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これは私の最初のStackFlow投稿ですので、何か不適切なことがあった場合はお知らせください。私は2番目のShinyAppに取り組んでいます。このアプリケーションは、ある種の分析のためにフォルダを読み込み、すべての* txtファイルを返すことになっています。私はこれを達成するためにshinyFilesアプリケーションとshinyDirChoose関数を使用しようとしています。 getVolumes（）を使用してアプリケーションを実行すると、自分のコンピュータのファイルやフォルダが表示されません。私のコードには他にもいくつかのエラーがあると確信していますが、正しくスキャンするためのフォルダを取得できない場合は、正しくデバッグすることができません。shinyDirchoose GetVolumes（）はディレクトリを返さない
library(ggplot2)
library(Cairo)
library(DT)
library(plyr)
library(shinythemes)
library(shinyFiles)
shinyApp(
ui = fluidPage(
theme = shinytheme("superhero"),
titlePanel(
fluidRow(
column(9, strong("Winnow Output")),
column(3, img(height = 150, width = 150, src = "spirit_black.jpg"))
)
),
sidebarLayout(
sidebarPanel(
fluidRow(
column(width=12,
shinyDirButton('directory', 'Folder select', 'Please select a folder'),
h3("Directory Selected"),
verbatimTextOutput("dirpath"),
div(class = "option_group",
radioButtons("plot_type", "Plot type",
c("Scatter", "Linear Rates", "AUC by MAE","True-Positives by False-Positives"), inline = FALSE),
conditionalPanel("input.plot_type === 'Scatter'",
selectInput("pvar1", "x-Var",
c("tp" = "tp",
"fp" = "fp"
),
selected = "tp"
),
selectInput("pvar2", "y-Var",
c("tp" = "tp",
"fp" = "fp"
),
selected = "fp"
)
),
conditionalPanel("input.plot_type === 'Linear Rates'",
selectInput("lvar1", "x-Var",
c("tpr" = "tpr",
"fpr" = "fpr"
),
selected = "tpr"
),
selectInput("lvar2", "y-Var",
c("tpr" = "tpr",
"fpr" = "fpr"
),
selected = "fpr"
)
),
conditionalPanel("input.plot_type === 'AUC by MAE'",
sliderInput("auc.min", "AUC axis minimum", min = 0, max = 2, value = .5,step=0.05),
sliderInput("auc.max", "AUC axis maximum", min = 0, max = 2, value = 1,step=0.05),
sliderInput("mae.min", "MAE axis minimum", min = 0, max = 2, value = 0,step=0.05),
sliderInput("mae.max", "MAE axis maximum", min = 0, max = 2, value = 1,step=0.05)
)
)#ends div
)#ends column
)#ends fluidRow
)#ends sidebarPanel
,
mainPanel(
tabsetPanel(
tabPanel("Plot",plotOutput("plot1")),
tabPanel("Table",
fluidRow(
column(4,
h4("means"),
tableOutput("meanresults")
),
column(4,
h4("sums"),
tableOutput("sumresults")
),
column(4,
h4("means"),
tableOutput("meanresults2")
)
)#end fluidRow
)#ends tablPale
)#ends tabsetPanel
)#ends mainPanel
)#ends sidebarLayout
),#ends ui = fluidPage
server = function(input, output) {
roots <- getVolumes()
folderInput1 <- reactive({
shinyDirChoose(input, 'directory', roots = roots, session = session,
restrictions = system.file(package = 'base'))
return(parseDirPath(volumes, input$directory))
})
output$directorypath = renderPrint({ folderInput1() })
files1 <- reactive({
list.files(path = folderInput1(), pattern = "*.txt", full.names = T)
})
nFiles1 <- reactive({ length(files1()) })
filenames <- reactive ({file_path_sans_ext(files1)})
myfiles <- reactive({lapply(files1, function(x) read.table(x, header=TRUE))})
tpmax<-function(list){
tps<-list()
for (i in 1:length(list)){
tps[[i]]<-list[[i]]$tp
}
y<-unlist(lapply(tps, max))
return(max(y))
}
ta<-reactive({tpmax(myfiles)})
#Determines minimum value of all true positives
tpmin<-function(list){
tps<-list()
for (i in 1:length(list)){
tps[[i]]<-list[[i]]$tp
}
y<-unlist(lapply(tps, min))
return(min(y))
}
tb<-reactive({tpmin(myfiles)})
