Vega-Lite 1.0 was released this past week. I had been meaning to play with it for a while but I’ve been burned before by working with unstable APIs and was waiting for this to bake to a stable release. Thankfully, there were no new shows in the Fire TV, Apple TV or Netflix queues, enabling some fast-paced nocturnal coding to make an R htmlwidgets interface to the Vega-Lite code before the week was out.

What is “Vega” and why “-Lite”? Vega is “a full declarative visualization grammar, suitable for expressive custom interactive visualization design and programmatic generation.” Vega-Lite “provides a higher-level grammar for visual analysis, comparable to ggplot or Tableau, that generates complete Vega specifications.” Vega-Lite compiles to Vega and is more compact and accessible than Vega (IMO). Both are just JSON data files with a particular schema that let you encode the data, encodings and aesthetics for statistical charts.

Even I don’t like to write JSON by hand and I can’t imagine anyone really wanting to do that. I see Vega and Vega-Lite as amazing ways to serialize statistical charts from ggplot2 or even Tableau (or any Grammar of Graphics-friendly creation tool) and to pass around for use in other programs—like Voyager or Pole★—or directly on the web. It is “glued” to D3 (given the way data transformations are encoded and colors are specified) but it’s a pretty weak glue and one could make a Vega or Vega-Lite spec render to anything given some elbow grease.

But, enough words! Here’s how to make a simple Vega-Lite bar chart using vegalite:

# devtools::install_github("hrbrmstr/vegalite")
library(vegalite)
 
dat <- jsonlite::fromJSON('[
    {"a": "A","b": 28}, {"a": "B","b": 55}, {"a": "C","b": 43},
    {"a": "D","b": 91}, {"a": "E","b": 81}, {"a": "F","b": 53},
    {"a": "G","b": 19}, {"a": "H","b": 87}, {"a": "I","b": 52}
  ]')
 
vegalite() %>% 
  add_data(dat) %>%
  encode_x("a", "ordinal") %>%
  encode_y("b", "quantitative") %>%
  mark_bar()

Note that bar graph you see above is not a PNG file or iframed widget. If you view-source: you’ll see that I was able to take the Vega-Lite generated spec for that widget code (done by piping the widget to to_spec()) and just insert it into this post via:

<style media="screen">.wpvegadiv { display:inline-block; margin:auto }</style>
 
<center><div id="vlvis1" class="wpvegadiv"></div></center>
 
<script>
var spec1 = JSON.parse('{"description":"","data":{"values":[{"a":"A","b":28},{"a":"B","b":55},{"a":"C","b":43},{"a":"D","b":91},{"a":"E","b":81},{"a":"F","b":53},{"a":"G","b":19},{"a":"H","b":87},{"a":"I","b":52}]},"mark":"bar","encoding":{"x":{"field":"a","type":"ordinal"},"y":{"field":"b","type":"quantitative"}},"config":[],"embed":{"renderer":"svg","actions":{"export":false,"source":false,"editor":false}}} ');
 
var embedSpec = { "mode": "vega-lite", "spec": spec1, "renderer": spec1.embed.renderer, "actions": spec1.embed.actions };
 
vg.embed("#vlvis1", embedSpec, function(error, result) {});
</script>

I did have have all the necessary js libs pre-loaded like you see in this example. You can use the embed_spec() function to generate most of that for you, too.

This means you can use R to gather, clean, tidy and analyze data. Then, generate a visualization based on that data with vegalite. Then generate a lightweight JSON spec from it and easily embed it anywhere without having to rig up a way to get a widget working or ship giant R markdown created files (like this one which has many full vegalite widgets on it).

One powerful feature of Vega/Vega-Lite is that the data does not have to be embedded in the spec.

Take this streamgraph visualization about unemployment levels across various industries over time:

vegalite() %>%
  cell_size(500, 300) %>%
  add_data("https://vega.github.io/vega-editor/app/data/unemployment-across-industries.json") %>%
  encode_x("date", "temporal") %>%
  encode_y("count", "quantitative", aggregate="sum") %>%
  encode_color("series", "nominal") %>%
  scale_color_nominal(range="category20b") %>%
  timeunit_x("yearmonth") %>%
  scale_x_time(nice="month") %>%
  axis_x(axisWidth=0, format="%Y", labelAngle=0) %>%
  mark_area(interpolate="basis", stack="center")

The URL you see in the R code is placed into the JSON spec. That means whenever that data changes and the visualization is refreshed, you see updated content without going back to R (or js code).

Now, dynamically-created visualizations are great, but what if you want to actually let your viewers have a copy of it? With Vega/Vega-Lite, you don’t need to resort to hackish bookmarklets, just change a configuration option to enable an export link:

vegalite(export=TRUE) %>%
  add_data("https://vega.github.io/vega-editor/app/data/seattle-weather.csv") %>%
  encode_x("date", "temporal") %>%
  encode_y("*", "quantitative", aggregate="count") %>%
  encode_color("weather", "nominal") %>%
  scale_color_nominal(domain=c("sun","fog","drizzle","rain","snow"),
                      range=c("#e7ba52","#c7c7c7","#aec7e8","#1f77b4","#9467bd")) %>%
  timeunit_x("month") %>%
  axis_x(title="Month") %>% 
  mark_bar()

(You can style/place that link however/wherever you want. It’s a simple classed <div>.)

If you choose a canvas renderer, the “export” option will be PNG vs SVG.

The package is nearly (~98%) feature complete to the 1.0 Vega-Lite standard. There are some tedious bits from the Vega-Lite spec remaining to be encoded. I’ve transcribed much of the Vega-Lite documentation to R function & package documentation with links back to the Vega-Lite sources if you need more detail.

I’m hoping to be able to code up an “as_spec()” function to enable quick conversion of ggplot2-created graphics to Vega-Lite (and support converting a ggplot2 object to a Vega-Lite spec in to_spec()) but that won’t be for a while unless someone wants to jump on board and implement an Vega expression creator/parser in R for me 🙂

You can work with the current code on github and/or jump on board to help with package development or file an issue with an idea or a bug. Please note that this package is under heavy development and the function interface is very likely to change as I and others work with it and develop more streamlined ways to handle the encodings. Check back to the github repo often to find out what’s different (there will be a NEWS file posted soon and maintained as well).