Shiny App on SOM

SOM - Server.R

# based on Manuel Bernal's code

######## SOM GENERATOR ###########
#Loading Packages needed
library(png) # For writePNG function
library(kohonen)
library(shiny)
library(ggplot2)
library(jsonlite)
library(Hmisc)
library(gridExtra)
options(shiny.maxRequestSize=500*1024^2)#maximum 500mb file


#Start shiny server
shinyServer(function(input, output, session) {
  #Reading file, inputs must be numeric
  dataInput <- reactive({
    inFile <- input$file1
    players<-read.csv(inFile$datapath, header=input$header, sep=input$sep,
                      stringsAsFactors=FALSE)

  })

  #Generate SOM
  generateSOM <- reactive({
    head(dataInput())
    players.sc <- scale(dataInput()) #Scale input
    set.seed(15675)
    players.som <- som(players.sc, grid = somgrid(input$slider1, input$slider2, input$fig)) #fig in "rectangular", "hexagonal"
  })

  #Generate Graphs
  generateGraph = function(){
    if (is.null(input$file1))
      return(NULL)
    else{
      coolBlueHotRed <- function(n, alpha = 1) {
        rainbow(n, end=4/6, alpha=alpha)[n:1]
      }
      plot(generateSOM(),type = input$select, main = "SOM", palette.name = coolBlueHotRed)
    }
  }

  #Converts SOM to table
  somToTable = function(){
    as.table( as.matrix(generateSOM()) )
  }

  #RenderGraph
  output$visual<-renderPlot({
    generateGraph()
  },height = 800, width = 800 )

  #DownloadImpage
  output$downloadData <- downloadHandler(
    filename = "shiny_som_plot.png",
    content = function(file) {
      png(file,width=800,height=800)
      generateGraph()
      dev.off()
    })

  #Download structure
  output$downloadTabla <- downloadHandler(
    filename = "shiny_som_data.csv",
    content = function(file) {
      write.table(somToTable(), file, sep = ",",row.names = FALSE)
    }
  )

  output$table <- DT::renderDataTable(
    if (is.null(input$file1))
      return(NULL)
    else{
      DT::datatable(dataInput(), options = list(searching = FALSE))
    }
  )

  output$histogram<-renderPlot({
    if (is.null(input$file1))
      return(NULL)
    else{
      hist.data.frame(dataInput())
    }
  },height = 800, width = 800 )

  output$property<-renderPlot({
    if (is.null(input$file1))
      return(NULL)
    else{
      coolBlueHotRed <- function(n, alpha = 1) {rainbow(n, end=4/6, alpha=alpha)[n:1]}
      som_model <- generateSOM()
      col <- ncol(som_model$data)
      if (col > 4) {par(mfrow=c(3,3))} else {par(mfrow=c(2,2))}
      for (i in 1:min(col, 9)){
         plot(som_model, type = "property", property = som_model$codes[,i], main=colnames(som_model$data)[i], palette.name=coolBlueHotRed)
      }
      # var <- 2 #define the variable to plot
      # var_unscaled <- aggregate(as.numeric(data_train[,var]), by=list(som_model$unit.classif), FUN=mean, simplify=TRUE)[,2]
      # plot(som_model, type = "property", property=var_unscaled, main=names(data_train)[var], palette.name=coolBlueHotRed)
    }
  },height = 800, width = 800 )

  output$cluster<-renderPlot({
    if (is.null(input$file1))
      return(NULL)
    else{
      som_model <- generateSOM()
      som_cluster <- cutree(hclust(dist(som_model$codes)), input$clusterSize)
      pretty_palette <- c("#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#76b7b2')
      if (input$clusterChart){
        par(mfrow=c(2,2))
        codes <- som_model$codes
        wss <- (nrow(codes)-1)*sum(apply(codes,2,var))
        for (i in 2:15) {
          wss[i] <- sum(kmeans(codes, centers=i)$withinss)
        }
        plot(wss, main = "Cluster WSS", xlab = "no of clusters")
        #plot.new()
        pca <- prcomp(som_model$codes, center = TRUE,scale. = TRUE)
        plot(pca$x[,1], pca$x[,2], col = som_cluster, main = "PCA", xlab = "PCA1", ylab = "PCA2")
        }else{
        par(mfrow=c(1,2))
      }
      plot(som_model, type="codes", main = "SOM Code")
      plot(som_model, type="mapping", bgcol = pretty_palette[som_cluster], main = "Clusters")
      add.cluster.boundaries(som_model, som_cluster)
    }
  },height = 800, width = 800 )

  output$info <- renderText({
    if (is.null(input$file1) | is.null(input$plot_click))
      return(NULL)
    else {
      x = 1
      y = 4.82
      xmax = 6
      ymax = 0.36
      y_offset = (ymax - y)/5
      yn = ceiling((input$plot_click$y - y) / y_offset)
      if (yn %% 2 == 0){
        x = x - 0.5
        xmax = xmax - 0.5
      }
      x_offset = (xmax - x)/5
      xn = ceiling((input$plot_click$x - x) / x_offset)
      paste0("x=", input$plot_click$x, "\ny=", input$plot_click$y, "\ncell = " , xn, ",", yn )

