Interactive graphics

Karl Broman

Biostatistics & Medical Informatics, University of Wisconsin – Madison

kbroman.org github.com/kbroman @kwbroman

slides: bit.ly/Broman2015-04

Outliers can be easily identified, with the right interactive plot. In R, you can use identify(), but who remembers how to do it? Usually, I’ll say, "Hmm...I want to figure out which point has weight 1 > 30 and weight 2 < 25."

Bandwidth = 1.00

D3 is awesome!

You just need to learn html, css, svg, and javascript.

And don’t forget .enter()

http://mbostock.github.io/d3/talk/20111018/collision.html

I’m using D3, a javascript library for data visualization. (D3 stands for “data-driven documents.”)

This is one of the images that convinced me that I wanted to learn D3. There are lots of additional examples at the D3 gallery.

There’s a ton to learn, and it’s rather forbidding (and much of it is dull).

My most common mistake is to forget to include .enter(). No error is given, but nothing shows up in the plot.

Summary

Interactive graphics require effort, but they

Facilitate exploration
Are great collaborative tools
Enable summaries with access to the details
Are great for teaching

Visualizations must be tailored to the data and questions
D3 is rather low level, but it

Is totally flexible
Provides hours of enjoyment
Can provide other hours of frustration