As part our series on new features in the RStudio v1.2 Preview Release, we’re pleased to announce the r2d3 package, a suite of tools for using custom D3 visualizations with R.

RStudio v1.2 includes several features to help optimize your development experience with r2d3. We’ll describe these features below, but first a bit more about the package. Features of r2d3 include:

• Translating R objects into D3 friendly data structures

• Publishing D3 visualizations to the web

• Incorporating D3 scripts into R
  Markdown reports, presentations,
  and dashboards

• Creating interacive D3 applications with
  Shiny

• Distributing D3 based htmlwidgets in R
  packages

With r2d3, you can bind data from R to D3 visualizations like the
ones found on https://github.com/d3/d3/wiki/Gallery,
https://bl.ocks.org/, and
https://vida.io/explore:

D3 visualizations created with r2d3 work just like R plots within
RStudio, R Markdown documents, and Shiny applications.

You can install the r2d3 package from CRAN as follows:

install.packages("r2d3")

D3 Scripts

To use r2d3, write a D3 script and then pass R data to it using the
r2d3() function. For example, here’s a simple D3 script that draws a
bar chart (“barchart.js”):

// !preview r2d3 data=c(0.3, 0.6, 0.8, 0.95, 0.40, 0.20)

var barHeight = Math.floor(height / data.length);

svg.selectAll('rect')
  .data(data)
  .enter().append('rect')
    .attr('width', function(d) { return d * width; })
    .attr('height', barHeight)
    .attr('y', function(d, i) { return i * barHeight; })
    .attr('fill', 'steelblue');

To render the script within R you call the r2d3() function:

library(r2d3)
r2d3(data=c(0.3, 0.6, 0.8, 0.95, 0.40, 0.20), script = "barchart.js")

Which results in the following visualization:

D3 Variables

Note that data is provided to the script using the data argument to
the r2d3() function. This data is then automatically made available to
the D3 script. There are a number of other special variables available
within D3 scripts, including:

• data — The R data converted to JavaScript.
• svg — The svg container for the visualization
• width — The current width of the container
• height — The current height of the container
• options — Additional options provided by the user
• theme — Colors for the current theme

When you are learning D3 or translating D3 examples for use with R it’s
important to keep in mind that D3 examples will generally include code
to load data, create an SVG or other root element, and establish a width
and height for the visualization.

On the other hand with r2d3, these variables are provided
automatically so do not need to be created. The reasons these variables
are provided automatically are:

1) So that you can dynamically bind data from R to visualizations; and

2) So that r2d3 can automatically handle dynamic resizing for your
visualization. Most D3 examples have a static size. This is fine for
an example but not very robust for including the visualization
within a report, dashboard, or application.

RStudio v1.2 and r2d3

The RStudio v1.2 preview
release
includes support for previewing D3 scripts as you write them. To try this out, install the preview release
then create a D3 script using the new file menu:

A simple template for a D3 script (the barchart.js example shown above)
is provided by default. You can use the Preview command
(Ctrl+Shift+Center) to render the
visualization:

You might wonder where the data comes from for the preview. Note that
there is a special comment at the top of the D3 script:

// !preview r2d3 data=c(0.3, 0.6, 0.8, 0.95, 0.40, 0.20)

This comment enables you to specify the data (along with any other
arguments to the r2d3() function) to use for the preview.

R Markdown

RStudio v1.2 also includes support for rendering r2d3 visualizations within R Markdown documents and R Notebooks. There is a new d3 R Markdown engine which works like this:

```{r setup}
library(r2d3)
bars <- c(10, 20, 30)
```

```{d3 data=bars, options=list(color = 'orange')}
svg.selectAll('rect')
  .data(data)
  .enter()
    .append('rect')
      .attr('width', function(d) { return d * 10; })
      .attr('height', '20px')
      .attr('y', function(d, i) { return i * 22; })
      .attr('fill', options.color);
```

Note that in order to use the d3 engine you need to add
library(r2d3) to the setup chunk (as illustrated above).

You can also of course call the r2d3() function from within
an R code chunk:

---
output: html_document
---

```{r}
library(r2d3)
r2d3(data=c(0.3, 0.6, 0.8, 0.95, 0.40, 0.20), script = "barchart.js")
```

Shiny

The renderD3() and d3Output() functions enable you to include D3
visualizations within Shiny applications:

library(shiny)
library(r2d3)

ui <- fluidPage(
  inputPanel(
    sliderInput("bar_max", label = "Max:",
                min = 0.1, max = 1.0, value = 0.2, step = 0.1)
  ),
  d3Output("d3")
)

server <- function(input, output) {
  output$d3 <- renderD3({
    r2d3(
      runif(5, 0, input$bar_max),
      script = system.file("examples/baranims.js", package = "r2d3")
    )
  })
}

shinyApp(ui = ui, server = server)

See the article on Using r2d3 with
Shiny to learn more
(including how to create custom Shiny inputs that respond to user
interaction with D3 visualizations).

Try It Out

To try out r2d3, start by installing the package from CRAN:

install.packages("r2d3")

Then, download the RStudio v1.2 Preview Release and head over to https://rstudio.github.io/r2d3/ for complete documentation on using the package.

If you aren’t familliar with D3, check out these links to learn the basics and see some examples that might inspire your
own work:

• Learning
  D3 —
  Suggested resources for learning how to create D3 visualizations.

• Gallery of
  Examples —
  Learn from a wide variety of example D3 visualizations.

We hope that the r2d3 package opens up many new horizons for creating custom interactive visualizations with R!