Simple 2d_density_plot

server.R

require(shiny)
require(ggplot2)

nObs <- 500
nCluster <- 7
dataset <- data.frame(X = rnorm(nObs) + 2 * rnorm(nObs/2) , Y = rnorm(nObs)) - 2 * rnorm(nObs/2)
kMeans <- kmeans(dataset, centers = nCluster)
dataset$Cluster <- as.factor(kMeans$cluster)

shinyServer(function(input, output) {

  dataset <- reactive(function() {
    val <- data.frame(X = rnorm(input$nObs) + 2 * rnorm(input$nObs/2) , Y = rnorm(input$nObs)) - 2 * rnorm(input$nObs/2)
    kMeans <- kmeans(val, centers = input$nCluster)
    val$Cluster <- as.factor(kMeans$cluster)
    val
  })
  
  output$plot <- reactivePlot(function() {
    
   p <- ggplot(dataset) + geom_point(aes(x = input$X, y = input$Y, colour = input$Cluster))
   print(p)
  
  }, height=700)
  
})



# library(shiny)
# library(ggplot2)
# 
# nObs <- 5000
# dataset <- rnorm(nObs)
# 
# shinyServer(function(input, output) {
#   
#   dataset <- reactive(function() {
#     rnorm(input$nObs)
#   })
#   
#   output$plot <- reactivePlot(function() {
#     
#     plot(rnorm(input$nObs))
#     
#   }, height=700)
#   
# })

ui.R

library(shiny)
library(ggplot2)

shinyUI(pageWithSidebar(

  headerPanel("2d statistics visualization"),
  
  sidebarPanel(

    sliderInput('nObs', 'nObs', min=10, max=10000,
                value=min(10, 10000), step=10, round=0),
    
    sliderInput('nCluster', 'nCluster', min=1, max=10,
                value=min(1, 10), step=1, round=0)
  ),

  mainPanel(
    plotOutput('plot')
  )
))