在 Shiny 中绘制动态 C5.0 决策树

Question

我正在开发一个 Shiny 应用程序，让用户根据需要选择因/自变量，然后执行 C5.0 以生成摘要和树形图。但是，生成绘图时出现错误消息。有谁知道解决方案？请找到代码：

library(shiny)

ui <- fluidPage(
  titlePanel('Plotting Decision Tree'),
  sidebarLayout(
    sidebarPanel(
      h3('iris data'),
      uiOutput('choose_y'),
      uiOutput('choose_x'),
      actionButton('c50', label = 'Generate C5.0 summary and plot')
    ),
    mainPanel(
      verbatimTextOutput('tree_summary'),
      plotOutput('tree_plot_c50')
    )
  )
)

# server.R
library(shiny)
library(C50)

server <- function(input, output) {
  output$choose_y <- renderUI({
    is_factor <- sapply(iris, FUN = is.factor)
    y_choices <- names(iris)[is_factor]
    selectInput('choose_y', label = 'Choose Target Variable', choices = y_choices)
  })

  output$choose_x <- renderUI({
    x_choices <- names(iris)[!names(iris) %in% input$choose_y]
    checkboxGroupInput('choose_x', label = 'Choose Predictors', choices = x_choices)
  })

  observeEvent(input$c50, {
    c50_fit <- C5.0(as.formula(paste(isolate(input$choose_y), '~', paste(isolate(input$choose_x), collapse = '+'))), data = iris)
    output$tree_summary <- renderPrint(summary(c50_fit))
    output$tree_plot_c50 <- renderPlot({
      plot(c50_fit)
    })
  })
}

shinyApp(ui, server)

Answer 1

在你的server.R试试

function(input, output) {
  output$choose_y <- renderUI({
    is_factor <- sapply(iris, FUN = is.factor)
    y_choices <- names(iris)[is_factor]
    selectInput('choose_y', label = 'Choose Target Variable', choices = y_choices)
  })

  output$choose_x <- renderUI({
    x_choices <- names(iris)[!names(iris) %in% input$choose_y]
    checkboxGroupInput('choose_x', label = 'Choose Predictors', choices = x_choices)
  })

  observeEvent(input$c50, {
    form <- paste(isolate(input$choose_y), '~', paste(isolate(input$choose_x), collapse = '+'))
    c50_fit <- eval(parse(text = sprintf("C5.0(%s, data = iris)", form)))
    output$tree_summary <- renderPrint(summary(c50_fit))
    output$tree_plot_c50 <- renderPlot({
      plot(c50_fit)
    })
  })
}

---

解释。plot 方法似乎正在寻找C5.0() 的返回值的call 元素中指定的术语，并在找不到它们时引发错误。在您的情况下，这指的是 input 对象。解决方法是调用C5.0()，并通过eval(parse(text = ...)) 构造完全指定公式（例如Species ~ Sepal.Length + Petal.Width）。