那些遇到过的问题

在 R Shiny 应用程序中渲染 ggplot2 和绘图对象

dan*_*dan 0 r render ggplot2 shiny plotly

我有一个R shiny应用程序使用R和来生成plotly和ggplot2显示数字。

因为渲染plotly图形shiny需要plotly函数，所以renderPlotly图形ggplot2会转换为零件plotly中的对象renderPlotly，这会让它们有点混乱。

这是一个例子。首先，生成一些数据：

set.seed(1)

meta.df <- data.frame(cell = c(paste0("c_",1:1000,"_1w"), paste0("c_",1:1000,"_2w"), paste0("c_",1:1000,"_3w")),
                      cluster = c(sample(c("cl1","cl2","cl3"),1000,replace=T)),
                      age = c(rep(1,1000),rep(2,1000),rep(3,1000)),
                      x = rnorm(3000), y = rnorm(3000))

expression.mat <- cbind(matrix(rnorm(20*1000,1,1), nrow=20, ncol=1000, dimnames=list(paste0("g",1:20),meta.df$cell[1:1000])),
                        matrix(rnorm(20*1000,2,1), nrow=20, ncol=1000, dimnames=list(paste0("g",1:20),meta.df$cell[1001:2000])),
                        matrix(rnorm(20*1000,3,1), nrow=20, ncol=1000, dimnames=list(paste0("g",1:20),meta.df$cell[2001:3000])))
Run Code Online (Sandbox Code Playgroud)

这是应用程序代码：

library(shiny)
library(dplyr)
library(ggplot2)
library(ggpmisc)

server <- function(input, output, session)
{
  output$gene <- renderUI({
      selectInput("gene", "Select Gene to Display", choices = rownames(expression.mat))
  })
  
  output$group <- renderUI({
    if(input$plotType == "Distribution Plot"){
      selectInput("group", "Select Group", choices = c("cluster","age"))
    }
  })
  
  scatter.plot <- reactive({
    scatter.plot <- NULL
    if(!is.null(input$gene)){
      gene.idx <- which(rownames(expression.mat) == input$gene)
      plot.df <- suppressWarnings(meta.df %>% dplyr::left_join(data.frame(cell=colnames(expression.mat),value=expression.mat[gene.idx,]),by=c("cell"="cell")))
      scatter.plot <- suppressWarnings(plotly::plot_ly(marker=list(size=3),type='scatter',mode="markers",color=plot.df$value,x=plot.df$x,y=plot.df$y,showlegend=F,colors=colorRamp(c("lightgray","darkred"))) %>%
                                         plotly::layout(title=input$gene,xaxis=list(zeroline=F,showticklabels=F,showgrid=F),yaxis=list(zeroline=F,showticklabels=F,showgrid=F)) %>%
                                         plotly::colorbar(limits=c(min(plot.df$value,na.rm=T),max(plot.df$value,na.rm=T)),len=0.4,title="Scaled Expression"))
    }
    return(scatter.plot)
  })
  
  distribution.plot <- reactive({
    distribution.plot <- NULL
    if(!is.null(input$gene) & !is.null(input$group)){
      gene.idx <- which(rownames(expression.mat) == input$gene)
      plot.df <- suppressWarnings(meta.df %>% dplyr::left_join(data.frame(cell=colnames(expression.mat),value=expression.mat[gene.idx,]),by=c("cell"="cell")))
      if(input$group == "cluster"){
        distribution.plot <- suppressWarnings(plotly::plot_ly(x=plot.df$cluster,y=plot.df$value,split=plot.df$cluster,type='violin',box=list(visible=T),points=T,color=plot.df$cluster,showlegend=F) %>%
                                                plotly::layout(title=input$gene,xaxis=list(title=input$group,zeroline=F),yaxis=list(title="Scaled Expression",zeroline=F)))
      } else{
        plot.df <- plot.df %>% dplyr::mutate(time=age) %>% dplyr::arrange(time)
        plot.df$age <- factor(plot.df$age,levels=unique(plot.df$age))
        distribution.plot <- suppressWarnings(ggplot(plot.df,aes(x=time,y=value)) +
                                                geom_violin(aes(fill=age,color=age),alpha=0.3) +
                                                geom_boxplot(width=0.1,aes(color=age),fill=NA) +
                                                geom_smooth(mapping=aes(x=time,y=value,group=cluster),color="black",method='lm',size=1,se=T) +
                                                stat_poly_eq(mapping=aes(x=time,y=value,group=cluster,label=stat(p.value.label)),formula=y~x,parse=T,npcx="center",npcy="bottom") +
                                                scale_x_discrete(name=NULL,labels=levels(plot.df$cluster),breaks=unique(plot.df$time)) +
                                                facet_wrap(~cluster) + theme_minimal() + ylab(paste0("#",input$gene," Scaled Expressioh"))+theme(legend.title=element_blank()))
      }
    }
    return(distribution.plot)
  })
  
