Stats Society NSW Monthly Talk: Hafen -- Visualising large complex data with Trelliscope

We have two talks in October, both on R-based tools.  

The first is by the lead developer in the Tessera project (http://tessera.io), an R-based
 "big data" framework.  One component of Tessera is the visualisation tool Trelliscope, 
which is the subject of this first October talk, the details of which appear below.  

The second talk toward the end of the month will be by Alumnus Justin Wishart on Shiny;
details to follow in a week or two.  

Cheers, 

Michael 

=========== 

Venue: Carslaw 373 

Time: Refreshments from 6pm, talk 6.30-7.30pm.  

Abstract: 

Trelliscope is an R-based tool for detailed, flexible, interactive visualization of
large complex data.  Trelliscope is based on Trellis Display, an effective approach to
visualizing complex data in which data are broken into subsets, the same visualization
method is applied to each subset, and the results are arranged in a grid for viewing.
Trelliscope extends Trellis Display by allowing the analyst to break potentially very
large data sets into many subsets, apply a visualization method to each subset, and then
interactively sample, sort, and filter the panels of the display on various quantities
of interest, called cognostics.  Trelliscope provides a system for specifying displays
and cognostics against large data sets, as well as an interactive web-based viewer for
exploring the displays.  Trelliscope is part of a larger project, Tessera, which aims to
provide a framework for deep analysis of large complex data.  In this talk, I will
provide an overview of Tessera, discuss some details of Trelliscope, and provide
examples of large-scale displays, including a display of hundreds of gigabytes of
financial data with one million panels.  More information is available at
http://tessera.io.  

Bio: 

Ryan Hafen is a statistical consultant and a remote adjunct assistant professor in the
Statistics Department at Purdue University.  Ryan’s research focuses on methodology,
tools, and applications in exploratory analysis, statistical model building, and machine
learning on large, complex datasets.  He is the developer of the datadr and Trelliscope
components of the Tessera project, as well as the rbokeh R visualization interface to
the Bokeh plotting library.  Prior to his work as a statistical consultant, Ryan worked
at Pacific Northwest National Laboratory doing applied work on large complex data
spanning many domains, including power systems engineering, nuclear forensics, high
energy physics, biology, and cyber security.  Ryan has a B.S.  in Statistics from Utah
State University, M.Stat.  in Mathematics from University of Utah, and Ph.D.  in
Statistics from Purdue University.  More information about Ryan is available at
http://ryanhafen.com.