Local RShiny Server not displaying app

[Anu Surendra]

Hi, 

I am fairly new to RShiny. I have installed the local RShiny Server on my Ubuntu desktop. I after reading the documentation, put my app file within a directory in the path "/srv/shiny-server". However, when i go to the "localhost:38/neuroapp" on the browser, i only see the file directory and not the app.

Below is my code for app.R: 

`

library(reshape2)

library(shiny)

library(ggplot2)

library(gridExtra)

library(DT)

library(dplyr)

library(plyr)

library(gtools)

# Define UI for application that draws a histogram

main.dir <- "."

cmi.m <- as.data.frame(read.delim(paste(main.dir,"Clinical_Metabolite_Information.tab",

                                              sep="/"),header=F,as.is=T, sep="\t"),stringAsFactors=F)

csi.m <- as.data.frame(read.delim(paste(main.dir,"Clinical_Subject_Information.tab",

                                              sep="/"),header=F,as.is=T, sep="\t"),stringAsFactors=F)

Muts.t.melt.mean.normalized <- as.data.frame(read.delim(paste(main.dir,"Muts.t.melt.mean.normailized.tab",

                                        sep="/"),header=T,as.is=T, sep="\t"),stringAsFactors=F)

Muts.t.melt.mean.normalized <- Muts.t.melt.mean.normalized[-which(Muts.t.melt.mean.normalized$Type=="Depressed"),]

Muts.t.melt.mean.normalized <- Muts.t.melt.mean.normalized[-which(Muts.t.melt.mean.normalized$Type=="Dementia"),]

colnames(cmi.m) <- cmi.m[1,]

colnames(csi.m) <- csi.m[1,]

g<-"Age_years (mean \u00b1 SD)"

Encoding(g)<-"UTF-8"

colnames(csi.m)[9] <- g

cmi.m <- cmi.m[-1,]

csi.m <- csi.m[-1,]

csi.m[,9] <- gsub("±.*","",csi.m[,9])

csi.m[,9] <- gsub("\\(.*","",csi.m[,9])

csi.m <- apply(csi.m,2,function(x) gsub("±.*","",x))

csi.m <- apply(csi.m,2,function(x) gsub("œ\?.*","",x))

csi.m <- apply(csi.m,2,function(x) gsub("\\(.*","",x))

csi.m <- apply(csi.m,2,function(x) gsub("N/A",NA,x))

cmi.m <- apply(cmi.m,2,function(x) gsub("±.*","",x))

cmi.m <- apply(cmi.m,2,function(x) gsub("œ\?.*","",x))

cmi.m <- apply(cmi.m,2,function(x) gsub("\\(.*","",x))

cmi.m <- apply(cmi.m,2,function(x) gsub("\\[.*","",x))

cmi.m <- apply(cmi.m,2,function(x) gsub("^-$",NA,x))

cmi.m <- apply(cmi.m,2,function(x) gsub("NULL",NA,x))

cmi.m <- apply(cmi.m,2,function(x) gsub("N/A",NA,x))

cmi.m[!nzchar(cmi.m)] <- NA

cmi.m[,"corrected_type"] <- gsub("AD.*","AD",as.vector(cmi.m[,"corrected_type"]))

cmi.m[,"corrected_type"] <- gsub("MCI.*","MCI",as.vector(cmi.m[,"corrected_type"]))

cmi.m[,"corrected_type"] <- gsub("Control.*","Control",as.vector(cmi.m[,"corrected_type"]))

cmi.m[,"corrected_type"] <- gsub(".*AD","AD",as.vector(cmi.m[,"corrected_type"]))

cmi.m[,"corrected_type"] <- gsub(".*MCI","MCI",as.vector(cmi.m[,"corrected_type"]))

cmi.m[,"corrected_type"] <- gsub(".*Control","Control",as.vector(cmi.m[,"corrected_type"]))

#z <- "[Cho] (choline) (corrected for atrophy)"

#Muts <- Muts.t.melt.mean.normalized[Muts.t.melt.mean.normalized$metabolite==z,]

#Muts <- melt(Muts[,-4], id=c("PMID", "metabolite","Type"))

#Muts[,1] <- factor(as.numeric(as.character(Muts[,1])),levels = sort(unique(as.numeric(as.character(Muts[,1])))))

#p1 <- ggplot(Muts, aes(fill=Type, y=as.numeric(value), x=PMID)) + 

#  geom_bar(position="stack", stat="identity") +

#  ggtitle(z) +

#  theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank(),

#        axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

u <- fluidPage(

  

  # Application title

  titlePanel("ADNeuromine"),

  

  # Sidebar with a slider input for the number of bins

  sidebarLayout(

    sidebarPanel(

      sliderInput("x", "Select Age:", 

                 min=20, max=100, value=50),

      selectInput("z", "Metabolite:", sort(colnames(cmi.m)[-1:-5]))

      

    ),

    

    # Show a plot of the generated distribution

    mainPanel(

      tabsetPanel(

        tabPanel("Plot",

                 fluidRow(

                   plotOutput("distPlot4"))

        ),tabPanel("Summary",  dataTableOutput("summary")))

    )

  )

)

s <- function(input, output) {

  

  mydata <- reactive({

    x <- input$x

    z <- input$z

    print(z)

    subset.indx <- which(as.numeric(csi.m[,9])>=x)

    subset.pmids <- unique(csi.m[subset.indx,2])

    Muts1 <- as.data.frame(cmi.m[which(cmi.m[,2]%in%subset.pmids),])

    #Muts2 <- as.data.frame(csi.m[subset.indx,])

    Muts3 <- Muts.t.melt.mean.normalized[which(Muts.t.melt.mean.normalized$PMID%in%subset.pmids),]

    Muts3 <- Muts3[which(Muts3$metabolite==z),]

    

    Muts3.subset <- Muts3[which(!is.na(Muts3$mean)),]

    print(Muts3.subset)

    

    Muts2 <- as.data.frame(csi.m[which(as.vector(csi.m[,2])%in%unique(Muts3.subset$PMID)),])

    

    print(Muts2)

    

    list(muts1=as.data.frame(Muts1),muts2=as.data.frame(Muts2),muts3=as.data.frame(Muts3))

  })

  output$distPlot4 <- renderPlot({

    z <- input$z

    Muts <- mydata()[[3]]

    Muts[,1] <- factor(as.numeric(as.character(Muts[,1])),levels = sort(unique(as.numeric(as.character(Muts[,1])))))

    

    hm <- sort(unique(Muts[,3]))

    hm <- hm[-which(hm=="Control")]

    hm <- c("AD","MCI","Control")

    

    Muts[,3] <- factor(Muts[,3],levels = hm)

    print(Muts)

    p1 <- ggplot(Muts, aes(fill=Type, y=as.numeric(normalized_mean), x=PMID)) + 

      geom_bar(position="stack", stat="identity") +

      ggtitle(paste(z," concentration across publications")) + xlab("PMID") +

      theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank(),

            axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

    

    grid.arrange(p1, ncol=1,widths = c(2))

  })

  output$summary <-  DT::renderDataTable({

    Muts <- mydata()[[2]][,c(2,4,5,7:10)]

    Muts

  })

}

shinyApp(u,s)

`

 

Any help would be much appriciated.