server.R

library(shiny)

  german_data <- read.table(file="http://archive.ics.uci.edu/ml/machine-learning-databases/statlog/german/german.data",
                            sep=" ", header=FALSE, stringsAsFactors=TRUE)
  names(german_data) <- c('ca_status','duration','credit_history','purpose','credit_amount','savings',
                          'present_employment_since','installment_rate_income','status_sex','other_debtors',
                          'present_residence_since','property','age','other_installment','housing','existing_credits',
                          'job','liable_maintenance_people','telephone','foreign_worker','gb')
  german_data$gb <- factor(german_data$gb, levels=c(2,1), labels=c("bad","good"))
  german_data <- german_data[,c("duration","age","credit_amount","gb")]

shinyServer(function(input, output) {
  
  # Show the first "n" observations
  output$gd <-  reactive({
   gd <- german_data[,c(input$variable,"gb")]
  })

  output$view <- renderTable({
    cuts <- sort(unique(c(input$g1,input$g2,input$g3,input$g4,input$g5,input$g6,input$g7,input$g8,input$g9)))
  # Table by g/b
  vartable  <- table(cut(gd[,1],breaks=c(min(gd[,1]),cuts,max(gd[,1]))),gd[,2])
  probtable <- prop.table(vartable,margin=2)
  woedata   <- data.frame(
    good    = vartable[,"good"],
    bad     = vartable[,"bad"],
    pctgood = probtable[,"good"],
    pctbad  = probtable[,"bad"],
    odds    = probtable[,"good"]/probtable[,"bad"],
    woe     = log(probtable[,"good"]/probtable[,"bad"]),
    miv     = log(probtable[,"good"]/probtable[,"bad"])  * (probtable[,"good"] - probtable[,"bad"])
  )
  woedata$class <- factor(rownames(woedata), levels=rownames(woedata))
  woedata
  })

  output$distPlot <- renderPlot({
    barplot(woedata$woe, names=levels(woedata$class))
  })


})

ui.R

library(shiny)

# Define UI for application that plots random distributions 
shinyUI(pageWithSidebar(
  
  # Application title
  headerPanel("Interactive Binning"),
  
  # Sidebar with a slider input for number of observations

  sidebarPanel(
    selectInput("variable", "Variable:", list("Age" = "age", "Duration" = "duration", "Credit Amount" = "credit_amount")),
    sliderInput("g1","g1",min = min(gd[,1]),max = max(gd[,1]), value = 10),
    sliderInput("g2","g2",min = min(gd[,1]),max = max(gd[,1]), value = 10),
    sliderInput("g3","g3",min = min(gd[,1]),max = max(gd[,1]), value = 10),
    sliderInput("g4","g4",min = min(gd[,1]),max = max(gd[,1]), value = 10),
    sliderInput("g5","g5",min = min(gd[,1]),max = max(gd[,1]), value = 10),
    sliderInput("g6","g6",min = min(gd[,1]),max = max(gd[,1]), value = 10),
    sliderInput("g7","g7",min = min(gd[,1]),max = max(gd[,1]), value = 10),
    sliderInput("g8","g8",min = min(gd[,1]),max = max(gd[,1]), value = 10),
    sliderInput("g9","g9",min = min(gd[,1]),max = max(gd[,1]), value = 10)
  ),
  
  # Show a plot of the generated distribution
  mainPanel(
    plotOutput("distPlot"),
    h4("Observations"),
    tableOutput("view")
  )
))