编辑：我已经简化了应用程序并使其所有代码都可重现。编辑 2：我刚刚发现，当我使用 navBarPage 时，我必须单击 Additional Parameters -> Colour。然后按预期着色。
我正在开发一个闪亮的应用程序，它过滤我的基因，然后绘制剩余基因的热图。最近，我发现了 shinyHeatmaply 包。我已经下载了他们的全局、UI 和服务器，当我在自己的计算机上尝试时，它们按预期工作。不幸的是，当我尝试使用 navbarPage 组合我的过滤器应用程序和它们的热图时，最后一个没有正确渲染。
我创建了一个极简主义示例，将 shinyheatmap 添加到https://shiny.rstudio.com/gallery/shiny-theme-selector.html应用程序中 navbarPage 的第二个 tabPanel，但无论如何我得到了相同的灰色渲染。
UI：Navbar 1 属于 shinytheme 应用程序，而 Navbar 2 的内容属于 shinyheatmaply
tagList(
shinythemes::themeSelector(),
navbarPage(
# theme = "cerulean", # <--- To use a theme, uncomment this
"shinythemes",
tabPanel("Navbar 1",
sidebarPanel(
fileInput("file", "File input:"),
textInput("txt", "Text input:", "general"),
sliderInput("slider", "Slider input:", 1, 100, 30),
tags$h5("Deafult actionButton:"),
actionButton("action", "Search"),
tags$h5("actionButton with CSS class:"),
actionButton("action2", "Action button", class = "btn-primary")
),
mainPanel(
tabsetPanel(
tabPanel("Tab 1",
h4("Table"),
tableOutput("table"),
h4("Verbatim text output"),
verbatimTextOutput("txtout"),
h1("Header 1"),
h2("Header 2"),
h3("Header 3"),
h4("Header 4"),
h5("Header 5")
),
tabPanel("Tab 2", "This panel is intentionally left blank"),
tabPanel("Tab 3", "This panel is intentionally left blank")
)
)
),
tabPanel("Navbar 2",
fluidPage(
sidebarLayout(
sidebarPanel(width=4,
h4('Data Selection'),
fileInput(inputId="mydata", label = "Import Data",multiple = T),
uiOutput('data'),
checkboxInput('showSample','Subset Data'),
conditionalPanel('input.showSample',uiOutput('sample')),
hr(),h4('Data Preprocessing'),
column(width=4,selectizeInput('transpose','Transpose',choices = c('No'=FALSE,'Yes'=TRUE),selected = FALSE)),
column(width=4,selectizeInput("transform_fun", "Transform", c(Identity=".",Sqrt='sqrt',log='log',Scale='scale',Normalize='normalize',Percentize='percentize',"Missing values"='is.na10', Correlation='cor'),selected = '.')),
uiOutput('annoVars'),
br(),hr(),h4('Row dendrogram'),
column(width=6,selectizeInput("distFun_row", "Distance method", c(Euclidean="euclidean",Maximum='maximum',Manhattan='manhattan',Canberra='canberra',Binary='binary',Minkowski='minkowski'),selected = 'euclidean')),
column(width=6,selectizeInput("hclustFun_row", "Clustering linkage", c(Complete= "complete",Single= "single",Average= "average",Mcquitty= "mcquitty",Median= "median",Centroid= "centroid",Ward.D= "ward.D",Ward.D2= "ward.D2"),selected = 'complete')),
column(width=12,sliderInput("r", "Number of Clusters", min = 1, max = 15, value = 2)),
#column(width=4,numericInput("r", "Number of Clusters", min = 1, max = 20, value = 2, step = 1)),
br(),hr(),h4('Column dendrogram'),
column(width=6,selectizeInput("distFun_col", "Distance method", c(Euclidean="euclidean",Maximum='maximum',Manhattan='manhattan',Canberra='canberra',Binary='binary',Minkowski='minkowski'),selected = 'euclidean')),
column(width=6,selectizeInput("hclustFun_col", "Clustering linkage", c(Complete= "complete",Single= "single",Average= "average",Mcquitty= "mcquitty",Median= "median",Centroid= "centroid",Ward.D= "ward.D",Ward.D2= "ward.D2"),selected = 'complete')),
column(width=12,sliderInput("c", "Number of Clusters", min = 1, max = 15, value = 2)),
#column(width=4,numericInput("c", "Number of Clusters", min = 1, max = 20, value = 2, step = 1)),
br(),hr(), h4('Additional Parameters'),
column(3,checkboxInput('showColor','Color')),
column(3,checkboxInput('showMargin','Layout')),
column(3,checkboxInput('showDendo','Dendrogram')),
hr(),
conditionalPanel('input.showColor==1',
hr(),
h4('Color Manipulation'),
uiOutput('colUI'),
