定义要绘制的变量的函数

如何解决定义要绘制的变量的函数

我想通过定义为 output$myplot1 、 output$myplot2 等的多个绘图函数绘制多个绘图，这些绘图当前在参数 selectInput 中定义，例如 {{1} } 是骨病。但是，我想使用除 p1 之外的另一个函数来定义要为 myplot1 和 myplot2 绘制哪些参数，因为我不希望它显示在侧边栏中。

如何用定义要绘制的临床参数但不在侧栏中显示的类似线替换此线：selectInput

我想在侧边栏中显示的唯一参数是 selectInput("p1","Clinical parameter",choices = c("Serum_M_component"))。

！更新了示例输入数据。

MicroRNA

解决方法

一种方法是使用 shinyjs 包的 hidden 选项。试试这个

                                                                                                                                                                           "Yes","Yes")),row.names = c(NA,3L),class = "data.frame")
library(shinyjs)

ui.miRNA.clinical <- dashboardPage(
  # Application title
  dashboardHeader(title=h4(HTML("MicroRNA expression <br/> in Multiple myeloma"))),dashboardSidebar(
    useShinyjs(),selectInput("gene","MicroRNA",choices = unique(data_prep$miRNA)),shinyjs::hidden(
      selectInput("p1","Clinical parameter",choices = c("Bone_disease"),),selectInput("p2",choices = c("Serum_M_component")),selectInput("p3",choices = c("ISS_stage")),selectInput("p4",choices = c("del17")),selectInput("p5",choices = c("t4_14"))
    )
    
    ),dashboardBody(
    tabsetPanel(
      tabPanel("Plot Bone_disease",plotOutput("myplot1",width = "400px",height = "300px")),tabPanel("Plot Serum_M_component",plotOutput("myplot2",tabPanel("Plot ISS_stage",plotOutput("myplot3",tabPanel("Plot del17",plotOutput("myplot4",tabPanel("Plot t4_14",plotOutput("myplot5",height = "300px"))
      
    )
  )
)

server.miRNA.clinical <- function(input,output,session) {
  
  # filter data by Gene
  data_selected <- reactive({
    filter(data_prep,miRNA %in% input$gene)
  })
  
  # Plot. use aes_string to simply use character input p
  #my_comparisons <- list( c("Yes","No"),c("Stage 1","Stage 3"))
  output$myplot1 <- renderPlot({
    ggplot(data_selected(),aes_string(input$p1,"value",fill = input$p1)) + 
      geom_boxplot() + theme_classic(base_size = 12) + labs(x="Clinical parameter",y="MicroRNA expression (cpm,log2)") +
      stat_compare_means(method = "wilcox.test")
    #method ="anova"
  })
  output$myplot2 <- renderPlot({
    ggplot(data_selected(),aes_string(input$p2,fill = input$p2)) + 
      geom_boxplot() + theme_classic(base_size = 12) + labs(x="Clinical parameter",log2)") +
      stat_compare_means(method = "anova") 
    #method ="anova"
  })
  
  output$myplot3 <- renderPlot({
    ggplot(data_selected(),aes_string(input$p3,fill = input$p3)) + 
      geom_boxplot() + theme_classic(base_size = 12) + labs(x="Clinical parameter",log2)") +
      stat_compare_means(method = "anova") 
    #method ="anova"
  })
  
  output$myplot4 <- renderPlot({
    ggplot(data_selected(),aes_string(input$p4,fill = input$p4)) + 
      geom_boxplot() + theme_classic(base_size = 12) + labs(x="Clinical parameter",log2)") +
      stat_compare_means(method = "anova") 
    #method ="anova"
  })
  
  output$myplot5 <- renderPlot({
    ggplot(data_selected(),aes_string(input$p5,fill = input$p5)) + 
      geom_boxplot() + theme_classic(base_size = 12) + labs(x="Clinical parameter",log2)") +
      stat_compare_means(method = "anova") 
    #method ="anova"
  })
}

shinyApp(ui.miRNA.clinical,server.miRNA.clinical)