Interactive Data Analysis Jeffrey Heer Stanford University
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InteractiveData Analysis
Jeffrey HeerStanford University
Node-link
Matrix
Matrix
Acquisition
Cleaning
Integration
Modeling
Visualization
Presentation
Dissemination
Acquisition
Cleaning
Integration
Modeling
Visualization
Presentation
Dissemination
How do people create visualizations?
Chart TypologyPick from a stock of templatesEasy-to-use but limited expressivenessProhibits novel designs, new data types
Component ArchitecturePermits more combinatorial possibilitiesNovel views require new operators, which requires software engineering.
Today's first task is not to invent wholly new [graphical] techniques, though these are needed. Rather we need most vitally to recognize and reorganize the essential of old techniques, to make easy their assembly in new ways, and to modify their external appearances to fit the new opportunities.
John W. TukeyThe Future of Data Analysis, 1962
Protovis: A Language for Visualization
A graphic is a composition of data-representative marks.
with Mike Bostock & Vadim Ogievetsky
Area Bar Dot Image
Line Label Rule Wedge
MARKS: Protovis graphical primitives
data λvisible λleft λbottom λwidth λheight λfillStyle λstrokeStyle λlineWidth λ… λ
λ: D → RMARK
datavisibleleftbottomwidthheightfillStylestrokeStylelineWidth…
λ: D → RBAR
trueλ: index*25
020
λ: datum*80blueblack
1.5…
1 1.2 1.7 1.5 0.7
datavisibleleftbottomwidthheightfillStylestrokeStylelineWidth…
λ: D → RBAR1 1.2 1.7 1.5 0.7
true0*25
020
1*80blueblack
1.5…
datavisibleleftbottomwidthheightfillStylestrokeStylelineWidth…
λ: D → RBAR1 1.2 1.7 1.5 0.7
true1*25
020
1.2*80blueblack
1.5…
datavisibleleftbottomwidthheightfillStylestrokeStylelineWidth…
λ: D → RBAR1 1.2 1.7 1.5 0.7
true2*25
020
1.7*80blueblack
1.5…
datavisibleleftbottomwidthheightfillStylestrokeStylelineWidth…
λ: D → RBAR1 1.2 1.7 1.5 0.7
true3*25
020
1.5*80blueblack
1.5…
datavisibleleftbottomwidthheightfillStylestrokeStylelineWidth…
λ: D → RBAR1 1.2 1.7 1.5 0.7
true4*25
020
0.7*80blueblack
1.5…
datavisibleleftbottomwidthheightfillStylestrokeStylelineWidth…
λ: D → RBAR
trueλ: index*25
020
λ: datum*80blueblack
1.5…
1 1.2 1.7 1.5 0.7
var vis = new pv.Panel();vis.add(pv.Bar).data([1, 1.2, 1.7, 1.5, .7]).visible(true).left(function() this.index * 25);.bottom(10).width(20).height(function(d) d * 80).fillStyle(“blue”).strokeStyle(“black”).lineWidth(1.5);
vis.render();
vis.add(pv.Rule).data([0,-10,-20,-30]).top(function(d) 300 - 2*d - 0.5).left(200).right(150).lineWidth(1).strokeStyle("#ccc").anchor("right").add(pv.Label)
.font("italic 10px Georgia")
.text(function(d) d+"°").textBaseline("center");
vis.add(pv.Line).data(napoleon.temp).left(lon).top(tmp) .strokeStyle("#0")
.add(pv.Label).top(function(d) 5 + tmp(d)).text(function(d) d.temp+"° "+d.date.substr(0,6)).textBaseline("top").font("italic 10px Georgia");
var army = pv.nest(napoleon.army, "dir", "group“);var vis = new pv.Panel();
var lines = vis.add(pv.Panel).data(army);lines.add(pv.Line).data(function() army[this.idx]).left(lon).top(lat).size(function(d) d.size/8000).strokeStyle(function() color[army[paneIndex][0].dir]);
vis.add(pv.Label).data(napoleon.cities).left(lon).top(lat).text(function(d) d.city).font("italic 10px Georgia").textAlign("center").textBaseline("middle");
Productivity - Faster Design Cycle, Less CodeComparison: 5x less code, 10x less dev time
Portability - Multiple ImplementationsJavaScript, Adobe Flash, Java/JVM
Performance - Optimization (in Protovis-Java)Just-in-time compilation; parallel executionHardware accelerated renderingUp to 20x scalability boost over prior toolkits
Interactive Graph Layout (Quad-Core MacPro)
20x20x
Graph Size (# Nodes, # Edges)
Fram
es p
er S
econ
d (f
ps)
d3.js Data-Driven Documents
with Mike Bostock & Vadim Ogievetsky
GitHub Rank…
12th most watched project on GitHub
d3 d3
Acquisition
Cleaning
Integration
Modeling
Visualization
Presentation
Dissemination
Acquisition
Cleaning
Integration
Modeling
Visualization
Presentation
Dissemination
I spend more than half of my time integrating, cleansing and transforming data without doing any actual analysis. Most of the time I’m lucky if I get to do any “analysis” at all.
Anonymous Data Scientistfrom our interview study, 2012
The Elephant in the Room
DataWrangler
with Sean Kandel, Philip Guo, Ravi Parikh, Andreas Paepcke & Joe Hellerstein
Wrangler in 2 Parts…
1. Declarative data transformation languageTuple mapping – split, merge, extract, deleteReshaping – fold, unfold (cross-tabulation)Lookups & joins – e.g., FIPS code to US stateSorting, aggregation, etc.
Informed by prior work in databases:Potter’s Wheel, SchemaSQL, AJAX
Wrangler in 2 Parts…
1. Declarative data transformation language
+2. Mixed-initiative interface for data transforms
User: Selects data elements of interestSystem: Suggests applicable transforms via
search over the space of viable transformsEnable rapid preview and refinement
Comparative Evaluation with Excel
Median completion time for Wrangler at leasttwice as fast in all tasks (p < 0.001).Suggestions and visual previews used heavily.
Extract
Impute
Reshape
Acquisition
Cleaning
Integration
Modeling
Visualization
Presentation
Dissemination
Acquisition
Cleaning
Integration
Modeling
Visualization
Presentation
Dissemination
GraphPrismwith Sanjay Kairam, Diana MacLean & Manolis Savva [AVI’12]
Stanford Dissertation Browserwith Jason Chuang, Dan Ramage & Chris Manning [CHI’12]
Stanford Dissertation Browserwith Jason Chuang, Dan Ramage & Chris Manning [CHI’12]
Termite Topic Model Viewerwith Jason Chuang & Chris Manning [AVI’12]
Acquisition
Cleaning
Integration
Modeling
Visualization
Presentation
Dissemination
InteractiveData Analysis
http://vis.stanford.edu
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