Data Visualization Nikhil Srivastava, 2015
Nikhil Srivastava
Wharton Data & Analytics Club
Data Visualization Nikhil Srivastava, 2015
Data Visualization Nikhil Srivastava, 2015
About this Lecture
• Shortened version of longer course
– Slides, demos, extra material
– Code samples and libraries
– Sample projects
• Questions
Data Visualization Nikhil Srivastava, 2015
About You
Data Visualization Nikhil Srivastava, 2015
• 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
Data Visualization Nikhil Srivastava, 2015
• 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
Data Visualization Nikhil Srivastava, 2015
Data Visualization
Information VisualizationScientific Visualization
Infographics
Statistical GraphicsInformative Art
ArtScience
Statistics
JournalismDesign
Visual Analytics
Business
Data Visualization Nikhil Srivastava, 2015
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
Data Visualization Nikhil Srivastava, 2015
• Which is the most populous
city in the list?
• Which state in the list has
the most cities?
• Which state in the list has
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
Data Visualization Nikhil Srivastava, 2015
Data Visualization Nikhil Srivastava, 2015
• Which is the most populous
city in the list?
• Which state in the list has
the most cities?
• Which state in the list has
the largest average city?
Data Visualization Nikhil Srivastava, 2015
• Which is the most populous
city in the list?
• Which state in the list has
the most cities?
• Which state in the list has
the largest average city?
• What is the population of
Montgomery, Alabama?
Data Visualization Nikhil Srivastava, 2015
Data Visualization is:• Useful
– Answers user questions
– Reduces user workload
(by design, not by default)
Data Visualization Nikhil Srivastava, 2015
Anscombe’s quartet (1973)
Data Visualization Nikhil Srivastava, 2015
Anscombe’s quartet (1973)
Data Visualization Nikhil Srivastava, 2015
Data Visualization is:• Useful
– Understand structure and patterns
– Resolve ambiguity
– Locate outliers
Data Visualization Nikhil Srivastava, 2015
Data Visualization Nikhil Srivastava, 2015
Data Visualization is:• Important
– Design decisions affect interpretation
Data Visualization Nikhil Srivastava, 2015
Crimean War Deaths
Florence Nightingale, 1858 (re-colorized)
Data Visualization Nikhil Srivastava, 2015
Data Visualization is:• Powerful
– Communicate, teach, inspire
Data Visualization Nikhil Srivastava, 2015
purpose communicate explore, analyze
data type numerical,categorical
text, maps, graphs, networks
method staticrepresentation
animation,interactivity
Our Focus
Data Visualization Nikhil Srivastava, 2015
• 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
Data Visualization Nikhil Srivastava, 2015
The Hardware
Data Visualization Nikhil Srivastava, 2015
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”
Data Visualization Nikhil Srivastava, 2015
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
Data Visualization Nikhil Srivastava, 2015
Task: Counting
How many 3’s?
1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686
Data Visualization Nikhil Srivastava, 2015
Task: Counting
How many 3’s?
1281768756138976546984506985604982826762 9809858458224509856358945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686
1281768756138976546984506985604982826762 9809858458224509856358945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686
Data Visualization Nikhil Srivastava, 2015
Task: Counting
Slow, sequential, conscious
Rapid, parallel, automatic
1281768756138976546984506985604982826762 9809858458224509856358945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686
1281768756138976546984506985604982826762 9809858458224509856358945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686
Data Visualization Nikhil Srivastava, 2015
Task: (Distracted) Search
Which side has the red circle?
Data Visualization Nikhil Srivastava, 2015
Task: (Distracted) Search
Which side has the red circle?
Data Visualization Nikhil Srivastava, 2015
Task: (Distracted) Search
Which side has the red circle?
Data Visualization Nikhil Srivastava, 2015
Task: (Distracted) Search
Which side has the red circle?
