intro to data visualization - evaluation ontario · 2016-06-23 · data exploration or conveying...

THINKING VISUALLY:AN INTRODUCTION TO DATA & INFORMATION VISUALIZATION

Learning Event for CES Ontario Li Ka Shing Knowledge Institute

June 22, 2016

Jesse Carliner

ACTING COMMUNICATIONS & REFERENCE LIBRARIAN

Leanne Trimble

DATA & STATISTICS LIBRARIAN

University of Toronto Libraries

WHY DATA VISUALIZATION?

COMMUNICATION

GAIN INSIGHTS

VISUALIZATION WORKFLOW

WORKFLOW DESIGN

Audience & Purpose

Select and Prepare Data

Select Visualization

Form


Elements

Visualize Data

Share

STAGE 1: AUDIENCE & PURPOSE

Audience & Purpose

STAGE 2: SELECT & PREPARE DATA

WORKFLOW DESIGN

Audience & Purpose



Form


Elements

Visualize Data

Share

STAGE 2: TYPES OF DATA

QUANTITATIVE

QUALITATIVE

STAGE 2: MEASUREMENT SCALE TYPE

CATEGORICAL (NOMINAL)

ORDINAL

INTERVAL

RATIO

STAGE 2: TYPES OF DATA

GEOGRAPHIC DATA

NETWORK DATA

TEMPORAL DATA

TOPICAL DATA

NUMERIC DATA

STAGE 2: UNIT OF OBSERVATION

Microdata: Unaggregated, responses at the individual level

Statistics: Aggregated from microdata, responses grouped into categories; shows counts, percentages, averages, etc.

STAGE 2: LEVEL OF ANALYSIS

SMALL LARGE

STAGE 2: OTHER FACTORS

PRIVACY?

HOW MANY VARIABLES?

STAGE 2: PREPARE YOUR DATA

WORKFLOW DESIGN

Audience & Purpose



Form


Elements

Visualize Data

Share


Aggregating

Dealing with missing values & outliers

Cleaning, sorting, labelling variables

Summary statistics, algorithms, correlation coefficients, etc.

Merging with other datasets

It’s iterative!


Before

After

STAGE 3: SELECT VISUALIZATION FORM

WORKFLOW DESIGN

Audience & Purpose



Form


Elements

Visualize Data

Share

STAGE 3: CHOOSE VISUALIZATION TYPE

Based on purpose

Data exploration or conveying thesis?Overall trends or accurate judgments?

Based on audience

Academic or hospital stakeholders?

Based on data

Quantitative, qualitative, categorical, scale (ordinal, interval), scope, temporal & geospatial considerations

Source: Cairo, Alberto. The Functional Art, p.120

Graphical perceptual accuracy,Cleveland & McGill 1984

RESOURCES FOR CHOOSING VISUALIZATION TYPES

http://datavizcatalogue.com/ Handout

PIE CHARTS

• Lack an inherent reference system (no x, y, z axis or grid lines)• Use angle to convey difference

BAR GRAPHS

• Use with when comparing quantitative variables across discrete categorical variables

• Counts or sums rather than characteristics of a distribution• Length or height more accurate than angles

b

BAR GRAPHS

• Use consistent axes when comparing two graphs

BAR GRAPHS

Use a zero baseline!

SCATTER PLOTS

• Effective for comparing how one variable is affected by another, e.g. correlation, cause & effect relationships

• Pros: Easy to spot outliers & trends• Cons: Hard to use with REALLY large datasets, occlusion issues with multivariate

scatterplots

SCATTER PLOTS

Best practices:• Prevent occlusion & ambiguity issues with easily distinguishable symbols • Consider colour, size, & symbols to capture more dimensions of your data

SCATTER PLOTS

Separate multivariate scatter plots with occlusion issues into multiple scatter plotsConsider scatterplot matrices (aka Trellis plots ) for large, multivariate sets

age

0.80 0.84 0.88 0.92 0.1 0.2 0.3 0.4 30000 50000 70000

25

35

45

55

0.8

00.8

60

.92

high_school

bach_degree

0.1

0.3

0.5

0.1

0.3

adv_degree

unemploy

0.0

80.1

20

.16

300

00

50

00

070

00

0

hh_income

25 35 45 55 0.1 0.2 0.3 0.4 0.5 0.08 0.12 0.16 300 500 700

300

50

07

00

rent

“SMALL MULTIPLES” & “SPARKLINES”

