定义要绘制的变量的函数

Question

我想通过定义为 output$myplot1 、 output$myplot2 等的几个绘图函数制作多个绘图，这些绘图目前在参数 selectInput 中定义，例如 p1 是 Bone_disease。但是，我想使用 selectInput 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。

如何将此行替换为定义要绘制的临床参数的类似行，但不要在侧边栏中显示它：selectInput("p1", "Clinical parameter", choices = c("Serum_M_component"))

我想在边栏中显示的唯一参数是 MicroRNA。

！更新了示例输入数据。

data_prep <- structure(list(miRNA = c("hsa-let-7a-3p", "hsa-let-7a-3p", "hsa-let-7a-3p"
), ID = c("86", "175", "217"), value = c(5.57979757386892, 5.21619202802748, 
5.42796072966512), Serum_M_component = c("IgG", "IgG", "Unknown"
), ISS_stage = c("Stage 3", "Stage 1", "Stage 3"), del17 = c("Poor Sample", 
"No", "No"), t4_14 = c("Poor Sample", "No", "Yes"), Bone_disease = c("No", 
"Yes", "Yes")), row.names = c(NA, 3L), class = "data.frame")

ui.miRNA.clinical <- dashboardPage(
  # Application title
  dashboardHeader(title=h4(HTML("MicroRNA expression <br/> in Multiple myeloma"))),
  dashboardSidebar(
    selectInput("gene", "MicroRNA", choices = unique(data_prep$miRNA)),
    selectInput("p1", "Clinical parameter", choices = c("Bone_disease"),),
    selectInput("p2", "Clinical parameter", choices = c("Serum_M_component")),
    selectInput("p3", "Clinical parameter", choices = c("ISS_stage")),
    selectInput("p4", "Clinical parameter", choices = c("del17")),
    selectInput("p5", "Clinical parameter", choices = c("t4_14"))),
  
  dashboardBody(
    tabsetPanel(
      tabPanel("Plot Bone_disease", plotOutput("myplot1", width = "400px", height = "300px")),
      tabPanel("Plot Serum_M_component", plotOutput("myplot2", width = "400px", height = "300px")),
      tabPanel("Plot ISS_stage", plotOutput("myplot3", width = "400px", height = "300px")),
      tabPanel("Plot del17", plotOutput("myplot4", width = "400px", height = "300px")),
      tabPanel("Plot t4_14", plotOutput("myplot5", width = "400px", 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$p2, "value", fill = input$p2)) + 
      geom_boxplot() + theme_classic(base_size = 12) + labs(x="Clinical parameter",y="MicroRNA expression (cpm,log2)") +
    stat_compare_means(method = "anova") 
    #method ="anova"
  })
  
  output$myplot3 <- renderPlot({
    ggplot(data_selected(), aes_string(input$p3, "value", fill = input$p3)) + 
      geom_boxplot() + theme_classic(base_size = 12) + labs(x="Clinical parameter",y="MicroRNA expression (cpm,log2)") +
    stat_compare_means(method = "anova") 
    #method ="anova"
  })
  
  output$myplot4 <- renderPlot({
    ggplot(data_selected(), aes_string(input$p4, "value", fill = input$p4)) + 
      geom_boxplot() + theme_classic(base_size = 12) + labs(x="Clinical parameter",y="MicroRNA expression (cpm,log2)") +
    stat_compare_means(method = "anova") 
    #method ="anova"
  })
  
  output$myplot5 <- renderPlot({
    ggplot(data_selected(), aes_string(input$p5, "value", fill = input$p5)) + 
      geom_boxplot() + theme_classic(base_size = 12) + labs(x="Clinical parameter",y="MicroRNA expression (cpm,log2)") +
    stat_compare_means(method = "anova") 
    #method ="anova"
  })
}

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

Answer 1

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", "Clinical parameter", choices = c("Serum_M_component")),
      selectInput("p3", "Clinical parameter", choices = c("ISS_stage")),
      selectInput("p4", "Clinical parameter", choices = c("del17")),
      selectInput("p5", "Clinical parameter", choices = c("t4_14"))
    )
    
    ),
  
  dashboardBody(
    tabsetPanel(
      tabPanel("Plot Bone_disease", plotOutput("myplot1", width = "400px", height = "300px")),
      tabPanel("Plot Serum_M_component", plotOutput("myplot2", width = "400px", height = "300px")),
      tabPanel("Plot ISS_stage", plotOutput("myplot3", width = "400px", height = "300px")),
      tabPanel("Plot del17", plotOutput("myplot4", width = "400px", height = "300px")),
      tabPanel("Plot t4_14", plotOutput("myplot5", width = "400px", 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$p2, "value", fill = input$p2)) + 
      geom_boxplot() + theme_classic(base_size = 12) + labs(x="Clinical parameter",y="MicroRNA expression (cpm,log2)") +
      stat_compare_means(method = "anova") 
    #method ="anova"
  })
  
  output$myplot3 <- renderPlot({
    ggplot(data_selected(), aes_string(input$p3, "value", fill = input$p3)) + 
      geom_boxplot() + theme_classic(base_size = 12) + labs(x="Clinical parameter",y="MicroRNA expression (cpm,log2)") +
      stat_compare_means(method = "anova") 
    #method ="anova"
  })
  
  output$myplot4 <- renderPlot({
    ggplot(data_selected(), aes_string(input$p4, "value", fill = input$p4)) + 
      geom_boxplot() + theme_classic(base_size = 12) + labs(x="Clinical parameter",y="MicroRNA expression (cpm,log2)") +
      stat_compare_means(method = "anova") 
    #method ="anova"
  })
  
  output$myplot5 <- renderPlot({
    ggplot(data_selected(), aes_string(input$p5, "value", fill = input$p5)) + 
      geom_boxplot() + theme_classic(base_size = 12) + labs(x="Clinical parameter",y="MicroRNA expression (cpm,log2)") +
      stat_compare_means(method = "anova") 
    #method ="anova"
  })
}

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

定义要绘制的变量的函数

Function to define variable to plot

r

shiny

shiny-server

shinydashboard