  output$out.plot <- plotly::renderPlotly({
    if(input$plotType == "Scatter Plot"){
      scatter.plot()
    } else if(input$plotType == "Distribution Plot"){
      distribution.plot()
    }
  })
}


ui <- fluidPage(
  titlePanel("Explorer"),
  sidebarLayout(
    sidebarPanel(
      tags$head(
        tags$style(HTML(".multicol {-webkit-column-count: 3; /* Chrome, Safari, Opera */-moz-column-count: 3; /* Firefox */column-count: 3;}")),
        tags$style(type="text/css", "#loadmessage {position: fixed;top: 0px;left: 0px;width: 100%;padding: 5px 0px 5px 0px;text-align: center;font-weight: bold;font-size: 100%;color: #000000;background-color: #CCFF66;z-index: 105;}"),
        tags$style(type="text/css",".shiny-output-error { visibility: hidden; }",".shiny-output-error:before { visibility: hidden; }")),
      conditionalPanel(condition="$('html').hasClass('shiny-busy')",tags$div("In Progress...",id="loadmessage")),
      selectInput("plotType", "Plot Type", choices = c("Scatter Plot","Distribution Plot")),
      uiOutput("gene"),
      uiOutput("group"),
    ),
    mainPanel(
      plotly::plotlyOutput("out.plot")
    )
  )
)

shinyApp(ui = ui, server = server)
Run Code Online (Sandbox Code Playgroud)

如果用户选择Distribution Plot Plot Type和“年龄” ，则使用和Group生成数字。作为对象，这些数字看起来像这样：ggplot2ggpmiscggplot2

在此输入图像描述

但作为一个plotly对象（我想象该plotly::renderPlotly部分ggplot2使用plotly'sggplotly函数从对象转换而来），它变成： 在此输入图像描述

正如您所看到的，底部的 P 值缺失，并且图例“行为不当”。

首选的解决方案是，plotly::renderPlotly如果对象是一个plotly对象，并且render如果它是一个ggplot2对象，则使用该函数，但我不知道如何实现它（我认为计算和纠正该函数的修改会更困难）ggplot2对象经历了向plotly对象的转换）。

任何想法？

sta*_*rja 5

我认为最简单的解决方案是定义 2 个输出，一个在plotly图前，一个用于ggplot图，并用于shinyjs根据输入显示/隐藏正确的图：

set.seed(1)

meta.df <- data.frame(cell = c(paste0("c_",1:1000,"_1w"), paste0("c_",1:1000,"_2w"), paste0("c_",1:1000,"_3w")),
                      cluster = c(sample(c("cl1","cl2","cl3"),1000,replace=T)),
                      age = c(rep(1,1000),rep(2,1000),rep(3,1000)),
                      x = rnorm(3000), y = rnorm(3000))

expression.mat <- cbind(matrix(rnorm(20*1000,1,1), nrow=20, ncol=1000, dimnames=list(paste0("g",1:20),meta.df$cell[1:1000])),
                        matrix(rnorm(20*1000,2,1), nrow=20, ncol=1000, dimnames=list(paste0("g",1:20),meta.df$cell[1001:2000])),
                        matrix(rnorm(20*1000,3,1), nrow=20, ncol=1000, dimnames=list(paste0("g",1:20),meta.df$cell[2001:3000])))

library(shiny)
library(dplyr)
library(ggplot2)
library(ggpmisc)
library(shinyjs)

server <- function(input, output, session)
{
  output$gene <- renderUI({
    selectInput("gene", "Select Gene to Display", choices = rownames(expression.mat))
  })
  
  output$group <- renderUI({
    if(input$plotType == "Distribution Plot"){
      selectInput("group", "Select Group", choices = c("cluster","age"))
    }
  })
  
  scatter.plot <- reactive({
    scatter.plot <- NULL
    if(!is.null(input$gene)){
      gene.idx <- which(rownames(expression.mat) == input$gene)
      plot.df <- suppressWarnings(meta.df %>% dplyr::left_join(data.frame(cell=colnames(expression.mat),value=expression.mat[gene.idx,]),by=c("cell"="cell")))
      scatter.plot <- suppressWarnings(plotly::plot_ly(marker=list(size=3),type='scatter',mode="markers",color=plot.df$value,x=plot.df$x,y=plot.df$y,showlegend=F,colors=colorRamp(c("lightgray","darkred"))) %>%
                                         plotly::layout(title=input$gene,xaxis=list(zeroline=F,showticklabels=F,showgrid=F),yaxis=list(zeroline=F,showticklabels=F,showgrid=F)) %>%
                                         plotly::colorbar(limits=c(min(plot.df$value,na.rm=T),max(plot.df$value,na.rm=T)),len=0.4,title="Scaled Expression"))
    }
    return(scatter.plot)
  })
  
