使用构建在闪亮服务器内的数据框进行两个输出

我正在开发一个简单的闪亮应用程序,以可视化方差分析中的变异源(总数、介于之间、之内)。基本上,我希望用户为三组单向方差分析场景输入“组n”、“平均数”和“sds”——然后,应用程序生成一个数据集来创建一个图和相应的方差分析表。

我还不知道如何在用户更改输入参数时同时更新绘图和方差分析表。我的大多数尝试都会导致绘图更新,但表不会更新。

我找到的最接近实际的解决方案是下面的“黑客/欺骗”方法,其中相同的数据集生成两次。然而,这显然是不必要的。我假设该解决方案与在服务器函数中创建一个可用于多个输出的“反应”数据集有关。这在原则上似乎应该是一件相当简单的事情。然而,我还不能在线拼凑教程/材料来找出如何做到这一点。如果您对此有任何帮助,我们将不胜感激。

代码:

# Visualizing partitioning variance for oneway ANOVA

library(shiny)

ui <- fluidPage(
   titlePanel("Partitioning Variance in a Oneway ANOVA"),

   sidebarLayout(
      sidebarPanel(
        sliderInput("N", "n for each group:",
                     min = 2, max = 50, value = 25),
        sliderInput("M1", "Mean for Control Group:",
                    min = 1, max = 100, value = 55),
        sliderInput("SD1", "SD for Control Group:",
                    min = 1, max = 20, value = 10),
        sliderInput("M2", "Mean for Treatment Group One:",
                    min = 1, max = 100, value = 55),
        sliderInput("SD2", "SD for Treatment Group One:",
                    min = 1, max = 20, value = 10),
        sliderInput("M3", "Mean for Treatment Group Two:",
               min = 1, max = 100, value = 55),
        sliderInput("SD3", "SD for Treatment Group Two:",
                    min = 1, max = 20, value = 10)
),
      mainPanel(
         plotOutput("varPlot"),
         verbatimTextOutput("anovaTable")
      )
   )
)

server <- function(input, output) {

   output$varPlot <- renderPlot({
     set.seed(1976)
     X1 <- rnorm(input$N, input$M1, input$SD1)
     X2 <- rnorm(input$N, input$M2, input$SD2)
     X3 <- rnorm(input$N, input$M3, input$SD3)
     datOutcome = data.frame(X1, X2, X3)
     library(tidyr)
     dat <- gather(datOutcome, group, outcome)
     dat$group <- factor(dat$group, levels = c("X1", "X2", "X3"), 
                         labels = c("Control", "Treatment One", "Treatment Two"))
     # attach the data
     attach(dat)

     library(car)
     mod <- Anova(lm(outcome ~ group, data = dat), type = "III")

     # make the stripcharts by group
     stripchart(outcome ~ group, method = "jitter", jitter = 0.05, 
                vertical = TRUE, pch = 1, col = "blue", 
                group.names = c("Control", "Treatment One", "Treatment Two"),
                xlim = c(.5,4.75),
                ylim = c((min(dat$outcome) - 5), (max(dat$outcome) + 5)),
                ylab = "Outcome Value",
                main = paste("Group n =", input$N, 
                             "\nRed = total variation, Blue = within groups variation, Green indicates between groups variation"))

     # label group means
     text(1.3, mean(X1), 
          paste("Control \nmean =", format(round(mean(X1), 2), nsmall = 2)), 
          col = "darkgreen", cex = .9)
     text(2.3, mean(X2), 
          paste("Treatment One \nmean =", format(round(mean(X2), 2), nsmall = 2)), 
          col = "darkgreen", cex = .9)
     text(3.3, mean(X3), 
          paste("Treatment Two\n mean =", format(round(mean(X3), 2), nsmall = 2)), 
          col = "darkgreen", cex = .9)

     # add diamonds to indicate the means for each group
     points(1, mean(X1), pch = 18, cex = 2, col = "darkgreen")
     points(2, mean(X2), pch = 18, cex = 2, col = "darkgreen")
     points(3, mean(X3), pch = 18, cex = 2, col = "darkgreen")

     # plot a stripchart for the grand mean
     stripchart( outcome, method="jitter" , jitter=0.05 , 
                 vertical=TRUE , pch=1 , col="red" , 
                 at = 4, add = TRUE, 
                 xlim=c(.5,3.75))

     # label grand mean and add dimaond to indicate mean
     text(4.3, mean(outcome), 
          paste("Grand \nmean =", format(round(mean(outcome), 2), nsmall = 2)), 
          col = "red", cex = .9)
     points(4, mean(outcome), pch = 18, cex = 2)

   })


   output$anovaTable <- renderPrint( {
     set.seed(1976)
     X1 <- rnorm(input$N, input$M1, input$SD1)
     X2 <- rnorm(input$N, input$M2, input$SD2)
     X3 <- rnorm(input$N, input$M3, input$SD3)
     datOutcome = data.frame(X1, X2, X3)
     library(tidyr)
     dat <- gather(datOutcome, group, outcome)
     dat$group <- factor(dat$group, levels = c("X1", "X2", "X3"), 
                         labels = c("Control", "Treatment One", "Treatment Two"))

     A <- Anova(aov(outcome ~ group, data = dat), type = "III")
     A
   }) 
}

shinyApp(ui = ui, server = server)