在shinydashboard中使用Plotly，按钮太大而无法看到情节
我在我的shinydashboard应用程序（https://plot.ly/r/shiny-tutorial/）中使用Plotly的教程闪亮。
出于某种原因，每当我运行应用程序并转到Plotly选项卡时，如果我尝试与绘图交互，通常位于Plotly绘图右上角的按钮变得如此之大，它们会掩盖情节。
这是我的ui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;
}
')
))
))
这是我的服务器代码：
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_$print()), collapse = '\n')
HTML(paste(c(cht_style, cht), collapse = '\n'))
}
}
output$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$bins
# a simple histogram of movie ratings
p <- plot_ly(movies, x = rating, autobinx = F, type = "histogram",
xbins = list(start = minx, end = maxx, size = size))
# style the xaxis
layout(p, xaxis = list(title = "Ratings", range = c(minx, maxx), autorange = F,
autotick = F, tick0 = minx, dtick = size))
})
}
目前有很多未使用的代码，因为我还没有完成ui。基本上我是从以前的闪亮代码中获取代码来组装一个shinydashboard。
3 个答案:
答案 0 :(得分：0)
我在寻找同一问题的解决方案时遇到过这篇文章（虽然我使用闪亮的app不使用shinydashboard）。我不确定为什么会这样，但前两个图标将占据整个情节输出。
我认为情节模式栏在我的情况下并不太重要，并决定删除它。使用：plot_ly(....) %>% config(displayModeBar = F)在此帖子中var node = XmlDoc.Descendants("InkZoneProfile").Where(x=> x.Attribute("Side") !=null && x.Attribute("Side").Value == "Front").FirstorDefault();
消除了情节模式栏和问题。
答案 1 :(得分：0)
这是一个更通用的解决方案：
.plotly .modebar-btn svg {
height: 1em!important;
width: 1em!important;
}
答案 2 :(得分：-1)
我面临同样的问题。
这绝对是因为$param$width与来自 rCharts 的param$height参数之间的冲突干扰情节。关于问题here
<强>更新 手动编辑html并替换所有
<div class="modebar modebar--hover">
与
<div class="modebar modebar--hover" style="
width: 200px;
height: 35px;">
是一个可能很脏的＆＃34; hacky＆＃34;溶液
必须有更清洁的方法来做到这一点，但在此之前这应该有所帮助。
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