rud.is

[Vega-Lite](http://vega.github.io/vega-lite/) 1.0 was [released this past week](https://medium.com/@uwdata/introducing-vega-lite-438f9215f09e#.yfkl0tp1c). 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 `htmlwidget`s interface](https://github.com/hrbrmstr/vegalite) to the Vega-Lite code before the week was out.

What is “Vega” and why “-Lite”? [Vega](http://vega.github.io/) 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](http://vega.github.io/voyager/) or [Pole★](http://vega.github.io/polestar/)—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 `iframe`d 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](http://vega.github.io/vega-lite/tutorials/getting_started.html#embed). 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](http://rud.is/projects/vegalite01.html) 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 `