我正在我的shinydashboard应用程序(https://plot.ly/r/shiny-tutorial/)中使用普洛特利的闪亮教程。

由于某种原因，每当我运行应用程序并打开Ploly选项卡时，如果我尝试与绘图交互，通常位于Ploly绘图右上角的按钮会变得太大，它们会遮住绘图。

以下是我的用户界面代码：

library(ggplot2)
library(plyr)
library(rCharts)
library(shiny)
library(xtable)
library(shinythemes)
library(shinydashboard)
library(dplyr)
library(plotly)
PopData <- read.csv("CountyPopLong.csv", header=T)
ChangeData <- read.csv("CountyChangeLong.csv", header=T)
CountyPop <- read.csv("CountyPopWide.csv", header=T)
CountyChange <- read.csv("CountyChangeWide.csv", header=T)
CntyInfo <- read.csv("CntyCensusInfo.csv", header=T)
CityPop <- read.csv("CityData.csv", header=T)
CityPopLong <- read.csv("CityPop.csv", header=T)
CityChange <- read.csv("CityChangeWide.csv", header=T)
CityChangeLong <- read.csv("CityChangeLong.csv", header=T)
CityInfo <- read.csv("CityCensusInfo.csv", header=T)
TwpInfo <- read.csv("TwpCensusInfo.csv", header=T)
TwpPop <- read.csv("TownshipPopWide.csv", header=T)
TwpPopLong <- read.csv("TownshipPopLongWithCounty.csv", header=T)
TwpChange <- read.csv("TwpChangeWide.csv", header=T)
TwpChangeLong <- read.csv("TwpChangeLong.csv", header=T)
MHIdata <- read.csv("CountyMHILong.csv", header=T)
PCIdata <- read.csv("CountyPCILong.csv", header=T)
CityACS <- read.csv("CityACSLong.csv", header=T)
TwpACS <- read.csv("TwpACSLong.csv", header=T)
CountyBGLong <- read.csv("CountyBG2000to2010long.csv", header=T)
CityBGLong <- read.csv("CityBG2000to2010long.csv", header=T)
TwpBGLong <- read.csv("TwpBrainGain2000to2010long.csv", header=T)
CountyBG90Long <- read.csv("CountyBG1990to2000long.csv", header=T)
CityBG90Long <- read.csv("CityBG1990to2000long.csv", header=T)
TwpBG90Long <- read.csv("TwpBrainGain1990to2000long.csv", header=T)
options(RCHART_WIDTH=500, RCHART_HEIGHT=500)

dashboardPage(
      header <- dashboardHeader(

      title = "CST Datapage"
      ),



      dashboardSidebar(
        sidebarMenu(
          sidebarMenu(
            menuItem("County Table", tabName="County-Table"),

            menuItem("Township", tabName="Township",
              menuSubItem("Table", tabName = "Table"),
              menuSubItem("Chart", tabName = "Chart")
          ),

            menuItem("Plotly Stuff", tabName = "Plotly-Stuff")
        )),

      tags$head(

        tags$style(HTML("
                        .sidebar{height: 90vh; overflow-y: auto; }
                        .dataTables_wrapper { overflow-x: scroll; }
                        .rChart {height: 500px, width: 100%}
                        "))
      )  

      ),

      dashboardBody(
      tabItems(
      tabItem(tabName =  "Table",

              tabBox(

              title = "Township Table",
              id= "TwpPopTable", height = "5000px", width = "1000px",
              tabPanel("Download Data", 
              fluidPage(
                              absolutePanel(draggable=F, top=125, left = 250, width = "40.2%",
                                            sidebarPanel(
                                              helpText("The township data is organized by county. To view a specific townshipâs population, first select the county it is located. You can compare multiple townships by holding down âCTRLâ while selecting your desired counties."),
                                              selectInput("County4", 
                                                          "Select a County:", 
                                                          c("All", 
                                                            unique(as.character(TwpPop$County))),
                                                          selected = "All",
                                                          multiple = TRUE
                                                          #                                                                               , selectize = FALSE
                                              ),
                                              helpText("To download the full dataset in .csv format, click the button below. Customized data can also be copied and pasted into an Excel spreadsheet with the formatting intact."),
                                              downloadButton('twpPopDownload', label = "Download Data"),
                                              width = 12)))),
              tabPanel("Table", 
                              mainPanel(dataTableOutput(outputId="TwpPopTable"),
                                        p("Source: U.S. Census Bureau, Decennial Censuses 1900-2010"))


