DataVisualization
DaveStinchcomb
October12,2016
WhatisDataVisualization?
• Datavisualization
isabout
COMMUNICATION
• Usingvisualdesignprinciples
tofacilitatecommunication
ofquantitativedata
• Keyquestions:
– Whoisyouraudience?
– Whatisthemainmessage?
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TheGalaxyofDataVisualization
• Staticgraphs,charts,andmaps
• Interactivegraphics
• Dashboards
• Infographics
• Reportdesign/webpagedesign
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ViewsoftheGalaxyͲ StaticGraphs
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ViewsoftheGalaxyͲ InteractiveGraphics
ChangesintheBritishDiet– http://britainsͲdiet.labs.theodi.org/
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ViewsoftheGalaxyͲ Dashboards
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ViewsoftheGalaxyͲ Infographics
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ViewsoftheGalaxy– ReportDesign
http://stephanieevergreen.com/designͲofͲanͲawardͲwinningͲreport/
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DataVizinCancerRegistryProjects
• Internalplanningandtracking
– Internalscheduletracking
– Reportingtoleadership
• Datacollection
– Trackingprogresstowardgoals
• Analysis
– Exploratorydatavisualization
•
•
•
•
Surveillancereports
Grantproposals
Academicpapers
Conferencepresentations
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PrinciplesofVisualDesign– VisualHierarchy
• Visualhierarchy
– Whatstandsout?
Whatdrawsyoureye?
– Elements:
• Size
• Colorandcontrast
• Typography
• Spacing
• Composition
– Dothe“squint”test– whatstandsout?
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VisualComparisons
• ClevelandandMcGill’scomparisonaccuracyscale:
1. Positionalongacommonscale
2. Positiononidentical
butnonalignedscales
3. Length,angle&direction
4. Area
5. Volume,curvature
6. Shading,colorsaturation
Cleveland&McGill,JASA,1984
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MakeItEasyontheViewer
• Avoidlegendsandencodedmeaning
• Ifalegendisneeded,useplacement,order,andcolortomake
interpretationeasier
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MakeItEasyontheViewer
• Avoiddiagonalorverticaltext
Betterredesign:
Adaptedfromhttp://annkemery.com/avoidingͲdiagonalͲtext/
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MakeItEasyontheViewer
• Avoidclutterand“chartjunk”
http://visual.ly/topͲ4ͲdisasterͲrecoveryͲfindings
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Transformation1– DataLookBetterNaked
http://www.darkhorseanalytics.com/blog/dataͲlooksͲbetterͲnaked/
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Transformation2– EvaluatorBreakfasts
Original:
Redesign:
StephanieEvergreenͲ http://stephanieevergreen.com/datavizͲchecklist/
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Makeover– Addingbarstodatatables
Original:
Redesign:
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Makeover– Barchartwithtimeseries
Original:
Percentofusersreportingthattheyagreewithpositivestatements
abouttheProgram’seffectiveness:2013,2010,and2008
Program
Program
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Makeover– Barchartwithtimeseries
Redesign:Ͳ timeserieslinechart
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Makeover– Stackedbarcharts
Original:
Percentofemployersreportingallreasonsforagreeingto
ProgramparticipateintheProgram
To improve ability to accomplish objectives
Believed that using the Program would allow
us to avoid an audit, raid, or fine
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Makeover– Stackedbarcharts
Pollresults:
Redesign– divergingstackedbarchart:
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Makeover– Clusteredcolumnchart
Original:
100%
90%
80%
70%
60%
50%
40%
30%
20%
38.5%
33.3%
31.6%
24.0% 25.5%
20.0%
20.0%
10.5%
10%
0.0%
0%
10.0%
16.0% 17.6%
0.0%
Ages 12 to 17
Cohort1
SPF SIG Cohort 1
Cohort5
SPF
SIG Cohort 5
23.1%
Ages 18 or Older
Cohort2
SPF
SIG Cohort 2
Cohort3
SPF
SIG Cohort 3
AllCohorts
All
SPF SIG
AllStates&D.C.