sliderInput("ncol", "Set Number of Colors", min = 1, max = 256, value = 256),
checkboxInput('colRngAuto','Auto Color Range',value = T),
conditionalPanel('!input.colRngAuto',uiOutput('colRng'))
),
conditionalPanel('input.showDendo==1',
hr(),
h4('Dendrogram Manipulation'),
selectInput('dendrogram','Dendrogram Type',choices = c("both", "row", "column", "none"),selected = 'both'),
selectizeInput("seriation", "Seriation", c(OLO="OLO",GW="GW",Mean="mean",None="none"),selected = 'OLO'),
sliderInput('branches_lwd','Dendrogram Branch Width',value = 0.6,min=0,max=5,step = 0.1)
),
conditionalPanel('input.showMargin==1',
hr(),
h4('Widget Layout'),
column(4,textInput('main','Title','')),
column(4,textInput('xlab','X Title','')),
column(4,textInput('ylab','Y Title','')),
sliderInput('row_text_angle','Row Text Angle',value = 0,min=0,max=180),
sliderInput('column_text_angle','Column Text Angle',value = 45,min=0,max=180),
sliderInput("l", "Set Margin Width", min = 0, max = 200, value = 130),
sliderInput("b", "Set Margin Height", min = 0, max = 200, value = 40)
)
),
mainPanel(
tabsetPanel(
tabPanel("Heatmaply",
tags$a(id = 'downloadData', class = paste("btn btn-default shiny-download-link",'mybutton'), href = "", target = "_blank", download = NA, icon("clone"), 'Download Heatmap as HTML'),
tags$head(tags$style(".mybutton{color:white;background-color:blue;} .skin-black .sidebar .mybutton{color: green;}") ),
plotlyOutput("heatout",height='600px')
),
tabPanel("Data",
DT::dataTableOutput('tables')
)
)
)
)
)
),
tabPanel("Navbar 3", "This panel is intentionally left blank")
)
)
服务器：关于服务器，前两个输出对应shinytheme，其他的属于shinyheatmaply
d=data(package='datasets')$results[,'Item']
d=d[!grepl('[\\()]',d)]
d=d[!d%in%c('UScitiesD','eurodist','sleep','warpbreaks')]
d=d[unlist(lapply(d,function(d.in) eval(parse(text=paste0('ncol(as.data.frame(datasets::',d.in,'))')))))>1]
d=d[-which(d=='mtcars')]
d=c('mtcars',d)
server <- shinyServer(function(input, output,session) {
####This to output belongs to the shinytheme application####
output$txtout <- renderText({
paste(input$txt, input$slider, format(input$date), sep = ", ")
})
output$table <- renderTable({
head(cars, 4)
})
#######################################################
#Up to here the code belongs to shinyheatmaply
output$txtout <- renderText({
paste(input$txt, input$slider, format(input$date), sep = ", ")
})
output$table <- renderTable({
head(cars, 4)
})
TEMPLIST<-new.env()
TEMPLIST$d<-d
#Annotation Variable UI ----
observeEvent(data.sel(),{
output$annoVars<-renderUI({
data.in=data.sel()
NM=NULL
if(any(sapply(data.in,class)=='factor')){
NM=names(data.in)[which(sapply(data.in,class)=='factor')]
}
column(width=4,
selectizeInput('annoVar','Annotation',choices = names(data.in),selected=NM,multiple=T,options = list(placeholder = 'select columns',plugins = list("remove_button")))
)
})
#Sampling UI ----
output$sample<-renderUI({
list(
column(4,textInput(inputId = 'setSeed',label = 'Seed',value = sample(1:10000,1))),
column(4,numericInput(inputId = 'selRows',label = 'Number of Rows',min=1,max=pmin(500,nrow(data.sel())),value = pmin(500,nrow(data.sel())))),
column(4,selectizeInput('selCols','Columns Subset',choices = names(data.sel()),multiple=T))
)
})
})
#Data Selection UI ----
output$data=renderUI({
if(!is.null(input$mydata)) TEMPLIST$d=c(input$mydata$name,TEMPLIST$d)
selData=head(TEMPLIST$d,1)
selectInput("data","Select Data",TEMPLIST$d,selected = selData)
})
#Color Pallete UI ----
output$colUI<-renderUI({
colSel='Vidiris'
if(input$transform_fun=='cor') colSel='RdBu'
if(input$transform_fun=='is.na10') colSel='grey.colors'