               ))),


      tabItem((tabName = "Chart"),

                fluidRow(  
                  column(width=5,
                      box(   
                      showOutput("TwpPopChart", "nvd3"), status= "primary",
                      p("Source: U.S. Census Bureau, Decennial Censuses 1900-2010")
                      , widths = c(1, 10))), 

                  absolutePanel(draggable=T, top=200, left = 1000, width = "20%",
                                helpText("The township data is organized by county. To view a specific townshipâs population, first select the county it is located. You can compare multiple townships by holding down âCTRLâ while selecting your desired counties. Then select the communities you want to compare by clicking on the legend."),
                                selectInput("County5", 
                                            "Select a County:", 
                                            c(unique(as.character(TwpPopLong$County))),
                                            selected = "Aitkin",
                                            multiple = TRUE
                                            #                                                                               , selectize = FALSE
                                )))

      ),

      tabItem(
        (tabName = "County-Table"),

              h2("Ayyyyyy")

              ),



      tabItem(
        (tabName = "Plotly-Stuff"),

          fluidRow( 
          mainPanel(plotlyOutput("trendPlot", width="100%")),


          sliderInput("bins", "Numbner of bins:", min=1,max=50,value=10))
      )),

      #Below is for colors display of the 

      tags$head(tags$style(HTML('
                .skin-blue .main-header .logo{
                  font-family: Georgia, Times, "Times New Roman", serif;
                  font-weight: bold;
                  font-size: 24px;
                  color: #FFFF1C;
                }

                .skin-blue .main-header .logo{
                  background-color: #FF9900;
                }

                .skin-blue .main-header .navbar{
                  background-color: #FF9900;
                }

                .skin-blue .main-sidebar{
                  background-color: #FF9900;
                }

                .skin-blue .main-sidebar{
                  font-family: Georgia, Times, "Times New Roman", serif;
                  font-weight: bold;
                  font-size: 12px;
                  color:#FFFF1C;
                }

                .skin-blue .main-sidebar .sidebar .sidebar-menu .active a{
                  color: #FFFF1C;
                }

                .skin-blue .main-header .logo:hover{
                  background-color: #FF9900;
                }

                ')


      ))

))

以下是我的服务器代码：

library(ggplot2)
  library(plyr)
  library(rCharts)
  library(shiny)
  library(xtable)
  library(RColorBrewer)
  library(shinythemes)
  library(shinydashboard)
  library(dplyr)
  library(plotly)
  PopData <- read.csv("CountyPopLong.csv", header=T)
  ChangeData <- read.csv("CountyChangeLong.csv", header=T)
  CountyPop <- read.csv("CountyPopWide.csv", header=T)
  CountyChange <- read.csv("CountyChangeWide.csv", header=T)
  CntyInfo <- read.csv("CntyCensusInfo.csv", header=T)
  CityPop <- read.csv("CityData.csv", header=T)
  CityPopLong <- read.csv("CityPop.csv", header=T)
  CityChange <- read.csv("CityChangeWide.csv", header=T)
  CityChangeLong <- read.csv("CityChangeLong.csv", header=T)
  CityInfo <- read.csv("CityCensusInfo.csv", header=T)
  TwpInfo <- read.csv("TwpCensusInfo.csv", header=T)
  TwpPop <- read.csv("TownshipPopWide.csv", header=T)
  TwpPopLong <- read.csv("TownshipPopLongWithCounty.csv", header=T)
  TwpChange <- read.csv("TwpChangeWide.csv", header=T)
  TwpChangeLong <- read.csv("TwpChangeLong.csv", header=T)
  MHIdata <- read.csv("CountyMHILong.csv", header=T)
  PCIdata <- read.csv("CountyPCILong.csv", header=T)
  CityACS <- read.csv("CityACSLong.csv", header=T)
  TwpACS <- read.csv("TwpACSLong.csv", header=T)
  CountyBGLong <- read.csv("CountyBG2000to2010long.csv", header=T)
  CityBGLong <- read.csv("CityBG2000to2010long.csv", header=T)
  TwpBGLong <- read.csv("TwpBrainGain2000to2010long.csv", header=T)
  CountyBG90Long <- read.csv("CountyBG1990to2000long.csv", header=T)
  CityBG90Long <- read.csv("CityBG1990to2000long.csv", header=T)
  TwpBG90Long <- read.csv("TwpBrainGain1990to2000long.csv", header=T)
  options(RCHART_WIDTH=800, RCHART_HEIGHT=600)


  function(input, output) {

    output$table <- renderDataTable(iris)

    set.seed(122)
    histdata <- rnorm(500)