#Determines median value of all true positives
tpmed<-function(list){
tps<-list()
for (i in 1:length(list)){
tps[[i]]<-list[[i]]$tp
}
y<-unlist(lapply(tps, median))
return(median(y))
}
tc<-reactive({tpmed(myfiles)})
#Determines maximum value of all false positives
fpmax<-function(list){
fps<-list()
for (i in 1:length(list)){
fps[[i]]<-list[[i]]$fp
}
y<-unlist(lapply(fps, max))
return(max(y))
}
fa<-reactive ({fpmax(myfiles)})
#Determines minimum value of all false positives
fpmin<-function(list){
fps<-list()
for (i in 1:length(list)){
fps[[i]]<-list[[i]]$fp
}
y<-unlist(lapply(fps, min))
return(min(y))
}
fb<-reactive({fpmin(myfiles)})
#Determines median value of all false positives
fpmed<-function(list){
fps<-list()
for (i in 1:length(list)){
fps[[i]]<-list[[i]]$fp
y<-unlist(lapply(fps, median))
return(median(y))
}
}
fc<-reactive({fpmed(myfiles)})
all.data<<-reactive ({do.call("rbind", myfiles)})
TPFP <- reactive ({ddply(all.data, .(tp, fp, filenames), summarize, count=length(filenames))})
output$plot1 = renderPlot({
if (input$plot_type == "Scatter") {
ggplot(all.data(),aes_string(x=input$pvar1,y=input$pvar2))+geom_point(color="firebrick")
} else if (input$plot_type == "Linear Rates") {
ggplot(all.data,aes_string(x=input$lvar1,y=input$lvar2))+geom_line(size = 1, alpha = 1)+
labs(title= "Comparison of Rates")
}
else if (input$plot_type == "AUC by MAE"){
plot(myfiles[[1]]$mae, myfiles[[1]]$auc, main="Plot of AUC by MAE", xlab="Mean Absolute Error (MAE)", ylab="Area under R-O Curve (AUC)",
pch=21, bg="black", xlim=c(input$mae.min, input$mae.max), ylim=c(input$auc.min,input$auc.max))
plotcol<-c("black")
if (length(myfiles) > 1){
#Create overlapping data plots to compare potentially by GWAS tool
#assuming that the length of the Winnow files is at least 2
for (i in 2:length(myfiles)){
points(myfiles[[i]]$mae, myfiles[[i]]$auc, main="Plot of AUC by MAE", xlab="Mean Absolute Error (MAE)", ylab="Area under R-O Curve (AUC)",
pch=21, bg=rainbow(i+1)[i], xlim=c(input$mae.min, input$mae.max), ylim=c(input$auc.min, input$auc.max))
plotcol[i]<-rainbow(i+1)[i]
}
}
}
else if (input$plot_type == "True-Positives by False-Positives"){
p <- ggplot(TPFP(), aes_string(x=fp, y=tp),environment=environment())
p2 <- p +
geom_rect(data=all.data[1,], aes(xmin=fc, xmax=fa, ymin=tc, ymax=ta),
alpha=0.2, fill="blue", linetype=0) +
geom_rect(data=all.data[1,], aes(xmin=fb, xmax=fc, ymin=tc, ymax=ta),
alpha=0.2,fill="green", linetype=0) +
geom_rect(data=all.data[1,], aes(xmin=fb, xmax=fc, ymin=tb, ymax=tc),
alpha=0.2, fill="blue", linetype=0) +
geom_rect(data=all.data[1,], aes(xmin=fc, xmax=fa, ymin=tb, ymax=tc),
alpha=0.2, fill="gray", linetype=0) +
theme(panel.background=element_rect(fill='white', colour='black')) +
theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank()) +
geom_point(aes(colour=filenames, size=count)) +
scale_size_continuous(range=c(2,8)) +
xlab("False Positives") +
ylab("True Positives") +
ggtitle("False Positives by True Positves") +
xlim(0, fa) + ylim(0, ta)
print(p2)
}
})
output$meanresults = renderTable({apply(all.data()[,1:5],2,mean)},rownames=TRUE)
output$sumresults = renderTable(apply(all.data()[,6:9],2,sum),rownames=TRUE)
output$meanresults2 = renderTable(apply(all.data()[,10:16],2,mean),rownames=TRUE)