Interaction beyond the “read-eval-print” loop

Working do with R through the command line imposes a certain laborious sequentiality to everything we do. The advantage to this mode of data-analytic computing is that every step is defined by code and every result can be viewed (and traced) as a sequence of explicit function evaluations on a well-defined environment.

An alternative mode of data-analytic computing uses graphical user interfaces (GUIs) in which mouse movements and button clicks are the primary means of specifying computations. This mode of interaction is sequential and traceable as well, but the path from mouse click to data-analytic function is technically complex and we will not pursue the matter any further.

The Rstudio group has produced a package called shiny URL that simplifies the creation of browser-driven GUIs for specific data-analytic activities. In this lab we’ll explore some of the potential of this package. We’ve added a “shiny app” to the ph525x package that we’ll now investigate.

Running the dfHclust function

The main display layout

The ph525x package includes the dfHclust function. This takes a data.frame instance as sole argument and starts a browser session. You can try it directly with


There is a sidebar panel on the left that accepts selections for

  • distance for object proximity
  • clustering method to form the hierarchy through object agglomeration
  • height for cutting the cluster dendrogram into groups of objects
  • features to use for computation of object distance

In this application, the objects are cars with different manufacturer and model types; the features are structural or operating characteristics of cars. Note that the application starts with certain defaults:

  • euclidean distance
  • ward.D as the agglomeration procedure
  • 40 as the cut height
  • the first two variables as features

The main panel to the right includes tabs for three types of display:

  • tree for the clustering dendrogram
  • pairs plot for direct pairwise scatterplot visualization
  • silhouette plot for assessing cluster quality at the selected cutting height

To fully understand the quantities displayed in the “tree” and “silh” tabs, you should review the definitions of distance, clustering method, and silhouette until you feel comfortable explaining these to a non-statistician.

  • Distance measures are very important in multivariate analysis
  • Hierarchical clustering procedures are complex; see the definition of Ward’s method in Wikipedia for a clear exposition of one important approach
  • Silhouette is defined clearly in the man page for silhouette in the cluster package

Interacting with the display

Notice that the defaults lead to a tree with three main lobes in the panel displayed when the “tree” tab is selected. The panel for the “silh” tab shows a measure of cluster membership for each observation in the dataset, using the height-to-cut setting to partition the data using the tree.

  • note that the average silhouette width at the defaults for mtcars is 0.56
  • if, leaving all other selections alone, you change the “Select height for cut” value to 70, the silhouette plot changes to show an average silhouette of 0.59 – an improvement, but now we have only two clusters, and one observation with a negative silhouette value
  • for an amusing observation, set the distance to “euclidean” and the clustering method to single – now the closest neighbor to the Mercedes 450 SLC is the AMC Javelin. I guess the Javelin wasn’t such a bad car after all….

A view of the code

In this subsection we will break up the main dfHclust function and explain its elements. The code was written in a very naive manner but even so has three virtues:

  • it is relatively short and self-contained
  • it works and does something useful in interactive data exploration
  • it is easy to extend by replicating and modifying short subparts

Some aspects that are likely to need improvement

  • handling of global variables for use in the ui component
  • excessive repetition of data.frame subsetting in the server component

Starting out

We have a simple fixed interface and fail if we don’t get a data.frame with at least two columns. We fail if the necessary software is not in place.

dfHclust = function(df) {
# validate input
   stopifnot(inherits(df, "data.frame"))
# obtain software

Some vectors to populate interface option sets

# global variables ... 
   nms = names(df)
   cmeths = c("ward.D", "ward.D2",
             "single", "complete", "average", "mcquitty",
             "median", "centroid")
   dmeths = c("euclidean", "maximum", "manhattan", "canberra",

The layout of the user interface

There are several high-level options that can control the browser interface. We’ll use a flexible approach called fluidPage; see also the shinydashboard package.

# main shiny components: ui and server
#   ui: defines page layout and components
#   server: defines operations
   ui <- fluidPage(
# we will have four components on sidebar: selectors for 
# distance, agglomeration method, height for tree cut, and variables to use

Title and sidebar

     titlePanel(paste(substitute(df), "hclust")),
          helpText(paste("Select distance:" )),
             selectInput("dmeth", NULL, choices=dmeths,
          helpText(paste("Select clustering method:" )),
             selectInput("meth", NULL, choices=cmeths,
          helpText(paste("Select height for cut:" )),
             numericInput("cutval", NULL, value=40, min=0, max=Inf, step=1)),
          helpText(paste("Select variables for clustering from", substitute(df), ":" )),
             checkboxGroupInput("vars", NULL, choices=nms,

Tabs for the primary displays

# main panel is a simple plot
  )  # end fluidPage

Defining the server

# a function with up to three arguments (input, output, session)
# can be used to define the server component of the app
# renderPlot makes it reactive, so when input components are altered,
# data frame in use and plot are updated
   server <- function(input, output) {
     output$plot1 <- renderPlot({
       xv = df[,input$vars]
       plot(hclust(dist(data.matrix(xv),method=input$dmeth), method=input$meth),
         xlab=paste(input$dmeth, "distance;", input$meth, "clustering"))
       abline(h=input$cutval, lty=2, col="gray")
     output$pairsplot <- renderPlot({
       xv = df[,input$vars]
     output$silplot <- renderPlot({
       xv = df[,input$vars]
       dm = dist(data.matrix(xv),method=input$dmeth)
       hc = hclust(dist(data.matrix(xv),method=input$dmeth), method=input$meth)
       ct = cutree(hc, h=input$cutval)
       plot(silhouette(ct, dm))
   shinyApp(ui, server)

Some gory details

A “shiny app” consists of two main components, a user interface and a server function. To use the infrastructure the shiny library must be attached to an R session. The application can be started in various ways; we use shinyApp and supply two arguments, ui and server.

  • ui is an instance of shiny.tag.list. You can get a feel for this by inspecting the result of fluidPage().

  • server is a function of three arguments input, output and session; the latter is optional. input is a list with bindings given values in the ui component, and output will be populated with elements in the server for rendering in the UI.

An example with fluidPage

To appreciate what shiny is doing for you, try the following:

  titlePanel("a title"),
    selectInput("seli1", "some letters", letters[1:4])

Set sink(file="dem.html") and run the above code again. Then issue sink(NULL); browseURL("dem.html"). R will fire up the browser and show a fairly anemic page. There will be a select control with options a, …, d. This particular example would be easy to code by hand, but the selectInput function allows you to develop controls with choices defined by any R vector. Furthermore, high-level R functions are defined to allow different kinds of input, which may be driven by mouse events.