shinyaframe {shinyaframe}

WebVR Data Visualizations

Description

Make R data available in Web-based virtual reality experiences for immersive, cross-platform data visualizations. Includes the 'gg-aframe' JavaScript package for a Grammar of Graphics declarative HTML syntax to create 3-dimensional visualizations.

Examples

# Example Shiny app from package vignette
if (interactive()) {
  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))
      })
    }
  )
}