问题描述
我正在尝试将 corrplot 的输出保存到 PDF 文件，并可以选择为其指定唯一的文件名。当我点击下载按钮时，它会在我的 PDF 查看器中打开 PDF 并为其分配一个随机文件名 - 欢迎任何帮助，因为我是一个完整的新手并且已经使用了一周。
library(shiny)
library(shinythemes)
library(psych)
library(corrplot)
library(RColorBrewer)
library(Cairo)
library(Grdevices)
# Correlation plot app with fdr option,which takes a CSV file
# containing species OTUs/ASVs and Metabolites or clinical data
# see corrplot_test_data.csv
# Define UI for data upload app
# this section deals with the user interface (UI)
# and its design and format ----
not_sel <- "Not Selected"
about_page <- tabPanel(title = "About",titlePanel("About"),br(),"Correlation plot for species and Metadata with False discovery rate adjustment")
instruction_page <- tabPanel(title = "Instructions",titlePanel("Instructions"),h4 ("Make sure all your inpit files are Comma seperated variable files (CSV)","Your input file needs to be in the format of Columns = species and variables (Metabolite concs) and Rows = samples and as a CSV file","Your input file must not have any hyphens - or spaces in any of the rows and column headers and names."," Follow the running order i.e. start at 1 and then move to 2 etc","When you've finished click on the Analysis tab to see your plot"))
#ui <- fluidPage(
# App title ----
#titlePanel("Uploading Files"),# Sidebar layout with input and output deFinitions ----
main_page <- tabPanel(title = "Analysis",titlePanel("Analysis"),sidebarLayout(
# Sidebar panel for inputs ----
sidebarPanel(
# Input: Select number of rows to display ----
# radioButtons("disp","1: What do you wish to display in the files?",# choices = c(Head = "head",# All = "all"),# selected = "head"),# Input: Select distance method ----
radioButtons("dist","1: Select distance method for Correlation",inline = TRUE,choices = c(Spearman = "spearman",Pearson = "pearson",Kendall = "kendall"),selected = ";"),# Input: Select fdr method ----
radioButtons("adjust_method","2: Select false discovery rate adjustment method for Correlation",choices = c(Holm = "holm",Hochberg = "hochberg",Hommel = "hommel",Bonferroni = "bonferroni",Benjamini_Hochberg = "BH",Benjamini_Yekutieli = "BY",fdr = "fdr",Kendall = "kendall",None = "none"),# Input: Select display method ----
radioButtons("shape","3: Select the shape of the symbol in your plot",choices = c(Circle = "circle",Square = "square",Ellipse = "ellipse",Number = "number",Shade = "shade",Colour = "color",Pie = "pie"),# Input: Select layout for matrix ----
radioButtons("layout","4: Select the layout of your plot",choices = c(Full = "full",Upper = "upper",Lower = "lower"),# Input: Select ordering of the correlation matrix ----
radioButtons("reordered","5: Select the layout of your plot",choices = c(AOE = "AOE",First_principal_component = "FPC",Alphabet = "alphabet",Hclust = "hclust"),# Input: Select ordering of the correlation matrix ----
radioButtons("hclust_method","6: Select the Hclust method if you chose this in 5",choices = c(Ward = "ward",Single = "single",Complete = "complete",McQuitty = "mcquitty",Median = "median",Centroid = "centroid"),# Size of labels in plot line ----
sliderInput("text_size","Text size for labels:",min = 0.05,max = 2,value = 0.5),# Size of labels in plot line ----
sliderInput("number_size","Text size for correlation coefficient when Number is chosen in 3:",min = 0.01,# Input: Select number of rows to display ----
radioButtons("disp","What do you wish to display in the Input data matrix panel?",choices = c(Head = "head",All = "all"),selected = "head"),# Input: Select file 1 for Input data into corr.test ----
fileInput("file1","6: Choose Input data CSV File",multiple = TRUE,accept = c("text/csv","text/comma-separated-values,text/plain",".csv")),# Download the plot ----
downloadButton('downloadplot','Download Plot'),tags$hr(),),# Main panel for displaying outputs ----
mainPanel(
# Output: Data file ----
tabsetPanel(
tabPanel(title = "Input data matrix",tableOutput("shared")),# first panel
tabPanel(title = "Correlation plot",plotOutput("corr_plot")) # second panel
)
#tableOutput("Metadata")
)
)
)
# Define server logic to read selected file
# this section deals with the functions and
# analysis that will be performed in the
# the app e.g. plots---
server <- function(input,output) {
options(shiny.usecairo=T)