selectizeInput(inputId ="pal", label ="Select Color Palette",
choices = c('Vidiris (Sequential)'="viridis",
'Magma (Sequential)'="magma",
'Plasma (Sequential)'="plasma",
'Inferno (Sequential)'="inferno",
'Magma (Sequential)'="magma",
'Magma (Sequential)'="magma",
'RdBu (Diverging)'="RdBu",
'RdYlBu (Diverging)'="RdYlBu",
'RdYlGn (Diverging)'="RdYlGn",
'BrBG (Diverging)'="BrBG",
'Spectral (Diverging)'="Spectral",
'BuGn (Sequential)'='BuGn',
'PuBuGn (Sequential)'='PuBuGn',
'YlOrRd (Sequential)'='YlOrRd',
'Heat (Sequential)'='heat.colors',
'Grey (Sequential)'='grey.colors'),
selected=colSel)
})
#Manual Color Range UI ----
output$colRng=renderUI({
if(!is.null(data.sel())) {
rng=range(data.sel(),na.rm = TRUE)
}else{
rng=range(mtcars) # TODO: this should probably be changed
}
# sliderInput("colorRng", "Set Color Range", min = round(rng[1],1), max = round(rng[2],1), step = .1, value = rng)
n_data = nrow(data.sel())
min_min_range = ifelse(input$transform_fun=='cor',-1,-Inf)
min_max_range = ifelse(input$transform_fun=='cor',1,rng[1])
min_value = ifelse(input$transform_fun=='cor',-1,rng[1])
max_min_range = ifelse(input$transform_fun=='cor',-1,rng[2])
max_max_range = ifelse(input$transform_fun=='cor',1,Inf)
max_value = ifelse(input$transform_fun=='cor',1,rng[2])
a_good_step = 0.1 # (max_range-min_range) / n_data
list(
numericInput("colorRng_min", "Set Color Range (min)", value = min_value, min = min_min_range, max = min_max_range, step = a_good_step),
numericInput("colorRng_max", "Set Color Range (max)", value = max_value, min = max_min_range, max = max_max_range, step = a_good_step)
)
})
#Import/Select Data ----
data.sel=eventReactive(input$data,{
if(input$data%in%d){
eval(parse(text=paste0('data.in=as.data.frame(datasets::',input$data,')')))
}else{
data.in=importSwitch(input$mydata[input$mydata$name%in%input$data,])
}
data.in=as.data.frame(data.in)
# data.in=data.in[,sapply(data.in,function(x) class(x))%in%c('numeric','integer')] # no need for this
return(data.in)
})
#Building heatmaply ----
interactiveHeatmap<- reactive({
data.in=data.sel()
if(input$showSample){
if(!is.null(input$selRows)){
set.seed(input$setSeed)
if((input$selRows >= 2) & (input$selRows < nrow(data.in))){
# if input$selRows == nrow(data.in) then we should not do anything (this save refreshing when clicking the subset button)
if(length(input$selCols)<=1) data.in=data.in[sample(1:nrow(data.in),pmin(500,input$selRows)),]
if(length(input$selCols)>1) data.in=data.in[sample(1:nrow(data.in),pmin(500,input$selRows)),input$selCols]
}
}
}
# ss_num = sapply(data.in,function(x) class(x)) %in% c('numeric','integer') # in order to only transform the numeric values
if(length(input$annoVar)>0){
if(all(input$annoVar%in%names(data.in)))
data.in <- data.in%>%mutate_at(funs(factor),.vars=vars(input$annoVar))
}
ss_num = sapply(data.in, is.numeric) # in order to only transform the numeric values
if(input$transpose) data.in=t(data.in)
if(input$transform_fun!='.'){
if(input$transform_fun=='is.na10'){
updateCheckboxInput(session = session,inputId = 'showColor',value = T)
data.in[, ss_num]=is.na10(data.in[, ss_num])
}
if(input$transform_fun=='cor'){
updateCheckboxInput(session = session,inputId = 'showColor',value = T)
updateCheckboxInput(session = session,inputId = 'colRngAuto',value = F)
data.in=cor(data.in[, ss_num],use = "pairwise.complete.obs")
}
if(input$transform_fun=='log') data.in[, ss_num]= apply(data.in[, ss_num],2,log)
if(input$transform_fun=='sqrt') data.in[, ss_num]= apply(data.in[, ss_num],2,sqrt)
if(input$transform_fun=='normalize') data.in=heatmaply::normalize(data.in)