    ######################## POPULATION OUTPUTS ########################
    output$PopTable <- renderDataTable({
      data <- CountyPop
      if (input$County != "All"){
        data <- data[data$County %in% input$County,]
      }
      data
    }, options =list(aoColumnDefs = list(list(sClass="alignCenter",aTargets=c(list("_all")) )),  pageLength=10))

    output$PopChart <- renderChart({
      names(PopData) = gsub("\.", "", names(PopData))
      n1 <- nPlot(Population ~ Year, data = PopData, group = "County", type = 'lineChart')
      n1$addParams(dom = 'PopChart')
      n1$yAxis(axisLabel = "Population", width = 62)      
      n1$xAxis(axisLabel = "Year")
      n1$chart(forceY = c(0, 1))
      n1$xAxis(tickValues=c(1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010))
      return(n1)
    })

    output$countyPopDownload <- downloadHandler(
      filename = function() { paste('MNCountyPopData', '.csv', sep='') },
      content = function(file) {
        write.csv(PopData, file)
      }) 

    output$CityPopTable <- renderDataTable({
      data <- CityPop
      if (input$County2 != "All"){
        data <- data[data$County %in% input$County2,]
      }
      data
    }, options =list(aoColumnDefs = list(list(sClass="alignCenter",aTargets=c(list("_all")) )),  pageLength=10))

    output$CityPopChart <- renderChart({
      var <- reactive({input$County3})
      DF <- subset(CityPopLong, County %in% var())
      names(DF) = gsub("\.", "", names(DF))
      n2 <- nPlot(Population ~ Year, data = DF, group = "Name", type = 'lineChart')
      n2$addParams(dom = 'CityPopChart')
      n2$yAxis(axisLabel = "Population", width = 62)      
      n2$xAxis(axisLabel = "Year")
      n2$xAxis(tickValues=c(1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010))
      n2$chart(forceY = c(0, 1))
      return(n2)
    })

    output$cityPopDownload <- downloadHandler(
      filename = function() { paste('MNCityPopData', '.csv', sep='') },
      content = function(file) {
        write.csv(CityPop, file)
      }) 

    output$TwpPopTable <- renderDataTable({
      data <- TwpPop
      if (input$County4 != "All"){
        data <- data[data$County %in% input$County4,]
      }
      data
    }, options =list(aoColumnDefs = list(list( sClass="alignCenter",aTargets=c(list("_all"))  )),  pageLength=10))


    renderChart_pct <- function(expr, env = parent.frame(), quoted = FALSE) {
      func <- shiny::exprToFunction(expr, env, quoted)
      function() {
        rChart_ <- func()
        cht_style <- sprintf("<style>.rChart {width: %s; height: %s} </style>",
                             #### change these here to desired %
                             "100%", "100%")
        cht <- paste(capture.output(rChart_$print()), collapse = '
')
        HTML(paste(c(cht_style, cht), collapse = '
'))
      }
    }

    output$TwpPopChart <- renderChart({
      var <- reactive({input$County5})
      DF <- subset(TwpPopLong, County %in% var())
      names(DF) = sub("\.", "", names(DF))
      n2 <- nPlot(Population ~ Year, data = DF, group = "Name", type = 'lineChart')
      n2$addParams(dom = 'TwpPopChart')
      n2$yAxis(axisLabel = "Population", width = 62)      
      n2$xAxis(axisLabel = "Year")
      n2$xAxis(tickValues=c(1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010))
      n2$chart(forceY = c(0, 1))
      n2$chart(height=500)
      n2$set(width=500)
      return(n2)
    })

    output$twpPopDownload <- downloadHandler(
      filename = function() { paste('MNTownshipPopData', '.csv', sep='') },
      content = function(file) {
        write.csv(TwpPop, file)
      })

