Data & Analytics Club - Data Visualization Workshop

Data Visualization Nikhil Srivastava, 2015

Nikhil Srivastava

Wharton Data & Analytics Club


[email protected]


About this Lecture

• Shortened version of longer course

– Slides, demos, extra material

– Code samples and libraries

– Sample projects

• Questions

http://nsrivast.github.io/data-viz-nairobi/


About You


• What is Data Visualization?

• Thinking and Seeing

• From Data to Graphics

• Principles and Guidelines

• Building Visualizations

• Advanced

introduction

foundation & theory

building blocks

design & critique

construction

Outline







• Advanced

introduction

foundation & theory

building blocks

design & critique

construction


Data Visualization

Information VisualizationScientific Visualization

Infographics

Statistical GraphicsInformative Art

ArtScience

Statistics

JournalismDesign

Visual Analytics

Business


City State Population Baton Rouge Louisiana 191,741 Birmingham Alabama 220,927 Broken Arrow Oklahoma 58,018 Eugene Oregon 115,890 Glendale Arizona 245,868 Huntsville Alabama 55,741 Lafayette Louisiana 87,737 Mobile Alabama 98,147 Montgomery Alabama 126,250 New Orleans Louisiana 322,172 Norman Oklahoma 101,590 Peoria Arizona 167,868 Portland Oregon 514,108 Salem Oregon 147,631 Scottsdale Arizona 134,335 Shreveport Louisiana 68,756 Surprise Arizona 90,548 Tempe Arizona 143,369 Tulsa Oklahoma 392,138


• Which is the most populous

city in the list?

• Which state in the list has

the most cities?


the largest average city?

City State Population Baton Rouge Louisiana 191,741 Birmingham Alabama 220,927 Broken Arrow Oklahoma 58,018 Eugene Oregon 115,890 Glendale Arizona 245,868 Huntsville Alabama 55,741 Lafayette Louisiana 87,737 Mobile Alabama 98,147 Montgomery Alabama 126,250 New Orleans Louisiana 322,172 Norman Oklahoma 101,590 Peoria Arizona 167,868 Portland Oregon 514,108 Salem Oregon 147,631 Scottsdale Arizona 134,335 Shreveport Louisiana 68,756 Surprise Arizona 90,548 Tempe Arizona 143,369 Tulsa Oklahoma 392,138



city in the list?


the most cities?





city in the list?


the most cities?



• What is the population of

Montgomery, Alabama?


Data Visualization is:• Useful

– Answers user questions

– Reduces user workload

(by design, not by default)


Anscombe’s quartet (1973)


Data Visualization is:• Useful

– Understand structure and patterns

– Resolve ambiguity

– Locate outliers


Data Visualization is:• Important

– Design decisions affect interpretation


Crimean War Deaths

Florence Nightingale, 1858 (re-colorized)


Gapminder Foundation

http://www.gapminder.org/world/


Data Visualization is:• Powerful

– Communicate, teach, inspire


purpose communicate explore, analyze

data type numerical,categorical

text, maps, graphs, networks

method staticrepresentation

animation,interactivity

Our Focus







• Advanced

introduction

foundation & theory

building blocks

design & critique

construction


The Hardware


The Software• High-level concepts: objects, symbols

• Involves working memory

• Slower, serial, conscious

• Sensory input

• Low-level features: orientation,

shape, color, movement

• Rapid, parallel, automatic

Visual Perception

“Bottom-up”


The Software• High-level concepts: objects, symbols

• Involves working memory

• Slow, sequential, conscious

• Sensory input

• Low-level features: orientation,

shape, color, movement

• Rapid, parallel, automatic

“Bottom-up”

“Top-down”

Visual Perception


Task: Counting

How many 3’s?

1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686


Task: Counting

How many 3’s?

1281768756138976546984506985604982826762 9809858458224509856358945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686

1281768756138976546984506985604982826762 9809858458224509856358945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686


Task: Counting

Slow, sequential, conscious

Rapid, parallel, automatic

1281768756138976546984506985604982826762 9809858458224509856358945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686

1281768756138976546984506985604982826762 9809858458224509856358945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686


Task: (Distracted) Search

Which side has the red circle?