output$shared <- renderTable({
# input$file2 will be NULL initially. After the user selects
# and uploads a file,head of that data file by default,# or all rows if selected,will be shown.
req(input$file1)
df <- read.csv(input$file1$datapath,header = TRUE,sep = ',',quote = input$quote)
if(input$disp == "head") {
return(head(df))
}
else {
return(df)
}
})
output$corr_plot <- renderPlot({
req(input$file1)
# take CSV file of OTU data and make PCoA class for plotting
rawdata <- read.csv(input$file1$datapath,header=T,row.names=1)
mat <- as.data.frame(rawdata)
# create correlation matrix
corr_mat <- corr.test(mat,use = "pairwise",method=input$dist,adjust=input$adjust_method,alpha=.05,ci=TRUE,minlength=5)
corr_r_values <- corr_mat$r
corr_p_values <- corr_mat$p
# set colour palette for heatmap
col=brewer.pal(n=11,name="RdYlBu")
#plot heatmap of correlation matrix
corrplot(corr_r_values,method = input$shape,col=rev(col),type = input$layout,hclust.method = input$hclust_method,order = input$reordered,p.mat = corr_p_values,sig.level = 0.05,insig = "blank",addgrid.col = "#D3D3D3",# adjust for grid colour
tl.cex= input$text_size,# adjust for text size
number.cex = input$number_size,#adjust for number in plot size
pch.cex = "0.5",# input$text_size,tl.col = "black",# adjust for text colour
diag = FALSE,title=" ")
})
#download corrplot output as PDF file
output$downloadplot <- downloadHandler(
filename = function(){paste(" ",'.pdf',sep = '')},content = function(file){
cairo_pdf(filename = file,width = 18,height = 10,pointsize = 12,family = "sans",bg = "transparent",antialias = "subpixel",fallback_resolution = 300)
corrplot(corr_r_values,# adjust for grid colour
tl.cex= input$text_size,# adjust for text size
number.cex = input$number_size,#adjust for number in plot size
pch.cex = "0.5",# adjust for text colour
diag = FALSE,title=" ")
dev.off()
},contentType = "application/pdf"
)
}
# Run the app ----
ui <- navbarPage(title = "PCoA creator",theme = shinytheme('cerulean'),instruction_page,main_page,about_page)
shinyApp(ui = ui,server = server)
解决方法
我就是这样做的
library(shiny)
library(shinythemes)
library(psych)
library(corrplot)
library(RColorBrewer)
# Correlation plot app with FDR option,which takes a CSV file
# containing species OTUs/ASVs and metabolites or clinical data
# see corrplot_test_data.csv
# Define UI for data upload app
# this section deals with the user interface (UI)
# and its design and format ----
not_sel <- "Not Selected"
about_page <- tabPanel(title = "About",titlePanel("About"),br(),"Correlation plot for species and metadata with False discovery rate adjustment")
instruction_page <- tabPanel(title = "Instructions",titlePanel("Instructions"),h4 ("Make sure all your inpit files are Comma seperated variable files (CSV)","Your input file needs to be in the format of Columns = species and variables (metabolite concs) and Rows = samples and as a CSV file","Your input file must not have any hyphens - or spaces in any of the rows and column headers and names."," Follow the running order i.e. start at 1 and then move to 2 etc","When you've finished click on the Analysis tab to see your plot"))
#ui <- fluidPage(
# App title ----
#titlePanel("Uploading Files"),# Sidebar layout with input and output definitions ----
main_page <- tabPanel(title = "Analysis",titlePanel("Analysis"),sidebarLayout(
# Sidebar panel for inputs ----
sidebarPanel(
# Input: Select distance method ----
selectInput("dist","1: Select Distance method for Correlation:",list(`Distance Method` = list("spearman","pearson","kendall"))
),# Input: Select FDR method ----
selectInput("adjust_method","2: Select false discovery rate adjustment method for Correlation:",list(`Adjustment Method` = list("holm","hochberg","hommel","bonferroni","BH","BY","fdr","kendall","none"))
),# Input: Select display method ----
selectInput("shape","3: Select the shape of the symbol in your plot",list(`Shape` = list("square","ellipse","number","shade","color","pie"))
),# Input: Select layout for matrix ----
selectInput("layout","4: Select the layout of your plot",list(`Layout` = list("full","upper","lower"))
),# Input: Select ordering of the correlation matrix ----
selectInput("reordered","5: Select the layout of your plot",list(`Ordering of data` = list("AOE","FPC","alphabet","hclust"))
),# Input: Select ordering of the correlation matrix ----
