3D Scatterplots with gg-aframe

Building Blocks

The gg-aframe JavaScript package, also from this package’s author, is included in shinyaframe and provides a declarative “Grammar of Graphics” style HTML syntax for constructing 3-Dimensional data visualizations for WebVR. An example of that syntax for a basic scatter plot is shown below; refer to the link above for complete documentation.

<a-scene>
  <!-- position and rotation set the plot location in 3D space -->
  <a-entity plot scale-shape position="0 1.6 -1.38" rotation="0 35 0">
    <a-entity layer-point 
      data-binding__sepal.length="target: layer-point.x"
      data-binding__sepal.width="target: layer-point.y"
      data-binding__petal.length="target: layer-point.z"
      data-binding__species="target: layer-point.shape"
      data-binding__petal.width.size="target: layer-point.size">
    </a-entity>
  </a-entity>
</a-scene>

The data-binding attributes in the gg-aframe map from a central data repository to the aesthetic properties of the plot components, and this is where shinyaframe applies. The aDataScene function takes as input data from R and adds it to the data-binding repository using the htmlwidgets R package. Repeat calls will update the repository and the plot so that interactive visualizations are simple to implement in a Shiny app.

The example below shows the R code to provide the data for the gg-aframe plot above. Note that variables mapped to position and size are scaled in R before sending to gg-aframe whereas shape is not. In the plot specification, the scale-shape attribute enables gg-aframe to take raw input data for shape and map it to a polyhedral scale.

library(dplyr)
library(scales)
library(shinyaframe)

names(iris) <- tolower(names(iris))
iris %>%
  # scale positional data to (0,1)
  mutate_if(is.numeric, rescale) %>%
  # scale size data to relative percentage
  mutate(petal.width.size = rescale(petal.width, to = c(0.5, 2))) %>%
  aDataScene()

Creating a WebVR Shiny App

Shiny provides the glue to connect the HTML and R in the above examples. The Shiny app ui will create the HTML gg-aframe syntax, and the server will transform and send the data. The Shiny binding functions in shinyaframe are aDataSceneOuput for the ui, which will take all additional gg-aframe syntax as arguments, and renderADataScene for the server, which will process the data and return a call to aDataScene. Also included in shinyaframe are functions to render the custom HTML elements used by A-Frame and gg-aframe from a ui written in R, and they are exported in the atags list for convenient access.

The example below creates an iris data 3D scatterplot in a Shiny app. In addition to the code from the examples above, it also includes mapping and output for the axes and legend guides.

  library(shiny)
  library(dplyr)
  library(scales)
  library(shinyaframe)

  shinyApp(
    ui = fluidPage(
      aDataSceneOutput(
        # attributes and child elements provided as arguments
        # server output variable name
        outputId = "mydatascene",
        # add backdrop
        environment = "",
        # gg-aframe plot syntax
        atags$entity(
          # an empty string sets attributes with no additional properties
          plot = "",
          # sizable scale option uses polyhedra scaled for equivalent volumes
          `scale-shape` = "sizable",
          position = "0 1.6 -1.38",
          atags$entity(
            `layer-point` = "",
            `data-binding__sepal.length`="target: layer-point.x",
            `data-binding__sepal.width`="target: layer-point.y",
            `data-binding__petal.length`="target: layer-point.z",
            `data-binding__species`="target: layer-point.shape",
            `data-binding__petal.width.size`="target: layer-point.size",
            `data-binding__species.color`="target: layer-point.color"
          ),
          atags$entity(
            `guide-axis` = "axis: x",
            `data-binding__xbreaks` = "target: guide-axis.breaks",
            `data-binding__xlabels` = "target: guide-axis.labels",
            `data-binding__xtitle` = "target: guide-axis.title"
          ),
          atags$entity(
            `guide-axis` = "axis: y",
            `data-binding__ybreaks` = "target: guide-axis.breaks",
            `data-binding__ylabels` = "target: guide-axis.labels",
            `data-binding__ytitle` = "target: guide-axis.title"
          ),
          atags$entity(
            `guide-axis` = "axis: z",
            `data-binding__zbreaks` = "target: guide-axis.breaks",
            `data-binding__zlabels` = "target: guide-axis.labels",
            `data-binding__ztitle` = "target: guide-axis.title"
          ),
          atags$entity(
            `guide-legend` = "aesthetic: shape",
            `data-binding__shapetitle` = "target: guide-legend.title"
          ),
          atags$entity(
            `guide-legend` = "aesthetic: size",
            `data-binding__sizebreaks` = "target: guide-legend.breaks",
            `data-binding__sizelabels` = "target: guide-legend.labels",
            `data-binding__sizetitle` = "target: guide-legend.title"
          ),
          atags$entity(
            `guide-legend` = "aesthetic: color",
            `data-binding__colorbreaks` = "target: guide-legend.breaks",
            `data-binding__colorlabels` = "target: guide-legend.labels",
            `data-binding__colortitle` = "target: guide-legend.title"
          ),
          # animate the plot rotation
          atags$other('animation', attribute = "rotation",
                      from = "0 45 0", to = "0 405 0",
                      dur = "10000", `repeat` = "indefinite")
        )
      )
    ),
    server = function(input, output, session) {
      output$mydatascene <- renderADataScene({
        names(iris) <- tolower(names(iris))
        # Margin in (0,1) scale keeps polyhedra from sticking out of plot area
        positional_to <- c(0.01, 0.99)
        # convert to #RRGGBB color
        color_scale = setNames(rainbow(3, 0.75, 0.5, alpha = NULL),
                               unique(iris$species))
        iris %>%
          # scale positional data
          mutate_if(is.numeric, rescale, to = positional_to) %>%
          # scale size data to relative percentage, using cube root to correct
          # for radius->volume perception bias
          mutate(petal.width.size = rescale(petal.width^(1/3), to = c(0.5, 2)),
                 species.color = color_scale[species]) ->
          iris_scaled

        # provide guide info
        make_guide <- function (var, aes, breaks = c(0.01, 0.5, 0.99)) {
          guide = list()
          domain = range(iris[[var]])
          guide[[paste0(aes, "breaks")]] <- breaks
          guide[[paste0(aes, "labels")]] <- c(domain[1],
                                              round(mean(domain), 2),
                                              domain[2])
          guide[[paste0(aes, "title")]] <- var
          guide
        }
        Map(make_guide,
            var = c("sepal.length", "sepal.width", "petal.length"),
            aes = c("x", "y", "z")) %>%
          # repeat radius adjustment in the guide
          c(list(make_guide("petal.width", "size", c(0.5, 1.25, 2)^(1/3)))) %>%
          Reduce(f = c) ->
          guides
        guides$shapetitle = "species"
        guides$colortitle = "species"
        guides$colorbreaks = color_scale
        guides$colorlabels = names(color_scale)

        # convert data frame to list and combine with guides list
        aDataScene(c(iris_scaled, guides))
      })
    }
  )

The output of this Shiny app would appear as below:

You can run this example to view the interactive version by calling example("shinyaframe") (open the app in a Web browser; it will not function in the RStudio viewer).