This has been an exciting week for d3.js and R with the

1. release of vega by the data vis powerhouses at Trifacta
2. launch of clickme and already significant rewrite to accommodate vega
3. inception of a very promising d3 templates DexCharts described in multiple posts.

I am glad to have had time to play with all three, and I have actually already used them for legitimate purposes. I only understand the basics, but I thought I would post how we can combine a clickme ractive and a vega template to produce the lifelines example included in vega. I like the lifelines example because it is the one with the most complex data source and number of d3 elements. Fortunately, both projects are well documented, especially for early releases. I strongly recommend reading through both wikis to quickly progress along the learning curve. I will try to fill in some gaps in the clickme “clickme and vega” wiki page.

vega frameworks

vega frameworks are JSON objects to ease the construction of interactive d3 visualizations. In the wiki, the authors liken vega to ggplot2 but say

However, in service of rapid specification these systems make a number of decisions on behalf of the user, and also impose limits on the type of visualizations one can create. vega is intended to be lower-level, enabling fine-gained control of the visualization design. vega is intended to be lower-level, enabling fine-gained control of the visualization design.

ggplot2 and lattice users should immediately be familiar with words like “axes”,“scales”, “data”, and “marks”.

clickme ractives

clickme ractives are a directory structure with files that provide at a minimum a R markdown template (template.rmd) for a visualization and a R translator (translator.r) to allow the use of R data and calculations in the finished HTML5 rendering. Although clickme was developed unaware of vega, the author immediately saw the potential of combining both and rewrote clickme to allow easy integration. The synergy of the two is demonstrated by the ability to create 7 vega examples with all the same template.rmd and translator.r. vega templates now fall in the spec subdirectory of the data directory of a clickme ractive.

clickme filling vega

If we look at the original lifelines vega spec, we will see some spots where we might like R to provide the information, such as

...
  "width": 400,
  "height": 100,
  "padding": {"top": 60, "left": 5, "bottom": 30, "right": 30},
  "data": [
    {
      "name": "people",
      "values": [
        {"label":"Washington", "born":-7506057600000, "died":-5365324800000, 
         "enter":-5701424400000, "leave":-5453884800000},
...
      "name": "events",
      "format": {"type":"json", "parse":{"when":"date"}},
      "values": [
        {"name":"Decl. of Independence", "when":"July 4, 1776"},

I am guessing that some R users might stumble a little with the data section of this JSON, so let’s translate into lists, something I hope might be a little more familiar.

data = list(
    list(name="people",
       values=list(
                      list(label="Washington", born=-7506057600000, died=-5365324800000, enter=-5701424400000, leave=-5453884800000),
                      list(label="Adams", born=-7389766800000, died=-4528285200000, enter=-5453884800000, leave=-5327740800000),
                      list(label="Jefferson", born=-7154586000000, died=-4528285200000, enter=-5327740800000, leave=-5075280000000),
                      list(label="Madison", born=-6904544400000, died=-4213184400000, enter=-5075280000000, leave=-4822819200000),
                      list(label="Monroe", born=-6679904400000, died=-4370518800000, enter=-4822819200000, leave=-4570358400000)
                  )
        ),
    list(
      name= "events",
      format= list(type="json", parse=list(when="date")),
      values= list(
                list(name="Decl. of Independence", when="July 4, 1776"),
                list(name="U.S. Constitution",     when="3/4/1789"),
                list(name="Louisiana Purchase",    when="April 30, 1803"),
                list(name="Monroe Doctrine",       when="Dec 2, 1823")
              )

    )
)

Then in the translator.R part of our ractive, we can use the rjson package to translate the list into a JSON equivalent. Most of the data for d3 and vega can usually  just come from data.frames. The clickme author actually also wrote df2json to better handle the translation of data.frames to JSON, and there are numerous ractive examples using data.frames in the clickme package.

  get_data_as_json <- function(opts) {
  ...
    } else {
      library(rjson)
      json_data <- toJSON(opts$data) ##opts$data comes from the data parameter of the clickme function   
    }
      json_data
  }  

Now we just need to fill the vega spec with our data. We can replace the data section with

"data": {{ get_data_as_json(opts) }}

When we run the clickme_vega function, clickme will use the knit_expand function from knitr to expand/run the get_data_as_json function on the data supplied as a parameter to clickme_vega and replace like a mail merge with our translated JSON data representation.  With our ractive, we will also specify height, width, margins, title, etc. We can produce our HTML page with just one line.

clickme_vega(data,"lifelines",params=list(height=100,width=400,padding=list(top=60, left=5, bottom= 30, right=30)))

R to clickme to vega to d3 visualization workflow

Assuming we already have a predefined clickme ractive and vega spec, the workflow from R to a pretty d3 visualization becomes ridiculously easy:

1. Just like we would if we were creating an R graph, we get our data, clean our data, and run our calculations
   
2. in R, we run clickme_vega(data=our_data_from_step1, ractive=nameofourractive)
   
3. show off and use our amazing, beautiful, and interactive visualization (might need a simple http server for some cases).

If we do not have a predefined clickme ractive, then we can easily borrow/steal from the unbelievable repository of d3 examples or a possible future vega repository, and follow the instructions in the clickme wiki to convert into a ractive.

Live Example

if embed does not show go to http://bl.ocks.org/timelyportfolio/5316682.

Reproduce me

Below is all the code to run this specific example. The ractive and vega spec are in this Git repo.

#if not already installed, uncomment the two lines below
#library(devtools)
#install_github("clickme", "nachocab")

require(clickme)
#set location where you put your multiline ractive
set_root_path("path to your ractive/r")

data = list(
    list(name="people",
       values=list(
                      list(label="Washington", born=-7506057600000, died=-5365324800000, enter=-5701424400000, leave=-5453884800000),
                      list(label="Adams", born=-7389766800000, died=-4528285200000, enter=-5453884800000, leave=-5327740800000),
                      list(label="Jefferson", born=-7154586000000, died=-4528285200000, enter=-5327740800000, leave=-5075280000000),
                      list(label="Madison", born=-6904544400000, died=-4213184400000, enter=-5075280000000, leave=-4822819200000),
                      list(label="Monroe", born=-6679904400000, died=-4370518800000, enter=-4822819200000, leave=-4570358400000)
                  )
        ),
    list(
      name= "events",
      format= list(type="json", parse=list(when="date")),
      values= list(
                list(name="Decl. of Independence", when="July 4, 1776"),
                list(name="U.S. Constitution",     when="3/4/1789"),
                list(name="Louisiana Purchase",    when="April 30, 1803"),
                list(name="Monroe Doctrine",       when="Dec 2, 1823")
              )

    )
)

clickme_vega(data,"lifelines",params=list(height=100,width=400,padding=list(top=60, left=5, bottom= 30, right=30)))