HEAT MAPS

VISUALIZING PARTS OF A WHOLE: TREEMAPS

positionlengthdirectionangleareavolumecurvatureshadingcolour

Accuracy

VISUALIZING PARTS OF A WHOLE: STREAMGRAPHS

BOX PLOTS

• Effective for displaying distribution of data• Data dense: displays outliers, minimum, maximum, median, quartiles• Take up little space

NETWORK GRAPHS

• Track activity & relationships across temporal, geo., & categorical variables• Can analyze hubs & clusters of activity• May need to apply algorithms to fix layout issues• Labels can often get obscured

TABLES

Use for a small set of quantitative data that can’t be quickly summarized in text

Edward Tufte’s rule: Fewer than 20 numbers, just use a table*

Takes up less space than graphs

Author submission guidelines often spell out when to just use a table

*Tufte, E. R. (1983). The visual display of quantitative information. Cheshire, Conn. : Graphics Press. p.56

EXERCISE

1. I want to compare obesity rates across countries across a 5 year span.

Which variables will you need?

What are you trying to observe? (e.g. distribution, relationships, data overtime, etc.)

Which visualization type(s) would you use?

Country Obese % of population2005

Obese % of population 2010

Canada 23.7 25.4

New Zealand 26 27.8

United States 33 36.1

Spain etc. etc.

Russia etc. etc.

Use http://datavizcatalogue.com or the handout

http://datavizcatalogue.com/

EXERCISE

2. I want to explore the relationship between obesity rates across countries, across a 5 year span in relation to GDP per capita.

Which variables will you need?

What are you trying to observe? (e.g. distribution, relationships, data overtime, etc.)

Which visualization type(s) would you use?

Country Obese % of population2005

Obese % of population2010

GDP per capita2005

GDP per capita2010

Canada 23.7 25.4 36000 47465

New Zealand

26 27.8 27525 32845

United States

33 36.1 44307 48377

Russia etc. etc. etc. etc.

Use http://datavizcatalogue.com or the handout

http://datavizcatalogue.com/

STAGE 4: SELECT VISUAL ELEMENTS

WORKFLOW DESIGN

Audience & Purpose



Form


Elements

Visualize Data

Share



Elements

POSITION FORM COLOUR OPTICS TEXTURE


POSITION


FORM


FORM

Visualization from Information is beautiful: http://www.informationisbeautiful.net/visualizations/best-in-show-whats-the-top-data-dog/


FORM

Visualization from Information is beautiful: http://www.informationisbeautiful.net/visualizations/snake-oil-supplements/


COLOUR


COLOUR ACCESSIBILITY

COLOUR BLINDNESS SIMULATOR: http://www.color-blindness.com/coblis-color-blindness-simulator/ :

http://www.color-blindness.com/coblis-color-blindness-simulator/


COLOUR

COLOUR BREWER: http://www.colorbrewer2.org/#

http://www.colorbrewer2.org/


QUALITATIVECOLOUR PALETTE

Visualization from Information is beautiful: http://www.informationisbeautiful.net/visualizations/oil-well-every-cooking-oil-compared/


SEQUENTIALCOLOUR PALETTE

Visualization from Guardian: http://www.theguardian.com/money/ng-interactive/2014/may/23/-sp-see-how-house-prices-have-risen


DIVERGINGCOLOUR PALETTE

Visualization from Guardian:http://www.theguardian.com/society/ng-interactive/2015/sep/02/unaffordable-country-where-can-you-afford-to-buy-a-house


WHAT COLOUR PALETTE (S) WOULD YOU CHOOSE FOR THIS DATA SET?

:COLOUR BREWER: http://www.colorbrewer2.org/#

FAMILIESACRES OF LAND

RANK

RIDOLFI 30 5

SALVATTI 15 3

ALBIZZI 20 4

GINORI 10 2

http://www.colorbrewer2.org/

STAGE 4: CREATE TEXT

INCLUDE ALL

NECESSARY CONTENT

CLARITY

LESS IS MORE

TYPOGRAPHIC

BEST PRACTICES

STAGE 4: LAYOUT AND CREATION

DATA-INK RATIO

GESTALT

GRIDS

STAGE 4: DATA-INK RATIO

LOW HIGH

Images from: http://www.infovis-wiki.net/index.php/Data-Ink_Ratio


GESTALT


SIMILARITY

PROXIMITY


GROUPING


VISUAL COMPLETION

OR

LESS IS MORE


AVOID

DISTRACTIONS

THIS OR THIS?