Data Visualization Nikhil Srivastava, 2015
Task: (Distracted) SearchSlow, sequential, conscious
Rapid, parallel, automatic
Data Visualization Nikhil Srivastava, 2015
Task: (Distracted) Search
Data Visualization Nikhil Srivastava, 2015
Task: (Distracted) Search
Data Visualization Nikhil Srivastava, 2015
Task: (Distracted) Search
Data Visualization Nikhil Srivastava, 2015
Task: (Distracted) SearchSlow, sequential, conscious
Rapid, parallel, automatic
(n=7)
(n=5)
(n=3)
Data Visualization Nikhil Srivastava, 2015
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
Data Visualization Nikhil Srivastava, 2015
Example: Inefficient Attributes
Data Visualization Nikhil Srivastava, 2015
Example: Too Many Attributes
Data Visualization Nikhil Srivastava, 2015
• 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
Data Visualization Nikhil Srivastava, 2015
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 Visualization Nikhil Srivastava, 2015
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 Visualization Nikhil Srivastava, 2015
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 Visualization Nikhil Srivastava, 2015
Data Types (Advanced)
• Networks/Graphs
– Hierarchies/Trees
• Text
• Maps: points, regions, routes
Data Visualization Nikhil Srivastava, 2015
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 Visualization Nikhil Srivastava, 2015
Visual Encodings
Marks
point
line
area
volume
Channels
position
size
shape
color
angle/tilt
Data Visualization Nikhil Srivastava, 2015
Channel Effectiveness
Data Visualization Nikhil Srivastava, 2015
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”
Data Visualization Nikhil Srivastava, 2015
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
Data Visualization Nikhil Srivastava, 2015
type mark channel data represented
Scatter Plot point position 2 quantitative
Data Visualization Nikhil Srivastava, 2015
type mark channel data represented
Scatter + Hue point position,color
2 quantitative, 1 categorical
Data Visualization Nikhil Srivastava, 2015
type mark channel data represented
Scatter + Size (“Bubble”)
point position,size
3 quantitative
Data Visualization Nikhil Srivastava, 2015
Scatter Plot – Applications
RELATIONSHIP GROUPING OUTLIERS
Data Visualization Nikhil Srivastava, 2015
Scatter Plot – Dangers
OCCLUSION (DENSITY)
OCCLUSION (OVERLAP)
3-D
Data Visualization Nikhil Srivastava, 2015
type mark channel data represented
Line Chart line position(orientation)
2 quantitative
Data Visualization Nikhil Srivastava, 2015
type mark channel data represented
Area Chart area size (length) 2 quantitative
Data Visualization Nikhil Srivastava, 2015
Line Chart – Applications
PATTERN OVER TIME COMPARISON
Data Visualization Nikhil Srivastava, 2015
Line Chart – Dangers
Y SCALING
X SCALING
OVERLOAD
Data Visualization Nikhil Srivastava, 2015
type mark channel data represented
Bar Chart line size (length) 1 categorical,1 quantitative
Data Visualization Nikhil Srivastava, 2015
type mark channel data represented
Histogram line size (length) 1 ordinal/quantitative,1 quantitative (count)
Data Visualization Nikhil Srivastava, 2015
Bar Chart – Applications
COMPARE CATEGORIES DISTRIBUTION
Data Visualization Nikhil Srivastava, 2015
Bar Chart – Dangers
TOO MANY CATEGORIES
POORLY SORTED CATEGORIES
ZERO AXIS
Data Visualization Nikhil Srivastava, 2015
type mark channel data represented
Pie Chart area size (angle) 1 quantitative
Data Visualization Nikhil Srivastava, 2015
Pie Chart – Dangers
AREA/ANGLE SCALE SIMILAR AREAS OVERLOAD
Data Visualization Nikhil Srivastava, 2015
Multi-Series: Bar
“GROUPED” BAR CHART
“STACKED” BAR CHART
Data Visualization Nikhil Srivastava, 2015
Multi-Series: Line
MULTIPLE LINE
STACKED AREA CHART
Data Visualization Nikhil Srivastava, 2015
Normalization
