Shiny 为选定行和临床参数绘制曲线图

我正在尝试设置一个闪亮的地方，我想为数据中的值做一个方框图，并为“基因”和临床参数（临床中的colnames）创建一个下拉列表。在本例中，临床下拉列表将为

TumorLoad

和

Stage

以及

Gene1-6

我尝试过breifly，但这可能是错误的方法：

library(shiny)



ui <- fluidPage(
  selectInput("variable", "Clinical",choices = names(clinical)),
  plotOutput("myplot"))

server <- function(input, output, session) {
  
  output$myplot <- renderPlot({
    
    ggplot(data.m.plot,aes(input$variable,value,fill=input$variable)) + geom_boxplot()
    
    
  })
}

shinyApp(ui, server)

库（闪亮）
ui可以这样实现：

我没有在应用程序内部熔化和连接数据，而是在应用程序外部进行所有数据争用


添加第二个selectintput


使用ggplot进行绘图，其中我使用aes_字符串简单地使用字符输入


试试这个：

clinical <- data.frame(ID=c("Patient1","Patient2","Patient3","Patient4","Patient5","Patient6"),TumorLoad=c("High","High","High","Low","Low","Low"),Stage=c("1","1","1","3","3","3"))


data <- structure(list(Gene = structure(1:6, .Label = c("Gene1", "Gene2", 
                                                        "Gene3", "Gene4", "Gene5", "Gene6"), class = "factor"), Patient1 = c(24, 
                                                                                                                             42, 42, 4, 24, 24), Patient2 = c(23, 342, 4232, 4, 214, 244), 
                       Patient3 = c(24, 432, 4232, 4, 244, 214), Patient4 = c(424, 
                                                                              142, 412, 4, 234, 214), Patient5 = c(24, 432, 423, 24, 24, 
                                                                                                                   24), Patient6 = c(24, 432, 412, 4, 23, 241)), class = "data.frame", row.names = c(NA, 
                                                                                                                                                                                                     -6L))


library(dplyr)
library(shiny)
library(tidyr)
library(ggplot2)

# Prepare dataset. Make long (melt) and join
data_prep <- data %>% 
  tidyr::pivot_longer(-Gene, names_to = "ID") %>% 
  left_join(clinical)


ui <- fluidPage(
  selectInput("p", "Clinical p", choices = c("TumorLoad", "Stage")),
  selectInput("gene", "Gene", choices = unique(data_prep$Gene)),
  plotOutput("myplot"))

server <- function(input, output, session) {
  
  # filter data by Gene
  data_selected <- reactive({
    filter(data_prep, Gene %in% input$gene)
  })
  
  # Plot. use aes_string to simply use character input p
  output$myplot <- renderPlot({
    ggplot(data_selected(), aes_string(input$p, "value", fill = input$p)) + 
      geom_boxplot()
  })
}

shinyApp(ui, server)

临床
data.selected <- data[grep("Gene1",data$Gene),] #Dropdown menu for $Gene
data.m <- melt(data.selected,id.vars=("Gene"))
data.m.plot <- cbind(clinical,data.m) 
ggplot(data.m.plot,aes(data.m.plot$TumorLoad,value,fill=data.m.plot$TumorLoad)) + geom_boxplot() #Dropdown menu for clinical, in this example $TumorLoad is plotted

clinical <- data.frame(ID=c("Patient1","Patient2","Patient3","Patient4","Patient5","Patient6"),TumorLoad=c("High","High","High","Low","Low","Low"),Stage=c("1","1","1","3","3","3"))


data <- structure(list(Gene = structure(1:6, .Label = c("Gene1", "Gene2", 
                                                        "Gene3", "Gene4", "Gene5", "Gene6"), class = "factor"), Patient1 = c(24, 
                                                                                                                             42, 42, 4, 24, 24), Patient2 = c(23, 342, 4232, 4, 214, 244), 
                       Patient3 = c(24, 432, 4232, 4, 244, 214), Patient4 = c(424, 
                                                                              142, 412, 4, 234, 214), Patient5 = c(24, 432, 423, 24, 24, 
                                                                                                                   24), Patient6 = c(24, 432, 412, 4, 23, 241)), class = "data.frame", row.names = c(NA, 
                                                                                                                                                                                                     -6L))


library(dplyr)
library(shiny)
library(tidyr)
library(ggplot2)

# Prepare dataset. Make long (melt) and join
data_prep <- data %>% 
  tidyr::pivot_longer(-Gene, names_to = "ID") %>% 
  left_join(clinical)


ui <- fluidPage(
  selectInput("p", "Clinical p", choices = c("TumorLoad", "Stage")),
  selectInput("gene", "Gene", choices = unique(data_prep$Gene)),
  plotOutput("myplot"))

server <- function(input, output, session) {
  
  # filter data by Gene
  data_selected <- reactive({
    filter(data_prep, Gene %in% input$gene)
  })
  
  # Plot. use aes_string to simply use character input p
  output$myplot <- renderPlot({
    ggplot(data_selected(), aes_string(input$p, "value", fill = input$p)) + 
      geom_boxplot()
  })
}

shinyApp(ui, server)