first push

Dockerfile

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+FROM rocker/r-ver
+
+# basic shiny functionality
+RUN R -q -e "install.packages(c('shiny', 'rmarkdown'))"
+
+# copy the app to the image
+WORKDIR /shinyapp
+COPY --link Rprofile.site /usr/local/lib/R/etc/
+COPY --link app /shinyapp/
+
+EXPOSE 7860
+CMD ["R", "-q", "-e", "shiny::runApp('/shinyapp')"]

Rprofile.site

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+local({
+   options(shiny.port = 7860, shiny.host = "0.0.0.0")
+})

app/server.R

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+function(input, output, session) {
+
+  # Combine the selected variables into a new data frame
+  selectedData <- reactive({
+    iris[, c(input$xcol, input$ycol)]
+  })
+
+  clusters <- reactive({
+    kmeans(selectedData(), input$clusters)
+  })
+
+  output$plot1 <- renderPlot({
+    palette(c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3",
+      "#FF7F00", "#FFFF33", "#A65628", "#F781BF", "#999999"))
+
+    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)
+  })
+
+}

app/ui.R

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+vars <- setdiff(names(iris), "Species")
+
+pageWithSidebar(
+  headerPanel('Iris k-means clustering'),
+  sidebarPanel(
+    selectInput('xcol', 'X Variable', vars),
+    selectInput('ycol', 'Y Variable', vars, selected = vars[[2]]),
+    numericInput('clusters', 'Cluster count', 3, min = 1, max = 9)
+  ),
+  mainPanel(
+    plotOutput('plot1')
+  )
+)