  distribution.plot <- reactive({
    distribution.plot <- NULL
    if(!is.null(input$gene) & !is.null(input$group)){
      gene.idx <- which(rownames(expression.mat) == input$gene)
      plot.df <- suppressWarnings(meta.df %>% dplyr::left_join(data.frame(cell=colnames(expression.mat),value=expression.mat[gene.idx,]),by=c("cell"="cell")))
      if(input$group == "cluster"){
        distribution.plot <- suppressWarnings(plotly::plot_ly(x=plot.df$cluster,y=plot.df$value,split=plot.df$cluster,type='violin',box=list(visible=T),points=T,color=plot.df$cluster,showlegend=F) %>%
                                                plotly::layout(title=input$gene,xaxis=list(title=input$group,zeroline=F),yaxis=list(title="Scaled Expression",zeroline=F)))
      } else{
        plot.df <- plot.df %>% dplyr::mutate(time=age) %>% dplyr::arrange(time)
        plot.df$age <- factor(plot.df$age,levels=unique(plot.df$age))
        distribution.plot <- suppressWarnings(ggplot(plot.df,aes(x=time,y=value)) +
                                                geom_violin(aes(fill=age,color=age),alpha=0.3) +
                                                geom_boxplot(width=0.1,aes(color=age),fill=NA) +
                                                geom_smooth(mapping=aes(x=time,y=value,group=cluster),color="black",method='lm',size=1,se=T) +
                                                stat_poly_eq(mapping=aes(x=time,y=value,group=cluster,label=stat(p.value.label)),formula=y~x,parse=T,npcx="center",npcy="bottom") +
                                                scale_x_discrete(name=NULL,labels=levels(plot.df$cluster),breaks=unique(plot.df$time)) +
                                                facet_wrap(~cluster) + theme_minimal() + ylab(paste0("#",input$gene," Scaled Expressioh"))+theme(legend.title=element_blank()))
      }
    }
    return(distribution.plot)
  })
  
  output$out.plot_plotly <- plotly::renderPlotly({
    if(input$plotType == "Scatter Plot"){
      scatter.plot()
    } else {
      req(input$group)
      if (input$plotType == "Distribution Plot" && input$group != "age"){
        distribution.plot()
      }
    }
  })
  
  output$out.plot_plot <- renderPlot({
    req(input$group)
    if (input$plotType == "Distribution Plot" && input$group == "age") {
      distribution.plot()
    }
  })
  
  observeEvent(c(input$group, input$plotType), {
    req(input$group)
    if (input$group == "age" && input$plotType == "Distribution Plot") {
      hide("out.plot_plotly")
      show("out.plot_plot")
    } else {
      hide("out.plot_plot")
      show("out.plot_plotly")
    }
  })
}


ui <- fluidPage(
  titlePanel("Explorer"),
  useShinyjs(),
  sidebarLayout(
    sidebarPanel(
      tags$head(
        tags$style(HTML(".multicol {-webkit-column-count: 3; /* Chrome, Safari, Opera */-moz-column-count: 3; /* Firefox */column-count: 3;}")),
        tags$style(type="text/css", "#loadmessage {position: fixed;top: 0px;left: 0px;width: 100%;padding: 5px 0px 5px 0px;text-align: center;font-weight: bold;font-size: 100%;color: #000000;background-color: #CCFF66;z-index: 105;}"),
        tags$style(type="text/css",".shiny-output-error { visibility: hidden; }",".shiny-output-error:before { visibility: hidden; }")),
      conditionalPanel(condition="$('html').hasClass('shiny-busy')",tags$div("In Progress...",id="loadmessage")),
      selectInput("plotType", "Plot Type", choices = c("Scatter Plot","Distribution Plot")),
      uiOutput("gene"),
      uiOutput("group"),
    ),
    mainPanel(
      plotly::plotlyOutput("out.plot_plotly"),
      plotOutput("out.plot_plot")
    )
  )
)

shinyApp(ui = ui, server = server)
Run Code Online (Sandbox Code Playgroud)

归档时间：

查看次数：

2123 次

最近记录：

5 年，10 月 前
相关归档
R中是否存在三元运算符？ 162
在循环中填充R中的数据框 27
随机抽样数据框中的一定百分比的行 16
RMarkdown: Floating TOC and TOC at beginning 16
从R中的二项分布生成相关随机数 10
plotly js:如何在加载绘图图像后运行我的javascript 9
为同一 ggplot 中使用的不同 data.frames 手动添加额外的图例 9
删除绘图点击事件数据 6
ggplot2的barplot不会考虑所有值 3
如何在knitr中隐藏来自ggplot的消息 1
难疑归档
如何将某些内容附加到数组中？ 2895
如何让Git忽略文件模式(chmod)的变化？ 2188
如何在Ruby中编写switch语句 2026
如何用Vim中的换行符替换字符？ 1870
在SQL表中查找重复值 1789
如何在Java中打破嵌套循环？ 1751
从客户端检测到潜在危险的Request.Form值 1437
在YAML中,如何在多行中断字符串？ 1388
接口与抽象类(通用OO) 1372
什么是在Vim中评论/取消注释行的快速方法？ 1081