在R中创建基线比较数据-复制输入的数据帧

本练习的目的是允许用户根据输入比较两种不同的模型。为此，我创建了一个操作按钮，要求用户指定其基本模型，并创建了一个重置按钮，将数据集恢复到添加基线之前的状态。“基”逻辑确定用户是否希望包括基

单击添加基线操作按钮后，data.frame的当前状态将被保存，分组变量将被重命名，并在其前面添加“基线”（使用粘贴）。用户可以选择一个不同的模型，与此静态基础进行比较

由于某些原因，我无法让observe事件更改数据集。observe事件创建基线数据集fine（使用print（）测试），但是，if（）函数不会更改“data”，因此会停止添加到ggplot的基。这样编写代码有两个原因。1） 通过在observe事件之后包含if（）函数，对数据的任何进一步更改只会更改“data”，然后将其添加到未更改的基线数据中。2） 还允许创建重置按钮，该按钮仅在RBIND发生之前将data.frame重置为

这个小问题激怒了我，我看不出我错在哪里。提前为人们能提供的任何帮助欢呼。有更简单的方法可以做到这一点（开放的建议），然而，虹膜数据只是一个例子的功能，而实际的版本更复杂

library("ggplot2")
if (interactive()) {

 ui <- fluidPage(
selectInput("rows", label = h3("Choose your species"), 
            choices = list("setosa", "versicolor", "virginica")
            ),
actionButton("base", "Create baseline"),
actionButton("reset", "Reset baseline"),
plotOutput(outputId = "plot")

            )     # close fluid page

 server <- function(input, output) {


output$plot <- renderPlot({      # create plot
  base <- "no"                   # create baseline indicator which we can change once the observeevent below is changed
  data <- iris

  data <- iris[which(data$Species == input$rows),]         # Get datasubset based on user input

  observeEvent(input$base, {                                                   # If base is Pressed, run code below:
    baseline <- data                                                           # Make Baseline Data by duplicating the users' specification
    baseline$Species <- paste("Baseline", 
                        data$Species, sep = "_")                                # Rename the grouping variable to add Baseline B4 it
    base <- "yes"                # Change our indicator of whether a baseline had been made to yes
  })                                        # Close observe Event

  observeEvent(input$reset, {    

    base <- "no"    # This is placed before the rbind so that if we want to reset it will stop the merging of the two dataframes before it happens.
  })



  if (base == "yes") {

    data <- rbind(data, baseline)       # Run once the observe event has changed baseline to yes.This is kept seperatel that way any subsequent changes to data will not effect 
                                        # the final data. This command will simple add the base onto the changed "data" before plotting
  }

  observeEvent(input$reset, {    

    base <- "no"
                             })


  ggplot(data, aes(x=Petal.Width, y = as.numeric(Sepal.Width), colour = Species)) +    # variable = each dataset selected, value = respective values for that model
    labs(x="Hypothetical X", y="Hypothetical X") +
    geom_line() 
})                                          # Close Render Plot
  }                                            # Close Serve Function
  shinyApp(ui, server)
                                 }

库（“ggplot2”）
if（interactive（））{
ui在反应式表达式中有观察者，我在更正闪亮代码时多次看到这会导致问题。创建反应式表达式（绘图函数）和观察者仅指定哪一个是物种的基线值（字符串），然后将其提供给绘图函数中的过滤数据：

library(shiny)
library(ggplot2)
ui <- fluidPage(
  selectInput("rows", label = h3("Choose your species"), 
              choices = list("setosa", "versicolor", "virginica")
  ),
  actionButton("base", "Create baseline"),
  actionButton("reset", "Reset baseline"),
  plotOutput(outputId = "plot")

