# # This is a Shiny web application. You can run the application by clicking # the 'Run App' button above. # # Find out more about building applications with Shiny here: # # http://shiny.rstudio.com/ # ##Adding a save button. ## Suppose we have found a data set we like, and want to use/save elsewhere. For a complex app, this could be used ## to record user feedback or something. Note that because you are allowing an external user to write things to your ## server space, this involves some possible risks--they could try to fill up your computer's memory and crash your system. ## ## ## To do this, we have to consider a couple things--what is it we want to save? How do we want to save it? And ## should we try to simply 'log' information to an existing file, or completely overwrite things. ## ## for the moment, let's say we want to create a logging file that simply adds the information in the summary table column freq to an ## existing .csv file. This is pretty straightforward to do with the append=T argument of write.csv. ## ## To do this, we need to: ## 1. create an action button in the interface. ## 2. create something that responds to an update in the action button. that depends on the state of the action button. ## 3. have this second thing do the file saving, relying on the data table. Perhaps making that data table reactive itself. ## The new part is #2. We can use a different kind of reactive expression called observe() or observeEvent() to do this. ## This gets added as a part of the server function. ## observeEvent will take as its first argument the value you want to react to (i.e., the button), and as its second argument code to run. library(shiny) # Define UI for application that draws a histogram ui <- fluidPage( # Application title titlePanel("Random distribution generation"), # Sidebar with a slider input for number of bins sidebarLayout( sidebarPanel( sliderInput("bins", "Number of bins:", min = 1, max = 50, value = 30), selectInput("type",label="Select form of distribution", choices=c("normal","uniform")), numericInput("number","Number of samples",500,min=100,max=10000,step=100,width='100%'), actionButton("save","Save to file"), tags$a("Link to wikipedia",href="http://en.wikipedia.org"), tags$p(), tags$hr(), tags$a( tags$img(src="https://upload.wikimedia.org/wikipedia/en/c/c9/Michigan_Technological_University_logo.svg"), href="http://mtu.edu") ), # Show a plot of the generated distribution mainPanel( h1("Histograms"), plotOutput("distPlot"), plotOutput("distPlot2",width=400, height=400), tableOutput("table"), h3("Overview in text:"), textOutput("textdesc") ) ) ) server <- function(input, output,session) { data <- reactive( { if( input$type=="normal") { x <- rnorm(input$number) updateNumericInput(session,"bins",value=25) }else if(input$type=="uniform") { x <- runif(input$number) updateNumericInput(session,"bins",value=10) } x } ) output$distPlot <- renderPlot({ x <- data() bins <- seq(min(x), max(x), length.out = input$bins + 1) par(mfrow=c(1,1)) # draw the histogram with the specified number of bins hist(x, breaks = bins, col = 'gold', border = 'white') }) output$distPlot2 <- renderPlot({ x <- data() par(mfrow=c(1,1)) # draw the histogram with the specified number of bins qqnorm(x) }) output$table<- renderTable({ print(input$save) x <- data() summary(data.frame(x)) }) output$textdesc <- renderText( paste("using ",input$type," distribution with a mean of ", round(mean(data()),3), " and standard devation of",round(sd(data()),3),"\n") ) observeEvent(input$save, { tmp <- summary(data()) print(tmp) write.table(t(as.vector(tmp)),file="logfile.csv",append=T,sep=",", col.names=F) print("writing log file") }) } # Run the application shinyApp(ui = ui, server = server)