inst/extdata/iris_cluster.R

In shinymgr: A Framework for Building, Managing, and Stitching 'shiny' Modules into Reproducible Workflows

iris_cluster_ui <- function(id){
  # create the module's namespace 
  ns <- NS(id)
  
  sidebarLayout(
    sidebarPanel(
      
      # create tagList of inputs
      tagList(
        
        # add the dropdown for the X variable
        selectInput(
          ns("xcol"),
          label = "X Variable", 
          choices = c(
            "Sepal.Length", 
            "Sepal.Width", 
            "Petal.Length", 
            "Petal.Width"),
          selected = "Sepal.Length"),
        
        # add the dropdown for the Y variable
        selectInput(
          ns("ycol"), 
          label = "Y Variable", 
          choices = c(
            "Sepal.Length", 
            "Sepal.Width", 
            "Petal.Length", 
            "Petal.Width"),
          selected = "Sepal.Width"),
        # add input box for the cluster number
        
        numericInput(
          ns("clusters"), 
          label = "Cluster count", 
          value = 3, 
          min = 1, 
          max = 9)
      ) # end of input tagList
      
    ), # end of sidebarPanel
    
    mainPanel(
      
      # create tagList of outputs
      tagList(
        plotOutput(
          ns("plot1")
        )
      ) # end of output tagList
      
    ) # end of mainPanel
  ) # end of sidebarLayout
  
  
} # end of UI function

iris_cluster_server <- function(id) { 
  
  moduleServer(id, function(input, output, session) {
  
  # combine variables into new data frame
  selectedData <- reactive({
    iris[, c(input$xcol, input$ycol)]
    })
  
  # run kmeans algorithm 
  clusters <- reactive({
    kmeans(
      x = selectedData(), 
      centers = input$clusters)
    })
  
  output$plot1 <- renderPlot({
    oldpar <- par('mar')
    par(mar = c(5.1, 4.1, 0, 1))
    p <- plot(
      selectedData(),
      col = clusters()$cluster,
      pch = 20, 
      cex = 3)
    par(mar=oldpar)
    p
    })

  }) # end of moduleServer function
  
} # end of irisCluster function