv',
'text/comma-separated-values,text/plain',
'.csv')),
conditionalPanel(
condition = "output.fileUploaded",
fileInput('file2', 'Choose Future CSV File',
accept=c('text/csv',
'text/comma-separated-values,text/plain',
'.csv')),
downloadButton("downloadData", "Download Prediction")
)
),
mainPanel(
tabsetPanel(type = "tabs",
tabPanel('Results', (DT::dataTableOutput('table'))),
tabPanel("Model Summary",
verbatimTextOutput("summary"))
)
)
)
)
)
shinyServer(function(input, output) {
# hide the output
output$fileUploaded <- reactive({
return(!is.null(input$file1))
})
outputOptions(output, 'fileUploaded', suspendWhenHidden=FALSE)
data <- reactive({
File <- input$file1
if (is.null(File))
return(NULL)
complete <- read.csv(File$datapath,header=T,na.strings=c(""))
File1 <- input$file2
if (is.null(File1))
return(NULL)
raw.data <- read.csv(File1$datapath,header=T,na.strings=c(""))
#Change all variable to factor
complete[] <- lapply(complete, factor)
complete$Target <- recode(complete$Target," 'YES' = 1; 'Yes' = 1; 'NO' = 0 " )
set.seed(33)
splitIndex <- createDataPartition(complete$Target, p = .75, list = FALSE, times = 1)
trainData <- complete[ splitIndex,]
testData <- complete[-splitIndex,]
fitControl <- trainControl(method = "repeatedcv", number = 4, repeats = 4)
set.seed(33)
gbmFit1 <- train(as.factor(Target) ~ ., data = trainData, method = "gbm", trControl = fitControl,verbose = FALSE)
pred <- predict(gbmFit1, testData,type= "prob")[,2]
perf = prediction(pred, testData$Target)
pred1 = performance(perf, "tpr","fpr")
acc.perf <- performance(perf, "acc")
ind = which.max( slot(acc.perf, "y.values")[[1]])
acc = slot(acc.perf, "y.values")[[1]][ind]
output$summary <- renderPrint({
print(c(Accuracy=acc))
})
raw.data[] <- lapply(raw.data, factor)
testpred <- predict(gbmFit1, raw.data,type= "prob")[,2]
final = cbind(raw.data, testpred)
final
})
output$table = DT::renderDataTable({
final <- data()
DT::datatable(
data(), options = list(
pageLength = 5)
)
})
output$downloadData <- downloadHandler(
filename = function() { paste('SLA Prediction', '.csv', sep='') },
content = function(file) {
write.csv(data(),file)
}
)
return(output)
})
模型是使用第一个输入文件创建的，我的要求是用户应该被要求上传第二个输入文件（他们想要预测结果）只有当使用存储在变量acc中的第一个输入文件计算的模型精度应该大于0.9时，我无法为此获得解决方案，任何人都可以帮助我。
答案 0 :(得分：0)
现在第二个文件输入取决于变量acc，并且仅在大于0.9时显示。我还做了一些更改，主要是因为你的代码在我的笔记本电脑上没有工作:)。您可以使用函数return(NULL)代替req来确保值可用。
library(shiny)
library(shinysky)
library(shinythemes)
library(caret)
library(gbm)
library(ROCR)
library(car)
ui <- shinyUI(
fluidPage(
theme = shinytheme("united"), # added new theme from the package 'shinythemes'
titlePanel("Prediction"),
sidebarLayout(
sidebarPanel(
fileInput('file1', 'Choose Past CSV File',
accept=c('text/csv',
'text/comma-separated-values,text/plain',
'.csv')),
uiOutput("dynamic")
),
mainPanel(
# added busyIndicator
busyIndicator(text = "Calculation in progress..",
img = "shinysky/busyIndicator/ajaxloaderq.gif", wait = 500),
tabsetPanel(type = "tabs",
tabPanel('Results',
(DT::dataTableOutput('table'))),
tabPanel("Model Summary",
verbatimTextOutput("summary")),
tabPanel("Predictions",
DT::dataTableOutput('tablePred'))
)
)
)
)
)
server <- shinyServer(function(input, output) {
# hide the output
output$fileUploaded <- reactive({
return(!is.null(input$file1))
})
outputOptions(output, 'fileUploaded', suspendWhenHidden=FALSE)
data <- reactive({
File <- input$file1
req(File)
complete <- read.csv(File$datapath,header=T,na.strings=c(""))
complete
})
model <- reactive({
complete <- lapply(data(), factor)
complete$Target <- recode(data()$Target," 'YES' = 1; 'Yes' = 1; 'NO' = 0 " )
set.seed(33)
splitIndex <- createDataPartition(data()$Target, p = .75, list = FALSE, times = 1)
trainData <- data()[ splitIndex,]
testData <- data()[-splitIndex,]
fitControl <- trainControl(method = "repeatedcv", number = 4, repeats = 4)
set.seed(33)
gbmFit1 <- train(as.factor(Target) ~ ., data = trainData, method = "gbm", trControl = fitControl,verbose = FALSE)
pred <- predict(gbmFit1, testData, type= "prob")[,2]
perf = prediction(pred, testData$Target)
pred1 = performance(perf, "tpr","fpr")
acc.perf <- performance(perf, "acc")
ind = which.max( slot(acc.perf, "y.values")[[1]])
acc = slot(acc.perf, "y.values")[[1]][ind]
retval <- list(model = gbmFit1, accuracy = acc)
return(retval)
})
output$summary <- renderPrint({
req(model())
print(model())
})
output$dynamic <- renderUI({
req(model())
if (model()$accuracy >= 0.9)
list(
fileInput('file2', 'Choose Future CSV File',
accept=c('text/csv',
'text/comma-separated-values,text/plain',
'.csv')),
downloadButton("downloadData", "Download Prediction")
)
})
data2 <- reactive({
req(input$file2)
File1 <- input$file2
raw.data <- read.csv(File1$datapath,header=T,na.strings=c(""))
raw.data
})
preds <- reactive({
raw.data <- data2()
testpred <- predict(model()$model, raw.data,type= "prob")[,2]
print(testpred)
final = cbind(raw.data, testpred)
final
})
output$table = DT::renderDataTable({
DT::datatable(data(), options = list(pageLength = 15))
})
output$tablePred = DT::renderDataTable({
req(input$file2)
DT::datatable(preds(), options = list(pageLength = 15))
})
output$downloadData <- downloadHandler(
filename = function() { paste('SLA Prediction', '.csv', sep='') },
content = function(file) {
write.csv(preds(),file)
}
)
return(output)
})
shinyApp(ui, server)