GRIDS

/:

STAGE 5: SHARE YOUR VISUALIZATION

WORKFLOW DESIGN

Audience & Purpose



Form


Elements

Visualize Data

Share

STAGE 5: SHARING YOUR VISUALIZATION

SHARE

GET FEEDBACK

WAS IT EFFECTIVE?

REVISEShare

EXERCISE: IMPROVING VISUALIZATIONS

In groups of 2 or 3, examine the visualization on your handout.

These are problematic visualizations. What are the issues and how could they be improved?

What’s up with the x axis?

How the countries being plotted on the chart?

Appropriate use of visualization type?

Enough contextual information?

What’s up with the colour scale?

Appropriate colour choices?

Efficient way to display info?

Watch out for dual scales!

Exaggerating data?

Appropriate colour choice?

Proximity causes the reader to group together similar images.

Consistency with scales if drawing comparisons btw visualizations.

STATISTICAL SOFTWARE PACKAGES

SPSS: Viz types: Standard graphs, charts, plotsProgramming?: Not requiredStorage: Computer hard driveCost: Annual license

*note: Produces low quality images, not suitable for publication, consider rebuilding visualizations in Excel or using R or Stata

STATISTICAL SOFTWARE PACKAGES

R: Viz types: Almost any two dimensional visualization typesProgramming?: Requires knowledge of R language, steep learning curve, but

robust sets of tutorials & learning material onlineStorage: Computer hard driveCost: Free, open source

*note: • Higher quality images• Install ggplot2 & lattice packages for excellent

visualization libraries• New packages continuously being developed

COMMERCIAL VISUALIZATION SOFTWARE

Tableau: Viz types: Many 2-d & 3-d visualization types, including dynamic &

geospatial visualizationsProgramming?: None, drag & drop functionalityStorage: Computer hard driveCost: Expensive $$

*note: • High quality images• Allows users to build custom interactive

dashboard• Suggests visualization types based on your data• Free version with limited functionality, Tableau

Public

EXCEL

Viz types: Most standard 2-d visualizationsProgramming?: None, easy to use drag & drop functionalityStorage: Computer hard driveCost: Included in Microsoft Office

*note: • Produces serviceable visualizations• Don’t settle for the default charts!• Download additional Excel templates for more

visualization types• See Jorge Comoes’ blog for tutorials:

http://www.excelcharts.com/blog/

OPEN SOURCE VISUALIZATION TOOLS

Google Fusion TablesViz types: Most standard 2-d visualizations, network graphs, & geospatial

visualizations with Google Maps’ data, static & dynamic visualizations

Programming?: None, easy to use drag & drop functionalityStorage: Cloud based, attached to Google accountCost: Free, open source


Raw DensityViz types: Dynamic visualizations, visualizations that show relationships &

hierarchies, tree maps, scatterplots, network visualizationsProgramming?: None, easy to use drag & drop functionalityStorage: Cloud basedCost: Free, open source


d3.jsViz types: Interactive, geospatial, & network visualizationsProgramming?: Light javascript, many tutorialsStorage: Computer hard drive, interactive visualization onlineCost: Free, open source


Gephi & Sci2Viz types: Dynamic visualizations, visualizations that show relationships &

hierarchies, scatterplots, networks, geospatial visualizationsProgramming?: Light Python, easy to use drag & drop functionalityStorage: Cloud basedCost: Free, open source

IMAGE EDITORS & COLOUR TOOLS

InkscapeOpen source image editor

Useful for cleaning up images, editing axes or adding legends

ColorBrewer2Choose appropriate colour schemes according to brewer palatesCreates RGB, CMYK, & HEX codes for coloursPerforms various colour blindness tests

ADDITIONAL RESOURCES

The Data Visualization Catalogue: Detailed descriptions of data visualization types, their uses, & limitations

http://www.datavizcatalogue.com/

Tools organized by visualization type, Duke University Libraries:http://bit.ly/1EHjs5K

Curated list of visualization toolshttp://selection.datavisualization.ch/

Books

Cairo, A. (2013). The functional art: An introduction to information graphics and visualization.

Bo ̈rner, K., & Polley, D. E. (2014). Visual insights: A practical guide to making sense of data.

Tufte, E. R. (1983). The visual display of quantitative information.

Nature Methods Points of View Columns:

http://blogs.nature.com/methagora/2013/07/data-visualization-points-of-view.html

QUESTIONS?

Jesse Carliner

[email protected]

Leanne Trimble

[email protected]

mailto:[email protected]

mailto:[email protected]

THANK YOU!

intro to data visualization - evaluation ontario · 2016-06-23 · data exploration or conveying...

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