The theory of data
visualisation
v2.1
Simon Andrews, Phil Ewels
simon.andrews@babraham.ac.uk
phil.ewels@scilifelab.se
Data Visualisation
• A scientific discipline involving the creation
and study of the visual representation of data
whose goal is to communicate information
clearly and efficiently to users.
• Data Visualisation is both an art and a science.
Sample A Sample B
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Sample B
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ISBN-10: 1466508914
http://www.cs.ubc.ca/~tmm/talks.html
Data Viz Process
Collect Raw Data
Process and Filter
Data
Clean Dataset
Exploratory
Analysis
Generate
Visualisation
Generate
Conclusion
A data visualisation should…
•
•
•
•
•
Show the data
Not distort the data
Summarise to make things clearer
Serve a clear purpose
Link to the accompanying text and statistics
Things you can illustrate
Graphical Representations
• Basic questions
– How are you going to turn the data into a
graphical form (weight becomes length etc.)
– How are you going to arrange things in space
– How are you going to use colours, shapes etc. to
clarify the point you want to make
Marks and Channels
• Marks
– Geometric primitives
• Lines
• Points
• Areas
– Used to represent
data sets
• Channels
– Graphical appearance of a mark
•
•
•
•
Colour
Length
Position
Angle
– Used to encode data
Figures are a combination of marks
and channels
4.5
1 Mark = Rectangle
1 Channel = Length of longest side
4
3.5
3
2.5
2
1.5
1 Mark = Circle segment
1 Channel = Angle
1
0.5
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10
1 Mark = Diamond shape
2 Channels = X position, Y position
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1 Mark = Circle
4 Channels:
X position
Y position
Area
Colour
Golden Rules
• Effectiveness
– Encode the most important information with the
most effective channel
• Expressiveness
– Match the properties of the data and channel
Types of channel
• Quantitative
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–
–
–
–
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Position on scale
Length
Angle
Area
Colour (saturation)
Colour (lightness)
• Qualitative
– Spatial Grouping
– Colour (hue)
– Shape
Colour
• Technical representations of colour
– Red + Green + Blue (RGB)
– Cyan + Magenta + Yellow + Black (CMYK)
• Perceptual representation of colour
– Hue + Saturation + Lightness (HSL)
HSL Representation
• Hue
= Shade of colour = Qualitative
• Saturation = Amount of colour = Quantitative
• Lightness = Amount of white = Quantitative
• Humans have no innate quantitative perception of hue
but we have learned some (cold – hot, rainbow etc.)
• Our perception of hue is not linear
Types of channel
• Quantitative
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–
–
–
–
Position on scale
Length
Angle
Area
Colour (saturation)
Colour (lightness)
• Qualitative
– Spatial Grouping
– Colour (hue)
– Shape
Data Types
• Quantitative
– Height, Length, Weight, Expression etc.
• Ordered
– Small, Medium, Large
– January, February, March
• Categorical
– WT, Mutant1, Mutant2
– GeneA, GeneB, GeneC
Golden Rules
• Effectiveness
– Encode the most important information with the
most effective channel
• Expressiveness
– Match the properties of the data and channel
Golden Rules
• Effectiveness
– Encode the most important information with the
most effective channel
• Expressiveness
– Match the properties of the data and channel
Effectiveness of quantitation
10
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4.5X
1.1
2X
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16X
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Quantitation Perception
Golden Rules
• Effectiveness
– Encode the most important information with the
most effective channel
• Expressiveness
– Match the properties of the data and channel
Most Quantitative Representations
Good quantitation
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•
•
•
•
•
•
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Poor quantitation
Bar chart
Stacked bar chart with common start
Stacked bar chart with different starts
Pie charts
Bubble plots (circular area)
Rectangular area
Colour (luminance)
Colour (saturation)
Discriminability
• If you encode categorical data are the
differences between categories easy for the
user to perceive correctly?
Qualitative Discrimination
• How many colours can you discriminate?
Qualitative Discrimination
• How many (fillable) shapes can you
discriminate?
• Can combine with colour, but need to
maintain similar fillable areas
Separability
• The effectiveness of a channel does not always
survive being combined with a second channel.
• There are large variations in how much two
different channels interfere with each other
• Trying to put too much information on a figure
can erode the impact of the main point you’re
trying to make
Separability
There is no
confusion between
the two channels
Larger points are
easier to
discriminate than
smaller ones
We tend to focus
on the area of the
shape rather than
the height/width
separately
Humans are very
bad at separating
combined colours
Popout
• A distinct item immediately stands out from
the others
• Triggered by our low level visual system
• You don’t need to actively look at every point
(slow!) to see it
Popout
(find the red circle)
Popout
Speed of identification is independent of the number of distracting points
Popout
(Find the circle)
Popout
Colour pops out more than shape
Popout
Mixing channels removes the effect
(Find the red circle)
Use of space
• Where you want a viewer to focus on specific
subsets of data you can help their perception
by using the layout or highlighting of data to
draw their attention to the point you’re
making
Grouping
80
70
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10
0
Grouping
80
70
60
50
40
30
20
10
0
CpG CHH CHG
CpG CHH CHG
Exon
CGI
CpG CHH CHG
Intron
CpG CHH CHG
Repeat
Containment
Containment
Containment
Wild Type
80
70
60
50
40
30
20
10
0
CpG CHH CHG
CpG CHH CHG
CpG CHH CHG
CpG CHH CHG
CpG CHH CHG
CpG CHH CHG
CpG CHH CHG
CpG CHH CHG
Mutant
80
70
60
50
40
30
20
10
0
Linking
1
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25
20
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15
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25
20
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5
0
Linking
80
70
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2
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Ordering
• Is a monkey heavier than a dog?
140
120
Weight (kg)
100
80
60
40
20
0
cat
aardvark
fish
aardvark
cow
cat
dog
monkey
fish
dog
horse
cow
monkey
horse
Validation
• Always try to validate plots you create
• You have seen your data too often to get an unbiased
view
• Show the plot to someone not familiar with the data
– What does this plot tell you?
– Is this the message you wanted to convey?
– If they pick multiple points, do they choose the most
important one first?
General Rules
• No unnecessary figures
– Does a graphical representation make things clearer?
– Would a table be better?
• One point per figure
– Design each figure to illustrate a single point
– Adding complexity compromises the effectiveness of the main point
• No absolute reliance on colour
– Figures should ideally still work in black and white
– Colour should help perception
• No 3D
– 3D is hardly ever justified and makes things less clear
• Figures should be self-contained
– Must be understandable without additional information