rstats

React in R

This post is courtesy of Displayr who have generously offered to sponsor a series of independently authored posts about interactive visualization with R and JavaScript. Thank you so much Displayr for this opportunity.

crossposted at buildingwidgets and jsinr

In this post, we will pivot from iterative tree visualization to using the very popular JavaScript thing called React in R. With some assistance from the helper R package reactR, we will learn to incorporate Reactcomponents in our output and make a Semiotic chart from R data. I would recommend reading and working through the React tutorial before beginning this post, but I bet you can follow along even if you ignore this recommendation.

reactR

Most React projects require at least two things:

  1. React and ReactDOM JavaScript dependencies
  2. babel compiler to convert JSX and/or ES2015 (and beyond) to plain old JavaScript.

To ease this burden for the R user of React, I built the package reactRwhich allows us to accomplish both of the above requirements.reactR::html_dependency_react() provides up-to-date JavaScript dependencies for React and ReactDOM for use in Rmarkdown, Shiny, or other html projects. reactR::babel_transform() uses the V8 package to compile your JSX and ES2015 (and beyond) all within your R session.

Pattern for React and R

We will use the following generic pattern as we attempt to combine React with R.

library(htmltools)
library(reactR)

tagList(
  # add JavaScript dependencies React and ReactDOM
  reactR::html_dependency_react(),
  tags$div(...),
  tags$script(HTML(
    # babel_transform is only necessary if we plan to use
    #   ES2015 and/or JSX.  Most of the React examples out
    #   there will use one or both.
    reactR::babel_transform(
      sprintf(...)
    )
  ))
)

First Example

Let’s try it with a real example similar to the React Hello World! example. In our example, we will use React to render a heading h1 along with some text.

library(htmltools)
library(reactR)

tagList(
  reactR::html_dependency_react(),
  tags$div(id = "example"),
  tags$script(HTML(
    babel_transform(
"
ReactDOM.render(
  <div>
    <h1>React + R = BFF</h1>
    <p>This should probably be airbrushed Spring Break style
    on a t-shirt or license plate.
    </p>
  </div>,
  document.getElementById('example')
)
"
    )
  ))
)
reactR_example1.gif

Often, quotes " and ' are the most frustrating part about combining JavaScript and R. I tend to use " for R and ' for JavaScript.

Office React Components in R

I know that most R purists have eliminated Microsoft Office from their workflows, but we can bring a little bit of the “good” from Microsoft Office with the very well-built and helpful Office UI Fabric components for React. And yes you can use these with Shiny.

library(htmltools)
library(reactR)

fabric <- htmlDependency(
  name = "office-fabric-ui-react",
  version = "5.23.0",
  src = c(href="https://unpkg.com/office-ui-fabric-react/dist"),
  script = "office-ui-fabric-react.js",
  stylesheet = "css/fabric.min.css"
)

browsable(
  tagList(
    html_dependency_react(offline=FALSE),
    fabric,
    tags$div(id="pivot-example"),
    tags$script(HTML(babel_transform(
"
class PivotBasicExample extends React.Component {
  render() {
    return (
      <div>
        <Fabric.Pivot>
          <Fabric.PivotItem linkText='My Files'>
            <Fabric.Label>Pivot #1</Fabric.Label>
          </Fabric.PivotItem>
          <Fabric.PivotItem linkText='Recent'>
            <Fabric.Label>Pivot #2</Fabric.Label>
          </Fabric.PivotItem>
          <Fabric.PivotItem linkText='Shared with me'>
            <Fabric.Label>Pivot #3</Fabric.Label>
          </Fabric.PivotItem>
        </Fabric.Pivot>
      </div>
    );
  }
}
ReactDOM.render(<PivotBasicExample />, document.querySelector('#pivot-example'));
"
    )))
  )
)
office-ui-fabric React component from R

office-ui-fabric React component from R

Now you might have noticed that the RStudio Viewer showed up as blank. This seems to be an issue with non-local JavaScript dependencies in RStudio Viewer. I think the only way around this problem is to store the dependencies locally. A package housing these dependencies similar to reactR is probably the best option.

antd React Components to Step Through lm

antd is another set of very nice React components. Let’s walk through a lm from R using the step-through antd component.

Now we are getting much closer to our ultimate objective of using R data with React with a synergistic result.

Visualizations with Semiotic

Elijah Meeks has very generously contributed the React-based visualization library Semiotic. We will recreate one of the examples, but we’ll do the data part in R. Data from R and interactive vis from JavaScript hopefully will continue to become a popular and common workflow.

An htmlwidget for Semiotic would offer the ideal method of full integration, but I have not yet determined a good pattern for creating React-based htmlwidgets. Please let me know if you have thoughts or would like to collaborate towards achieving this goal.

Next Steps

An obvious next step would be integrating React with Shiny, and as I said before, this is possible. Also, there is another very popular JavaScript framework called Vue that I actually think is even easier to integrate with R. In the next post, we’ll demonstrate R + Vue.

Visualizing Trees | Partition + Tree

This post is courtesy of Displayr who have generously offered to sponsor a series of independently authored posts about interactive visualization with R and JavaScript. Thank you so much Displayr for this opportunity.

crossposted at buildingwidgets and Medium

Before I start on the second post on the series, I wanted to make sure all my R readers knew that the charts in this post are created in R using htmltools. Also, each chart should have a link to reproducible code.

In our first attempt at improving hierarchical visualization, we combined d3.tree() with d3-sankey. Our sankeytree concoction allows us to convey size or flow from one level to the next while maintaining some sense of the tree, but the sankeytree still suffers from the universal constant node size (height and width).

We are left with extra wasted space that possibly distracts from the message of the visualization. In this post we will see if we can eliminate some of this space with d3.partition() assisted by d3.treemap(). Let’s call this one parttree.

d3.partition()

Partition, or icicle, visualizations fill space much like a diced treemap or side-by-side stacked bar chart. The visualizations are commonly used in debugging and programming optimization. In this context, they are called flame graphs.

flame graph from Chrome debugger

flame graph from Chrome debugger

Since we are trying to eliminate some of the wasted space from our sankeytree, let’s see if we might be able to leverage the “space-filling” d3.partition(). For consistency, let’s continue to use the Titanic dataset from R and create a partition.

While d3.partition() efficiently fills the space, these charts in this context do not reveal the hierarchical nature of the underlying data as much as I would like. Also, in my opinion, the above chart is not very inviting or “fun”.

What if we start with d3.partition() and then use a node size smaller than the partition-assigned size? Then, we might have some space to draw links like a d3.tree() or d3-sankey. Seeing is believing, so let’s make the suggested adjustment to the partitioned Titanic and then animate the transformation.

I consider this good progress, and our new parttree imparts a sense of hierarchy with an efficient and compact portrayal of size and flow. I should note that we sprinkled in some assistance from d3.stack() and d3.treemap(). However, the straight angled links might be a little rigid. This can be solved with help from d3.linkHorizontal.

Finishing Touches

A little curve in our links might be nice. However, just a line with width defined by stroke-width can limit us in ways we might discuss in future posts, so let’s define a path with four points to draw our link.

Just imagine if we add proper labels, good color, and interactivity.

Next

If we like our new creation, then next steps will be to create a more formal d3 layout and then build a reusable chart based on the layout. As mentioned in the post, drawing the links as a path with four points instead of a line with two points will allow us the ability to add even more encoding and information in our links. In the next post, we will explore what we can do with our new powers.