NORMALIZED BAR NORMALIZED AREA
Data Visualization Nikhil Srivastava, 2015
• 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
Data Visualization Nikhil Srivastava, 2015
From Science to Art
• Design principles*
• Style guidelines*
*dependent on context and objective (and author)
Data Visualization Nikhil Srivastava, 2015
Design Principles
Data Visualization Nikhil Srivastava, 2015
Design Principles
• Integrity
– Tell the truth with data
• Effectiveness
– Achieve visualization objectives
• Aesthetics
– Be compelling, vivid, beautiful
Data Visualization Nikhil Srivastava, 2015
Integrity
Lie Ratio = size of effect in graphic
size of effect in data
Data Visualization Nikhil Srivastava, 2015
Integrity
Data Visualization Nikhil Srivastava, 2015
Integrity
“show data variation, not design variation”
Data Visualization Nikhil Srivastava, 2015
Effectiveness*
Data/Ink Ratio = ink representing data
total ink
*Tufte
Data Visualization Nikhil Srivastava, 2015
Effectiveness* *Tufte
avoid “chart junk”
Data Visualization Nikhil Srivastava, 2015
Avoid Chart Junk
Data Visualization Nikhil Srivastava, 2015
Avoid Chart Junk
Data Visualization Nikhil Srivastava, 2015
Avoid Chart Junk
Data Visualization Nikhil Srivastava, 2015
Avoid Chart Junk
Data Visualization Nikhil Srivastava, 2015
Avoid Chart Junk
Data Visualization Nikhil Srivastava, 2015
Avoid Chart Junk
Data Visualization Nikhil Srivastava, 2015
Effectiveness (Few)
Data Visualization Nikhil Srivastava, 2015
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
Data Visualization Nikhil Srivastava, 2015
A Note on Color
• To label
• To emphasize
• To liven or decorate
Data Visualization Nikhil Srivastava, 2015
Color as a ChannelCategorical Quantitative
Hue Good (6-8 max)
Poor
Value Poor Good
Saturation Poor Okay
Data Visualization Nikhil Srivastava, 2015
Bad Color
Data Visualization Nikhil Srivastava, 2015
Good Color
Data Visualization Nikhil Srivastava, 2015
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
Data Visualization Nikhil Srivastava, 2015
• 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
Data Visualization Nikhil Srivastava, 2015
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
Data Visualization Nikhil Srivastava, 2015
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
Data Visualization Nikhil Srivastava, 2015
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
Data Visualization Nikhil Srivastava, 2015
Cheat Sheets
• For Hackathon participants
• Otherwise, email me
Data Visualization Nikhil Srivastava, 2015
• 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
Data Visualization Nikhil Srivastava, 2015
Small Multiples
Data Visualization Nikhil Srivastava, 2015
Treemap(Hierarchical Data)
Strengths: nested relationships
Concerns: order, aspect ratio
Data Visualization Nikhil Srivastava, 2015
Multi-Level Pie Chart(Hierarchical Data)
Strengths: nested relationships
Concerns: readability
Data Visualization Nikhil Srivastava, 2015
Heat Map
(Table/Field Data) Strengths: pattern/outlier detectionConcerns: ordering, clustering, color
Data Visualization Nikhil Srivastava, 2015
Choropleth(Region Data)
Strengths: geography
Concerns: region sizecolor
Data Visualization Nikhil Srivastava, 2015
Cartogram
(Region Data) Strengths: geographic patternConcerns: base map knowledge
Data Visualization Nikhil Srivastava, 2015
The Ebb and Flow of Movies
NY Times, 2008
Streamgraph
Data Visualization Nikhil Srivastava, 2015
“Data Visualization” Wikipedia PageWordle
Word Cloud
Data Visualization Nikhil Srivastava, 2015
Data Visualization Nikhil Srivastava, 2015
Twitter NetworksPJ Lamberson, 2012
Data Visualization Nikhil Srivastava, 2015
Blogs/Reference
• Infosthetics.com
• Visualizing.org
• FlowingData.com
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