)     # close fluid page

server <- function(input, output) {
  rVals = reactiveValues()
  rVals[['data']] = iris
  rVals[['baseline']] = NULL
  output$plot <- renderPlot({
    # here we duplicate table to manipulate it before rendering
    # the reason for duplicate is that you dont want to affect your
    # base data as it may be used elsewhere
    # note that due to R's copy-on-write this may be expensive operation and 
    # have impact on app performance
    # in all cases using data.table package is recommended to mitigate 
    # some of the CoW implications
    render.data = rVals[['data']][rVals[['data']][['Species']] %in% c(rVals[['baseline']],input$rows),]

    # here manipulate render.data

    # and then continue with plot
    ggplot(data=render.data, 
           aes(x=Petal.Width, y = as.numeric(Sepal.Width), colour = Species,group=Species)
    ) +    
      labs(x="Hypothetical X", y="Hypothetical X") +
      geom_line() 
  })
  observeEvent(input$base,{
    rVals[['baseline']]=input$rows
  })
  observeEvent(input$reset,{
    rVals[['baseline']]=NULL
  })

}                                            
shinyApp(ui, server)

库（闪亮）
图书馆（GG2）
ui Sweet感谢您的输入，您的解决方案可以很好地将基础数据存储为基线，并使用rVals对象添加。您认为有没有一种方法可以在renderPlot内绘图之前简单地将数据集添加到一起？这将允许我修改输入值（例如，在选定的数据集上运行函数）并将其存储为基础？即，在输入$行和绘图之间有几个计算步骤。可能在RVAL[['baseline']]之间有一行代码=NULL和output$plot感谢Peter在此主题上提供的所有帮助，被动输入会很好地工作！我在这个主题上的最后一个问题。如果我在管道的早期将输入与数据集断开，您的解决方案会是什么样子？我已经使用您的解决方案和我的目标包含了相同add的修改版本oment它只会在控制台中循环一个错误。提前感谢您的所有时间和建议。在Shining函数中，反应、观察、观察、事件反应和renderXYZ会对函数中的输入做出响应（如果存在），区别在于对它们进行评估时。有一些关于Shining Responsive environment的主题需要阅读以掌握更多信息在我的示例中，rVals[['baseline']]是一个辅助变量，它假定为空状态或输入$rows中的值以帮助筛选主数据，您不应该将表分配给它。今天晚些时候我将查看您的代码并提出解决方案感谢@PeterS！我示例中的代码的后半部分使用reactiveObject遵循您的建议。它似乎可以工作，直到compa按下“重新操作”按钮。当这种情况发生时，图形变暗。这表明rVal[[
Final
]]工作，直到它与基线和输入数据合并，然后产生问题。
library(shiny)
library(ggplot2)
ui <- fluidPage(
  selectInput("rows", label = h3("Choose your species"), 
              choices = list("setosa", "versicolor", "virginica")
  ),
  actionButton("base", "Create baseline"),
  actionButton("reset", "Reset baseline"),
  plotOutput(outputId = "plot")

)     # close fluid page

server <- function(input, output) {
  rVals = reactiveValues()
  rVals[['data']] = iris
  rVals[['baseline']] = NULL
  output$plot <- renderPlot({
    # here we duplicate table to manipulate it before rendering
    # the reason for duplicate is that you dont want to affect your
    # base data as it may be used elsewhere
    # note that due to R's copy-on-write this may be expensive operation and 
    # have impact on app performance
    # in all cases using data.table package is recommended to mitigate 
    # some of the CoW implications
    render.data = rVals[['data']][rVals[['data']][['Species']] %in% c(rVals[['baseline']],input$rows),]

    # here manipulate render.data

    # and then continue with plot
    ggplot(data=render.data, 
           aes(x=Petal.Width, y = as.numeric(Sepal.Width), colour = Species,group=Species)
    ) +    
      labs(x="Hypothetical X", y="Hypothetical X") +
      geom_line() 
  })
  observeEvent(input$base,{
    rVals[['baseline']]=input$rows
  })
  observeEvent(input$reset,{
    rVals[['baseline']]=NULL
  })

}                                            
shinyApp(ui, server)