r

Build Your Own React-based htmlwidget

Making of nivocal

nivocal was built to be a working package, but while I was at it I wanted to record each step in the creation journey as a reference for future authors of reactR-based htmlwidgets. For reference, the package in its initial working state required less than 30 minutes of effort. I never left my RStudio window, and I only had to write 1.5 lines of JavaScript.

One Time on the Internet I Found …

There are some amazing React comoponents out there. If one day on the Internet, you find something you like then it can be ready to use in R in less than an hour (hopefully shorter if you read this document). Take for example the @nivo set of components. I’d like to use the Github-style calendar.

screenshot of nivo calendar

Starting the Package

usethis allows us to create a package in one line of code. Let’s begin our journey here.

usethis::create_package("nivocal")

screencast of creating package with use this

Scaffolding

Once we have a package, we’ll open it up and then build a scaffold. Sometimes finding the npmPkgs argument can be a little tricky. Usually, the best clues are in the docs, but we can also use unpkg.com–the CRAN of Node JavaScript–for some help. End the url with / to see the contents of the package and find the most recent version. For the calender, we do https://unpkg.com/@nivo/calendar/. Remember the /. Try https://unpkg.com/@nivo/calendar to see the difference.

scaffoldReactWidget(
  "nivocal",
  npmPkgs = c("@nivo/calendar" = "0.52.1")
)

screencast of scaffolding the widget

Now we have all the files we need for a working htmlwidget but unfortunately not working in the way we want.

1.5 Lines of JavaScript and Build

In the JavaScript, we will need to import the module we want to use. For nivocal we want ResponsiveCalendar. import in JavaScript is very similar to library() in R.

import { ResponsiveCalendar } from '@nivo/calendar'

The JavaScript build toolchain can get complicated, but fortunately reactR takes care of much of this for us. I hate to tell you, but you will need to install node and yarn. I promise this is not hard or scary though. Once you have both installed, we will build/webpack our JavaScript in the RStudio terminal or other terminal/console.

yarn install
yarn run webpack

screencast of building the JavaScript

The built JavaScript will be copied into the /inst/htmlwidgets directory ready for use in our R htmlwidget.

Build R Package

We have a couple more things to do on the R side. For now, let’s see if the package builds. In RStudio, we can CTRL + Shift + B or

devtools::document()
devtools::install(quick = TRUE)

screencast of building the R package

If all goes well, then our package is working, but as I said just not quite in the way we want.

Add Some Arguments

Now we need a way to go from R to JavaScript. We’ll add arguments for the data, from, and to component props in our R function.

screencast of add R function arguments

Change the Tag

The scaffold uses div, but we want to use the ResponsiveCalendar component. React components are always capitalized.

screencast of change tag to component

Add More Props/options and Do Some R Work

There are a lot of other options for the calendar. For a well-built R package, I think each of these should be dcoumented arguments, but for now we’ll use ... to pass other options from R to JavaScript.

data, from, and to are required for the calendar component. Eventually, we want to accept various forms of data from the user, but for now we will assume the user provides a data.frame with two columns day and value. htmlwidgets communicate data.frames as an array of arrays but ResponsiveCalendar wants the equivalent of dataframe = "rows" in jsonlite::toJSON(). We’ll use mapply to do this, but as described in the data transformation article we have other methods to achieve this. The most common form – using JavaScript HTMLWidgets.dataframeToD3() – does not currently work well with reactR-based htmlwidgets.

Without from and to, the calendar will not render, so let’s assume the user wants from to be the first row of the data and to to be the last row.

screencast of use ellipsis for other options and munge arguments

It’s Working

Now we have a working htmlwidget. Build the package with CTRL+Shift+B or

devtools::document()
devtools::install(quick = TRUE)

Give it some data and see an interactive calendar.

library(nivocal)

# fake data of 500 records/days starting 2017-03-15
df <- data.frame(
  day = seq.Date(
    from = as.Date("2017-03-15"),
    length.out = 500,
    by = "days"
  ),
  value = round(runif(500)*1000, 0)
)

nivocal(df)

screencast of working package widget

Customize

Remember we added ... for further customization. Let’s see how this works.

library(nivocal)

# fake data of 500 records/days starting 2017-03-15
df <- data.frame(
  day = seq.Date(
    from = as.Date("2017-03-15"),
    length.out = 500,
    by = "days"
  ),
  value = round(runif(500)*1000, 0)
)

nivocal(
  df,
  direction = "vertical",
  colors = RColorBrewer::brewer.pal(n=9, "Blues")
)

screencast of more customization

More Resources

Even though all of this is fairly new, we have tried to offer examples and resources to ease the learning curve. The react-R Github organization is intended to be a friendly central location for all things R + React. Please join in the fun.

Vue

We’d like to do the same for Vue. Please let us know if you have interest. vueR would be a good starting point.

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 + Sankey

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 jsinr.me and Medium

This will be the last post in our iterations in visualizing trees. In the next post, we’ll move on to how we can use the very popular React in R.

Our first post yielded a sankeytree concoction.

sankey + tree

sankey + tree

Then our second post made a more compact parttree.

Although the parttree is more compact, we can compress even further by stacking our link paths to reflect the leaf details and eliminating the leaf nodes. Then, we can blend in some interactivity to help the user process the visualization and examine the data.

Eliminating Repetition

At the leaf level of our parttree, the repetition of "Yes" and "No" could interfere with our ability to compare survival at each level.

partsankey_partree_repetition.png

Let’s see what happens if we instead convey the survival information in our link paths that connect the nodes. This is similar to a stacked bar or streamgraph. To accomplish this, we will use d3.stack().

Interactivity

We save some space, but a user might get confused. For example, the "Yes" does not flow through the nodes. Some interactivity might help clear up the confusion.

partition + sankey (interactive)

partition + sankey (interactive)

Next

Iterating through our last three posts demonstrates how creative blending can result in unique representations of tree hierarchies from R or JavaScript. We’ll stop here with our iteration, but we could easily transform this or other visualizations into htmlwidgets for even easier consumption by R users.

Much of the recent innovation in JavaScript visualization has happened in the newest frameworks, such as React and Vue. We’ll see in the next couple of posts how to use these new frameworks in R.

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.

JavaScript in R Series of Posts

One of the very fine folks at Displayr asked if I might be interested in writing a series of posts extending my 2015 htmlwidget/week project.  I said yes as long as I have "creative" license (never thought I would be requesting that in my lifetime), so away we go.  I plan to narrowly focus on combining JavaScript with R for visualization and data science.  The posts will appear on this site along with crossposts at my new site JSinR and on Medium to satisfy your viewing preference.

For those interested in workflow, the site is built Using RStudio/Yihui Xie’s blogdown package. The package perfectly illustrates the power of combination blending R, Rmarkdown, markdown, and Go. Netlify deploys the static site with every push to Github. Although this might sound difficult to the R user, I promise blogdown and Netlify makes all of this straightforward. Please let me know if I can help.

Thanks so much to Displayr for sponsoring the first set of posts.