      # grid <- som_model$grid$pts
      # gridFrame <- data.frame(xvar = grid[,1], yvar = grid[,2])
      # print(str(gridFrame))
      # print(nearPoints(som_model, input$plot_click, xvar = "xvar", yvar = "yvar", maxpoints = 1))
    }

  })

  output$distribution<-renderPlot({
    if (is.null(input$file1) | is.null(input$plot_click))
      return(NULL)
    else{
      x = 1
      y = 4.82
      xmax = 6
      ymax = 0.36
      y_offset = (ymax - y)/5
      yn = ceiling((input$plot_click$y - y) / y_offset)
      if (yn %% 2 == 0){
        x = x - 0.5
        xmax = xmax - 0.5
      }
      x_offset = (xmax - x)/5
      xn = ceiling((input$plot_click$x - x) / x_offset)
      som_model <- generateSOM()
      population <- cbind(dataInput(), som = som_model$unit.classif)
      column = input$slider1
      index = xn+(yn-1)*column
      print(index)
      cell <- population[population$som == index,]
      combined <- rbind(cbind(population, label = 'population'), cbind(cell, label = 'cell'))
      par(mfrow=c(2,2))
      p1 <- ggplot(combined, aes(x=combined[, 1]) ) + geom_density(aes(fill = label), alpha=0.2) + xlab(names(combined)[1]) + guides(fill=FALSE)
      p2 <- ggplot(combined, aes(x=combined[, 2]) ) + geom_density(aes(fill = label), alpha=0.2) + xlab(names(combined)[2]) + guides(fill=FALSE)
      p3 <- ggplot(combined, aes(x=combined[, 3]) ) + geom_density(aes(fill = label), alpha=0.2) + xlab(names(combined)[3]) + guides(fill=FALSE)
      p4 <- ggplot(combined, aes(x=combined[, 4]) ) + geom_density(aes(fill = label), alpha=0.2) + xlab(names(combined)[4]) + guides(fill=FALSE)
      grid.arrange(p1, p2, p3, p4, ncol=2)
    }
  })


})

SOM - UI.R

# based on Manuel Bernal's code
library(shiny)

shinyUI(fluidPage(
  titlePanel("Self-Organized-Maps"),
  
  fluidRow(
    sidebarPanel(
      radioButtons('select', 'Graph Type', choices = c("Codebook" = "code",
                                            "Training mean distance" = "changes",
                                            "Objects per unit" = "counts",
                                            "Distance to neighbours - class boundary have higher distance" = "dist.neighbours",
                                            "Distance to codebook - lower the better" = "quality"), selected = 1),
      selectInput("fig", label = "Shape", 
                  choices = list("Hexagonal" = "hexagonal", "Rectangular" = "rectangular"),
                  selected = 1),
      
      sliderInput("slider1", label = "Granularity X", min = 1, 
                  max = 50, value = 5),
      sliderInput("slider2", label = "Granularity Y", min = 1, 
                  max = 50, value = 5),
      sliderInput("clusterSize", label = "Cluster Size", min = 2, 
                  max = 10, value = 4),
      checkboxInput('clusterChart', 'Cluster wss plot ', TRUE),
      
      tags$hr(),
      tags$br(),
      fileInput('file1', 'Select CSV',accept=c('text/csv','text/comma-separated-values,text/plain','.csv')),
      checkboxInput('header', 'Header', TRUE),
      selectInput("sep", label = "Separator", 
                  choices = list("Coma" = ",", "Semicolon" = ";", "Tab" = '\t'),
                  selected = 1),
     # selectInput("quote", label = "Quote", 
       #           choices = list("Any"='','Double Quotes'='"','Single Quotes'="'"),
      #            selected = 1),
      downloadButton('downloadData', 'Save as (PNG)'),
      downloadButton('downloadTabla', 'Save SOM as (CSV)')
    , verbatimTextOutput("info"),
      plotOutput('distribution', width = "100%")
     
    ),
    
    mainPanel(
      navbarPage(
        title = 'Generate SOM',
        tabPanel('Table',      helpText(" This table shows the content of the entered data. In order to generate the diagrams all values must be numeric."),
                 DT::dataTableOutput('table')   ),
        tabPanel('Histogram',      helpText(" Generate histograms to inspect data"),
                 plotOutput('histogram'), width = "100%"  ),
        tabPanel('SOM',     plotOutput('visual', width = "100%",click = "plot_click",hover = "plot_hover") ),
        tabPanel('SOM Property',     plotOutput('property', width = "100%") ),
        tabPanel('SOM Cluster',     plotOutput('cluster', width = "100%") )
        
        
      )
    )  
  )
))