SAPT
20% Set-Aside
Cohort4
SPF SIG Cohort 4
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Makeover– Clusteredcolumnchart
Redesign– backͲtoͲbackbarchart(withlegend):
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Makeover– Makeiteasyontheviewer
Original:
Productusebyincomelevel
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Makeover– Makeiteasyontheviewer
Redesign– horizontalbarchart,divergingcolorscheme:
Productusebyincomelevel
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Makeover– Piecharts
Original:
PercentParticipationbyOrganization
Redesign:
PercentParticipationbyOrganization
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Makeover– changefrombaseline
Original:
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Makeover– changefrombaseline
Redesign– slopegraph,
highlightsignificantresults:
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Makeover– Confidenceintervalsintables
Redesign–
Original: addgraphtothetable:
Group1
Overall
Overall
Previous
Revised
Percent
change (LCL,UCL)
Ͳ2.2 (Ͳ4.7,0.4)
0.1
(Ͳ0.8,1.1)
NonͲHispanic Previous
NonͲHispanic Revised
1.2
0.2
(0.1,2.2)
(Ͳ0.8,1.2)
Hispanic
Hispanic
Previous
Revised
2.7
7.9
(Ͳ2.2,7.9)
(2.8,13.3)
Previous
Revised
Ͳ0.5
0.0
(Ͳ1.5,0.5)
(Ͳ0.9,0.9)
NonͲHispanic Previous
NonͲHispanic Revised
0.5
0.0
(Ͳ0.5,1.5)
(Ͳ0.9,1)
Hispanic
Hispanic
5.7
10.7
(Ͳ0.3,12)
(4.4,17.4)
Group2
Overall
Overall
Previous
Revised
Ͳ5
0
5
10
15
20
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Makeover– Confidenceintervalsintables
Redesign–
Original: addgraphtothetable:
Group1
Overall
Overall
Previous
Revised
Percent
change (LCL,UCL)
Ͳ2.2 (Ͳ4.7,0.4)
0.1
(Ͳ0.8,1.1)
NonͲHispanic Previous
NonͲHispanic Revised
1.2
0.2
(0.1,2.2)
(Ͳ0.8,1.2)
Hispanic
Hispanic
Previous
Revised
2.7
7.9
(Ͳ2.2,7.9)
(2.8,13.3)
Previous
Revised
Ͳ0.5
0.0
(Ͳ1.5,0.5)
(Ͳ0.9,0.9)
NonͲHispanic Previous
NonͲHispanic Revised
0.5
0.0
(Ͳ0.5,1.5)
(Ͳ0.9,1)
Hispanic
Hispanic
5.7
10.7
(Ͳ0.3,12)
(4.4,17.4)
Group2
Overall
Overall
Previous
Revised
Ͳ5
0
5
10
15
20
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Makeover– Confidenceintervalsingraphs
Original:
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Makeover– Confidenceintervalsingraphs
Redesign– errorbands:
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Makeover– Multiplelinegraph
Original:
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Makeover– Multiplelinegraph
Redesign–
smallmultiples:
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Tools– ChoosingtheRightChartType
• DataVisualizationCatalog
http://www.datavizcatalogue.com/
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Tools– DataVizChecklist
http://stephanieevergreen.com/datavizͲchecklist/
Textsizeishierarchicalandreadable
210n/a
Dataarelabeleddirectly
210n/a
GraphistwoͲdimensional
210n/a
Colorisusedtohighlightkeypatterns
210n/a
Colorislegibleforpeoplewithcolorblindness
210n/a
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Tools– Creatingstaticgraphs
• Excelisyourfriend
– MostexampleshavebeendonewithExcel
• ButExcelgraphdefaultsareNOT yourfriends!
– Getinthehabitofmodifyingeachgraph
http://thewhyaxis.info/defaults/
• Programmingtoolsforstaticgraphs:
– Randthe“ggplot2”package
– SASstatisticalgraphics:
SGprocedures(SGPLOT,SGSCATTER,SGPANEL)
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Tools– ExcelHowͲToGuides
• EvergreenData stepͲbyͲstep
• YouTubevideosbyDougH
http://stephanieevergreen.com/tag/stepͲbyͲstep/
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InteractiveDataVisualizationTools
• Interactivedatavisualization:
– Tableau
• Commercialtoolforquickinteractivegraphsanddashboards
• Inexpensiveforpublicdata;canbepriceyforprivatedata
– BrowserͲbasedJavaScriptlibraries:
• Highcharts
• D3(popularandpowerful)
• Dygraphs
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GeneralDataVizResources
• DataVizBlogs:
– EvergreenData– StephanieEvergreen
• http://stephanieevergreen.com/blog/
– PeltierTechBlog– JonPeltier
• http://peltiertech.com/
– FlowingData – NathanYau
• http://flowingdata.com/
• Booksby:
–
–
–
–
EdwardTufte
StephenFew
StephanieEvergreen
ColeNussbaumerKnaflic
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Conclusions– KeyPoints
• Datavisualizationisaboutcommunication
– Bringdatatolife
– Whoistheaudience;whatisthekeymessage?
• Builddatavisualizationintoregistryplans
– Operations,datacollection,analysis,reporting
• Visualdesignprinciples:
– Visualhierarchy
– Effectivevisualcomparisons
– Makingiteasyontheviewer
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Conclusions– TheBottomLine
• Datavisualizationcanmaketheworkyoudo
– Moreaccessible
– Moreimpactful
– Moreeffective
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ThankYou
UsefulLinks
•
•
•
•
•
•
DataVizCatalog
http://www.datavizcatalogue.com/
DataVizChecklist
http://stephanieevergreen.com/datavizͲchecklist/
BreakingExcelDefaults
http://thewhyaxis.info/defaults/
EvergreenData StepͲbyͲstep
http://stephanieevergreen.com/tag/stepͲbyͲstep/
Colorselection:
http://colorbrewer2.org/
Colorblindtestingtools:
http://www.colorͲblindness.com/coblisͲcolorͲblindnessͲsimulator/
http://www.vischeck.com/vischeck/
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