Task: (Distracted) SearchSlow, sequential, conscious



Task: (Distracted) Search


Task: (Distracted) SearchSlow, sequential, conscious


(n=7)

(n=5)

(n=3)


Lessons for Visualization

• Use “pre-attentive” attributes when possible

– Color, shape, orientation (depth, motion)

– Faster, higher bandwidth

• Caveats

– Beware limits of working memory (<7)

– Be careful mixing attributes


Example: Inefficient Attributes


Example: Too Many Attributes







• Advanced

introduction

foundation & theory

building blocks

design & critique

construction


What kind of data do we

have?

How can we represent the data

visually?

How can we organize this into a

visualization?

Visual Encoding


Data TypesCATEGORICAL ORDINAL NUMERICAL

Interval Ratio

Male / Female

Asia / Africa / Europe

True / False

Small / Med / Large

Low / High

Yes / Maybe / No

Latitude/Longitude

Compass direction

Time (event)

Length

Count

Time (duration)

= = = =< > < > < >

- + -* /


Data TypesCATEGORICAL ORDINAL NUMERICAL

Interval Ratio

Male / Female

Asia / Africa / Europe

True / False

Small / Med / Large

Low / High

Yes / Maybe / No

Latitude/Longitude

Compass direction

Time (event)

Length

Count

Time (duration)

Bin/Categorize

Difference/Normalize


Data Types (Advanced)

• Networks/Graphs

– Hierarchies/Trees

• Text

• Maps: points, regions, routes


What kind of data do we

have?

How can we represent the data

visually?

How can we organize this into a

visualization?

Visual Encoding


Visual Encodings

Marks

point

line

area

volume

Channels

position

size

shape

color

angle/tilt


Channel Effectiveness


Channel Effectiveness

“Spatial position is such a good visual

coding of data that the first decision of

visualization design is which variables get

spatial encoding at the expense of others”


What kind of data do we have?

How can we represent the data visually?

How can we organize this into a visualization?

Athi River Machakos 139,380

Awasi Kisumu 93,369

Kangundo-Tala Machakos 218,557

Karuri Kiambu 129,934

Kiambu Kiambu 88,869

Kikuyu Kiambu 233,231

Kisumu Kisumu 409,928

Kitale Trans-Nzoia 106,187

Kitui Kitui 155,896

Limuru Kiambu 104,282

Machakos Machakos 150,041

Molo Nakuru 107,806

Mwingi Kitui 83,803

Naivasha Nakuru 181,966

Nakuru Nakuru 307,990

Nandi Hills Trans-Nzoia 73,626


type mark channel data represented

Scatter Plot point position 2 quantitative



Scatter + Hue point position,color

2 quantitative, 1 categorical



Scatter + Size (“Bubble”)

point position,size

3 quantitative


Scatter Plot – Applications

RELATIONSHIP GROUPING OUTLIERS


Scatter Plot – Dangers

OCCLUSION (DENSITY)

OCCLUSION (OVERLAP)

3-D



Line Chart line position(orientation)

2 quantitative



Area Chart area size (length) 2 quantitative


Line Chart – Applications

PATTERN OVER TIME COMPARISON


Line Chart – Dangers

Y SCALING

X SCALING

OVERLOAD



Bar Chart line size (length) 1 categorical,1 quantitative



Histogram line size (length) 1 ordinal/quantitative,1 quantitative (count)


Bar Chart – Applications

COMPARE CATEGORIES DISTRIBUTION


Bar Chart – Dangers

TOO MANY CATEGORIES

POORLY SORTED CATEGORIES

ZERO AXIS



Pie Chart area size (angle) 1 quantitative


Pie Chart – Dangers

AREA/ANGLE SCALE SIMILAR AREAS OVERLOAD


Multi-Series: Bar

“GROUPED” BAR CHART

“STACKED” BAR CHART


Multi-Series: Line

MULTIPLE LINE

STACKED AREA CHART


Normalization

NORMALIZED BAR NORMALIZED AREA







• Advanced

introduction

foundation & theory

building blocks

design & critique

construction


From Science to Art

• Design principles*

• Style guidelines*

*dependent on context and objective (and author)