selectInput("hclust_method","6: Select the Hclust method if you chose this in 5",list(`Clustering method` = list("ward","single","complete","mcquitty","median","centroid"))
),# Size of labels in plot line ----
sliderInput("text_size","Text size for labels:",min = 0.05,max = 2,value = 0.5),# Size of labels in plot line ----
sliderInput("number_size","Text size for correlation coefficient when Number is chosen in 3:",min = 0.01,# Input: Select number of rows to display ----
radioButtons("disp","What do you wish to display in the Input data matrix panel?",choices = c(Head = "head",All = "all"),selected = "head"),# Input: Select file 1 for Input data into corr.test ----
fileInput("file1","6: Choose Input data CSV File",multiple = TRUE,accept = c("text/csv","text/comma-separated-values,text/plain",".csv")),# Height slider bar
sliderInput(inputId = "plot_height",label = "Plot heigth",min = 200,max = 1000,step = 10,value = 400),# Width slider bar
sliderInput(inputId = "plot_width",label = "Plot width",min = 300,max = 1500,value = 500),# Download the plot ----
downloadButton('dbtn','Download Plot'),tags$hr(),),# Main panel for displaying outputs ----
mainPanel(
# Output: Data file ----
tabsetPanel(
tabPanel(title = "Input data matrix",tableOutput("shared")),# first panel
tabPanel(title = "Correlation plot",plotOutput("corr_plot",width = "100%",height ="100%")) # second panel
)
#tableOutput("metadata")
)
)
)
# Define server logic to read selected file
# this section deals with the functions and
# analysis that will be performed in the
# the app e.g. plots---
server <- function(input,output) {
output$shared <- renderTable({
# input$file2 will be NULL initially. After the user selects
# and uploads a file,head of that data file by default,# or all rows if selected,will be shown.
req(input$file1)
df <- read.csv(input$file1$datapath,header = TRUE,check.names = FALSE,sep = ',',quote = input$quote)
if(input$disp == "head") {
return(head(df))
}
else {
return(df)
}
})
# creates a plot object called plot_obj which can be rendered and downloaded
plot_obj <- reactive({
req(input$file1)
# take CSV file of OTU data and make PCoA class for plotting
rawdata <- read.csv(input$file1$datapath,header=T,row.names=1,check.names=FALSE)
mat <- as.data.frame(rawdata)
# create correlation matrix
corr_mat <- corr.test(mat,use = "pairwise",method=input$dist,adjust=input$adjust_method,alpha=.05,ci=TRUE,minlength=5)
# extract correlation values from corr_mat
corr_r_values <- corr_mat$r
# extract p values from corr_mat
corr_p_values <- corr_mat$p
# set colour palette for heatmap
col=brewer.pal(n=11,name="RdYlBu")
#plot heatmap of correlation matrix
corrplot(corr_r_values,method = input$shape,col=rev(col),type = input$layout,hclust.method = input$hclust_method,order = input$reordered,p.mat = corr_p_values,sig.level = 0.05,insig = "blank",addgrid.col = "#D3D3D3",# adjust for grid colour
tl.cex= input$text_size,# adjust for text size
number.cex = input$number_size,#adjust for number in plot size
pch.cex = "0.5",# input$text_size,tl.col = "black",# adjust for text colour
diag = FALSE,title=" ")
})
# this part renders the plot object for presentation in the appropriate panel
#data$Group <- env_data[[input$sel]]
output$corr_plot <- renderPlot({req(plot_obj())},height = exprToFunction(input$plot_height),width = exprToFunction(input$plot_width))
#download corrplot output as PDF file
output$dbtn <- downloadHandler(
filename = function(){paste(" ",'.pdf',sep = '')},content = function(file) {
pdf(file)
#corrplot(plot_obj())
req(input$file1)
# take CSV file of OTU data and make PCoA class for plotting
rawdata <- read.csv(input$file1$datapath,check.names = FALSE)
mat <- as.data.frame(rawdata)
# create correlation matrix
corr_mat <- corr.test(mat,minlength=5)
# extract correlation values from corr_mat
corr_r_values <- corr_mat$r
# extract p values from corr_mat
corr_p_values <- corr_mat$p
# set colour palette for heatmap
col=brewer.pal(n=11,name="RdYlBu")
corrplot(corr_r_values,# adjust for grid colour
tl.cex= input$text_size,# adjust for text size
number.cex = input$number_size,#adjust for number in plot size
pch.cex = "0.5",# adjust for text colour
diag = FALSE,title=" ")
dev.off()
}
)
}
# Run the app ----
ui <- navbarPage(title = "PCoA creator",theme = shinytheme('cerulean'),instruction_page,main_page,about_page)
shinyApp(ui = ui,server = server)