    ######################## POPULATION CHANGE OUTPUTS ########################

    output$ChangeTable <- renderDataTable({
      data <- CountyChange
      if (input$County6 != "All"){
        data <- data[data$County %in% input$County6,]
      }
      data
    }, options =list(aoColumnDefs = list(list(sClass="alignCenter",aTargets=c(list("_all"))  )),  pageLength=10))  

    output$ChangeChart <- renderChart({
      names(ChangeData) = gsub("\.", "", names(ChangeData))
      n3 <- nPlot(PercentChange ~ Year, data = ChangeData, group = "County", type = 'lineChart')
      n3$addParams(dom = 'ChangeChart')
      #     n3$yAxis(tickValues=function(d) {return(d + "%")})
      ##    ^infinite recursion
      #     n3$yAxis(tickFormat="#!function(d) {return d3.format('%Y'));}!#" )
      ##    ^all axis values gone, chart turns static
      #     n3$yAxis(labelType='percent')
      ##     ^ only for pie charts
      #     n3$yAxis(tickFormat=function(d) { return(parseInt(d, 10) + "%")})
      ##     ^ "could not find function 'd3.format'"
      n3$yAxis(axisLabel = "Percent Change from Previous Decade", width = 62)      
      n3$xAxis(axisLabel = "Year")
      n3$xAxis(tickValues=c(1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010))
      return(n3)
    })

    output$countyChangeDownload <- downloadHandler(
      filename = function() { paste('MNCountyPopChangeData', '.csv', sep='') },
      content = function(file) {
        write.csv(ChangeData, file)
      }) 

    output$cityChangeTable <- renderDataTable({
      data <- CityChange
      if (input$County7 != "All"){
        data <- data[data$County %in% input$County7,]
      }
      data
    }, options =list(aoColumnDefs = list(list(sClass="alignCenter",aTargets=c(list("_all"))  )),  pageLength=10))  

    output$cityChangeChart <- renderChart({
      var <- reactive({input$County8})
      DF <- subset(CityChangeLong, County %in% var())
      names(DF) = gsub("\.", "", names(DF))
      n4 <- nPlot(PercentChange ~ Year, data = DF, group = "Name", type = 'lineChart')
      n4$addParams(dom = 'cityChangeChart')
      n4$yAxis(axisLabel = "Percent Change from Previous Decade", width = 62)      
      n4$xAxis(axisLabel = "Year")
      n4$xAxis(tickValues=c(1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010))
      #     n3$chart(forceY = c(-100, 100))
      return(n4)
    })

    output$cityChangeDownload <- downloadHandler(
      filename = function() { paste('MNCityPopChangeData', '.csv', sep='') },
      content = function(file) {
        write.csv(CityChange, file)
      })

    output$twpChangeTable <- renderDataTable({
      data <- TwpChange
      if (input$County9 != "All"){
        data <- data[data$County %in% input$County9,]
      }
      data
    }, options =list(aoColumnDefs = list(list(sClass="alignCenter",aTargets=c(list("_all"))  )), pageLength=10))  

    output$twpChangeChart <- renderChart({
      var <- reactive({input$County10})
      DF <- subset(TwpChangeLong, County %in% var())
      names(DF) = gsub("\.", "", names(DF))
      n5 <- nPlot(PercentChange ~ Year, data = DF, group = "Name", type = 'lineChart')
      n5$addParams(dom = 'twpChangeChart')
      n5$yAxis(axisLabel = "Percent Change from Previous Decade", width = 62)      
      n5$xAxis(axisLabel = "Year")
      n5$xAxis(tickValues=c(1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010))
      #     n3$chart(forceY = c(-100, 100))
      return(n5)
    })

    output$twpChangeDownload <- downloadHandler(
      filename = function() { paste('MNTownshipPopChangeData', '.csv', sep='') },
      content = function(file) {
        write.csv(TwpChange, file)
      })


    data(movies, package = "ggplot2")
    minx <- min(movies$rating)
    maxx <- max(movies$rating)

      output$trendPlot <- renderPlotly({
        # size of the bins depend on the input 'bins'
        size <- (maxx - minx) / input$bins

        # a simple histogram of movie ratings
        p <- plot_ly(movies, x = rating, autobinx = F, type = "histogram",
                     xbins = list(start = minx, end = maxx, size = size))
        # style the xaxis
        layout(p, xaxis = list(title = "Ratings", range = c(minx, maxx), autorange = F,
                               autotick = F, tick0 = minx, dtick = size))
      })




  }

目前有很多未使用的代码，因为我还没有完成UI。从本质上讲，我是从前面闪亮的代码中提取代码来组装一个shinydashboard。

推荐答案

这里有一个更通用的解决方案：

.plotly .modebar-btn svg {
  height: 1em!important;
  width: 1em!important;
}