Design Principles


Design Principles

• Integrity

– Tell the truth with data

• Effectiveness

– Achieve visualization objectives

• Aesthetics

– Be compelling, vivid, beautiful


Integrity

Lie Ratio = size of effect in graphic

size of effect in data


Integrity


Integrity

“show data variation, not design variation”


Effectiveness*

Data/Ink Ratio = ink representing data

total ink

*Tufte


Effectiveness* *Tufte

avoid “chart junk”


Avoid Chart Junk


Effectiveness (Few)

https://speakerdeck.com/cherdarchuk/remove-to-improve-the-data-ink-ratio


Practical Guidelines

• Avoid 3-D charts

• Focus on substance over graphics

• Avoid separate legends and keys

• Use faint grids/guidelines

• Avoid unnecessary textures and colors


A Note on Color

• To label

• To emphasize

• To liven or decorate


Color as a ChannelCategorical Quantitative

Hue Good (6-8 max)

Poor

Value Poor Good

Saturation Poor Okay


Bad Color


Good Color


More Color Guidelines

• Use color only when necessary

• Saturated colors for small areas, labels

• Less saturated colors for large areas,

backgrounds

• Use tools like ColorBrewer







• Advanced

introduction

foundation & theory

building blocks

design & critique

construction


What Tools to Use? Athi River Machakos 139,380

Awasi Kisumu 93,369

Kangundo-Tala Machakos 218,557

Karuri Kiambu 129,934

Kiambu Kiambu 88,869

Kikuyu Kiambu 233,231

Kisumu Kisumu 409,928

Kitale Trans-Nzoia 106,187

Kitui Kitui 155,896

Limuru Kiambu 104,282

Machakos Machakos 150,041

Molo Nakuru 107,806

Mwingi Kitui 83,803

Naivasha Nakuru 181,966

Nakuru Nakuru 307,990

Nandi Hills Trans-Nzoia 73,626

CleanRestructure

ExploreAnalyze

DATA

Visualization Goals


Visualization Tools

Excel

TableauPlotly

Python

R

Matlab

Ubiq/Silk

How hard is it to learn?

How powerful & flexible is it?

I’ll have to write code


Visualization Tools

Excel

TableauPlotly

Python

R

Matlab

Ubiq/Silk

How hard is it to learn?

How powerful & flexible is it?

Google Charts

Highcharts

d3

I’ll have to write code


Cheat Sheets

• For Hackathon participants

• Otherwise, email me







• Advanced

introduction

foundation & theory

building blocks

design & critique

construction


Small Multiples


Treemap(Hierarchical Data)

Strengths: nested relationships

Concerns: order, aspect ratio


Multi-Level Pie Chart(Hierarchical Data)

Strengths: nested relationships

Concerns: readability


Heat Map

(Table/Field Data) Strengths: pattern/outlier detectionConcerns: ordering, clustering, color


Choropleth(Region Data)

Strengths: geography

Concerns: region sizecolor


Cartogram

(Region Data) Strengths: geographic patternConcerns: base map knowledge


The Ebb and Flow of Movies

NY Times, 2008

Streamgraph

http://www.nytimes.com/interactive/2008/02/23/movies/20080223_REVENUE_GRAPHIC.html


“Data Visualization” Wikipedia PageWordle

Word Cloud

http://www.wordle.net/


Twitter NetworksPJ Lamberson, 2012

http://social-dynamics.org/twitter-network-data/


Blogs/Reference

• Infosthetics.com

• Visualizing.org

• FlowingData.com


Nikhil Srivastava

[email protected]

Data & Analytics Club - Data Visualization Workshop

Technology

Transcript of Data & Analytics Club - Data Visualization Workshop