OneSample

server.R

library(shiny)

# Define server logic required to draw a histogram
shinyServer(function(input, output) {
  
  
  Data <- reactive({
    # output$contents <- renderTable({
    # input$file1 will be NULL initially. After the user selects
    # and uploads a file, it will be a data frame with 'name',
    # 'size', 'type', and 'datapath' columns. The 'datapath'
    # column will contain the local filenames where the data can
    # be found.
    inFile <- input$file1
    if (is.null(inFile))
      return(NULL)
    read.csv(inFile$datapath, header=input$header, sep=input$sep,
             quote=input$quote)
    
  }) 
  
  
  output$contents <- renderTable({
    my.df <- Data()
    if (is.null(my.df)){return(NULL)}
    my.df})   
  
  
  
  output$fivenum <- renderTable({
    my.df <- Data()
    
    if (is.null(my.df)){return(NULL)}
    
    
    
    quantiles <- quantile(my.df[,1], c(0.25, 0.5, 0.975))
    mins <- min(my.df[,1])
    maxs <- max(my.df[,1])
    fivenum <- data.frame(min = mins, lower=quantiles[1],
                          median=quantiles[2],upper=quantiles[3],
                          max = maxs)
    return(fivenum)
  })
  

  output$fivenumlogs <- renderTable({
    my.df <- Data()
    
    if (is.null(my.df)){return(NULL)}
    
    if (!is.null(my.df)){
    if (min(my.df[,1])>=0){
      my.df[,1] <- log(my.df[,1])}}
    
    quantiles <- quantile(my.df[,1], c(0.25, 0.5, 0.975))
    mins <- min(my.df[,1])
    maxs <- max(my.df[,1])
    fivenum <- data.frame(min = mins, lower=quantiles[1],
                          median=quantiles[2],upper=quantiles[3],
                          max = maxs)
    return(fivenum)
  })
  
  
 
 
  
  output$parametric <- renderTable({
    my.df <- Data()
    if (is.null(my.df)){return(NULL)}
    means <- mean(my.df[,1])
    sds <- sd(my.df[,1])
    ses<- sd(my.df[,1])/sqrt(length(my.df[,1]))
    parametric <- data.frame(mean = c(means[1]), sd=c(sds[1]), se=c(ses[1]))
    return(parametric)
  }) 
  
  
  
  
  
  output$parametriclogs <- renderTable({
    my.df <- Data()
    
    if (is.null(my.df)){return(NULL)}
    
    if (!is.null(my.df)){
    
    if (min(my.df[,1])>=0){
      my.df[,1] <- log(my.df[,1])}
    }
    means <- mean(my.df[,1])
    sds <- sd(my.df[,1])
    ses<- sd(my.df[,1])/sqrt(length(my.df[,1]))
    parametric <- data.frame(mean = c(means[1]), sd=c(sds[1]), se=c(ses[1]))
    return(parametric)
  }) 
  
  
  
  # Expression that generates a histogram. The expression is
  # wrapped in a call to renderPlot to indicate that:
  #
  #  1) It is "reactive" and therefore should re-execute automatically
  #     when inputs change
  #  2) Its output type is a plot
  
  
  
  output$distPlot <- renderPlot({
    my.df <- Data()
    if (is.null(my.df)){return(NULL)}
    x    <- my.df[,1]  # Old Faithful Geyser data
    bins <- seq(min(x), max(x), length.out = input$bins + 1)
    
    # draw the histogram with the specified number of bins
    hist(x, breaks = bins, col = 'salmon', border = 'white')
  })
  
  
  output$distPlotlogs <- renderPlot({
    my.df <- Data()
    if (is.null(my.df)){return(NULL)}
    if (min(my.df[,1])>=0){
      my.df[,1] <- log(my.df[,1])}
    
   
    x    <- my.df[,1]  # Old Faithful Geyser data
    bins <- seq(min(x), max(x), length.out = input$logbins + 1)
    
    # draw the histogram with the specified number of bins
    hist(x, breaks = bins, col = 'brown', border = 'white', main = "Log transformed data")
  })
  
  
})

ui.R

library(shiny)

# Define UI for application that draws a histogram
shinyUI(fluidPage(
  
  # Application title
  titlePanel("Single Variable"),
  
  # Sidebar with a slider input for the number of bins
  sidebarLayout(
    sidebarPanel(
      
      
        fileInput('file1', 'Choose CSV File',
                  accept=c('text/csv',
                           'text/comma-separated-values,text/plain',
                           '.csv')),
        tags$hr(),
        checkboxInput('header', 'Header', TRUE),
        radioButtons('sep', 'Separator',
                     c(Comma=',',
                       Semicolon=';',
                       Tab='\t'),
                     ','),
        radioButtons('quote', 'Quote',
                     c(None='',
                       'Double Quote'='"',
                       'Single Quote'="'"),
                     '"'), 
      
      
      sliderInput("bins",
                  "Number of bins:",
                  min = 1,
                  max = 50,
                  value = 30),
      selectInput("uselogs", "Use logs", c(Yes="Yes", No="No"), "No"),
      conditionalPanel(condition = "input.uselogs== 'Yes'",
        sliderInput("logbins",
                    "Number of bins on log plot:",
                    min = 1,
                    max = 50,
                    value = 30))
        
    ),
    
    # Show a plot of the generated distribution
    mainPanel(
      plotOutput("distPlot"),
      p("The min, lower quartile, median, upper quartile and max are:"),
      tableOutput('fivenum'), 
      p("The observed sample statistics were:"),
      tableOutput('parametric'),
      conditionalPanel(condition = "input.uselogs== 'Yes'",
      plotOutput("distPlotlogs"),
      p("The min, lower quartile, median, upper quartile and max for the log transformed data are:"),
      tableOutput('fivenumlogs'), 
      p("The observed sample statistics for the log transformed data are:"),
      tableOutput('parametriclogs'))
      
      
    )
  )
))