Iris K-Means Clustering

# load data in 'global' chunk so it can be shared by all users of the dashboard
library(datasets)
data(iris)

Column {.sidebar}

selectInput('xcol', 'X Variable', names(iris))

selectInput('ycol', 'Y Variable', names(iris),
                selected=names(iris)[[2]])

numericInput('clusters', 'Cluster count', 3,
              min = 1, max = 9)

Column

K Means

palette(c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3",
  "#FF7F00", "#FFFF33", "#A65628", "#F781BF", "#999999"))

# Combine the selected variables into a new data frame
selectedData <- reactive({
  iris[, c(input$xcol, input$ycol)]
})

clusters <- reactive({
  kmeans(selectedData(), input$clusters)
})

renderPlot({
  par(mar = c(5.1, 4.1, 0, 1))
  plot(selectedData(),
       col = clusters()$cluster,
       pch = 20, cex = 3)
  points(clusters()$centers, pch = 4, cex = 4, lwd = 4)
})


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flexdashboard documentation built on July 8, 2020, 7:32 p.m.