Shiny＆ggplot：ggplot的aes（）映射语句中无法识别的数字变量

2 回答

• 4

  考虑更简单的例子

  # works
  ggplot(iris, aes(Sepal.Width)) + geom_density(aes(y=..density.. * 5))
  # doesn't work
  N <- 5
  ggplot(iris, aes(Sepal.Width)) + geom_density(aes(y=..density.. * N))
  

  对于为您进行计算的ggplot图层，他们需要创建自己的变量，当他们这样做时，他们无法访问他们没有创建的值（至少它是当前实现的方式） .

  所以你有两个我能想到的选择：1）自己计算密度，或2）动态构建表达式，使其中没有其他未评估的变量 .

  对于选项一，可能看起来像

  dens <- density(iris$Sepal.Width, kernel = "gaussian") #geom_density equivalent
  N <- 5
  ggplot(iris, aes(Sepal.Width)) +
      geom_histogram() + 
      geom_area(aes(x, y*N), data=data.frame(x=dens$x, y=dens$y))
  

  对于选项2，您可以这样做

  N <- 5
  dens_map <- eval(bquote(aes(y = ..density..* .(N))))
  ggplot(iris, aes(Sepal.Width)) +
      geom_histogram() + 
      geom_density(dens_map)
  

  它基本上将变量名称扩展为它的数值 .

  回复于 2024-04-29T09:41:50+08:00
• 0

  虽然之前的答案已经足够好了，但是 stat_density 允许我们提取我们可以进行算术运算并构建图层的密度值，只是想分享这种方法 .

  if(interactive()){
    #
    # Cookie Cutting Analytics
    #
    # Author: Cody
    # Date:   10/16/2017
    # Descr:  An application to analyze David and Sharon's cookie cutting efficiency.
    #
  
    # Libraries -----------------------------------------------------------
    suppressWarnings(library(dplyr))
    suppressWarnings(library(ggplot2))
    suppressWarnings(library(shiny))
  
  
    # User Interface ------------------------------------------------------
    ui <- fluidPage(
  
      # App Title
      titlePanel("Cookie Cutting Analytics"),
  
      # Sidebar layout
      sidebarLayout(
  
        # Sidebar panel for Input
        sidebarPanel(
  
          # Input: Proc Name Dropdown
          selectInput("cutterPerson", "Cookie Cutter:",
                      c("David", "Sharon")),
  
          # Input: Histogram Bin Count Slider
          sliderInput("binCount", "Number of Bins:",
                      min = 1,
                      max = 10,
                      value = 5)
  
        ),
  
        # Main panel for displaying outputs
        mainPanel(
  
          tabsetPanel(type = "tabs",
                      tabPanel("Plots",
                               br(),
                               plotOutput("histogram"),
                               br(),
                               plotOutput("boxPlot")),
                      tabPanel("Stats", verbatimTextOutput("summary")),
                      tabPanel("Data", tableOutput("table"))
          )
  
        )
  
      )
    )
  
  
    # Server Logic --------------------------------------------------------
    server <- function(input, output) {
  
      # Reactive Expression: Cookie Data
      cookieData.df <- reactive ({
        person <- c("David","Sharon","David","Sharon","David","Sharon","David","Sharon","David","Sharon","David","Sharon","David","Sharon","David","Sharon","David","Sharon","David","Sharon","David","Sharon","David","Sharon","David","Sharon","David","Sharon","David","Sharon","David","Sharon","David","Sharon","David","Sharon","David","Sharon","David","Sharon")
        cutTime <- c(5,10,8,12,6,9,8,8,4,15,9,14,5,9,7,12,6,13,8,11,6,12,6,10,8,13,9,8,5,11,4,13,7,10,5,12,6,10,5,15)
        data.frame(person, cutTime)
      })
  
      # Reactive Expression: Person Cutting
      cutterName <- reactive({
        input$cutterPerson
      })
  
      # Reactive Expression: Filtered Data
      filteredData.df <- reactive({
        cookieData.df() %>% select(person, cutTime) %>% filter(person == input$cutterPerson)
      })
  
      # Output: Histogram
      output$histogram <- renderPlot({
        #N <- nrow(filteredData.df())
        #binWidth = (max(filteredData.df()$cutTime)-min(filteredData.df()$cutTime) / input$binCount)
        #N <- 20
        #binWidth = 1
        n <- ggplot(filteredData.df(), aes(cutTime))
  
        n.data <- ggplot_build (n + stat_density(aes(y =..density..)))[['data']][[1]]
  
        n.data$y <- n.data$y * N() * binWidth()
  
        #n.data$y <- n.data$y * 20 * 1
  
        n + geom_histogram(aes(y = ..count..), bins = input$binCount) + 
  
          geom_line(data = n.data, aes(x = x, y = y), color = 'red') + 
  
  
          #geom_density(aes(y = ..density.. * 20 * 1), color = "red") + # David Curve: N = 10, binWidth = (max(cutTime)-min(cutTime))/binCount = (9-4)/5 = 1
          #geom_density(aes(y = ..density.. * 20 * 1.4), color = "red") + # Sharon Curve: N = 10, binWidth = (max(cutTime)-min(cutTime))/binCount = (15-8)/5 = 1.4
          labs(title = "Histogram of Cookie Cut Times", x = "Cut Duration (s)", y = "Frequency") +
          theme(plot.title = element_text(size = 25, face = "bold"),
                axis.title = element_text(size = 15, face = "bold"))
  
        #m <- print(m)
  
      })
  
  
      # Reactive Expression: (N) Filtered Data
      N <- reactive({
        nrow(filteredData.df())
      })
  
      # Reactive Expression: (binWidth) Filtered Data
      binWidth <- reactive({
        (max(filteredData.df()$cutTime) - min(filteredData.df()$cutTime)) / input$binCount
      })
  
    }
  
  
    shinyApp(ui, server)
  }
  

  截图：

  

  回复于 2024-04-29T09:41:50+08:00