if(input$transform_fun=='scale') data.in[, ss_num] = scale(data.in[, ss_num])
if(input$transform_fun=='percentize') data.in=heatmaply::percentize(data.in)
}
if(!is.null(input$tables_true_search_columns))
data.in=data.in[activeRows(input$tables_true_search_columns,data.in),]
if(input$colRngAuto){
ColLimits=NULL
}else{
ColLimits=c(input$colorRng_min, input$colorRng_max)
}
distfun_row = function(x) dist(x, method = input$distFun_row)
distfun_col = function(x) dist(x, method = input$distFun_col)
hclustfun_row = function(x) hclust(x, method = input$hclustFun_row)
hclustfun_col = function(x) hclust(x, method = input$hclustFun_col)
p <- heatmaply(data.in,
main = input$main,xlab = input$xlab,ylab = input$ylab,
row_text_angle = input$row_text_angle,
column_text_angle = input$column_text_angle,
dendrogram = input$dendrogram,
branches_lwd = input$branches_lwd,
seriate = input$seriation,
colors=eval(parse(text=paste0(input$pal,'(',input$ncol,')'))),
distfun_row = distfun_row,
hclustfun_row = hclustfun_row,
distfun_col = distfun_col,
hclustfun_col = hclustfun_col,
k_col = input$c,
k_row = input$r,
limits = ColLimits) %>%
layout(margin = list(l = input$l, b = input$b, r='0px'))
p$elementId <- NULL
p
})
#Render Plot ----
observeEvent(input$data,{
output$heatout <- renderPlotly({
if(!is.null(input$data))
interactiveHeatmap()
})
})
#Render Data Table ----
output$tables=DT::renderDataTable(data.sel(),server = T,filter='top',
extensions = c('Scroller','FixedHeader','FixedColumns','Buttons','ColReorder'),
options = list(
dom = 't',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print','colvis'),
colReorder = TRUE,
scrollX = TRUE,
fixedColumns = TRUE,
fixedHeader = TRUE,
deferRender = TRUE,
scrollY = 500,
scroller = TRUE
))
#Clone Heatmap ----
observeEvent({interactiveHeatmap()},{
h<-interactiveHeatmap()
l<-list(main = input$main,xlab = input$xlab,ylab = input$ylab,
row_text_angle = input$row_text_angle,
column_text_angle = input$column_text_angle,
dendrogram = input$dendrogram,
branches_lwd = input$branches_lwd,
seriate = input$seriation,
colors=paste0(input$pal,'(',input$ncol,')'),
distfun_row = input$distFun_row,
hclustfun_row = input$hclustFun_row,
distfun_col = input$distFun_col,
hclustfun_col = input$hclustFun_col,
k_col = input$c,
k_row = input$r,
limits = paste(c(input$colorRng_min, input$colorRng_max),collapse=',')
)
#l=l[!l=='']
l=data.frame(Parameter=names(l),Value=do.call('rbind',l),row.names = NULL,stringsAsFactors = F)
l[which(l$Value==''),2]='NULL'
paramTbl=print(xtable::xtable(l),type = 'html',include.rownames=FALSE,print.results = F,html.table.attributes = c('border=0'))
h$width='100%'
h$height='800px'
s<-tags$div(style="position: relative; bottom: 5px;",
HTML(paramTbl),
tags$em('This heatmap visualization was created using',
tags$a(href="https://github.com/yonicd/shinyHeatmaply/",target="_blank",'shinyHeatmaply'),
Sys.time()
)
)
output$downloadData <- downloadHandler(
filename = function() {
paste("heatmaply-", gsub(' ','_',Sys.time()), ".html", sep="")
},
content = function(file) {
libdir <- paste(tools::file_path_sans_ext(basename(file)),"_files", sep = "")
htmltools::save_html(htmltools::browsable(htmltools::tagList(h,s)),file=file,libdir = libdir)
if (!htmlwidgets:::pandoc_available()) {
stop("Saving a widget with selfcontained = TRUE requires pandoc. For details see:\n",
"https://github.com/rstudio/rmarkdown/blob/master/PANDOC.md")
}
htmlwidgets:::pandoc_self_contained_html(file, file)
unlink(libdir, recursive = TRUE)
}
)
})
#End of Code ----
})
提前感谢解决这个问题的英雄。最好的奖励，丹尼尔。